Perficient’s vibrant culture is fueled by leaders who bring sharp thinking, deep expertise, and a collaborative spirit to their day-to-day work. We recently connected with John Vylasek, senior solution architect, whose journey from military intelligence and commodity trading to client strategy has shaped his bold, visionary approach to problem-solving.  

John currently leads strategic data efforts at Perficient, using AI, analytics, and deep business knowledge to help clients drive impact. In this People of Perficient profile, we’ll explore how John’s diverse background and passion for innovation empower Perficient to deliver transformative, AI-first solutions with purpose and impact. 

What is your role? Describe a typical day in the life.    

As a data strategist, I help solve a wide range of client challenges. My day usually starts with reviewing my plan from the previous day while also checking emails and Teams for any new adjustments. I’m currently engaged full-time as Data Delivery Director with a major global financial services client, where things can shift quickly due to regulatory and business requirement changes. Thankfully, my team is always responsive and able to adapt as needed, which makes Perficient a great strategic partner. 

How did your experience in the military shape your approach to leadership? 

My time in military intelligence taught me how to break down complex problems, communicate clearly up the chain of command, and lead with both confidence and humility. I was asked at the age of 19 to assemble a team of intelligence analysts with the skills required to tackle an important and ambiguous challenge. We developed effective new techniques, and just months before Operation Desert Storm began, we applied our solutions to great effect. That experience taught me the value of building the right team, being bold yet respectful, and focusing on meaningful impact. One of the biggest lessons I carry is to “just make it happen”—a mindset I apply daily, whether with clients or internal teams. 

What brought you to Perficient, and how do your past experiences align with your current role? 

I joined Perficient while consulting independently, thanks to a referral, and it just clicked. My background in tech, leadership, analytics, and decomplicating ambiguity makes data strategy a great fit. I’m still coding, diagramming, translating complexity into clarity, and diving headfirst into new challenges. The consulting side was new to me at this scale, but because I’ve solved similar problems before, I adjusted quickly. I love helping clients align, adapt, and problem-solve with confidence, and I get genuinely excited when a new curveball comes my way. 

Whether big or small, how do you make a difference for our clients, colleagues, communities, or teams? 

I make a difference by asking many questions. It might seem small, but it helps the entire team learn and speak up. I often hear, “I was wondering the same thing,” which creates a more open, collaborative environment. I also love sharing resources that have helped me grow, like Cloud Data Architectures Demystified or helpful Udemy courses. As a lifelong learner, I’m always generous when passing along what’s worked for me. 

READ MORE: Hear From Our Colleagues About How They Prioritize Learning and Development 

Whether it’s creating a quick diagram during a meeting or reframing a problem in clearer terms, I break things down into understandable chunks so people can understand our goals and act on next steps with confidence. I take the same approach in client discovery—asking leading questions, listening closely, and creating a safe space for open and transparent communication. Being humble and approachable makes it easier for others to do the same, and that’s where real progress happens. 

What was the most rewarding part about serving as a mentor in the Mark Cuban AI Bootcamp? 

The most rewarding part of mentoring in the Mark Cuban AI Bootcamp was watching a group of bright high school students come together, collaborate, and build something meaningful. With some light coaching on inclusion and teamwork, they quickly aligned and focused on a shared goal: using AI to help people in the physical world.  

We developed a gesture recognition model to help people with communication challenges express specific needs through custom-trained motions, even when away from familiar caregivers. It was powerful to see how that idea took shape through trial, iteration, and collaboration. 

By the end of the program, not only did our model work, but my students also won the final “Shark Tank” pitch for the most impactful idea. One of them said, “We never would have gotten here if everyone didn’t think differently,” which really stuck with me. That diversity of thought, and the chance to help guide it, was incredibly rewarding. Hearing later that one student had highlighted her experience working with me as a key takeaway from the program made it even more meaningful. 

READ MORE: Perficient’s Award-Winning Partnership With the Mark Cuban Foundation 

What advice would you give to colleagues who are just starting their career with Perficient?

My first piece of advice is simple: handle your basics. Get your timesheets in, complete your training, and manage your time like a professional while always learning and improving yourself. It may seem small and obvious, but it sets the tone for everything else you do. 

Second, understand that how you show up internally at Perficient may need to be different than how you show up with a client. With clients, you lead through collaboration and patience, bringing people along at their own pace. Internally, especially during pursuits, business moves fast, and it helps to be more direct and decisive. Know your role, understand who’s leading, and stay aligned. If you have a concern, think about whether it’s the right time to raise it. I’ve coached and mentored people on this—sometimes it’s better to hold off on a small technical rabbit hole detail rather than disrupt momentum when the group is already aligned. I’m still learning and adapting myself, but this distinction has been key to working effectively. 

Why are you #ProudlyPerficient?    

I’m #ProudlyPerficient because I get to work alongside sharp, highly adaptive people who are always ready to dive in and get things done. Internally, there’s a fast pace and a bias toward action, so I’ve learned that often you need to step up, assign roles, and lead decisively. It’s not about being the loudest voice in the room; it’s about clarity, support, and knowing when to speak up and when to stay focused on the goal. That kind of teamwork and trust is what gets stuff done. 

I appreciate how differently we show up for clients— collaborative, patient, meeting them where they are. That flexibility between both modes while staying grounded in the work is what makes Perficient special. We’re not just delivering solutions; we’re building alignment. I’m proud to be a part of that. 

How has collaborating with our global teams shaped your growth journey at Perficient?
I’ve been leading global teams for many years, and the approach is consistent—find the people who make the extra effort to communicate, align, and get things done. Whether they’re in Latin America, India, or elsewhere, those relationships are what get “it” done. Building that network, finding your go-to experts, and recognizing talent across borders have been the most rewarding parts of my journey. 

LEARN MORE: Perficient’s Global Footprint Enables Genuine Connection and Collaboration 

How does staying up to date with evolving technologies help you better serve clients?   

One of my goals is to deepen my understanding of how to use local large language models (LLMs) in secure, practical ways. It’s an incredible accelerator for learning and staying up to speed. With a manufacturing client, I used an LLM to help map two complex database schemas. By feeding in just the field names and a few sample rows, the model was able to do most of the heavy lifting in identifying how the old system aligned with the new one. It wasn’t perfect, but it saved a lot of time and gave us a strong head start. Continuing to explore how AI can support data strategy and problem-solving is a key part of my growth path. 

What does being an AI-first company mean to you?
To me, being an AI-first company means starting from a place where AI is always considered not only as a solution itself but also as an accelerator to identify the solution and the steps to get there. It is a new way of thinking that saves us time and our clients’ money.  

 At Perficient, we approach AI with purpose and lead conversations when it makes sense to lead with it and when it does not. That level of thoughtfulness is part of what sets us apart. 

LEARN MORE: How We Are Building an AI-First Enterprise 

What are you passionate about outside of work?   

Outside of work, I spend a lot of time with my family. My oldest son lives just four doors down, and we’re often outside fishing with the grandkids. I also support my wife, who went from being a stay-at-home mom to now serving as Dean of the School of Health Sciences over several programs. I’m the primary cook at home and like making healthy meals. I’m also into photography, especially aurora and space photography. I’ve been able to get some great shots even from our backyard in the city. Staying active is also a big focus of mine, so we spend a lot of time out in nature. 

SEE MORE PEOPLE OF PERFICIENT  

It’s no secret our success is because of our people. No matter the technology or time zone, our colleagues are committed to delivering innovative, end-to-end digital solutions for the world’s biggest brands, and we bring a collaborative spirit to every interaction. We’re always seeking the best and brightest to work with us. Join our team and experience a culture that challenges, champions, and celebrates our people.  

Learn more about what it’s like to work at Perficient at our Careers page. See open jobs or join our talent community for career tips, job openings, company updates, and more!  

Go inside Life at Perficient and connect with us on LinkedIn, YouTube, X, Facebook, and Instagram. 

From Self-Service to Self-Driving: How Agentic AI Will Transform Analytics in the Next 3 Years

Imagine starting your workday with an alert not from a human analyst, but from an AI agent. While you slept, this agent sifted through last night’s sales data, spotted an emerging decline in a key region, and already generated a mini-dashboard highlighting the issue and recommending a targeted promotion. No one asked it to; it acted on its own. This scenario isn’t science fiction or some distant future; it’s the imminent reality of agentic AI in enterprise analytics. Businesses have spent years perfecting dashboards and self-service BI, empowering users to explore data on their own. However, in a world where conditions are constantly changing, even the most advanced dashboard may feel excessively slow. Enter agentic AI: the next frontier where intelligent agents don’t just inform decisions; they make and even execute decisions autonomously. Over the next 1–3 years, this shift toward AI-driven “autonomous BI” is poised to redefine how we interact with data, how analytics teams operate, and how insights are delivered across organizations.

In this post, we’ll clarify what agentic AI means in the context of enterprise analytics and explore how it differs from traditional automation or self-service BI. We’ll forecast specific changes this paradigm will bring, from business users getting proactive insights to data teams overseeing AI collaborators, and call out real examples (think AI agents auto-generating dashboards, orchestrating data pipelines, or flagging anomalies in real time). We’ll also consider the cultural and organizational implications of this evolution, such as trust and governance, and conclude with a point of view on how enterprises can prepare for the agentic AI era.

What is Agentic AI in Enterprise Analytics?

Agentic AI (often called agentic analytics in BI circles) refers to analytics systems powered by AI “agents” that can autonomously analyze data and take action without needing constant human prompts. In traditional BI, a human analyst or business user queries data, interprets results, and decides on an action. By contrast, an agentic AI system is goal-driven and proactive; it continuously monitors data, interprets changes, and initiates responses aligned with business objectives on its own. In other words, it shifts the analytics model from simply supporting human decisions to executing or recommending decisions independently.

Put simply, agentic analytics enables autonomous, goal-driven analytic agents that behave like tireless virtual analysts. They’re designed to think, plan, and act much like a human analyst would, but at machine speed and scale. Instead of waiting for someone to run a report or ask a question, these AI agents proactively scan data streams, reason over what they find, and trigger the appropriate next steps. For example, an agent might detect that a KPI is off track and automatically send an alert or even adjust a parameter in a system, closing the loop between insight and action. This stands in contrast to earlier “augmented analytics” or alerting tools that, while they could highlight patterns or outliers, were fundamentally passive; they still waited for a human to log in or respond. Agentic AI, by definition, carries the initiative: it doesn’t just explain what’s happening; it helps change what happens next.

It’s worth noting that the term “agentic” implies having agency, the capacity to act autonomously. In enterprise analytics, this means the AI isn’t just crunching numbers; it’s making choices about what analyses to perform and what operational actions to trigger based on those analyses. This could range from generating a new visualization to writing back results into a CRM to launching a workflow in response to a detected trend. Crucially, agentic AI doesn’t operate in isolation of humans’ goals. These agents are usually configured around explicit business objectives or KPIs (e.g., reduce churn, optimize inventory). They aim to carry out the intent set by business leaders, just without needing a person to micromanage each step.

Beyond Automation and Self-Service – How Agentic AI Differs from Today’s BI

It’s important to distinguish agentic AI from the traditional automation and self-service BI approaches that many enterprises have implemented over the past decade. While those were important steps in modernizing analytics, agentic AI goes a step further in several key ways:

• Proactive vs. Reactive: Traditional BI systems (even self-service ones) are fundamentally reactive. They provide dashboards, reports, or alerts that a human must actively check or respond to. Automation in classic BI (like scheduled reports or rule-based alerts) can trigger predefined actions, but only for anticipated scenarios. Agentic AI flips this model: AI agents continuously monitor data streams and autonomously identify anomalies or opportunities in real time, acting without waiting for a human query or a pre-scheduled job. The system doesn’t sit idle until someone asks a question; it searches for questions to answer and problems to solve on its own. This drastically reduces decision latency, as actions can be taken at the moment conditions warrant, not hours or days later when a person finally notices.
• Decision Execution vs. Decision Support: Self-service BI and automation tools have largely been about supporting human decision-making, surfacing insights faster, or auto-refreshing data, but ultimately leaving the interpretation and follow-up to people. Agentic AI shifts to decision execution. An agentic analytics platform can decide on and carry out a next step in the business process. Rather than just emailing you an alert about a sudden dip in revenue, an agent might also initiate a discounted offer to at-risk customers or reallocate ad spend, actions a human analyst might have taken, now handled by the AI. It’s a move from insight to outcome. As one industry observer put it, “agentic analytics executes and orchestrates actions… a shift from insights for humans to outcomes through machines.” Importantly, this doesn’t mean removing humans entirely; think of it as humans setting the goals and guardrails, while the AI agent carries out the routine decisions within those boundaries (often phrased as moving from human-in-the-loop to human-on-the-loop oversight).
• Adaptive Learning vs. Static Rules: Traditional automation often runs on static, predefined rules or scripts (e.g., “if KPI X drops below Y, send alert”). Agentic AI agents are typically powered by advanced AI (including machine learning and large language models) that allow them to learn and adapt. They maintain memory of past events, learn from feedback, and improve their recommendations over time. This means the agent can handle novel situations better than a fixed rule could. For instance, if an agent took an action that didn’t have the desired outcome, it can adjust its strategy next time. This continuous learning loop is something traditional BI tools lack; they’re only as good as their initial programming, whereas an agentic system can get “smarter” and more personalized with each iteration.
• Natural Interaction and Democratization: Self-service BI lowered the technical barrier for users to get insights (e.g., drag-and-drop dashboards, natural language query features). Agentic AI lowers it even further by allowing conversational or even hands-off interaction. Business users might simply state goals or ask questions in plain English, and the AI agent handles the heavy lifting of data analysis and presentation. For example, a user could ask, “Why did our conversion rate drop last week?” and receive an explanation with charts, without writing a single formula. More impressively, an agent might notify the user of the drop before they even ask, complete with a diagnosis of causes. In effect, everyone gets access to a “personal data analyst” that works 24/7. This continues the BI trend of democratizing data, but with agentic AI, even non-technical users can leverage advanced analytics because the AI translates raw data into succinct, contextual insights. The result is more people in the organization can harness data effortlessly, through intuitive interactions, without sacrificing trust or accuracy, although ensuring that trust is maintained brings us to important governance considerations, which we’ll discuss later.

In summary, agentic AI goes beyond what traditional automation or self-service BI can do. If a classic self-service dashboard was like a GPS map you had to read, an agentic AI is like a self-driving car; you tell it where you want to go, and it navigates there (while you watch and ensure it stays on track). This evolution is happening now because of converging advances in technology: more powerful AI models, API-accessible cloud tools, and enterprises’ appetite for real-time, automated decisions. With the groundwork laid, analytics is moving from a manual, human-driven endeavor to a collaborative human-AI partnership, and often, the AI will take the first action.

The Coming Changes: How Agentic AI Will Impact Users, Teams, and Analytics Delivery

What practical changes should we expect as agentic AI becomes part of enterprise analytics in the next 1–3 years? Let’s explore the forecast across three dimensions: how business users interact with data, how data and analytics teams work, and how analytics capabilities are delivered in organizations.

Impact on Business Users: From Asking for Insights to Acting on Conversations

For business users, the managers, analysts, and non-technical staff who consume data, agentic AI will make analytics feel more like a conversation and less like a hunt for answers. Instead of clicking through dashboards or waiting for weekly reports, users will have AI assistants that deliver insights proactively and in real-time.

• Proactive Insights and Alerts: Users will increasingly find that key insights come to them without asking. AI agents will continuously watch metrics and immediately flag anomalies or trends in real time, for instance, spotting a sudden spike in support tickets or a dip in conversion rate, and notify the relevant users with an explanation. This might happen via the tools people already use (a Slack message, an email, a mobile notification) rather than a BI portal. Crucially, the agent doesn’t just raise a flag; it provides context (e.g., “Conversion rates dropped 5% today, mainly in the Northeast region, possibly due to a pricing change”) and might even suggest a next step. Business users move from being discoverers of insights to responders to insights surfaced autonomously.
• Conversational Data Interaction: The mode of interacting with analytics will shift toward natural language. We’re already seeing early versions of this with chatbots in analytics tools, but agentic AI will make it far more powerful. Users will be able to ask follow-up questions in plain English and get instant answers with relevant charts or predictions, effectively having a dialog with their data. For example, a marketing VP could ask, “Agent, why is our Q3 pipeline behind plan?” and get a dynamically generated explanation that the agent figured out by correlating CRM data and marketing metrics. If the answer isn’t clear, the VP can ask, “Can you break that down by product line and suggest any fixes?”, and the agent will drill down and even propose actions (like increasing budget on a lagging campaign). This means less time training business users on BI tools and more time acting on insights, since the AI handles the mechanics of data analysis.
• Higher Trust (with Transparency): Initially, some users may be wary of an AI making suggestions or decisions; trust is a big cultural factor. Over the next few years, expect agentic AI tools to integrate explainability features to earn user trust. For instance, an agent might not only send a recommendation but also a brief rationale: “I’m suggesting a price drop on Product X because sales are 20% below forecast and inventory is high.” This transparency, along with the option for users to provide feedback or override decisions, will be key. As users see that the agents’ tips are grounded in data and often helpful, comfort with “AI co-workers” will grow. In fact, by offloading routine analysis to AI, business users can focus more on strategic thinking, and paradoxically increase their data literacy by engaging in more high-level questioning of the data (the AI does the number crunching, but users still exercise judgment on the recommendations).
• Example, Daily “Agent” Briefings: To illustrate, imagine a finance director gets a daily briefing generated by an AI agent each morning. It’s a short narrative: “Good morning. Today’s cash flow is on track, but I noticed an unusual expense spike in marketing, 30% above average. I’ve attached a breakdown chart and alerted the marketing lead. Also, three regional sales agents missed their targets; I’ve scheduled a meeting on their calendars to review. Let me know if you want me to take any action on budget reallocations.” This kind of hands-off insight delivery, where the agent surfaces what matters and even kicks off next steps, could become a routine part of business life. Business users essentially gain a virtual analyst that watches over their domain continuously.

Overall, for business users, the next few years with agentic AI will feel like analytics has turned from a static product (dashboards and reports you check) into an interactive service (an intelligent assistant that tells you what you need to know and helps you act on it). The organizations that embrace this will likely see faster decision cycles and a more data-informed workforce, as employees spend less time gathering insights and more time using them.

Impact on Data Teams: From Builders of Reports to Trainers of AI Partners

For data and analytics teams (data analysts, BI developers, data engineers, data scientists), agentic AI will bring a significant shift in roles and workflows. Rather than manually producing every insight or report, these teams will collaborate with AI agents and focus on enabling and governing these agents.

• Shift to Higher-Value Tasks: Much of a data team’s routine workload today, writing SQL queries, building dashboards, updating reports, and troubleshooting minor data issues, can be time-consuming. As AI agents start handling tasks like generating analyses or spotting data issues automatically, human analysts will be freed up for more high-value activities. For example, if an agent can automatically produce a weekly KPI overview and pinpoint the outliers, the analyst can spend their time investigating the why behind those outliers and planning strategic responses, rather than crunching the numbers. Data scientists might similarly delegate basic model monitoring or data prep to AI routines and focus on designing better experiments or algorithms. In essence, the human experts become more like strategic supervisors and domain experts, guiding the AI on what problems to tackle and validating how the AI’s insights are used.
• New Collaboration with AI (“Centaur” Teams): We’ll likely see the rise of “centaur” analytics teams, a term borrowed from human-computer chess teams, where human analysts and AI agents work together on analytics projects. A data analyst might ask an AI agent to fetch and preprocess certain data, test dozens of correlations, or even draft an analytic report. The analyst then reviews, corrects, and adds domain context. This iterative partnership can drastically speed up analysis cycles. Data teams will need to develop skills in prompting and guiding AI agents, much like a lead analyst guiding a junior employee. The next 1–3 years might even see specialized roles emerge, such as Analytics AI Trainers or AI Wrangler, people who specialize in configuring these agents, tuning their behavior (for example, setting the logic for when an agent should escalate an issue to a human), and feeding them the right context.
• Focus on Data Pipeline Orchestration and Quality: Agentic AI is only as good as the data it can access. Data engineers will find their work more crucial than ever, not in manually running pipelines, but in ensuring robust, real-time data infrastructure for the agents. In fact, one of the big changes is that AI agents themselves may orchestrate data pipelines or integration tasks as needed. For instance, if an analytics agent determines it needs fresh data from a new source (say, a marketing system) to analyze a trend, it could automatically trigger an ETL job or API call to pull that data, rather than waiting on a data engineer’s backlog. We’re already seeing early architectures where an agent, empowered with the right APIs, can initiate workflows across the data stack. Data teams, therefore, will put more effort into building composable, API-driven data platforms that agents can plug into on the fly. They will also need to set up monitoring. If an agent’s automated pipeline run fails or produces weird results, it should alert the team or retry, which ties into governance (discussed below).
• Example, AI Orchestrating a Pipeline: Consider a data engineering scenario: an AI agent in charge of analytics notices that a particular report is missing data about a new product line. Traditionally, an engineer might have to add the new data source and rebuild the pipeline. In an agentic AI setup, the agent itself might call a data integration tool via API to pull in the new product data and update the data model, then regenerate the dashboard with that data included. All of this could happen in minutes, whereas a manual process might take days. The data team’s job in this case was to make sure the integration tool and data model were accessible and that the agent had the proper permissions and guidelines. This kind of autonomous pipeline management could become more common, with humans overseeing the exceptions.
• Guardians of Governance: Perhaps the most critical role for data teams will be governing the AI agents. They will define the guardrails, what the agents are allowed to do autonomously vs. where human sign-off is required, how to avoid the AI making erroneous or harmful decisions, and how to monitor the AI’s performance. Data governance and security professionals will work closely with analytics teams to implement policy-based controls on these agents. For example, an agent might be permitted to send an internal Slack alert or create a Jira ticket on its own, but not to send a message directly to a client without approval. Every action an agent takes will likely be logged and auditable. The next few years will see companies extending their data governance frameworks to cover AI behavior, ensuring transparency, preventing “rogue” actions, and maintaining compliance. Data teams will need to build trust dashboards of their own, showing how often agents are intervening, what outcomes resulted, and flagging any questionable AI decisions for review.

In short, data teams will transition from being the sole producers of analytics output to being the enablers and overseers of AI-driven analytics. Their success will be measured not just by the reports they build, but by how well they can leverage AI to scale insights. This means stronger emphasis on data quality, real-time data availability, and robust governance. Culturally, it may require a mindset shift: accepting that some of the work traditionally done “by hand” can be delegated to machines, and that the value of the team is in how they guide those machines and interpret the results, rather than in producing every chart themselves. Organizations that prepare their data talent for this augmented role, through training in AI tools and proactive change management, will handle the transition more smoothly.

Impact on Analytics Delivery: Insights When and Where They’re Needed

Agentic AI will also transform how analytics capabilities are delivered and consumed in the enterprise. Today, the typical delivery mechanism is a dashboard, report, or perhaps a scheduled email, in other words, the user has to go to a tool or receive a static packet of information. In the coming years, analytics delivery will become more embedded, continuous, and personalized, largely thanks to AI agents working behind the scenes.

• From Dashboards to Embedded Insights: We may witness the beginning of the end of the standalone, static dashboard as the primary analytics product. Instead, insights will be delivered in the flow of work. AI agents can push insights into chat applications, business software (CRM, ERP), or even directly into operational dashboards in real-time. For example, rather than expecting a manager to log into a BI tool, an agent might integrate with Slack or Microsoft Teams to post a daily metrics summary, or inject an alert into a sales system (“this customer is at risk of churning; here’s why…” as a note on the account). This embedded approach has been called “headless BI” or “analytics anywhere,” and agentic AI accelerates it, because the agents can operate through APIs; they aren’t tied to a single UI. The result: analytics becomes more ubiquitous but less visible; users just experience their software getting smarter with data-driven guidance at every turn, courtesy of AI.
• Autonomous Report Generation: The creation of analytic content itself will increasingly be automated. Need a new report or visualization? In many cases, you won’t file a request to IT or even drag-and-drop it yourself; an AI agent can generate it on the fly. For instance, if a department head wonders about a trend, the agent can compile a quick dashboard or narrative report addressing that query, using templates and visualization libraries. These reports might be ephemeral (created for that moment and then discarded or refreshed later). Over the next few years, as agentic AI gets better at understanding business context, we’ll see “self-serve” taken to the next level: the system serves itself on behalf of the user. One concrete example today is AI that generates Power BI or Tableau dashboards from natural language questions. Going forward, an agent might proactively create an entire dashboard for a quarterly business review meeting, unprompted, because it knows what metrics the meeting usually covers and has detected some changes worth highlighting. Indeed, some modern BI platforms are already hinting at this capability; e.g., Tableau’s upcoming “Pulse” and ThoughtSpot’s Spotter agent aim to deliver key metrics and even generate charts without manual effort.
• Real-Time Anomaly Detection and Action: Real-time analytics isn’t new, but agentic AI will broaden its impact. Rather than just streaming charts updating in real time, an agentic approach means the moment an anomaly occurs, it’s not only detected, but something happens. This is analytics delivery as an event-driven process. If a sudden spike in website latency is detected, an AI agent might immediately create an incident ticket and ping the on-call engineer with diagnostic info attached. If sales on a new product are surging beyond forecast, an agent might auto-adjust the supply chain parameters or at least alert the inventory planner to stock up. These kinds of immediate, cross-system actions blur the line between analytics and operations. In effect, analytics outputs (insights) and business inputs (actions) merge. The next few years will likely see BI tools integrating more tightly with automation/workflow platforms so that insight-to-action loops can be closed programmatically. As one example, agents could leverage workflow tools (like Salesforce Flow or Azure Logic Apps) to trigger multi-step processes when certain data conditions are met. The vision is an “autonomous enterprise” where routine decisions and responses happen at machine speed, with humans intervening only for exceptions or strategic choices.
• Continuous Personalization: Analytics delivery will also become more tailored to each user’s context, thanks to AI’s ability to personalize. An agent could learn what each user cares about (their role, their usual queries, and their past behavior) and customize the insights delivered. For example, a VP of Sales might get alerts about big deals slipping, while a CFO’s agent curates financial risk indicators. Both are looking at the same underlying data universe, but their AI agents filter and format insights to what’s most relevant to each. This personalization extends to timing and format; the AI might learn that a particular manager prefers a text summary vs. a chart and deliver information accordingly. In the near term, this might simply mean smarter defaults and recommendations in BI tools. Within a few years, it could mean each executive essentially has a bespoke analytics feed curated by an AI that knows their priorities.

To sum up, analytics capabilities will be delivered more fluidly and in an integrated fashion. Rather than thinking of “going to analytics,” the analytics will come to you, often initiated by an agent. Dashboards and reports will not disappear overnight (they still have their place for deep dives and record-keeping), but the center of gravity will shift toward timely insights injected into decision points. The business impact is significant: decisions can be made faster and in context, and fewer opportunities or risks will slip through unnoticed between reporting cycles. It’s a world where, ideally, nothing important waits for the next report; your AI agent has already informed the right people or taken action.

Organizational Implications: Trust, Culture, and Governance in the Age of AI Agents

The technical capabilities of agentic AI are exciting, but enterprises must also grapple with cultural and organizational implications. Introducing autonomous AI into analytics workflows will affect how people feel about trust, control, and their own roles. Here are some key considerations:

• Building Trust in AI Decisions: Trust is paramount. If business stakeholders don’t trust the AI outputs or actions, they’ll resist using them. Early in the adoption of agentic AI, organizations should invest in explainability and transparency. Ensure the AI agents can show the rationale behind their conclusions (audit trails, plain-language explanations) to demystify their “thinking.” Start with agents making low-risk decisions and proving their reliability. For instance, let an agent flag anomalies and suggest actions for a period of time, and have humans review its accuracy. As confidence grows, the agent can be allowed to take more autonomous actions. It’s also wise to maintain a human-in-the-loop for critical decisions; for example, an agent might draft an email to a client or a change to pricing, but a human approves it until the AI has earned trust. According to best practices, a well-architected agentic system will log every action and enable easy overrides or rollbacks. Demonstrating these safety nets goes a long way in getting team buy-in.
• Governance and Ethical Use: Alongside trust is the need for robust governance. Companies will need to update their data governance policies to include AI agent behavior. This means defining what data an agent can access (to prevent privacy violations), what types of decisions it’s allowed to make, and how to handle errors or “hallucinations” (when an AI produces incorrect output). Establish clear accountability: if an AI agent makes a mistake, who checks it and corrects it? Setting up an AI governance committee or expanding the remit of existing data governance boards can help oversee these issues. They should define guidelines like: AI agents must identify themselves as such when communicating (so people know it’s an algorithm), they must adhere to company compliance rules (e.g., not sending sensitive data externally), and they should escalate to humans when a situation is ambiguous or high-stakes. Fortunately, many agentic AI platforms recognize this need and offer role-based controls and audit features. Enterprises should take advantage of those and not treat an autonomous agent as a “set and forget” technology; continuous monitoring is key. Essentially, trust but verify: let the agents run, but keep dashboards for AI performance and a way to quickly intervene if something looks off.
• Job Roles and Skills Evolution: Understandably, some employees may fear that more AI autonomy could threaten jobs (the classic “will AI replace me?” concern). It’s critical for leadership to address this proactively as part of cultural change. The narrative should be that agentic AI is meant to augment human talent, not replace it, taking over drudgery and enabling people to focus on higher-value work. In many cases, new roles will emerge (as discussed for data teams), and existing roles will shift to incorporate AI supervision. Training and upskilling programs will be important so that staff know how to work with AI agents. For example, train business analysts to interpret AI-generated insights and ask the right questions of the system, or train data scientists on how to embed AI agents into workflows. Equally, encourage development of “soft skills” like critical thinking and data storytelling, because while the AI can crunch data, humans still need to translate insights into decisions and convince others of a course of action. Organizations that treat this as an opportunity for employees to become more strategic and tech-savvy will find the cultural transition much smoother than those that simply impose the technology. Including end-users in pilot projects (so they can give feedback on the agent’s behaviors and feel ownership) is another good practice to ease adoption.
• Data Literacy and Decision Culture: With AI taking on more analytics tasks, one might worry that employees’ data skills will atrophy. On the contrary, if rolled out correctly, agentic AI can actually raise the baseline of data literacy in the company. When AI agents provide insights in accessible language, it can educate users on what the data means. People might start to internalize, for example, which factors typically influence sales because their AI assistant frequently points them out. However, there’s a flip side: employees must be educated not to blindly follow AI. A culture of healthy skepticism and validation should be maintained, e.g., encouraging users to double-check critical suggestions or understand the “why” behind agent actions. Essentially, “trust the AI, but verify the results” should be a mantra. Businesses should continue investing in data literacy programs, now including AI literacy: teaching staff the basics of how these analytics agents work, their limitations, and how to interpret their outputs. This will empower employees to use AI as a tool rather than see it as a mysterious black box or, worse, a threat.
• Change Management and Communication: Rolling out agentic AI capabilities enterprise-wide is a major change that touches processes and people across departments. A strong change management plan is essential. Communicate early and often about what agentic AI is, why the company is adopting it, and how it will benefit both the organization and individual employees (e.g., “It will free you from manual spreadsheet updates so you can spend more time with clients”). Highlight success stories from pilot tests; for instance, if the sales team’s new AI agent helped them respond faster to lead changes, share that story. Address concerns in open forums. And provide channels for feedback once it’s in use: users should have a way to report if the AI agent did something weird or if they have ideas for improvements. Culturally, leadership should champion a mindset of responsible experimentation, encourage teams to try these new AI-driven workflows while also reinforcing that ethical considerations and human judgment remain paramount. Over the next few years, companies that actively shape their culture around human-AI collaboration will likely outperform those that simply deploy the tech and hope people figure it out.

Preparing for the Agentic AI Era: Recommendations for Enterprises

Agentic AI in analytics is on the horizon, and the time to prepare is now. Here’s a forward-thinking game plan for enterprises to get ready for this shift:

• Strengthen Data Foundations: Ensure your data house is in order. Agentic AI thrives on timely, high-quality data. Invest in data readiness, integrate your data sources, clean up quality issues, and build the pipelines for real or near-real-time data access. Consider modern data architectures (like data lakes or warehouses with streaming capabilities) that an AI agent can tap into on demand. The next 1–3 years should see upgrades to data infrastructure with an eye toward supporting AI: e.g., adopting tools that allow easy API access to data, implementing robust data catalogs/semantic layers (so the AI agents understand business definitions), and generally making data more available and trustworthy. Simply put, if your data is fragmented or slow, an AI agent won’t magically fix that; lay the groundwork now.
• Start with Pilot Projects: Rather than flipping a switch enterprise-wide, start by introducing agentic AI on a smaller scale to learn what works. Identify a use case with clear value, for example, an AI agent to monitor financial metrics for anomalies, or an agent to handle marketing campaign optimization suggestions. Pilot it in one department or process. This allows you to fine-tune the technology and the human processes around it. In the pilot, closely involve the end-users and gather feedback: Did the agent provide useful insights? Did it make any mistakes? How was the user experience? Use these lessons to refine your approach before scaling up. Early successes will also build momentum and buy-in within the organization. By experimenting in the next year, you’ll develop internal expertise and champions who can lead broader adoption in years 2 and 3.
• Invest in Skills and Change Management: Prepare your people, not just your tech. Launch training programs and workshops to familiarize employees with the concepts of AI-driven analytics. Train your data teams on the specific AI tools or platforms you plan to use (maybe it’s a feature in your BI software, or a custom AI solution using Python frameworks). Also, upskill business users on how to interpret AI outputs, for instance, how to converse with a data chatbot effectively, or how to verify an AI-generated insight. Simultaneously, engage in change management: communicate the vision that agentic AI will augment everyone’s capabilities. Address the “what does this mean for my job” questions head-on (perhaps emphasizing that the organization will re-invest efficiency gains into growth, not just headcount cuts, to quell fears). Encourage a culture of continuous learning so employees see this as an opportunity to learn new tools and advance their roles. Essentially, prepare the human minds for the change, not just the IT systems.
• Define Governance and Guardrails: Before unleashing AI agents, define the governance policies that will keep them in check. Assemble the relevant stakeholders (IT, data governance, legal, business leaders) to map out scenarios: What decisions can the AI make autonomously? What data is it allowed to use? How will we handle errors or unexpected outcomes? Draft guidelines such as “AI must tag any outbound communication as AI-generated” or “For decisions impacting spend over $X, require human approval”. Set up an oversight process, maybe a periodic review of AI agent logs and outcomes by a governance board. This preparation will help prevent incidents and also reassure everyone that there are safety nets. Additionally, explore your tool’s capabilities for setting roles/permissions for agents. Many modern analytics platforms embed governance features (for example, ensuring the AI only uses governed data sources or limiting integration points to approved systems). Leverage those. In short, treat your AI agent like a new team member: it needs a “job description” and supervision.
• Reimagine Processes and Roles: Be proactive in redesigning workflows to integrate AI agents. Don’t just slap AI onto existing processes; think about where decisions or handoffs could be made more efficient. For example, if marketing currently meets weekly to adjust campaigns, could an AI agent handle adjustments daily and the meeting shift to strategy? If data engineers spend time on routine pipeline fixes, can an agent auto-detect and resolve some of those? Start mapping these possibilities and adjusting team roles accordingly. You might formally assign someone as an “AI operations” lead to monitor all agent activity. You might need to update incident response playbooks to include AI-generated alerts. Also consider KPI changes: perhaps include metrics like “number of autonomous decisions executed” or “AI agent precision (accuracy of its recommendations)” as new performance indicators for the analytics program. By envisioning these changes early, you can guide the transition rather than just reacting to it.
• Develop a Clear Vision and Executive Support: Finally, ensure there is a clear point of view from leadership on why the organization is embracing agentic AI. Tie it to business goals (faster insights, more competitive decisions, empowered employees, etc.). When leadership articulates a positive vision, e.g., “In three years, we aim to have AI copilots assisting every team, elevating our decision-making and freeing us to focus on innovation,” it gives the effort purpose and urgency. Secure executive sponsorship to allocate budget and to champion the change across departments. Enterprises should also track the industry and learn from others: join communities or forums on AI in analytics, and perhaps partner with vendors or consultants who specialize in this area (since they can share best practices from multiple client experiences). A clear, supported strategy will help coordinate the technical and cultural preparation into a successful transformation.

Agentic AI represents a bold leap in the evolution of business intelligence, from tools that we operate to intelligent agents that work alongside us (and sometimes ahead of us). In the next 1–3 years, we can expect early forms of these AI agents to become part of everyday analytics in forward-thinking enterprises. They will likely start by tackling well-defined tasks: automatically generating reports, sending alerts for anomalies, and answering common analytical questions. Over time, as trust and sophistication grow, their autonomy will increase to more complex orchestrations and decision executions. The payoff can be substantial: faster decision cycles, decisions that are more data-driven and less prone to human overlook, and analytics capabilities that truly scale across an organization. Companies that embrace this shift early could gain a competitive edge, outpacing those stuck in manual analytics with speed, agility, and insights that are both deeper and more timely.

Yet, success with agentic AI won’t come just from buying the latest AI tool. It requires a thoughtful approach to technology, process, and people. The enterprises that thrive will be those that pair innovation with governance, enthusiasm with education, and automation with a human touch. By laying the groundwork now, improving data infrastructure, cultivating AI-friendly skills, and establishing clear rules, organizations can confidently welcome their new AI “colleagues” and harness their potential. In the near future, your most trusted analyst might not be a person at all, but an algorithmic agent that never sleeps, never gets tired, and continuously learns. The question is, will your organization be ready to partner with it and leap ahead into this new age of analytics?

Sources:

• Ryan Aytay, Tableau, “Agentic Analytics: A New Paradigm for Business Intelligence”, Tableau Blog (April 2025)
• Arend Verschueren, Biztory, “Agentic Analytics: The Future of Autonomous BI” (June 2025)
• Shuchismita Sahu, Medium, “Agentic BI: Your Intelligent Data Analyst Revolution” (May 2025)
• Will Thrash, Perficient Blogs, “Elevate Your Analytics: Overcoming the Roadblocks to AI-Driven Insights” (Jan 2025)
• Will Thrash, Perficient Blogs, “Headless BI?” (Nov 2023)

At Perficient, our Sales team is at the forefront of shaping the future of AI-first transformation for some of the world’s most innovative enterprises and admired brands. Our sales professionals serve as trusted advisors, strategic partners, and essential contributors to creating bold solutions that define who we are as a global digital consultancy. 

We offer a platform for sellers to directly impact major industries around the world, and the opportunities we offer here are boundless. Professional and personal growth starts at Perficient.  

What Sets Our Sales Team Apart?  

Perficient’s Sales team builds trusted partnerships with Fortune 500 and Global 2000 clients, helping them rethink, reimagine, and redefine their digital futures. We deliver solutions that move business forward by combining the entrepreneurial spirit and agility of a startup with the stability and reputation of an award-winning, AI-first global consultancy.  

READ MORE: Perficient Accelerates Growth for the Biggest Brands 

Another major advantage of being part of Perficient’s Sales team is the opportunity to sell alongside the biggest names in tech. We are elite partners of Adobe, Salesforce, Microsoft, AWS, and Google, among other industry-leading technology innovators. These partnerships bring great value to the conversations our sales teams have with clients, offering credibility, co-selling opportunities, and access to cutting-edge solutions. 

Our sellers receive the enablement and partner support they need to build strategic pipelines and close high-impact deals. At the same time, they gain the advantage of working on innovative solutions that go beyond the basics—integrating AI, data, and design in ways that truly differentiate our offerings in the market. 

“Perficient is a place where you can grow, it’s a very demanding environment but with hard work and success there is lots of opportunity,” said Jake Corn, account developer. “We have a real entrepreneurial culture; you are expected to own your own business but you have all of the resources to help you. Leadership will step in to help you out wherever needed.” 

LEARN MORE: Perficient’s Strategic Partnerships 

Culture Driven by Collaboration 

Our teams of skilled strategists and technologists around the world bring an unmatched level of dedication, drive, and passion in everything we do to boldly advance business. We are committed to building the future of AI and making a difference. These universal traits not only make Perficient a formidable force in the market, but they also contribute to a unique people experience. In fact, we’ve been named a USA Today Top Workplace for two years in a row, serving as a reflection of our commitment to building a people-first culture and positive employee experience. 

t for two years in a row, serving as a reflection of our commitment to building a people-first culture and positive employee experience. 

Perficient understands that it’s our people who make a difference, which is why we’ve made a promise to challenge, champion and celebrate our people through the Perficient People Promise. 

READ MORE: Unveiling the New Perficient People Promise 

As part of our people-first approach, our Growth for Everyone initiative drives continuous professional development with real pathways for advancement.  From leadership training and mentorship programs to enablement resources built specifically for sellers, we’re committed to helping you move forward—whether that’s into new markets, new roles, or new levels of impact. And because our Sales teams are embedded across industries and technologies, you’ll always be learning something new, surrounded by peers who are just as invested in your success as you are. Our sales professionals work side-by-side with delivery experts, technical consultants, and partner managers to shatter boundaries for our clients, ensuring they receive end-to-end support at every stage of the project. 

LEARN MORE: Shattering Boundaries with Perficient’s Digital Expertise and Global Strategy 

When you join Perficient, you’re joining a global team that’s forging the future, together. 

What We Look for in Sales Talent 

We’re always looking for curious, driven professionals who know how to open doors and build real relationships. If you’ve sold consulting or professional services before, and you enjoy connecting the dots between business needs and digital solutions, you’ll feel right at home here. 

Many of our sales roles involve working closely with clients in local markets, so having a strong network and staying informed about regional opportunities can be a significant advantage, especially for those focused on growing existing accounts. We’re also focusing on roles tied to specific technologies and would welcome those with a background in navigating the world of platforms like Adobe, Salesforce, Microsoft, or Oracle.  

Whether you’re stepping into your first consulting sales role or bringing years of experience to the table, you’ll find the support, mentorship, and growth opportunities to chart your own career path. 

Ready to be part of an AI-First, collaborative, and purpose-driven culture? 

Explore our open sales roles and take the next step in your career. 

I’ve been coding in notebooks for so long, I forgot how much I missed a nice, deployed application. I also didn’t realize how this was limiting my solution space. Then I started working with Databricks Apps.

Databricks Apps are designed to extend the functionality of the Databricks platform, providing users with enriched features and capabilities tailored to specific data needs. These apps can significantly enhance the data processing and analysis experience, offering bespoke solutions to address complex business requirements.

Key Features of Databricks Apps

1. Custom Solutions for Diverse Needs: Databricks Apps are built to cater to a wide range of use cases, from data transformation and orchestration to predictive analytics and AI-based insights. This versatility allows organizations to deploy applications that directly align with their specific business objectives.
2. Seamless Integration: The apps integrate smoothly within the existing Databricks environment, maintaining the platform’s renowned ease of use and ensuring that deployment does not disrupt current data processes. This seamless integration is crucial for maintaining operational efficiency and minimizing transition challenges.
3. Scalability and Flexibility: Databricks Apps are designed to scale with your organization’s needs, ensuring that as your data requirements grow, the solutions deployed through these apps can expand to meet those demands without compromising performance.
4. Enhanced Collaboration: By leveraging apps that foster collaboration, teams can work more effectively across different departments, sharing insights and aligning strategic goals with more precision and cohesion.

Benefits for Architects

1. Tailored Data Solutions: Databricks Apps enables architects to deploy tailored solutions that meet their unique data challenges, ensuring that technical capabilities are closely aligned with strategic business goals.
2. Accelerated Analytics Workflow: By using specialized apps, organizations can significantly speed up their data analytics workflows, leading to faster insights and more agile decision-making processes, essential in today’s fast-paced business environment.
3. Cost Efficiency: The capability to integrate custom-built apps reduces the need for additional third-party tools, potentially lowering overall costs and simplifying vendor management.
4. Future-Proofing Data Strategies: With the rapid evolution of technology, having access to a continuously expanding library of Databricks Apps helps organizations stay ahead of trends and adapt swiftly to new data opportunities and challenges.

Strategies for Effectively Leveraging Databricks Apps

To maximize the potential of Databricks Apps, CIOs and CDOs should consider the following approaches:

• Identify Specific Use Cases: Before adopting new apps, identify the specific data operations and challenges your organization is facing. This targeted approach ensures that the apps you choose provide the most value.
• Engage with App Developers: Collaborate with app developers who specialize in delivering comprehensive solutions tailored to your industry. Their expertise can enhance the implementation process and provide insights into best practices.
• Promote Cross-Department Collaboration: Encourage departments across your organization to utilize these apps collaboratively. The synergistic use of advanced data solutions can drive more insightful analyses and foster a unified strategic direction.
• Assess ROI Regularly: Continuously assess the return on investment from using Databricks Apps. This evaluation will help in determining their effectiveness and in making data-driven decisions regarding future app deployments.

Conclusion

Databricks Apps present a powerful opportunity for CIOs and CDOs to refine and advance their data strategies by offering tailored, scalable, and integrated solutions. By embracing these tools, organizations can transform their data-driven operations to gain a competitive edge in an increasingly complex business landscape.

Contact us to learn more about how to empower your teams with the right tools, processes, and training to unlock Databricks’ full potential across your enterprise.

Perficient’s longstanding partnership with Salesforce is a testament to our belief in this platform’s transformative power in solving complex business challenges and designing superior digital experiences.   

We strive to stay ahead of the evolving Salesforce ecosystem and actively shape its future. Our talented Salesforce professionals go beyond mere implementation; we focus on driving innovation, fostering connections, and creating meaningful change for our clients and the broader technology landscape.  

Whether you’re an experienced Salesforce expert or looking to develop your skills, joining Perficient’s Salesforce team gives you a refreshing opportunity to step into a role that challenges and inspires you to drive impact at a global scale.   

So, what makes Perficient’s Salesforce practice truly exceptional? Keep reading to learn more about:  

• The ways we’re redefining Salesforce innovation across industries  

• How our people and collaborative culture serve as the backbone of our Salesforce practice  

• How our ongoing impact through Salesforce solutions will facilitate continuous professional growth  

Spearheading Advanced Solutions with Salesforce  

Our colleagues are not just Salesforce experts—they are digital innovators. Perficient’s Salesforce team is dedicated to harnessing the full potential of Salesforce’s robust ecosystem, including Data Cloud, Einstein AI, Marketing Cloud, and Experience Cloud. 

We partner with clients across industries such as healthcare, life sciences, manufacturing, and financial services to solve complex problems, create personalized digital experiences, and drive sustainable growth.  

Perficient’s commitment to crafting meaningful and measurable solutions doesn’t end with clients; we ensure that our Salesforce professionals have access to continuous learning and development opportunities to stay ahead of industry trends and technologies.  

Our goal is simple—to deliver digital solutions that matter, while fostering a work environment where your skills and ideas are championed.  

“Our clients are always excited to see how the changes we make will help make their lives easier,” said Katie Wilson, who started as an associate technical consultant and is now a technical architect on our Salesforce team in Fargo. 

READ MORE: Katie Wilson Develops Her Career Through Client Relationships  

Delivering Real Impact Through Collaboration  

Our Salesforce experts don’t work in silos. We foster a collaborative culture where our consultants, architects, engineers, and strategists come together to build tailored solutions.  

“Perficient’s collaborative spirit is what truly sets us apart,” said Jideofor Onyeneho, lead technical consultant, Salesforce. “We work together seamlessly to develop effective solutions that address our clients’ critical business challenges.”   

READ MORE: Jideofor’s Contributions to Perficient’s Salesforce Team  

We leverage Salesforce products and expertise to address industry-specific challenges—whether that’s transforming patient experiences in healthcare, improving partner engagement in manufacturing, or empowering advisors in financial services.   

“I transitioned from web development to Salesforce because problem-solving has always been my passion,” says Carl Thress, technical architect, Salesforce. “The most rewarding part of my job is delivering Salesforce solutions that simplify our clients’ lives. I’m currently pursuing my sixth Salesforce certification to keep growing in my career.”   

LEARN MORE: Carl Thress Learns on the Job and Continues to Thrive   

 Our people are at the core of our success. We believe in creating an environment where professionals with a problem-solving mindset, a collaborator’s approach, and a creator’s mentality can thrive.  

READ MORE:  Highlights from Our Demo Jam Events 

Our promise to challenge, champion, and celebrate our people is at the heart of everything we do. We offer a supportive environment where your ideas matter and foster growth for everyone. Whether through our mentorship programs, our commitment to work-life balance, or our collaborative culture, you’ll find that Perficient is a place where you can thrive personally and professionally. 

Driving Success Across Industries  

• Healthcare  

To expand its community impact and improve patient engagement, a leading pediatric health system partnered with Perficient to modernize its outreach strategy. The organization relied on a legacy CRM that lacked the scalability needed to support its growth ambitions. Managing critical data such as leads, forms, and campaign information became increasingly difficult, limiting its ability to connect with patients effectively. 

By integrating Salesforce Health Cloud with the organization’s existing Marketing Cloud, we created a unified platform that streamlined patient data management and enhanced communication capabilities. Additionally, we optimized system functionality to improve workflows and outreach efforts, making engagement more seamless and efficient.  

The results of this transformation were significant. Improved patient engagement became possible through enhanced data visibility, enabling more effective and personalized communications. Operational efficiency also saw a boost, as the integrated Salesforce ecosystem eliminated manual processes and streamlined workflows. 

This success story highlights the power of digital transformation in healthcare. As Perficient continues to partner with healthcare organizations, we remain committed to driving innovation that enhances patient care, optimizes operations, and strengthens community impact. 

READ MORE: Learn more about how we’re reshaping healthcare technology solutions. 

• Finance 

By leveraging Salesforce to streamline operations and unify data, financial organizations can improve efficiency, enhance customer experiences, and drive better business outcomes. 

For one of our leading clients in Mexico, inefficiencies in its lease platform caused integration gaps, reporting challenges, and manual processes that hindered call center performance. We automated dealer communication, digitized third-party vendor data, and updated Salesforce to address these issues to prevent future business disruptions. As a result, internal processes became more efficient, agent productivity improved, and management gained greater visibility into business operations. 

In another instance, a global financial organization sought to unify its marketing processes by consolidating multiple marketing automation platforms into Salesforce Marketing Cloud Engagement (MCE). Perficient developed a roadmap and architecture integrating multiple CRMs using Multi-Org in MCE and companion orgs in Data Cloud. 

This solution provided the marketing team with a centralized data hub, enabling consistent workflows, advanced segmentation, and seamless cross-organization communication.

• Manufacturing  

Manufacturing companies often face challenges in consolidating data from multiple systems and ensuring seamless sales operations. Perficient has helped industry leaders use Salesforce to streamline workflows, enhance data visibility, and drive revenue growth. 

For a leading manufacturer, the need to consolidate key sales data and generate actionable insights was critical to identifying new business opportunities. Perficient’s Agile global delivery team developed a custom digital sales analytics tool, integrating Google Cloud’s data storage and Tableau’s reporting into the Salesforce interface with a real-time API. This solution provided sales and marketing teams with a single, user-friendly platform to research prospects, follow up efficiently, and generate leads based on detailed asset and account data. As a result, productivity improved, and the company gained a scalable system for driving future revenue. 

Interested in learning more about our work within the manufacturing space? Click here.  

• Data 

In today’s digital-first world, businesses need a solid data foundation to deliver seamless, personalized experiences. For companies rolling out Agentforce, integrating Salesforce Data Cloud is a game-changer. By unifying customer data across multiple sources, Data Cloud enables agents and brokers to gain real-time insights, improve customer engagement, and make informed decisions that drive business growth. 

With AI-powered analytics, Salesforce Data Cloud enhances personalization by equipping sales teams with predictive insights, helping them anticipate customer needs and tailor interactions. Additionally, its scalability and security make it a future-proof solution for growing organizations. By centralizing and optimizing data, businesses can empower their teams to operate with agility and precision. 

READ MORE: Learn how Salesforce Data Cloud is transforming sales and customer engagement across industries. 

Insights From the Salesforce Manufacturing Summit 2025 

Our team had the opportunity to participate in the Salesforce Manufacturing Summit in Atlanta, an event that brought together over 800 attendees with more than 40 sessions and 20 product demonstrations. It was an exciting space for industry leaders, peers, and customers to engage in meaningful discussions about the future of manufacturing, with a strong focus on innovation and digital transformation. 

A key highlight of the summit was the increasing role of Generative AI in manufacturing, particularly within Salesforce’s Agentforce and Data Cloud solutions. These technologies are set to transform the industry by streamlining operations, enhancing efficiency, and improving customer satisfaction. According to the Salesforce State of Manufacturing Survey 2024, over 80% of manufacturers are now engaged with Generative AI. 

Salesforce’s Manufacturing Cloud was another major focus, with its goals centered on unifying digital experiences across the value chain. The platform aims to modernize commercial operations by streamlining sales and fulfillment processes, simplifying partner engagement by enhancing collaboration with suppliers and channel partners, and transforming service experiences by improving customer interactions and field service operations. 

The summit also featured exciting announcements, including new integrations with Revenue Cloud, agent-first field service capabilities, and expanded manufacturing-specific skills for Agentforce. These innovations further reinforce Salesforce’s commitment to helping manufacturers optimize operations and stay ahead in an evolving landscape. 

Read the full article here to dive deeper into the key takeaways, trends, and insights from the Salesforce Manufacturing Summit 2025. 

Join Our Salesforce Team   

If you’re ready to take the next step in your career and join a Salesforce team that’s leading the charge in digital transformation, we’d love to hear from you. With roles available such as lead technical consultant, senior project manager, and solutions architect, there’s a place for you to make an impact.  

Explore our open roles and become part of a global team dedicated to solving complex business problems through the power of Salesforce.   

Learn about current opportunities here. 

By joining Perficient, you’re not just advancing your career. You’re becoming part of a global community striving to shatter boundaries, obsess over outcomes, and forge the future.  

The global business landscape is complex, and responsible design has emerged as a critical imperative for organizations across sectors. It represents a fundamental shift from viewing design merely as a creative output to recognizing it as an ethical responsibility embedded within institutional structures and processes

True transformation toward responsible design practices cannot be achieved through superficial initiatives or isolated projects. Rather, it requires deep institutional commitment—reshaping governance frameworks, decision-making processes, and organizational cultures to prioritize human dignity, social equity, and environmental stewardship.

This framework explores how institutions can move beyond performative gestures toward authentic integration of responsible design principles throughout their operations, creating systems that consistently produce outcomes aligned with broader societal values and planetary boundaries.

The Institutional Imperative

What is Responsible Design?

Responsible design is the deliberate creation of products, services, and systems that prioritize human wellbeing, social equity, and environmental sustainability. While individual designers often champion ethical approaches, meaningful and lasting change requires institutional transformation. This framework explores how organizations can systematically embed responsible design principles into their core structures, cultures, and everyday practices.

Why Institutions Matter

The imperative for responsible design within institutions stems from their unique position of influence. Institutions have extensive reach, making their design choices impactful at scale. They establish standards and expectations for design professionals, effectively shaping the future direction of the field. Moreover, integrating responsible design practices yields tangible benefits: enhanced reputation, stronger stakeholder relationships, and significantly reduced ethical and operational risks.

Purpose of This Framework

This article examines the essential components of responsible design, showcases institutions that have successfully implemented ethical design practices, and provides practical strategies for navigating the challenges of organizational transformation. By addressing these dimensions systematically, organizations can transcend isolated ethical initiatives to build environments where responsible design becomes the institutional default—creating cultures where ethical considerations are woven into every decision rather than treated as exceptional concerns.

Defining Responsible Design

Responsible design encompasses four interconnected dimensions: ethical consideration, inclusivity, sustainability, and accountability. These dimensions form a comprehensive framework for evaluating the ethical, social, and environmental implications of design decisions, ultimately ensuring that design practices contribute to a more just and sustainable world.

Interconnected Dimensions

These four dimensions function not as isolated concepts but as integrated facets of a holistic approach to responsible design. Ethical consideration must guide inclusive practices to ensure diverse stakeholder perspectives are genuinely valued and incorporated. Sustainability principles should drive robust accountability measures that minimize environmental harm while maximizing social benefit. By weaving these dimensions together throughout the design process, institutions can cultivate a design culture that authentically champions human wellbeing, social equity, and environmental stewardship in every project.

A Framework for the Future

This framework serves as both compass and blueprint, guiding institutions toward design practices that meaningfully contribute to a more equitable and sustainable future. When organizations fully embrace these dimensions of responsible design, they align their creative outputs with their deepest values, enhance their societal impact, and participate in addressing our most pressing collective challenges. The result is design that not only serves immediate business goals but also advances the greater good across communities and generations.

Ethical Consideration

Understanding Ethical Design

Ethical consideration: A thoughtful evaluation of implications across diverse stakeholders. This process demands a comprehensive assessment of how design decisions might impact various communities, particularly those who are vulnerable or historically overlooked. Responsible designers must look beyond intended outcomes to anticipate potential unintended consequences that could emerge from their work.

Creating Positive Social Impact

Beyond harm prevention, ethical consideration actively pursues opportunities for positive social impact. This might involve designing solutions that address pressing social challenges or leveraging design to foster inclusion and community empowerment. When institutions weave ethical considerations throughout their design process, they position themselves to contribute meaningfully to social equity and justice through their creations.

Implementation Strategies

Organizations can embed ethical consideration into their practices through several concrete approaches: establishing dedicated ethical review panels, conducting thorough stakeholder engagement sessions, and developing robust ethical design frameworks. By placing ethics at the center of design decision-making, institutions ensure their work not only reflects their core values but also advances collective wellbeing across society.

Inclusive Practices

Understanding Inclusive Design

Inclusive practices: Creating designs that meaningfully serve and represent all populations, particularly those historically marginalized. This approach demands that designers actively seek diverse perspectives, challenge their inherent biases, and develop solutions that transcend physical, cognitive, cultural, and socioeconomic barriers. By centering previously excluded voices, inclusive design creates more robust and universally beneficial outcomes.

Empowering Marginalized Communities

True inclusive design transcends mere accommodation—it fundamentally shifts power dynamics by elevating marginalized communities from subjects to co-creators. This transformation might involve establishing paid consulting opportunities for community experts, creating accessible design workshops in underserved neighborhoods, or forming equitable partnerships where decision-making authority is genuinely shared. When institutions embrace these collaborative approaches, they produce designs that authentically address community needs while building lasting relationships based on mutual respect and shared purpose.

Implementation Strategies

Organizations can systematically embed inclusive practices by recruiting design teams that reflect diverse lived experiences, conducting immersive community-based research with appropriate compensation for participants, and establishing measurable inclusive design standards with accountability mechanisms. By integrating these approaches throughout their processes, institutions not only create more accessible and equitable designs but also contribute to dismantling systemic barriers that have historically limited full participation in society.

Sustainability

Definition and Core Principles

Sustainability: Minimizing environmental impact and resource consumption across the entire design lifecycle. This comprehensive approach spans from raw material sourcing through to end-of-life disposal, challenging designers to eliminate waste, preserve natural resources, and significantly reduce pollution. Sustainable design necessitates careful consideration of long-term environmental consequences, including addressing critical challenges like climate change, habitat destruction, and biodiversity loss.

Beyond Harm Reduction

True sustainability transcends mere harm reduction to actively generate positive environmental outcomes. This transformative approach creates products and services that harness renewable energy, conserve vital water resources, or restore damaged ecosystems. When institutions fully embrace sustainability principles, they contribute meaningfully to environmental resilience and help foster regenerative systems that benefit both present and future generations.

Implementation Strategies

Organizations can embed sustainability through strategic, measurable approaches including rigorous lifecycle assessments, integrated eco-design methodologies, and significant investments in renewable energy infrastructure and waste reduction technologies. By elevating sustainability to a core organizational value, institutions can dramatically reduce their ecological footprint while simultaneously driving innovation and contributing to planetary health and wellbeing.

Accountability

Definition and Core Principles

Accountability: Taking ownership of both intended and unintended outcomes of design decisions. This principle demands establishing robust systems for monitoring and evaluating design impacts, along with mechanisms for corrective action when necessary. Accountable designers maintain transparency throughout their process, actively seek stakeholder feedback, and acknowledge responsibility for any negative consequences, even those that were unforeseen. This foundation of responsibility ensures designs serve their intended purpose while minimizing potential harm.

Learning and Growth

True accountability transcends mere acknowledgment of errors—it transforms mistakes into catalysts for improvement. This transformative process involves critically examining design failures, implementing process refinements, enhancing designer training, and establishing more comprehensive ethical frameworks. When institutions embrace accountability as a pathway to excellence rather than just a response to failure, they cultivate stakeholder trust while continuously elevating the quality and integrity of their design practices.

Implementation Strategies

Organizations can foster a culture of accountability by establishing well-defined responsibility chains, implementing comprehensive monitoring systems, and creating accessible channels for feedback and remediation. Effective implementation includes regular ethical audits, transparent reporting practices, and systematic incorporation of lessons learned. By prioritizing accountability at every organizational level, institutions ensure their designs consistently uphold ethical standards, promote inclusivity, and advance sustainability goals.

Case Study: Patagonia’s Environmental Responsibility

• Environmental Integration in Design: Patagonia has revolutionized responsible design by weaving environmental considerations into the fabric of its product development process. The company’s groundbreaking “Worn Wear” program—which actively encourages repair and reuse over replacement—emerged organically from the organization’s core values rather than as a response to market trends. Patagonia’s governance structure reinforces this commitment through rigorous environmental impact assessments at every design stage, ensuring sustainability remains central rather than peripheral to innovation.
• Sustainability Initiatives: Patagonia demonstrates unwavering environmental responsibility through comprehensive initiatives that permeate all aspects of their operations. The company has pioneered the use of recycled and organic materials in outdoor apparel, dramatically reduced water consumption through innovative manufacturing processes, and committed to donating 1% of sales to grassroots environmental organizations, a pledge that has generated over $140 million in grants to date. These initiatives represent the concrete manifestation of Patagonia’s mission rather than superficial corporate social responsibility efforts.
• Environmental Leadership as a Competitive Advantage: 
  Patagonia’s remarkable business success powerfully illustrates how environmental responsibility can create lasting competitive advantage in the marketplace. By elevating environmental considerations from afterthought to guiding principle, the company has cultivated a fiercely loyal customer base willing to pay premium prices for products aligned with their values. Patagonia’s approach has redefined industry standards for sustainable business practices, serving as a compelling case study for organizations seeking to integrate responsible design into their operational DNA while achieving exceptional business results.

Case Study: IDEO’s Human-Centered Evolution

• Organizational Restructuring: IDEO transformed from a traditional product design firm into a responsible design leader through deliberate organizational change. The company revolutionized its project teams by integrating ethicists and community representatives alongside designers, ensuring diverse perspectives influence every creation. Their acclaimed “Little Book of Design Ethics” now serves as the foundational document guiding all projects, while their established ethics review board rigorously evaluates proposals against comprehensive responsible design criteria before approval.
• Ethical Integration in Design Process: IDEO’s evolution exemplifies the critical importance of embedding ethical considerations throughout the design process. By incorporating ethicists and community advocates directly into project teams, the company ensures that marginalized voices are heard, and ethical principles shape all design decisions from conception to implementation. The “Little Book of Design Ethics” functions not simply as a reference manual but as a living framework that empowers designers to navigate complex ethical challenges with confidence and integrity.
• Cultural Transformation: IDEO’s remarkable journey demonstrates that responsible design demands a fundamental cultural shift within organizations. The company has cultivated an environment where ethical awareness and accountability are celebrated as core values rather than compliance requirements. By prioritizing human impact alongside business outcomes, IDEO has established itself as the preeminent leader in genuinely human-centered design. Their case offers actionable insights for institutions seeking to implement responsible design practices while maintaining innovation and market leadership.

Addressing Resistance to Change

Institutional transformation inevitably encounters resistance. Change disrupts established routines and challenges comfort zones, often triggering reactions ranging from subtle hesitation to outright opposition. Overcoming this resistance requires thoughtful planning, transparent communication, and meaningful stakeholder engagement throughout the process.

Why People Resist Change.

Resistance typically stems from several key factors:

• Fear of the unknown and potential failure
• Perceived threats to job security, status, or expertise
• Skepticism about the benefits compared to required effort
• Attachment to established processes and organizational identity
• Past negative experiences with change initiatives

Effective Strategies for Change Management

• Phased implementation with clearly defined pilot projects that demonstrate value
• Identifying and empowering internal champions across departments to model and advocate for new approaches
• Creating safe spaces for constructive critique of existing practices without blame
• Developing narratives that connect responsible design to institutional identity and core values

Keys to Successful Transformation

By implementing these strategies, institutions can cultivate an environment that embraces rather than resists change. Transparent communication creates trust, active stakeholder engagement fosters ownership, and focusing on shared values helps align diverse perspectives. When people understand both the rationale for change and their role in the transformation process, resistance diminishes and the foundation for responsible design practices strengthens.

Balancing Competing Priorities

The complex tension between profit motives and ethical considerations demands sophisticated strategic approaches. Modern institutions navigate a challenging landscape of competing demands: maximizing shareholder value, meeting evolving customer needs, and fulfilling expanding social and environmental responsibilities. Successfully balancing these interconnected priorities requires thoughtful deliberation and strategic decision-making that acknowledges their interdependence.

Tensions in Modern Organizations

These inherent tensions can be effectively managed through:

• Developing comprehensive metrics that capture long-term value creation beyond quarterly financial results, including social impact assessments and sustainability indicators
• Identifying and prioritizing “win-win” opportunities where responsible design enhances market position, builds brand loyalty, and creates competitive advantages

Strategic Decision Frameworks

Creating robust decision frameworks that explicitly weigh ethical considerations alongside financial metrics, allowing for transparent evaluation of tradeoffs. Building compelling business cases that demonstrate how responsible design significantly reduces long-term risks related to regulation, reputation, and resource scarcity.

Long-term Value Integration

By thoughtfully integrating ethical considerations into core decision-making processes and developing nuanced metrics that capture multidimensional long-term value creation, institutions can successfully reconcile profit motives with responsible design principles. This strategic approach enables organizations to achieve sustainable financial success while meaningfully contributing to a more just, equitable, and environmentally sustainable world.

Beyond Token Inclusion

Meaningful participation requires addressing deep-rooted power imbalances in institutional structures. Too often, inclusion is reduced to superficial gestures—inviting representatives from marginalized communities to consultations while denying them genuine influence over outcomes and decisions that affect their lives.

The Challenge of Meaningful Participation

To achieve authentic participation, institutions must confront and transform these entrenched power dynamics. This means moving beyond symbolic representation to creating spaces where traditionally excluded voices carry substantial weight in shaping both processes and outcomes.

Key Requirements for True Inclusion:

• Redistributing decision-making authority through participatory governance structures that give community members voting rights on critical decisions
• Providing fair financial compensation for community members’ time, expertise, and design contributions—recognizing their input as valuable professional consultation
• Implementing responsive feedback mechanisms with sufficient authority to pause, redirect, or fundamentally reshape projects when community concerns arise
• Establishing community oversight boards with substantive veto power and resources to monitor implementation

Building Equity Through Empowerment

By fundamentally redistributing decision-making authority and genuinely empowering marginalized communities, institutions can transform design processes from extractive exercises to collaborative partnerships. This shift ensures that design benefits flow equitably to all community members, not just those with pre-existing privilege. Such transformation demands more than good intentions—it requires concrete commitments to equity, justice, and collective accountability.

Case Study: The Microsoft Inclusive Design Transformation

• Restructuring Design Hierarchy: Microsoft fundamentally transformed its design process by establishing direct reporting channels between accessibility teams and executive leadership. This strategic restructuring ensured inclusive design considerations could not be sidelined or overridden by product managers focused solely on deadlines or feature development. Additionally, they created a protected budget specifically for community engagement that was safeguarded from reallocation to other priorities—even during tight financial cycles.
• Elevating Accessibility Teams: This structural change demonstrates a commitment to inclusive design that transcends corporate rhetoric. By elevating accessibility specialists to positions with genuine organizational influence and providing them with unfiltered access to executive leadership, Microsoft ensures that inclusive design principles are embedded in strategic decisions at the highest levels of the organization. This repositioning signals to the entire company that accessibility is a core business value, not an optional consideration.
• Dedicated Community Engagement: The protected budget for community engagement reinforces this commitment through tangible resource allocation. By dedicating specific funding for meaningful partnerships with marginalized communities, Microsoft ensures diverse voices directly influence product development from conception through launch. This approach has yielded measurable improvements in product accessibility and market reach, demonstrating how institutional transformation of design processes can simultaneously advance inclusion, equity, and business outcomes.

Regulatory Alignment

Anticipating Regulatory Changes

Visionary institutions position themselves ahead of regulatory evolution rather than merely reacting to it. As global regulations on environmental sustainability, accessibility, and data privacy grow increasingly stringent, organizations that proactively integrate these considerations into their design processes create significant competitive advantages while minimizing disruption.

Case Study: Proactive Compliance

• European medical device leader Ottobock established a specialized regulatory forecasting team that maps emerging accessibility requirements across global market. Their “compliance plus” philosophy ensures designs exceed current standards by 20-30%, virtually eliminating costly redesigns when regulations tighten
• Benefits of Forward-Thinking Regulation Strategy: Proactive regulatory alignment transforms compliance from a burden into a strategic asset. Organizations that embrace this approach not only mitigate financial and reputational risks but also establish themselves as industry leaders in responsible design. This strategic positioning requires continuous environmental scanning and a genuine commitment to ethical design principles that transcend minimum requirements.

Market Differentiation

Rising Consumer Expectations

The evolving landscape of consumer expectations presents strategic opportunities to harmonize responsible design with market advantage. Today’s consumers are not merely preferring but actively demanding products and services that demonstrate ethical production standards, environmental sustainability practices, and social responsibility commitments. Organizations that authentically meet these heightened expectations can secure significant competitive advantages and cultivate deeply loyal customer relationships.

Real-World Success Stories

Consider these compelling examples:

• Herman Miller revolutionized the furniture industry through circular design principles, exemplified by their groundbreaking Aeron chair remanufacturing program
• This innovative initiative established a premium market position while substantially reducing material consumption and environmental impact

Creating Win-Win Outcomes

When organizations strategically align responsible design principles with market opportunities, they forge powerful win-win scenarios that simultaneously benefit business objectives and societal wellbeing. Success in this approach demands both nuanced understanding of evolving consumer expectations and unwavering commitment to developing innovative solutions that address these expectations while advancing sustainability goals.

Beyond Good Intentions

Concrete measurement systems are essential for true accountability. While noble intentions set the direction, only robust metrics can verify real progress in responsible design. Organizations must implement comprehensive measurement frameworks to track outcomes, identify improvement opportunities, and demonstrate genuine commitment.

Effective Measurement Systems

Leading examples include:

• IBM’s Responsible Design Dashboard, which provides quantifiable metrics across diverse product lines
• Google’s HEART framework (Happiness, Engagement, Adoption, Retention, Task success) that seamlessly integrates ethical dimensions into standard performance indicators
• Transparent annual responsible design audits with publicly accessible results that foster organizational accountability

Benefits of Implementation

By embracing data-driven measurement systems, organizations transform aspirational goals into verifiable outcomes. This approach demonstrates an authentic commitment to responsible design principles while creating a foundation for continuous improvement. The willingness to measure and transparently share both successes and challenges distinguishes truly responsible organizations from those with merely good intentions.

Incentive Restructuring

The Power of Aligned Incentives

Human behavior is fundamentally shaped by incentives. To foster responsible design practices, institutions must strategically align rewards systems with desired ethical outcomes. When designers and stakeholders are recognized and compensated for responsible design initiatives, they naturally prioritize these values in their work.

Implementation Strategies

Organizations are achieving this alignment through concrete approaches:

• Salesforce has integrated diversity and inclusion metrics directly into executive compensation packages, ensuring leadership accountability
• Leading firms like Frog Design have embedded responsible design outcomes as key criteria in employee performance reviews
• Structured recognition programs celebrate and amplify exemplary responsible design practices, increasing visibility and adoption

Creating a Culture of Responsible Design

Thoughtfully restructured incentives transform organizational culture by signaling what truly matters. When ethical, inclusive, and sustainable practices are rewarded, they become embedded in institutional values rather than treated as optional considerations. This transformation requires rigorous assessment of current incentive frameworks and bold leadership willing to realign reward systems with responsible design principles.

Institutional Culture and Learning Systems

Responsible design flourishes within robust learning ecosystems. Rather than a one-time achievement, responsible design represents an ongoing journey of discovery, adaptation, and refinement. Organizations must establish comprehensive learning infrastructures that nurture this evolutionary process and ensure design practices remain ethically sound, inclusive, and forward-thinking.

Key Components of Learning Infrastructure

An effective learning infrastructure incorporates:

• Rigorous post-implementation reviews that critically assess ethical outcomes and user impact
• Vibrant communities of practice that facilitate knowledge exchange and cross-pollination across departments
• Strategic partnerships with academic institutions to integrate cutting-edge ethical frameworks and research
• Diverse external advisory boards that provide constructive critique and alternative perspectives

Benefits of Learning Systems

By investing in robust learning infrastructure, organizations cultivate a culture of continuous improvement and adaptive excellence. These systems ensure responsible design practices evolve in response to emerging challenges, technological shifts, and evolving societal expectations. Success requires unwavering institutional commitment to evidence-based learning, collaborative problem-solving, and transparent communication across all levels of the organization.

Case Study: The Philips Healthcare Example

• The Responsibility Lab Initiative: Philips Healthcare established a groundbreaking “Responsibility Lab” where designers regularly rotate through immersive experiences with diverse users from various backgrounds and abilities. This innovative rotation system ensures that responsible design knowledge becomes deeply embedded across the organization rather than remaining isolated within a specialized team.
• Benefits of Experiential Learning: This approach powerfully demonstrates how experiential learning catalyzes responsible design practices. By immersing designers directly in the lived experiences of diverse users, Philips enables them to develop profound insights into the ethical, social, and environmental implications of their design decisions—insights that could not be gained through traditional research methods alone.
• Organizational Knowledge Distribution: The strategic rotation system ensures that valuable ethical design principles flow throughout the organization, transforming responsible design from a specialized function into a shared organizational capability. This case study exemplifies how institutions can build effective learning systems that not only foster a culture of responsible design but also make it an integral part of their operational DNA.

The Institutional Journey

A Continuous Transformation

Institutionalizing responsible design is not a destination but a dynamic journey of continuous evolution. It demands skillful navigation through competing priorities, entrenched power dynamics, and ever-shifting external pressures. Forward-thinking institutions recognize that responsible design is not merely adjacent to their core mission—it is fundamental to their long-term viability, relevance, and social license to operate in an increasingly conscientious marketplace.

Beyond Sporadic Initiatives

By addressing these dimensions systematically and holistically, organizations transcend fragmentary ethical initiatives to achieve truly institutionalized responsible design. This transformation creates environments where ethical considerations and responsible practices become the natural default—woven into the organizational DNA—rather than exceptional efforts requiring special attention or resources.

Embrace the Journey of Continuous Growth

Immerse yourself in a transformative journey that thrives on continuous learning, adaptive thinking, and cross-disciplinary collaboration. This mindset unlocks the potential for design practices that fuel a more just, equitable, and sustainable world. By embracing this profound shift, institutions can drive real change.

Achieving this radical transformation requires visionary leadership, ethical conduct, and an innovative culture. It demands the united courage to challenge outdated norms and champion a brighter future. When institutions embody this ethos, they become beacons of progress, inspiring others to follow suit.

The path forward is not without obstacles, but the rewards are immense. Institutions that lead with this mindset will not only transform their own practices but also catalyze systemic change across industries. They will set new standards, reshape markets, and pave the way for a more responsible, inclusive, and sustain.

As we progress through 2025, the banking industry is set for substantial transformation driven by several key trends. Digital transformation will remain a powerful force, with advancements in AI and machine learning enabling unparalleled operational efficiencies and hyper-personalized customer experiences. Banks will look to transform the way they do business by moving beyond their walls with the maturing of open banking and embedded finance.

To stay competitive, banks must adapt and embrace emerging industry trends. This will require being more inquisitive and innovative compared to previous years, as the adoption of AI and cloud technologies continues to expand.

Banking Trend #1: Hyper-Personalization for Customer Satisfaction

Customers increasingly demand personalized banking experiences tailored to their unique needs and preferences. Hyper-personalization transforms the traditional banking model into a customer-centric approach, significantly boosting satisfaction and retention rates. Banks can use advanced data analytics and AI to deliver highly personalized financial services, such as customized savings plans and tailored investment advice. By leveraging data, banks can anticipate customer needs and proactively offer solutions, like recommending mortgage products or providing financial wellness tools based on individual spending patterns. Delivering these tailored experiences will be a crucial differentiator for banks aiming to attract and retain customers.

Recommended Approach: Banks should leverage advanced data analytics, artificial intelligence (AI), and machine learning (ML) to create highly individualized experiences. By understanding customer preferences, behaviors, and financial needs in real-time, banks can offer tailored products, services, and communication, leading to a more seamless and intuitive customer experience. This approach improves engagement and loyalty, increases revenue opportunities, and provides competitive differentiation.

See Also: Transforming Industries, Powering Innovation

Banking Trend #2: Adapting to Regulatory Shifts

As the banking landscape evolves, staying compliant with regulatory requirements becomes increasingly challenging, especially with the rise of open banking, AI, and data privacy concerns. In 2025, banks will face a more complex regulatory environment, with new rules focused on data privacy, cybersecurity, and sustainability. The rise of digital banking, cryptocurrency, blockchain, and AI adoption across banking operations will prompt regulatory bodies to implement clearer frameworks and guidelines to ensure stability and consumer protection.

Recommended Approach: Navigating constant changes in risk and regulatory environments is crucial for banks in 2025. By ensuring compliance with regulations, banks mitigate risks and maintain trust with customers and regulatory authorities. To stay ahead, banks should adopt compliance technologies that automate regulatory reporting and help them stay agile in a rapidly changing landscape. Investing in advanced technologies like AI and machine learning can help identify potential risks and streamline compliance efforts. By adopting these strategies, banks can better manage the dynamic risk and regulatory environment, ensuring compliance while maintaining competitiveness and customer trust.

Related: Strategies + Solutions to Ensure Regulatory and Compliance Excellence

Banking Trend #3: Embedded Finance Enables Industry Crossover

Embedded finance integrates financial services into non-financial platforms, allowing users to access banking services within applications they already use. By embedding payment, lending, and insurance services into apps and websites, non-financial companies are able to offer financial products directly to their customers. For example, ride-sharing apps like Uber and Lyft offer in-app payment options, and e-commerce platforms provide financing options at checkout. By integrating financial services into non-financial platforms, banks can tap into new markets and customer bases, generating additional revenue.

Recommended Approach: Banks looking to start or deepen their embedded finance solutions should take several key steps. These steps include identifying strategic partnerships and collaborating with fintech companies and non-financial platforms that align with their goals. These partnerships provide the necessary technology and customer reach to implement embedded finance solutions effectively. Next, invest in technology by upgrading IT infrastructure to support embedded finance, including adopting cloud-based solutions and modern technologies that offer the agility and scalability needed for seamless integration. Create robust APIs that allow third-party platforms to integrate your financial services. Ensure these APIs are secure, reliable, and easy to use. Leverage advanced data analytics to understand customer behavior and preferences, helping you offer personalized financial services that meet the specific needs of your customers.

You May Enjoy: 6 Digital Payment Trends Set to Transform 2025

Banking Trend #4: Leveraging AI for Insights and Automation

AI and automation have evolved from buzzwords to essential components of banking operations. In 2025, AI will play a pivotal role in customer service, fraud detection, risk management, and personalized financial advice. AI-powered chatbots will handle routine customer inquiries, freeing human agents to tackle more complex issues. Additionally, AI-driven algorithms will enhance banks’ ability to detect emerging fraud patterns and mitigate risks more effectively. Automation will streamline internal processes, leading to cost reductions and improved operational efficiency.

Recommended Approach: To fully utilize AI, banks need to prioritize improving their data strategy, recognizing that high-quality, reliable, and trustworthy data is essential for AI to deliver significant outcomes. They need to align AI initiatives with the bank’s overall business goals. Then identify areas where AI can add the most value, such as customer service, fraud detection, and risk management and focus on value-driven AI use cases. They also need to invest in modern technology and infrastructure for agility and scalability.

Read More: Transform Your Business With Cutting-Edge AI and Automation Solutions

Banking Trend #5: Responsive Resilience

Responsive Resilience continues to be a key banking trend in 2025, emphasizing the ability of financial institutions to adapt swiftly and effectively to changing conditions. Banks must be prepared to respond to economic fluctuations, market changes, and unexpected events. This requires robust risk management frameworks and the ability to pivot strategies quickly. A compounding factor, the shift to digital has caused increased exposure to financial fraud and cyber threats.

Recommended Approach: Invest in advanced technologies such as AI, machine learning, and cloud computing to enhance operational efficiency and adaptability. These technologies can automate processes, improve decision-making, and provide real-time insights. Utilize data analytics to gain deeper insights into customer behavior, market trends, and potential risks. This data-driven approach allows banks to make informed decisions and respond swiftly to changes in the banking landscape.

Explore More: Reimagine Business for the Digital Age

Banking Trend #6: Embracing Open Banking and Connected Ecosystems

Although many institutions have deprioritized open banking in recent years, establishing an open banking strategy now offers significant benefits. Open banking continues to revolutionize the financial industry by enabling secure data sharing between banks and third-party providers through APIs. It unlocks a wealth of third-party financial data, enabling banks to better-tailor products and services, enhance customer loyalty, improve operational efficiencies, and open new revenue streams. This trend will change how banks engage with third-party financial service providers, fostering the development of innovative financial products and services. Open banking will drive greater competition, promote collaboration, and empower consumers with more choices and personalized banking experiences. Examples include budgeting apps that aggregate data from multiple bank accounts, personalized financial advice based on spending patterns and streamlined loan application processes by accessing a customer’s financial data to provide a more accurate and personalized credit score.

Recommended Approach: Banks must establish a clear vision for open banking that aligns with their overall business goals. They should determine specific objectives, such as driving innovation, improving customer experience, or expanding market reach. Additionally, banks need to update their IT infrastructure to support open banking. This includes developing secure and reliable APIs for data sharing and ensuring systems are resilient and scalable.

See Also: 1033 Open Banking Mandate Blueprint for Success

Unlocking New Opportunities in 2025

Our end-to-end digital solutions drive business outcomes, enhance experiences, and ensure robust risk and compliance management to improve operational efficiency and business resilience.

• Business Transformation: Align the business with an actionable roadmap to optimize operations, improve service, and enhance profitability.
• Modernization: Advance technologies to enhance digital capabilities and customer engagement.
• Data + Analytics: Proactively leverage integrated data and AI to refine banking product portfolios and mitigate risks.
• Risk + Compliance: Strengthen risk management frameworks, ensure adherence to banking regulations, and enhance operational stability.
• Consumer Experience: Improve customer satisfaction with hyper-personalized banking services and intuitive digital experiences.

Discover why we have been trusted by 18 of the 20 largest commercial banks. Explore our financial services expertise and contact us to learn more.

Augmented analytics—an approach that leverages machine learning (ML) and artificial intelligence (AI) to enhance data exploration and analysis—holds enormous potential for companies seeking to improve decision-making, boost operational efficiency, and secure a competitive advantage. Before examining the hurdles organizations often face when rolling out this technology, it’s important to understand the rewards associated with embracing augmented analytics.

Potential Advantages of Augmented Analytics

Organizations can reap several benefits by adopting augmented analytics:

• More Informed Decision-Making: Automation features and AI-driven insights enable quicker, more precise decisions.
• Reduced Bias: Augmented analytics can minimize the risk of biased outcomes through automated data processing and less human intervention.
• Accelerated Insight Generation: By expediting data analysis, these tools help teams respond with greater agility to market shifts.
• Heightened Accuracy: AI algorithms often identify patterns or outliers that human analysts might overlook, resulting in more precise insights.
• Operational Efficiency: Routine data handling is automated, freeing analysts to tackle higher-level strategic work.
• Enhanced Data Literacy: By making data more transparent, augmented analytics tools can foster better understanding and usability across the organization.
• Democratization of Insights: With easier access to analytic capabilities, more employees can participate in data-driven decision-making, promoting a culture of widespread data usage.

Having outlined these potential gains, we can now concentrate on the barriers that may arise during the implementation phase.

Common Implementation Challenges

Although augmented analytics offers significant advantages, organizations commonly encounter challenges in three broad categories: technological, organizational, and data-related.

Technological Challenges

• Integration with Legacy Systems: Merging augmented analytics platforms with existing tools and older infrastructures can be complex. Organizations might need to manage compatibility issues, enable smooth data transfers, and migrate legacy databases into newer environments.
• Scalability Concerns: Because augmented analytics thrives on large volumes of data, some companies struggle to secure adequate infrastructure and computing power to handle increasing data complexity. Adopting scalable cloud-based solutions or upgrading hardware may be required.
• Performance Constraints: Factors such as the amount of data, the complexity of models, and algorithmic efficiency all influence performance. Achieving optimal results depends on careful model tuning, database optimization, and potentially distributed computing.
• Accuracy and Contextual Relevance: If the insights generated do not align with the specific business scenario or are simply inaccurate, stakeholder trust may deteriorate. Thus, selecting suitable algorithms, rigorously validating data, and monitoring model outputs are essential.

Organizational Challenges

• Change Resistance: Employees might be wary of new technologies or feel unprepared to become “citizen data scientists.” Effective strategies to overcome this include transparent communication, thorough training, and fostering an environment where experimentation is encouraged.
• Cultural Realignment: A shift in corporate culture toward data-informed decision-making often requires breaking down silos, encouraging collaboration, and advocating for data-driven approaches.
• Job Security Fears: Automation can cause anxiety about job displacement. Alleviating these worries involves emphasizing how augmented analytics can empower staff with new competencies rather than eliminating their roles.
• “Black Box” Syndrome: Some augmented analytics solutions lack transparency regarding how their outputs are generated. Offering interpretable explanations and visualizations that clarify AI-driven outcomes helps address doubts.
• Complexity and User Adoption: Many augmented analytics platforms can be intricate, and users may need guidance to interpret analyses. Designing intuitive interfaces, providing relevant training, and offering ongoing user support are critical.

Data-Related Challenges

• Reliance on Data Quality: Inaccuracies or inconsistencies in input data undermine the reliability of an augmented analytics tool’s results. Organizations should invest in robust data governance and quality assurance to maintain trust in the platform.
• Data Bias: Any biases embedded in training datasets can lead to skewed outputs and, in turn, unfair or discriminatory outcomes. Companies must be vigilant in spotting and countering bias during both data preparation and model evaluation.
• Privacy and Security Risks: Because augmented analytics platforms often handle large quantities of sensitive data, stringent data governance and security measures—including compliance with relevant regulations—are essential.

Strategies for Overcoming Implementation Roadblocks

Addressing these challenges calls for a comprehensive approach that covers technical, organizational, and data-related dimensions.

Technological Strategies

• Gradual Rollout: Launch a pilot project targeting a specific, high-value use case to gain experience and demonstrate the viability of augmented analytics on a smaller scale.
• Choosing Compatible Solutions: Focus on tools that align well with existing infrastructures, offer robust security, and can scale to accommodate future growth.
• Upgrading Infrastructure: Evaluate whether computing power, storage solutions, and network capabilities are sufficient. In many cases, cloud-based solutions offer the scalability needed to handle larger datasets efficiently.

Organizational Strategies

• Build a Data-Focused Culture: Enhance collaboration and promote knowledge sharing to support data-driven decision-making. Training initiatives, cross-departmental collaboration, and visible leadership commitment to data initiatives play a critical role.
• Comprehensive Training: Develop programs to improve data literacy at different organizational levels. Focus on analytical methods, hands-on tool usage, and interpretation of outcomes.
• Proactive Change Management: Address worries about evolving job roles by highlighting how augmented analytics can open up professional development opportunities.
• Encourage Transparency: Opt for systems that explain how results are produced to instill confidence in the insights. Visual explanations and active participation from domain experts help solidify trust.
• Identify and Resolve User Pain Points: Conduct user research to understand existing workflow challenges and tailor augmented analytics solutions to real-world needs.
• Continuous Improvement: Maintain feedback loops by collecting user input and monitoring model performance, adjusting processes or algorithms as needed.
• Data Sharing Across Teams: Reduce silos by promoting inter-departmental data sharing. This leads to more comprehensive analyses and fosters a collaborative culture.

Data-Related Strategies

• Data Quality Improvements: Formalize data governance protocols—like cleansing, validation, and enrichment—to ensure the underlying data is accurate and dependable.
• Prioritize Data Security: Put robust encryption, access controls, and anonymization measures in place to protect sensitive information. These steps also ensure adherence to data privacy laws and regulations.

Best Practices for Data Modeling in Augmented Analytics

An effective data model is vital to a successful augmented analytics rollout. Consider these guidelines:

1. Start with Simplicity: Begin with a lean data model to minimize potential errors and validate initial outcomes. Scale up complexity once trust in the process grows.
2. Design for Adaptability: Because business goals and data sources evolve, data models should be designed with future modifications in mind.
3. Maintain a Data Dictionary: Store updated metadata to clarify each element’s purpose and structure, ensuring consistency across datasets.
4. Enforce Data Quality Standards: Integrate data freshness and integrity checks to uphold reliability.
5. Leverage Various Modeling Approaches: Evaluate techniques—like star schemas, materialized tables, or views—to optimize for performance and ease of access.

Types of Augmented Analytics Solutions

A wide range of augmented analytics platforms is available, each with unique strengths:

• Microsoft Power BI: Through its Copilot feature, Power BI integrates seamlessly with Microsoft’s ecosystem, offering AI-powered insights and natural language interactions.
• Tableau: Tableau’s Pulse and Agent Force features support natural language queries and automated insight generation. Its user-friendly interface appeals to both technical and non-technical audiences.
• Oracle Analytics Cloud: Delivers a robust suite of analytics capabilities—from automated data discovery to ML-driven predictions.
• IBM Cognos Analytics: Incorporates AI for automated data preparation, natural language querying, and in-depth pattern discovery.
• MicroStrategy: Embeds AI and ML functionalities, including automated insights and user-friendly data exploration.

Selecting the best platform depends on organizational priorities, infrastructure environments, and budgeting.

Financial Considerations

Organizations adopting augmented analytics should budget for both one-time and recurring expenses:

• Initial Outlay: These costs typically include software licensing, hardware or cloud service upgrades, data migration, and consulting or training fees.
• Ongoing Costs: Upkeep expenses may encompass software support, subscription fees for cloud hosting, data storage costs, and continuous staff development.

Carefully comparing these investments with anticipated returns helps shape a viable rollout plan.

Essential Skills and Resources

Implementing augmented analytics effectively requires a mix of competencies and personnel:

• Data Literacy: End users should have a baseline understanding of data concepts and analysis methods.
• Analytical Thinking: Although AI tools automate much of the process, human insight remains essential to interpret results and make strategic decisions.
• Domain Knowledge: Familiarity with the relevant business sector ensures data is interpreted within the proper context.
• Data Management Expertise: Professionals who can handle data governance, protect data quality, and ensure strong security measures are crucial.

Key roles include data scientists to develop and maintain ML models, data analysts to translate findings into actionable recommendations, IT professionals to manage infrastructure, and training facilitators to promote ongoing skill development.

Conclusion

Augmented analytics can revolutionize how organizations glean insights and strengthen their market position. Yet, achieving these benefits calls for a methodical plan that anticipates common difficulties and fosters a supportive, data-oriented environment. Organizations that understand and address these challenges early are more likely to see successful outcomes and long-term value.

Key actions for successful augmented analytics implementations:

1. Pilot with a Targeted Use Case: Demonstrate value on a smaller scale before wider adoption.
2. Select Appropriate Tools: Ensure compatibility with existing systems, future scalability, and robust data protection.
3. Emphasize Data Integrity: High-quality data is foundational to dependable insights.
4. Nurture a Data-Centric Culture: Encourage the organization to base decisions on analysis and evidence.
5. Provide Ongoing Training: Equip users with the knowledge they need to navigate tools effectively.
6. Manage Change Proactively: Address concerns around job security and clarify how the technology benefits employees.
7. Strengthen Privacy and Security Measures: Safeguard sensitive information and comply with privacy regulations.

By methodically planning for these considerations, organizations can better navigate the inevitable challenges and unleash the full value of augmented analytics.

Have you noticed your expenses rising lately? Eating out costs are up over 4% year over year, housing expenses have increased by 5-6%, and opening your auto insurance bill reveals a shocking 22% hike. These figures highlight the inflationary pressures impacting various sectors but are particularly severe in the property and casualty (P&C) insurance industry. 

Insurance Industry Challenges 

2023 was one of the costliest years on record for the P&C industry due to several factors: 

• Extreme weather 

• Inflationary pressures 

• • Labor costs have jumped nearly 12%.

• • Residential building costs have risen almost 28%.

As a result, industry combined ratios have reached 103.9%, forcing carriers to take corrective underwriting actions, including significant premium increases, to bring transparency in coverage processes (TCRs) back in line. 

Cost Optimization Strategies for Consumers and Carriers 

While consumer advocates encourage policyholders to shop for better rates, bundle multiple products for discounts, and optimize their policy structure, there are several strategies insurance carriers can adopt to retain and grow their customer base through effective expense management. 

Personalize Your Product 

According to a recent JD Power survey, nearly half of auto insurance consumers shopped their policy last year, and a staggering 29% switched from their current carriers. The driving force? Customers are more likely to engage with companies that genuinely understand their needs and relationships. Personalization offers the consumer a perceived ROI on the premiums they are paying – they are understood and (most importantly) protected.   

Successful carriers leverage their extensive operational data to generate actionable insights, creating integrated, seamless, and tailored customer experiences. Personalization goes beyond sending birthday acknowledgments. It involves continuously learning about your customers’ evolving needs and communicating with them in an authentic tone. 

The benefits of personalization are significant. A life insurance survey indicated that understanding the customer and tailoring offers can reduce customer acquisition costs by up to 50%, generate up to 10% more new premiums, and reduce customer churn by 30%. And, despite the rise in privacy regulations such as the GDPR or CPRA, 70% of global customers are willing to share their data in exchange for better pricing, experiences, or tailored offers from their carrier. 

Improve Internal Efficiencies 

The recent economic challenges have, without a doubt, exposed process inefficiencies, highlighting the need for greater automation in both customer-facing and back-office operations. The insurance industry has one of the highest ratios of labor expense to final product price. Industry forecasts estimate that within 15 years, 10-50% of current insurance processes will be automated, significantly reallocating resources and value propositions. 

By 2025, 60% of organizations will be using automation to address staffing challenges, moving human intervention to the highest priority work. Automated processes have been proven to reduce paperwork by 80% and speed up claims processing by 50%, resulting in substantial productivity gains. 

Automation Success In Action: Our client needed a claims processing platform that could handle high-volume operations, provide all business areas insight into best practices, and provide customers with self-service capabilities. We redesigned the underwriting and claims processing applications using Pega’s Claims for Insurance framework. We created customized workflows and case types, enabled document ingestion and indexing, and provided access to the first notice of loss reports. 

Embrace Artificial Intelligence and Machine Learning 

Beyond operational efficiencies, artificial intelligence (AI) and machine learning (ML) also offer promising advancements for the industry. Integrating data-driven analytics into core underwriting elements will enhance product pricing (e.g., telematics) and development. 

For claims organizations, AI can significantly improve settlement and fraud detection processes. Moreover, predictive and preventative services enabled by AI can help prevent risks before they occur. The use of predictive analytics has positively impacted loss ratios by 3-9%.  

While the current P&C rate increase is helping to bring down combined ratios, 2024 is likely to continue to experience more underwriting pressure, with AM Best predicting a combined ratio of 100.7%.  

AI Success in Action: We developed a virtual assistant to redirect 25% of our client’s incoming call volume of 5,000 calls per month to a self-service model. ​ By off-loading a large volume of third-party mortgage inquiries, our client could focus on providing personal attention to customers when they need it most while also saving significant amounts of labor on tasks that are ripe for automation.   

The Need for Digital Solutions 

Companies that invest in smart technology to future-proof their expense ratios will not only mitigate near-term profitability challenges but also establish a strong foundation for ongoing productivity and customer satisfaction enhancements. Embracing personalization, automation, and AI will enable carriers to navigate the evolving landscape of the insurance industry effectively. 

Your Expert Partner 

Are you prepared to embrace the future of insurance? 

We invite you to explore our insurance expertise, or contact us today to learn how we can optimize your insurance practice. 

Welcome to the world of Adobe Experience Platform (AEP), where digital transformation becomes a reality. If you’ve landed here, you’re already on your way to making significant strides in your organization or your career.

In a digital era where data reigns supreme, and the Customer Data Platform (CDP) landscape is ever-evolving, businesses strive to maximize their investments to thrive in a fiercely competitive market.

Whether you’re a marketer or an aspiring AEP developer, this blog is your go-to resource. Together, we’ll lay the foundation for building schemas and crafting strategies from scratch. Using a real-life example, I’ll break down the requirements and demonstrate how to translate them into a technical blueprint for your schemas.

Now, let’s dive into the core components: Adobe Experience Platform (AEP), XDM (Experience Data Model), Schemas, and Field Groups.

XDM: The Universal Language

Imagine XDM as the universal language for digital experiences. It’s like a rulebook crafted by Adobe to decipher customer experience data. When you work with AEP, ensuring your data speaks this XDM language is crucial. It streamlines data management, much like ensuring all puzzle pieces share the same shape for a perfect fit.

Schemas: The Blueprints

AEP relies on schemas, which act as templates, to maintain consistent and organized data. Schemas describe how your data looks and where it should reside within the platform, providing a structured framework to keep everything working in an orderly fashion.

Field Groups: The Organizers

Now, enter Field Groups – the unsung heroes within AEP. They resemble categorized drawers in your data cabinet, ensuring data consistency and organization within your schemas. Each Field Group is like a labelled drawer, helping you effectively organize your data points.

In practical terms, XDM is the language spoken by all the toys in your store. Schemas provide blueprints for your toy displays, and Field Groups are the labelled drawers that keep your toys organized. Together, they ensure your toy store runs smoothly, helping you offer personalized toy recommendations, like finding the perfect toy for each child in your store.

Now that we’ve grasped the fundamentals let’s apply them to a real-life scenario:

Real-Life Use Case: Lead Generation Example

Imagine you’re on a mission to enhance your data collection and personalization use cases using AEP.  Your goal is to send data to both Adobe Analytics [to keep your power users engaged while they level up their skills in Customer Journey Analytics] and Customer Journey Analytics [being future-ready for omnichannel journey analysis] simultaneously, ensuring a seamless analysis process. To achieve this, you need to configure data collection on your website and send specific data points.

Now, let’s get into the nitty-gritty. You’re running a lead generation site, and you want to track several data points:

• You aim to monitor all traffic data related to web page details.
• You’re keen on tracking interactions with Call-to-Action (CTA) links.
• You want to capture custom form tracking information, including the form name and the specific form event.
• Additionally, you have your eyes on tracking videos, complete with their names and the events associated with them.
• To top it off, once users authenticate, you intend to pass User ID information. More importantly, this ID will serve as a Person ID to stitch users across channels in future.
• And, of course, capturing valuable web page information such as the web page template, web page modification date, and the corresponding business unit.

Now that we’ve listed our requirements, the next step is translating them into an XDM schema. This schema will serve as the blueprint to encompass all these data points neatly and effectively.

Breaking Down the Requirements

Navigating the AEP Technical Landscape

To effectively implement data collection on our website using the AEP Web SDK, we’ll start by integrating the ‘AEP Web SDK ExperienceEvent’ predefined field group into our schema. This step ensures that our schema includes field definitions for data automatically collected by the AEP Web SDK (Alloy) library.

Additionally, considering that we’re dealing with website data, which involves time-series records (each with an associated timestamp), we’ll require an ‘Xperience event’ [class] type of schema. This schema is tailored to accommodate this specific data structure, ensuring seamless handling of our web-related records.

Let’s talk about Field Groups:

• Business Requirement: Select AEP Web SDK Experience Event Template in the schema to send data to AEP.
  • Field Group Type: Adobe’s Predefined Field Groups
  • Field GroupName/Path: Adobe Web SDK Experience Event Template.
    • This is a mandatory field group if you are capturing onsite data using web SDK.

• Business Requirement: Send data to Adobe Analytics from web SDK (traditional eVars,props,events).
  • Field Group Type: Adobe’s Predefined Field Groups
  • Field GroupName/Path: Adobe Analytics ExperienceEvent Template
    • This will take care of all your existing / new Adobe Analytics implementation needs.
    • Using this will eliminate the need to create a processing rule in the Adobe Analytics console if you map directly to evars/prop/events within this field group in schema via Adobe Launch setup.

• Business Requirement: Monitoring all traffic data related to web page details.
  • Field Group Type: Adobe’s Predefined Field Groups
  • Field GroupName/Path: Web Details
    • Path: web.webPageDetails

• Business Requirement: Tracking interactions with Call-to-Action (CTA) links.
  • Field Group Type: Adobe’s Predefined Field Groups
  • Field GroupName/Path: Web Details
    • Path: web.webInteraction

• Business Requirement: Capturing custom form tracking details, including form names and events.
  • Field Group Type: Hybrid: Adobe’s Predefined Field Groups + Custom Field Group.
  • Field GroupName/Path: Web Details
    • Path: web.webInteraction._democompany.form

    • web.webInteraction._democompany.form={
      formName:<form name>,
      formEvent:<form event such as start/complete/error>
      }

      

• Business Requirement: Keeping an eye on video interactions, including video names and associated events.
  • Field Group Type: Hybrid: Adobe’s Predefined Field Groups + Custom Field Group.
  • Field GroupName/Path: Web Details
    • Path: web.webInteraction._democompany.video

    • web.webInteraction._democompany.video={
      videoName:<video name>,
      videoEvent:<video event such as start,stop,milestones etc>
      }

      

• Business Requirement: Business specific custom web page information
  • Field Group Type: Hybrid: Adobe’s Predefined Field Groups + Custom Field Group.
  • Field GroupName/Path: Web Details
    • Path: web.webInteraction._democompany

    • web.webPageDetails._democompany={
      webPageTemplate:<custom web page template>,
      businessUnit:<business unit>
      }

• Business Requirement: Lastly, once users authenticate, pass user ID information.
  • Field Group Type: Custom Field group
  • Field GroupName/Path:_democompany.identity.userID. This is set at the root level
    • Assign this as an Identity, but not as primary [You may wonder why?  see below ].

*_democompany= _perficientincpartnersandbox for us as the tenant ID assigned to our account is perficientincpartnersandbox.

Key Points

Here are the key points and recommendations, explained in simpler terms:

• Understanding Field Groups: Field Groups are like organized drawers for your data. Each field within them is connected to a specific space known as a namespace. Predefined Field Groups come with predefined namespaces, while custom Field Groups are linked to your unique namespace, usually marked by an underscore (_) followed by your company’s name.
• Flexibility to Customize: You can modify predefined field groups, like Web Details, to match your needs. You can do this either through the user interface or using APIs. This flexible approach is what we call a “HYBRID field group.” It lets you adjust according to your requirements. As a result, your custom namespace (usually something like _<your tenant ID/company ID>) takes priority, and all customizations fall under this category. (You can check the final schema below for reference.)
  • Why Use HYBRID Field Groups: If you’re an architect or strategist, creating solutions that are reusable, efficient, and scalable is second nature. That’s why I highly recommend using HYBRID field groups whenever possible. These field groups offer the best of both worlds. They leverage the power of predefined field groups while allowing you to add your custom touch, all within a field group type. It’s like tailoring a ready-made suit to fit you perfectly, saving time and effort while ensuring the best results.
• Choosing a Primary ID: For website data, when it comes to identifying a user, we won’t set this user ID as the primary ID. You might wonder, “What should be the primary ID for on-site data, especially when you might need to connect it with offline data later?” You’re partially correct. While you can use this user ID as an identity to link with offline data, it doesn’t have to be the primary one.

• • Pro-tip: Use the Identity map to include all your possible custom identities by configuring this in the Identify map data element in Adobe Launch. By default, the ECID will be used as the primary identifier for stitching.
  • Using an XDM identityMap field, you can identify a device/user using multiple identities, set their authentication state, and decide which identifier is considered the primary one. If no identifier has been set as primary, the primary defaults to be the ECID

Important Note: Remember that if you specify a primary ID in the schema and it’s missing in a data entry (for example, on pages where users aren’t authenticated and don’t have a user ID), keep in mind that AEP will exclude those data entries because they lack the primary ID we’ve specified. This helps maintain data accuracy and integrity.

We’re making excellent headway! Our requirements have evolved into a detailed technical blueprint. Our XDM schema’s foundation is strong and ready to roll. Just remember, for website data, we use the “experience event” type schema with the experience event class. If we ever need to capture user profiles, we’ll craft an Experience Profile schema with the Experience Profile class. This adaptability ensures we’re prepared for diverse data scenarios.

Schema Creation 

With all the defined field groups, we can now combine this information to construct the schema. When it comes to building your schema, you’ve got two main paths to choose from:

• API-First Approach (Highly Recommended): This is the best approach if you want to align with AEP’s API-first philosophy.
• User-Friendly UI Interface (Great for Simple Use Cases): If the thought of working with APIs sounds intimidating, don’t worry! You can also create schemas through a user-friendly UI interface. This option is perfect for straightforward scenarios and when APIs might seem a bit daunting.

Final Schema Output

In this blog, we’ve opted for the UI method to construct our schema, and here’s the result:

In conclusion, Adobe Experience Platform empowers you to navigate the complex digital landscape easily. By understanding the language, creating blueprints, and organizing your data, you’ll unlock the potential to provide personalized experiences that resonate with your customers. Your journey to digital success has just begun!

CloudFront, Amazon’s Content Delivery Network (CDN), accelerates website performance by delivering content from geographically distributed edge locations. But how do you understand how users interact with your content and optimize CloudFront’s performance? The answer lies in CloudFront access logs, and a powerful tool called AWS Athena can help you unlock valuable insights from them. In this blog post, we’ll explore how you can leverage Amazon Athena to simplify log analysis for your CloudFront CDN service.

Why Analyze CloudFront Logs?

CloudFront delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. However, managing and analyzing the logs generated by CloudFront can be challenging due to their sheer volume and complexity.

These logs contain valuable information such as request details, response status codes, and latency metrics, which can help you gain insights into your application’s performance, user behavior, and security incidents. Analyzing this data manually or using traditional methods like log parsing scripts can be time-consuming and inefficient.

By analyzing these logs, you gain a deeper understanding of:

• User behaviour and access patterns: Identify popular content, user traffic patterns, and potential areas for improvement.
• Content popularity and resource usage: See which resources are accessed most frequently and optimize caching strategies.
• CDN performance metrics: Measure CloudFront’s effectiveness by analyzing hit rates, latency, and potential bottlenecks.
• Potential issues: Investigate spikes in errors, identify regions with slow response times, and proactively address issues.

Introducing AWS Athena: Your CloudFront Log Analysis Hero

Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. Here’s why Athena is perfect for CloudFront logs:

• Cost-Effective: You only pay for the queries you run, making it a budget-friendly solution.
• Serverless: No infrastructure to manage – Athena takes care of everything.
• Familiar Interface: Use standard SQL queries, eliminating the need to learn complex new languages.

Architecture:

Getting Started with Athena and CloudFront Logs

To begin using Amazon Athena for CloudFront log analysis, follow these steps:

1. Enable Logging in Amazon CloudFront

If you haven’t already done so, enable logging for your CloudFront distribution. This will start capturing detailed access logs for all requests made to your content.

2. Store Logs in Amazon S3

Configure CloudFront to store access logs in a designated Amazon S3 bucket. Ensure that you have the necessary permissions to access this bucket from Amazon Athena.

3. Create an Athena Table

Create an external table in Amazon Athena, specifying the schema that matches the structure of your CloudFront log files.

Below is the sample query we have used to create a Table :

 CREATE EXTERNAL TABLE IF NOT EXISTS cloudfront_logs (

  date STRING,

  time STRING,

  location STRING,

  bytes BIGINT,

  request_ip STRING,

  method STRING,

  host STRING,

  uri STRING,

  status INT,

  referrer STRING,

  user_agent STRING,

  query_string STRING,

  cookie STRING,

  result_type STRING,

  request_id STRING,

  host_header STRING,

  request_protocol STRING,

  request_bytes BIGINT,

  time_taken FLOAT,

  xforwarded_for STRING,

  ssl_protocol STRING,

  ssl_cipher STRING,

  response_result_type STRING,

  http_version STRING,

  fle_encrypted_fields STRING,

  fle_status STRING,

  unique_id STRING

)

ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t’ ESCAPED BY ‘\’ LINES TERMINATED BY ‘\n’

LOCATION ‘paste your s3 URI here’;

Click on the run button!

Extracting Insights with Athena Queries

Now comes the fun part – using Athena to answer your questions about CloudFront performance. Here are some sample queries to get you going:

Total Requests

Find the total number of requests served by CloudFront for a specific date range.

SQL

SELECT

    COUNT(*) AS total_requests

FROM

    cloudfront_logs

WHERE

    date BETWEEN ‘2023-12-01’ AND ‘2023-12-31’;

Most Requested Resources

Identify the top 10 most requested URLs from your CloudFront distribution. This query will give you a list of the top 10 most requested URLs along with their corresponding request counts. You can use this information to identify popular content and analyze user behavior on your CloudFront distribution.

SQL

SELECT

    uri,

    COUNT(*) AS request_count

FROM

    assetscs_cdn_logs

GROUP BY

    uri

ORDER BY

    request_count DESC

LIMIT 10;

Traffic by Region

Analyze traffic patterns by user location.

This query selects the location field from your CloudFront logs (which typically represents the geographical region of the user) and counts the number of requests for each location. It then groups the results by location and orders them in descending order based on the request count. This query will give you a breakdown of traffic by region, allowing you to analyze which regions generate the most requests to your CloudFront distribution. You can use this information to optimize content delivery, allocate resources, and tailor your services based on geographic demand.

SQL

SELECT

    location,

    COUNT(*) AS request_count

FROM

    cloudfront_logs

GROUP BY

    location

ORDER BY

    request_count DESC;

Average Response Time

Calculate the average response time for CloudFront requests. Executing this query will give you the average response time for all requests served by your CloudFront distribution. You can use this metric to monitor the performance of your CDN and identify any potential performance bottlenecks.

SQL

SELECT

    AVG(time_taken) AS average_response_time

FROM

    cloudfront_logs;

Number of Requests According to Status

The below query will provide you with a breakdown of the number of requests for each HTTP status code returned by CloudFront, allowing you to identify any patterns or anomalies in your CDN’s behavior.

SQL

SELECT status, COUNT(*) as count

FROM cloudfront_logs

GROUP BY status

ORDER BY count DESC;

Athena empowers you to create even more complex queries involving joins, aggregations, and filtering to uncover deeper insights from your CloudFront logs.

Optimizing CloudFront with Log Analysis

By analyzing CloudFront logs, you can identify areas for improvement:

• Resource Optimization: Resources with consistently high latency or low hit rates might benefit from being cached at more edge locations.
• Geographic Targeting: Regions with high traffic volume might warrant additional edge locations to enhance user experience.

Conclusion

AWS Athena and CloudFront access logs form a powerful duo for unlocking valuable insights into user behavior and CDN performance. With Athena’s cost-effective and user-friendly approach, you can gain a deeper understanding of your content delivery and make data-driven decisions to optimize your CloudFront deployment.

Ready to Unleash the Power of Your Logs?

Get started with AWS Athena today and unlock the hidden potential within your CloudFront logs. With its intuitive interface and serverless architecture, Athena empowers you to transform data into actionable insights for a faster, more performant CDN experience.

Background

Microsoft Fabric is emerging as one-stop solution to aspects revolving around the data. Before the introduction of Fabric, Power BI faced few limitations related to data ingestion, since Power Query offers limited ETL & data transformation functionality. Power Query M Language scripting lacks ease of development, compared to popular languages like Java / C# / Python etc., which might be the need for complex scenarios. Lakehouse of Microsoft Fabric eliminates this downside by providing power of Apache Spark, which can be used in Notebooks to handle complicated requirements. Traditionally, organizations used to provision multiple services of Azure Services, like Azure Storage, Azure Databricks, etc. Fabric brings all the required services into a single platform.

Case Study

A private equity organization wants to have a close eye on equity stocks it has invested in for their clients. They want to generate trends, predictions (using ML), and analyze data based on algorithms developed by their portfolio management team in collaboration with data scientists written in Python. Reporting Team wants to consume data for preparing Dashboards, using Power BI. Organization has subscription of Market Data API, which can pull live market data. This data needs to be ingested on a real-time basis into the warehouse, for further usage by the data scientist & data analyst team.

Terminologies Used

Below are few terms used in the blog. A better understand of these by visiting respective website is advisable for better understanding:

• Lakehouse: In layman terms, this is the storehouse which will store unstructured data like CSV files in folders and structured data i.e., table (in Delta lake format). To know more about Lakehouse, visit official documentation link: https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview
• Notebook: It is a place to store our Python code along with supporting documentation (in Markdown format). Visit this link for details on Fabric Notebook: https://learn.microsoft.com/en-us/fabric/data-engineering/how-to-use-notebook
• PySpark: Apache Spark is an in-memory engine for analysis of bigdata. Spark supports languages like Java / Scala / SQL / Python / R. PySpark is Python based SDK for Spark. More information on spark can be found on the official website: https://spark.apache.org/
• Semantic Model: Power BI Dataset is now re-named as Semantic Model.
• Postman: Postman is a popular tool mostly used for API testing (limited feature free edition available). Postman offers Graphical Interface to make HTTP requests & inspect their response in various format like JSON / HTML etc.
• Polygon.io: It is a market data platform offering API to query stock prices & related information.

Flow Diagram

Below is the flow diagram to help understand how Fabric components are interlinked to each other to achieve the result.

API Feed Data Capture

In this case study, a free account sign-up of website https://polygon.io was done, which allows querying End-of-Day data with cap of max 5 API request / minute. Considering this limitation, hourly data of only 3 securities have been ingested, to demonstrate POC (Proof-of-Concept). Viewers are encouraged to use a paid account, which supports real-time data with unlimited API request, for their development / testing / production usage.

Below is the screenshot of HTTP request with response made via postman for single security, to be implemented in Notebook, for data ingestion.

JSON response contains property named results, of type object array containing hourly status of specific security.
o = open / c = close / h = high / l = low / v = traded volume / t = timestamp (in Unix style)

Step 01: Create Fabric Capacity Workspace

For the POC, we will create a workspace named Security Market, for our portfolio management division, using New Workspace button (available to Fabric Administrator), with settings as per below screenshots.

It is crucial that in Premium tab of settings, one needs to choose Fabric capacity (or Trial), which offers Lakehouse (refer below screenshot).

Once created, it should look as below (refer below screenshot).

Step 02: Setup Lakehouse

Next, we will create a new Lakehouse to host API feed captured data. Click New button and choose more options (if Lakehouse is not visible in menu). A detailed page as shown in the screenshot below would appear.

Use Lakehouse option to create a new Lakehouse. Rename this Lakehouse as per your choice.

Lakehouse can host structured data Table & Semi-structured / Unstructured data Sub-Folder to store raw / processed files. We will create a sub-folder named EOD_Data to store data received from API request in CSV file format, which in-turn would be available for Data Scientist for further processing (refer below screenshot).

Step 03: Create Notebook

Once Lakehouse is ready, we can proceed towards the next step, where we will be writing Python code to capture & ingest data. Click on Open Notebook > New Notebook to initialize a blank Notebook (refer below screenshot).

This would open a blank Notebook. Copy-paste below Python code into code cell as shown in below screenshot.

import datetime as dt
import requests as httpclient
from notebookutils import mssparkutils

api_key = 'hfoZ81xxxxxxxxxxxxxxxx'  # Secret API Key
symbol_list = ['MSFT', 'GOOG', 'PRFT']  # Symbol list

target_date = dt.datetime.today()
file_content = 'symbol,timestamp,open,high,low,close,volume\n'  # insert CSV header
dt_YYYYMMDD = target_date.strftime('%Y-%m-%d')  # YYYYMMDD

for symbol in symbol_list:  # Iterate through each symbol (security)
    api_url = f'https://api.polygon.io/v2/aggs/ticker/{symbol}/range/1/hour/{dt_YYYYMMDD}/{dt_YYYYMMDD}/?apiKey={api_key}'
    resp_obj = httpclient.get(api_url).json()
    for r in resp_obj['results']:  # Iterate through each rows of security for respective frequency of timestamp
        price_open, price_close, price_high, price_low, trade_volume = r['o'], r['c'], r['h'], r['l'], r['v']
        timestamp = dt.datetime.fromtimestamp(r['t']/1000).strftime('%Y-%m-%dT%H:%M:%S') # decode unix timestamp
        file_content += f'{symbol},{timestamp},{price_open},{price_high},{price_low},{price_close},{trade_volume}\n' # append row
    
mssparkutils.fs.put(f'Files/EOD_Data/{dt_YYYYMMDD}.csv', file_content)  # Save file into Datalake with Date identifier
df = spark.read.load(f'Files/EOD_Data/{dt_YYYYMMDD}.csv', format='csv', header=True, inferSchema=True) # Read file into dataframe
df.write.saveAsTable('nasdaq', mode='append')  # Append dataframe rows to "nasdaq" table

Execute the above code, after the NASDAQ market is closed. Let us understand in nutshell, what this Python code does:

1. Every Market Data platform offers a secret API key, which needs to be provided in URL or HTTP header (as defined in API documentation).
2. Just to experiment, we have selected 3 securities MSFT (Microsoft Corp), GOOG (Alphabet Inc – Class C) and PRFT (Perficient Inc).
3. URL requires date to be in YYYY-MM-DD format, which variable dt_YYYYMMDD is holding.
4. Next, we run a loop for every security we want to query.
5. HTTP Get request is made to Market API platform by dynamically preparing URL with target date, security (symbol) and API key, setting frequency of hourly data to be returned.
6. In the JSON response, result property holds array of hourly data changes of security attributes (like open / close / high / low / etc.) as depicted in postman request screenshot. Kindly refer to respective market platform API documentation to know this in detail.
7. Next, we run a loop to iterate and capture hourly data and append them to a text variable named file_content in comma separated format, to prepare our CSV file (notice we already wrote CSV header in line no 9 of code).
8. Post execution of both the loops, in line no 20, a file with naming structure (YYYYMMDD.csv) is created under sub-folder EOD_Data.
9. In the last, this saved CSV file is read using spark reader into data frame, and the result is appended to a table named “nasdaq” (spark will auto create table if not found).

Let’s preview the data to ensure success of Python script. Navigate to Lakehouse, expand Tables, and ensure a table named “nasdaq” is created. Refer below screenshot for sample data.

Step 04: Schedule Job

This notebook code needs to be run every day. Notebook offers a feature of scheduling the code to run automatically on set frequency event. This option is available in Notebook under option Run > Schedule.

This would open detailed scheduling option page as below. Assuming 4.00 pm EST as closing timing and adding buffer of 30 min for safety, let us apply timer to execute this Notebook Daily at 4:30 pm (refer below image).

The job would run daily even on weekend when market is closed. Ideally this should not affect analytics, as for weekend Friday day-end position would continue. Data scientists are free to delete weekend data or ignore that data from their further calculation scripts.

Step 05: Generate Semantic Model

Semantic Model (previously known as Dataset) serves as data source for Power BI reports. Lakehouse contains an option to generate semantic model providing option to choose specific tables to be loaded into model required by BI developer (refer below screenshot).

BI Developer can further build upon that semantic model creating relationships & measures. Only limitation is that calculated columns cannot be added into tables from model editor, as in backend there is no Power Query. Columns need to be added using in Notebook.

Conclusion

The story does not end here but continue with authoring dashboards & reporting from Power BI based on the semantic model produced by Lakehouse. Fabric enables integration of team of data scientist, data engineers & data analyst on a single unified platform. Azure administrator just need to provision Fabric Capacity, which is scalable just like regular Azure Workload, based on CU (Consumption Units), which can be tweaked on hourly basis, to accommodate for peak workload hours. Blog intends to share few capabilities of Fabric for dealing real scenario. There are many components of Fabric like Data Activator, ML Model, Data Pipeline, which for further complex level use-cases, which can be a great for exploration.