Balancing AI Strategy With Human Wisdom 

“AI-first” has become a buzzword in executive conversations, but what does it really mean? Is it about using artificial intelligence at every turn, or applying it with intention and purpose? For analyst and researcher Susan Etlinger, it’s clearly the latter. 

On the latest episode of “What If? So What?”, Susan joins host Jim Hertzfeld to explore what it takes to build AI strategies that are both innovative and responsible. With a background that bridges the humanities and technology, she makes a compelling case for the critical role of human insight in an AI-driven world. 

When (and When Not) to Automate 

AI’s power lies not just in what it can do, but in knowing when not to use it. Susan argues that leaders must assess whether automation truly improves outcomes or risks eliminating valuable learning opportunities. 

She shares a story from early in her career, when manually compiling business data helped her develop essential skills like stakeholder management, strategic thinking, and financial literacy. Her point: AI can accelerate, but only human experience gives results meaning. 

From Generative to Agentic AI: Who’s in Control? 

The conversation explores the evolution from machine learning to Generative AI, and now to Agentic AI. Susan encourages leaders to ask:  

Who sets the goals? Who ensures alignment?  

While AI agents can handle tasks from start to finish, intention, ethics, and judgment remain the responsibility of humans. 

Smarter AI Strategies, Not Just More AI 

Susan’s key takeaway is clear:  

Organizations don’t need more AI; they need better AI strategies. 

Start with a clear use case, implement with intention, and learn from the outcome. The most effective approaches respect the limits of automation while amplifying human strengths. 

Keep People at the Center of Your AI Strategy 

For leaders shaping AI strategy, Susan offers a clear reminder:  progress isn’t about replacing human decision making, it’s about enhancing it. AI can accelerate outcomes, but it’s people who ensure those outcomes are purposeful, ethical, and aligned to your business goals. 

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Meet our Guest – Susan Etlinger

Susan Etlinger is a globally recognized expert on the business and societal impact of data and artificial intelligence and senior fellow at the Centre for International Governance Innovation, an independent, non-partisan think tank based in Canada. Her TED talk, “What Do We Do With All This Big Data?” has been translated into 25 languages and has been viewed more than 1.5 million times. Her research is used in university curricula around the world, and she has been quoted in numerous media outlets including The Wall Street Journal, The Atlantic, The New York Times and the BBC. Susan holds a Bachelor of Arts in Rhetoric from the University of California at Berkeley. 

Follow Susan on LinkedIn  

Learn More about Susan Etlinger

Meet our Host

Jim Hertzfeld is Area Vice President, Strategy for Perficient.

For over two decades, he has worked with clients to convert market insights into real-world digital products and customer experiences that actually grow their business. More than just a strategist, Jim is a pragmatic rebel known for challenging the conventional and turning grand visions into actionable steps. His candid demeanor, sprinkled with a dose of cynical optimism, shapes a narrative that challenges and inspires listeners.

Connect with Jim:

LinkedIn | Perficient

In recent years, AI chatbots like ChatGPT have gone from fun tools for answering questions to serious helpers in workplaces, education, and even personal decision-making. With ChatGPT-5 now being the latest and most advanced version, it’s no surprise that people are asking a critical question:

“Is my personal data safe when I use ChatGPT-5?”

First, What Is ChatGPT-5?

ChatGPT-5 is an AI language model created by OpenAI. You can think of it like a super-smart digital assistant that can:

• Answering questions across a wide range of topics
• Drafting emails, essays, and creative content
• Writing and debugging code
• Assisting with research and brainstorming
• Supporting productivity and learning

It learns from patterns in data, but here’s an important point – it doesn’t “remember” your conversations unless the developer has built a special memory feature and you’ve agreed to it.

How Your Data Is Used

When you chat with ChatGPT-5, your messages are processed to generate a response. Depending on the app or platform you use, your conversations may be:

• Temporarily stored to improve the AI’s performance
• Reviewed by humans (in rare cases) to train and fine-tune the system
• Deleted or anonymized after a specific period, depending on the service’s privacy policy

This is why reading the privacy policy is not just boring legal stuff – it’s how you find out precisely what happens to your data.

Real Security Risks to Be Aware Of

The concerns about ChatGPT-5 (and similar AI tools) are less about it being “evil” and more about how your data could be exposed if not appropriately handled.

Here are the main risks:

1. Accidental Sharing of Sensitive Information

Many users unknowingly type personal details – such as their full name, home address, phone number, passwords, or banking information – into AI chat windows. While the chatbot itself may not misuse this data, it is still transmitted over the internet and may be temporarily stored by the platform. If the platform suffers a data breach or if the information is accessed by unauthorized personnel, your sensitive data could be exposed or exploited.

Best Practice: Treat AI chats like public forums – never share confidential or personally identifiable information.

2. Data Retention by Third-Party Platforms

AI chatbots are often integrated into third-party platforms, such as browser extensions, productivity tools, or mobile apps. These integrations may collect and store your chat data on their own servers, sometimes without clearly informing you. Unlike official platforms with strict privacy policies, third-party services may lack robust security measures or transparency.

Risk Example: A browser extension that logs your AI chats could be hacked, exposing all stored conversations.

Best Practice: Use only trusted, official apps and review their privacy policies before granting access.

3. Misuse of Login Credentials

In rare but serious cases, malicious AI integrations or compromised platforms could capture login credentials you enter during a conversation. If you share usernames, passwords, or OTPs (one-time passwords), these could be used to access your accounts and perform unauthorized actions – such as placing orders, transferring money, or changing account settings.

Real-World Consequence: You might wake up to find that someone used your credentials to order expensive items or access private services.

Best Practice: Never enter login details into any AI chat, and always use two-factor authentication (2FA) for added protection.

4. Phishing & Targeted Attacks

If chat logs containing personal information are accessed by cybercriminals, they can use that data to craft highly convincing phishing emails or social engineering attacks. For example, knowing your name, location, or recent purchases allows attackers to impersonate trusted services and trick you into clicking malicious links or revealing more sensitive data.

Best Practice: Be cautious of unsolicited messages and verify the sender before responding or clicking links.

5. Overtrusting AI Responses

AI chatbots are trained on vast datasets, but they can still generate inaccurate, outdated, or misleading information. Relying on AI responses without verifying facts can lead to poor decisions, especially in areas like health, finance, or legal advice.

Risk Example: Acting on incorrect medical advice or sharing false information publicly could have serious consequences.

Best Practice: Always cross-check AI-generated content with reputable sources before taking action or sharing it.

How to Protect Yourself

Here are simple steps you can take:

• Never share sensitive login credentials or card details inside a chat.
• Stick to official apps and platforms to reduce the risk of malicious AI clones.
• Use 2-factor authentication (2FA) for all accounts, so even stolen passwords can’t be used easily.
• Check permissions before connecting ChatGPT-5 to any service – don’t allow unnecessary access.
• Regularly clear chat history if your platform stores conversations.

Final Thoughts

ChatGPT-5 is a tool, and like any tool, it can be used for good or misused. The AI itself isn’t plotting to steal your logins or credentials, but if you use it carelessly or through untrusted apps, your data could be at risk.

Golden rule: Enjoy the benefits of AI, but treat it like a stranger online – don’t overshare, and keep control of your personal data.

Have you ever wondered what happens if you ask AI to create an image, write a poem, or draft an email?
Most of us picture “the cloud” working its magic in a distant location. The twist is that the cloud is physical, real, and thirsty. Data centers require water, sometimes millions of gallons per day, to stay cool while AI is operating.

By 2025, it is impossible to overlook AI’s growing water footprint. But don’t worry, AI isn’t to blame here. It’s about comprehending the problem, the ingenious ways technology is attempting to solve it, and what we (as humans) can do to improve the situation.

Why does AI need water?

Doesn’t your laptop heat up quickly when you run it on overdrive for hours? Now multiply that by millions of machines that are constantly in operation and stacked in enormous warehouses. A data centre is that.

These facilities are cooled by air conditioning units, liquid cooling, or evaporative cooling to avoid overheating. And gallons of fresh water are lost every day due to evaporative cooling, in which water actually evaporates into the atmosphere to remove heat.

Therefore, there is an invisible cost associated with every chatbot interaction, artificial intelligence-powered search, and generated image: water.

How big is the problem in 2025?

Pretty Big—and expanding. According to a 2025 industry report, data centers related to artificial intelligence may use more than 6 billion cubic meters of water a year by the end of this decade. That is roughly equivalent to the annual consumption of a mid-sized nation.

In short, AI’s water consumption is no longer a “future problem.” The effects are already being felt by the communities that surround big data centers. Concerns regarding water stress during dry months have been voiced by residents in places like Arizona and Ireland.

But wait—can AI help solve this?

Surprisingly, yes. It is being saved by the same intelligence that requires water.

optimised cooling: Businesses are utilising AI to operate data centers more effectively by anticipating precisely when and how much cooling is required, which can reduce water waste by as much as 20–30%.

Technology for liquid cooling: Some new servers are moving to liquid cooling systems, which consume a lot less water than conventional techniques.

Green data centers: Major corporations, such as Google and Microsoft, are testing facilities that use recycled water rather than fresh water for cooling and are powered by renewable energy.

Therefore, “AI is the problem” is not the story. “AI is thirsty, but also learning how to drink smarter,” it says.

What about us—can regular people help?

Absolutely.Our decisions have an impact even though the majority of us do not manage data centers. Here’s how:

More intelligent use of AI: We can be aware of how frequently we execute complex AI tasks, just as we try to conserve energy. (Is 50 AI-generated versions of the same image really necessary?)

Encourage green tech: Selecting platforms and services that are dedicated to sustainable data practices encourages the sector to improve.

Community action: Cities can enact laws that promote the use of recycled water in data centers and openness regarding the effects of water use in their communities.

Consider it similar to electricity, whose hidden costs we initially hardly noticed. Efficiency and awareness, however, had a significant impact over time. Water and AI can have the same effect.

What’s the bigger picture?

AI is only one piece of the global water puzzle. Water stress is still primarily caused by industry, agriculture, and climate change. However, the emergence of AI makes us reevaluate how we want to engage with the planet’s most valuable resource in the digital future.

If this is done correctly, artificial intelligence (AI) has the potential to be a partner in sustainability, not only in terms of how it uses water but also in terms of how it aids in global water monitoring, forecasting, and conservation.

The Takeaway

The cloud isn’t magic. It’s water, energy, wires, and metal. And AI’s thirst increases with its growth. However, this is an opportunity for creativity rather than panic. Communities, engineers, and even artificial intelligence (AI) are already rethinking how to keep machines cool without depleting the planet.

Therefore, keep in mind that every pixel and word contains a hidden drop of water the next time you converse with AI or create an interesting image. Furthermore, the more information we have, the better decisions we can make to ensure the future continues.

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)

In today’s hyper-personalized digital world, delivering the right message to the right customer at the right time is non-negotiable. 

Adobe Commerce is a powerful eCommerce engine, but when coupled with Adobe Real-Time CDP (Customer Data Platform), it evolves into an intelligent experience machine, which is capable of deep AI-powered personalization, dynamic segmentation, and real-time responsiveness. 

What is Adobe Real-Time CDP? 

Adobe Real-Time CDP is a Customer Data Platform that collects and unifies data across various sources (websites, apps, CRM, etc.) into a single, comprehensive real-time customer profile. This data is then accessible to other systems for marketing, sales, and service. 

Key Capabilities of Real-time CDP

• Real-time data ingestion and activation.  
• Identity resolution across devices and platforms 
• AI-driven insights and audience segmentation 
• Data governance and privacy compliance tools 

Why Integrate Adobe Commerce with Adobe CDP? 

Adobe Commerce offers native customer segmentation, but it’s limited to session or behavior data within the commerce environment. When the customer data is vast, the native segmentation becomes very slow, impacting overall performance.  

What We Gain with Real-Time CDP

Use Cases

1. Win-back Campaign: Identify dormant users in CDP and activate personalized discounts  
2. Cart Recovery: Capture cart abandonment events. 
3. High-Intent Buyers: Target customers who browse premium products but didn’t convert 

Integration of Adobe Commerce with Adobe Real-Time CDP 

Data Layer Implementation

• Install Adobe Experience Platform Web SDK to enable real-time event tracking and identity collection.  
• Define and deploy a custom XDM schema aligned with Commerce events. 

Customer Identity Mapping

• Implement Adobe Identity Service to build unified customer profiles across anonymous and logged-in sessions. 
• Ensure login/signup events are tracked for persistent identification. 

Data Collection Configuration

• Tag key Commerce events (add to cart, purchase, product) to collect data. 
• Set up batch or streaming ingestion using the following extensions: 
  • audiences-activation 
  • experience-platform-connector
• Admin configuration for Organization ID, Dataset ID & Data Stream ID:  
  • System -> Services -> Data Connection 
  • System -> Services -> Commerce Service Connector 

Audience Segmentation & Activation

• Create dynamic audiences using behavioral, transactional, and CRM data.  
• Assign Audience in Adobe Commerce. 

Personalization Execution

• Leverage Adobe Target or Adobe Experience Manager (AEM) to serve personalized content.
• CDP can be used for decision making, like suppressing offers to churn customers. 

Challenges to Consider 

• Data Governance: Ensure GDPR/CCPA compliance with CDP’s consent management tools. 
• Identity Resolution Complexity: Work closely with marketing teams to define identity rules. 
• Cross-Team Collaboration: Integration touches data engineering, commerce, marketing, and legal teams.

Conclusion 

Integrating Adobe Commerce with CDP empowers both business and technical teams to unify profiles and stay ahead in a dynamic marketplace by delivering personalization.  

Adobe Real-Time CDP is not just a marketing tool, it’s an asset for creating commerce experiences that adapt to the customer in real-time.   

The pressure is on for healthcare organizations to deliver more—more value, more equity, more impact. That’s where a well-known approach is stepping back into the spotlight.

If you’ve been around healthcare conversations lately, you’ve probably heard the resurgence of term value-based care. And there’s a good reason for that. It’s not just a buzzword—it’s reshaping how we think about health, wellness, and the entire care experience.

What Is Value-Based Care, Really?

At its core, value-based care is a shift away from the old-school fee-for-service model, where providers got paid for every test, procedure, or visit, regardless of whether it actually helped the patient. Instead, value-based care rewards providers for delivering high-quality, efficient care that leads to better health outcomes.

It’s not about how much care is delivered, it’s about how effective that care is.

This shift matters because it places patients at the center of everything. It’s about making sure people get the right care, at the right time, in the right setting. That means fewer unnecessary tests, fewer duplicate procedures, and less of the fragmentation that’s plagued the system for decades.

The results? Better experiences for patients. Lower costs. Healthier communities.

Explore More: Access to Care Is Evolving: What Consumer Insights and Behavior Models Reveal

Benefits and Barriers of Value-Based Care in Healthcare Transformation

There’s a lot to be excited about, and for good reason! When we focus on prevention, chronic disease management, and whole-person wellness, we can avoid costly hospital stays and emergency room visits. That’s not just good for the healthcare system, it’s good for people, families, and communities. It moves us closer to the holy grail in healthcare: the quintuple aim. Achieving it means delivering better outcomes, elevating experiences for both patients and clinicians, reducing costs, and advancing health equity.

The challenge? Turning value-based care into a scalable, sustainable reality isn’t easy.

Despite more than a decade of pilots, programs, and well-intentioned reforms, only a small number of healthcare organizations have been able to scale their value-based care models effectively. Why? Because many still struggle with some pretty big roadblocks—like outdated technology, disconnected systems, siloed data, and limited ability to manage risk or coordinate care.

That’s where digital transformation comes in.

To make value-based care real and sustainable, healthcare organizations are rethinking their infrastructure from the ground up. They’re adopting cloud-based platforms and interoperable IT systems that allow for seamless data exchange across providers, payers, and patients. They’re tapping into advanced analytics, intelligent automation, and AI to identify at-risk patients, personalize care, and make smarter decisions faster.

As organizations work to enable VBC through digital transformation, it’s critical to really understand what the current research says. Our recent study, Access to Care: The Digital Imperative for Healthcare Leaders, backs up these trends, showing that digital convenience is no longer a differentiator—it’s a baseline expectation.

Findings show that nearly half of consumers have opted for digital-first care instead of visiting their regular physician or provider.

This shift highlights how important it is to offer simple and intuitive self-service digital tools that help people get what they need—fast. When it’s easy to find and access care, people are more likely to trust you, stick with you, and come back when they need you again.

You May Also Enjoy: How Innovative Healthcare Organizations Integrate Clinical Intelligence

Redesigning Care Models for a Consumer-Centric, Digitally Enabled Future

Care models are also evolving. Instead of reacting to illness, we’re seeing a stronger focus on prevention, early intervention, and proactive outreach. Consumer-centric tools like mobile apps, patient portals, and personalized health reminders are becoming the norm, not the exception. It’s all part of a broader movement to meet people where they are and give them more control over their health journey.

But here’s an important reminder: none of these efforts work in a vacuum.

Value-based care isn’t just a technology upgrade or a process tweak. It’s a cultural shift.

Success requires aligning people, processes, data, and technology in a way that’s intentional and strategic. It’s about creating an integrated system that’s designed to improve outcomes and then making those improvements stick.

So, while the road to value-based care may be long and winding, the destination is worth it. It’s not just a different way of delivering care—it’s a smarter, more sustainable one.

Success In Action: Empowering Healthcare Consumers and Their Care Ecosystems With Interoperable Data

Reimagine Healthcare Transformation With Confidence

If you’re exploring how to modernize your digital front door, consider starting with a strategic assessment. Align your goals, audit your content, and evaluate your tech stack. The path to better outcomes starts with a smarter, simpler way to help patients find care.

We combine strategy, industry best practices, and technology expertise to deliver award-winning results for leading healthcare organizations.

• Business Transformation: Activate strategy for transformative outcomes and health experiences.
• Modernization: Maximize technology to drive health innovation, efficiency, and interoperability.
• Data + Analytics: Power enterprise agility and accelerate healthcare insights.
• Consumer Experience: Connect, ease, and elevate impactful health journeys.

Our approach to designing and implementing AI and machine learning (ML) solutions promotes secure and responsible adoption and ensures demonstrated and sustainable business value.

Discover why we have been trusted by the 10 largest health systems and the 10 largest health insurers in the U.S. Explore our healthcare expertise and contact us to learn more.

Ready to go from “meh” to “whoa” with your AI coding assistant? Here’s how to get started.

You’ve installed GitHub Copilot. Now what?

Here’s how to actually get it to work for you – not just with you.

In the blog Using GitHub Copilot in VS Code, we have already seen how to use GitHub Copilot in VS Code.

1. Write for Copilot, Not Just Yourself

Copilot is like a teammate who’s really fast at coding but only understands what you clearly explain.

Start with Intention:

Use descriptive comments or function names to guide Copilot.

// Fetch user data from API and cache it locally
function fetchUserData() {

Copilot will often generate useful logic based on that. It works best when you think one step ahead.

2. Break Problems Into Small Pieces

Copilot shines when your code is modular.

Instead of writing:

function processEverything() {
  // 50 lines of logic
}

Break it down:

// Validate form input
function validateInput(data) {

}

// Submit form to backend
function submitForm(data) {

}

This way, you get smarter, more accurate completions.

3. Use Keyboard Shortcuts to Stay in Flow

Speed = flow. These shortcuts help you ride Copilot without breaking rhythm:

Power Tip: Hold Tab to preview full suggestion before accepting it.

4. Experiment With Different Prompts

Don’t settle for the first suggestion. Try giving Copilot:

• Function names like: generateInvoicePDF()
• Comments like: // Merge two sorted arrays
• Descriptions like: // Validate email format

Copilot might generate multiple versions. Pick or tweak the one that fits best.

5. Review & Refactor – Always

Copilot is smart, but not perfect.

• Always read the output. Don’t blindly accept.
• Add your own edge case handling and error checks.
• Use tools like ESLint or TypeScript for safety.

Think of Copilot as your fast-thinking intern. You still need to double-check their work.

6. Use It Across File Types

Copilot isn’t just for JS or Python. Try it in:

• HTML/CSS → Suggest complete sections
• SQL → Generate queries from comments
• Markdown → Draft docs and README files
• Dockerfiles, .env, YAML, Regex patterns

Write a comment like # Dockerfile for Node.js app – and watch the magic.

7. Pair It With Unit Tests

Use Copilot to write your test cases too:

// Test case for addTwoNumbers function
describe('addTwoNumbers', () => {

It will generate a full Jest test block. Use this to write tests faster – especially for legacy code.

8. Learn From Copilot (Not Just Use It)

Treat Copilot suggestions as learning opportunities:

• Ask: “Why did it suggest that?”
• Compare with your original approach
• Check docs or MDN if you see unfamiliar code

It’s like having a senior dev whispering best practices in your ear.

9. Use Copilot Chat (If Available)

If you have access to GitHub Copilot Chat, try it. Ask questions like:

• What does this error mean?
• Explain this function
• Suggest improvements for this code

It works like a Stack Overflow built into your IDE.

Quick Recap

Final Thoughts: Practice With Purpose

To truly master Copilot:

• Build small projects and let Copilot help
• Refactor old code using Copilot suggestions
• Try documenting your code with its help

You’ll slowly build trust – and skill.

For most of  people, JavaScript still conjures images of simple web interactions like toggling menus, validating forms, or animating buttons. But that perception is rapidly changing.

JavaScript has quietly transformed into a powerful tool for machine learning, not in data centers or cloud clusters, but right in your browser. No need for Python scripts or backend servers, client-side intelligence, powered by frameworks like TensorFlow.js and Brain.js.

This shift means developers can now build smart, responsive applications that learn and adapt all without leaving the browser window.

What Is AI at the Edge?

AI at the edge means running artificial intelligence models directly on your device, whether it’s your phone, laptop, or even a micro-controller rather than relying on cloud servers.

Why does this matter?

• Faster :  No need to send data to a remote server and wait for a response.
• More Private : Your data stays on your device, reducing privacy risks.
• Offline-Friendly: Works without internet connectivity, ideal for remote or bandwidth constrained environments.

This opens the door to amazing new features for users, like recognizing hand movements, detecting faces, or translating languages instantly. And the best part It works right inside your browser, without needing any extra software.

What is TensorFlow.js?

TensorFlow.js is a JavaScript library created by Google that lets you build and run machine learning models directly in your browser or in a Node.js environment.

Key Features:

• Run Pre-trained Models: Use models that are already trained for tasks like image recognition, face detection, or text analysis.
• Train Your Own Models: You can train models using data from the user, all inside the browser.
• Convert Python Models:  If you’ve built a model in Python using TensorFlow, you can convert it to JavaScript and run it on the web.
• GPU Acceleration:  WebGL, TensorFlow.js can use your device’s GPU for faster performance.

Why JavaScript Is a Natural Fit for Edge AI

JavaScript is changing how we use AI on devices like phones, laptops, and tablets  right in the browser. Here’s why it’s so powerful:

• Works Everywhere: If your device has a browser, it can run JavaScript. No need to install anything extra.
• Fast and Interactive: JavaScript is great at handling real-time stuff, like tracking hand movements, analyzing video, or listening to voice.
• Keeps Your Data Private: Everything runs on your device, so your personal data doesn’t get sent to a server.
• Easy for Developers: Web developers already use JavaScript. Now, with tools like TensorFlow.js, they can build smart AI apps without learning a whole new language.

Real-World Examples

• Teachable Machine (Google): Train AI models using webcam or mic, no coding needed, all in-browser.
• Fitness Coaches: Use pose detection to give live feedback on workout form.
• Voice Assistants: Convert speech to text directly in the browser, no cloud required.
• Accessibility Tools: Control devices using facial gestures ,great for users with disabilities.

Limitations:

• Performance: It’s still slower than low-level languages like C++ or Rust.
• Model Size : Large language models like GPT and complex image generators are currently too heavy to run entirely in the browser.
• Memory Constraints : Edge devices have limited memory and compute power, which can limit the size and complexity of models.

However, the gap is narrowing. Tools like WebAssembly, model quantization, and on device hardware acceleration (e.g. WebGPU) are rapidly improving JavaScript’s capabilities in the AI domain.

The Future Is Now: JavaScript

• JavaScript is no longer just for buttons and forms, it’s powering real-time AI right in your browser.
• Accessible: Anyone with a browser can use or create AI tools.
• Secure: Data stays on your device,no need to send it to the cloud.
• Responsive: Instant feedback without delays or server calls.
• Web developers: You don’t need to learn Python or set up servers.

Conclusion:

JavaScript is quietly yet powerfully reshaping the future of AI at the edge. What was once a humble scripting language is now a gateway to real-time, intelligent experiences that run directly in the browser.

The lines between web development and machine learning are blurring and that’s a good thing.

• More developers can build smarter apps.
• More users can benefit from instant, secure AI.
• More innovation can happen without waiting on cloud infrastructure.

Machine Learning (ML) is no longer limited to research labs — it’s actively driving decisions in real estate, finance, healthcare, and more. But deploying and managing ML models in production is a different ballgame. That’s where MLOps comes in.

In this blog, we’ll walk through a practical MLOps learning project — building a House Price Predictor using Azure DevOps as the CI/CD backbone. We’ll explore the evolution from DevOps to MLOps, understand the model development lifecycle, and see how to automate and manage it effectively.

What is MLOps?

MLOps (Machine Learning Operations) is the discipline of combining Machine Learning, DevOps, and Data Engineering to streamline the end-to-end ML lifecycle.

It aims to:

• Automate training, testing, and deployment of models
• Enable reproducibility and version control for data and models
• Support continuous integration and delivery (CI/CD) for ML workflows
• Monitor model performance in production

MLOps ensures that your model doesn’t just work in Jupyter notebooks but continues to deliver accurate predictions in production environments over time.

From DevOps to MLOps: The Evolution

DevOps revolutionized software engineering by integrating development and operations through automation, CI/CD, and infrastructure as code (IaC). However, ML projects add new complexity:

So, MLOps builds on DevOps but extends it with data-centric workflows, experimentation tracking, and model governance.

House Price Prediction: Project Overview

Our goal is to build an ML model that predicts house prices based on input features like square footage, number of bedrooms, location, etc. This learning project is structured to follow MLOps best practices, using Azure DevOps pipelines for automation.

 Project Structure

house-price-predictor/

├── configs/               # Model configurations stored in YAML format

├── data/                  # Contains both raw and processed data files

├── deployment/  

│    └── mlflow/           # Docker Compose files to set up MLflow tracking

├── models/                # Saved model artifacts and preprocessing objects

├── notebooks/             # Jupyter notebooks for exploratory analysis and prototyping

├── src/

│    ├── data/             # Scripts for data preparation and transformation

│    ├── features/         # Logic for generating and engineering features

│    ├── models/           # Code for model building, training, and validation

├── k8s/

│    ├── deployment.yaml        # Kubernetes specs to deploy the Streamlit frontend

│    └── fast_model.yaml        # Kubernetes specs to deploy the FastAPI model service

├── requirements.txt       # List of required Python packages

 Setting Up Your Development Environment

Before getting started, make sure the following tools are installed on your machine:

• Python 3.11
• Git
• Visual Studio Code (or any other code editor you prefer)
• UV– A modern Python package and environment manager
• Docker Desktop or Podman Desktop

 Preparing Your Environment

• Fork this repo on GitHub to your personal or organization account.
• Clone your forked repository

# Replace 'xxxxxx' with your GitHub username or organization
git clone https://github.com/xxxxxx/house-price-predictor.git
cd house-price-predictor

• Create a virtual environment using UV:

uv venv --python python3.11
source .venv/bin/activate

• Install the required Python packages:

uv pip install -r requirements.txt

 Configure MLflow for Experiment Tracking

To enable experiment and model run tracking with MLflow:

cd deployment/mlflow
docker compose -f mlflow-docker-compose.yml up -d
docker compose ps

 Using Podman Instead of Docker?

podman compose -f mlflow-docker-compose.yml up -d
podman compose ps

Access the MLflow UI. Once running, open your browser and navigate to http://localhost:5555

Model Workflow

 Step 1: Data Processing

Perform cleaning and preprocessing on the raw housing dataset:

python src/data/run_processing.py   --input data/raw/house_data.csv   --output data/processed/cleaned_house_data.csv

 Step 2: Feature Engineering

Perform data transformations and feature generation:

python src/features/engineer.py   --input data/processed/cleaned_house_data.csv   --output data/processed/featured_house_data.csv   --preprocessor models/trained/preprocessor.pkl

 Step 3: Modeling & Experimentation

Train the model and track all metrics using MLflow:

python src/models/train_model.py   --config configs/model_config.yaml   --data data/processed/featured_house_data.csv   --models-dir models   --mlflow-tracking-uri http://localhost:5555

Step 4: Building FastAPI and Streamlit

The source code for both applications — the FastAPI backend and the Streamlit frontend — is already available in the src/api and streamlit_app directories, respectively. To build and launch these applications:

• Add a Dockerfile in the src/api directory to containerize the FastAPI service.
• Add a Dockerfile inside streamlit_app/ to package the Streamlit interface.
• Create a docker-compose.yaml file at the project root to orchestrate both containers.
  Make sure to set the environment variable API_URL=http://fastapi:8000 for the Streamlit app to connect to the FastAPI backend.

Once both services are up and running, you can access the Streamlit web UI in your browser to make predictions.

You can also test the prediction API directly by sending requests to the FastAPI endpoint.

curl -X POST "http://localhost:8000/predict" \

-H "Content-Type: application/json" \

-d '{

  "sqft": 1500,

  "bedrooms": 3,

  "bathrooms": 2,

  "location": "suburban",

  "year_built": 2000,

  "condition": fair

}'

Be sure to replace http://localhost:8000/predict with the actual endpoint based on where it’s running.

At this stage, your project is running locally. Now it’s time to implement the same workflow using Azure DevOps.

Prerequisites for Implementing This Approach in Azure DevOps.

To implement a similar MLOps pipeline using Azure DevOps, the following prerequisites must be in place:

1. Azure Service Connection (Workload Identity-based)
   • Create a Workload Identity Service Connection in Azure DevOps.
   • Assign it Contributor access to the target Azure subscription or resource group.
   • This enables secure and passwordless access to Azure resources from the pipeline.
2. Azure Kubernetes Service (AKS) Cluster
   • Provision an AKS cluster to serve as the deployment environment for your ML application.
   • Ensure the service connection has sufficient permissions (e.g., Azure Kubernetes Service Cluster User RBAC role) to interact with the cluster.

Start by cloning the existing GitHub repository into your Azure Repos. Inside the repository, you’ll find the azure-pipeline.yaml file, which defines the Azure DevOps CI/CD pipeline consisting of the following four stages:

1. Data Processing Stage – Handles data cleaning and preparation.
2. Model Training Stage – Trains the machine learning model and logs experiments.
3. Build and Publish Stage – Builds Docker images and publishes them to the container registry.
4. Deploy to AKS Stage – Deploys the application components to Azure Kubernetes Service (AKS).

This pipeline automates the end-to-end ML workflow from raw data to production deployment.

The CI/CD pipeline is already defined in the existing YAML file and is configured to run manually based on the parameters specified at runtime.

This pipeline is manually triggered (no automatic trigger on commits or pull requests) and supports the conditional execution of specific stages using parameters.

It consists of four stages, each representing a step in the MLOps lifecycle:

1. Data Processing Stage

Condition: Runs if run_all or run_data_processing is set to true.

What it does:

• Check out the code.
• Sets up Python 3.11.13 and installs dependencies.
• Runs scripts to:
  • Clean and preprocess the raw dataset.
  • Perform feature engineering.
• Publishes the processed data and the trained preprocessor as pipeline artifacts

2. Model Training Stage

Depends on: DataProcessing
Condition: Runs if run_all or run_model_training is set to true.

What it does:

• Downloads the processed data artifact.
• Spins up an MLflow server using Docker.
• Waits for MLflow to be ready.
• Trains the machine learning model using the processed data.
• Logs the training results to MLflow.
• Publishes the trained model as a pipeline artifact.
• Stops and removes the temporary MLflow container.

3. Build and Publish Stage

Depends on: ModelTraining
Condition: Runs if run_all or run_build_and_publish is set to true.

What it does:

• Downloads trained model and preprocessor artifacts.
• Builds Docker images for:
  • FastAPI (model API)
  • Streamlit (frontend)
• Tag both images using the current commit hash and the latest.
• Runs and tests both containers locally (verifies /health and web access).
• Pushes the tested Docker images to Docker Hub using credentials stored in the pipeline.

4. Deploy to AKS Stage

Depends on: BuildAndPublish
Condition: Runs only if the previous stages succeed.

What it does:

• Uses the Azure CLI to:
  • Set the AKS cluster context. #Make sure to update the cluster name
  • Update Kubernetes deployment YAML files with the new Docker image tags.
  • Apply the updated deployment configurations to the AKS cluster using kubectl.

Now, the next step is to set up the Kubernetes deployment and service configuration for both components of the application:

• Streamlit App: This serves as the frontend interface for users.
• FastAPI App: This functions as the backend, handling API requests from the Streamlit frontend and returning model predictions.

Both deployment and service YAML files for these components are already present in the k8s/ folder and will be used for deploying to Azure Kubernetes Service (AKS).

This k8s/deployment.yaml file sets up a Streamlit app on Kubernetes with two key components:

• Deployment: Runs 2 replicas of the Streamlit app using a Docker image. It exposes port 8501 and sets the API_URL environment variable to connect with the FastAPI backend.
• Service: Creates a LoadBalancer service that exposes the app on port 80, making it accessible externally.

In short, it deploys the Streamlit frontend and makes it publicly accessible while connecting it to the FastAPI backend for predictions.

This k8s/fastapi_model.yaml file deploys the FastAPI backend for the house price prediction app:

• It creates a Deployment named house-price-api with 2 replicas running the FastAPI app on port 8000.
• A LoadBalancer Service named house-price-api-service exposes the app externally on port 8000, allowing other services (like Streamlit) or users to access the API.

In short, it runs the backend API in Kubernetes and makes it accessible for predictions.

Now it’s time for the final run to verify the deployment on the AKS cluster. Trigger the pipeline by selecting the run_all parameter.

After the pipeline completes successfully, all four stages and their corresponding jobs will be executed, confirming that the application has been successfully deployed to the AKS cluster.

Now, log in to the Azure portal and retrieve the external IP address of the Streamlit app service. Once accessed in your browser, you’ll see the House Price Prediction Streamlit application up and running.

Now, go ahead and perform model inference by selecting the appropriate parameter values and clicking on “Predict Price” to see how the model generates the prediction.

Conclusion

In this blog, we explored the fundamentals of MLOps and how it bridges the gap between machine learning development and scalable, production-ready deployment. We walked through a complete MLOps workflow—from data processing and feature engineering to model training, packaging, and deployment—using modern tools like FastAPI, Streamlit, and MLflow.

Using Azure DevOps, we implemented a robust CI/CD pipeline to automate each step of the ML lifecycle. Finally, we deployed the complete House Price Predictor application on an Azure Kubernetes Service (AKS) cluster, enabling a user-friendly frontend (Streamlit) to interact seamlessly with a predictive backend (FastAPI).

This end-to-end project not only showcases how MLOps principles can be applied in real-world scenarios but also provides a strong foundation for deploying scalable and maintainable ML solutions in production.

Let’s be honest – coding isn’t always easy. Some days, you’re laser-focused, knocking out feature after feature. Other days, you stare at your screen, wondering,
“What’s the fastest way to write this function?”
“Is there a cleaner way to loop through this data?”

That’s where GitHub Copilot comes in.

If you haven’t tried it yet, you’re seriously missing out on one of the biggest productivity boosters available to developers today. In this blog, I’ll walk you through how to use GitHub Copilot with Visual Studio Code (VS Code), share my personal experience, and help you decide if it’s worth adding to your workflow.

What is GitHub Copilot?

Think of GitHub Copilot as your AI pair programmer.
It’s trained on billions of lines of public code from GitHub repositories and can:

• Suggest whole lines of code or entire functions
• Autocomplete loops, conditions, or boilerplate code
• Help you learn new frameworks or syntaxes on the fly

It’s like having a coding buddy that never sleeps, doesn’t get tired, and is always ready to assist.

Setting Up Copilot in VS Code

Getting started is easy. Here’s a step-by-step guide:

Step 1: Install Visual Studio Code

If you don’t have VS Code installed yet, you can install it from here.

Step 2: Install the GitHub Copilot Extension

• Open VS Code
• Go to the Extensions tab (Ctrl+Shift+X)
• Search for GitHub Copilot
• Click Install

Or directly visit here to find the extension.

Step 3: Sign in with GitHub

After installing, you’ll be prompted to sign in using your GitHub account.

Note: GitHub Copilot is a paid service (currently), but there’s usually a free trial to test it out.

How Does Copilot Work?

Once set up, Copilot starts making suggestions as you code. It’s kind of magical.

Here’s how it typically works:

• Type a comment describing what you want
  • Example:

// Function to reverse a string

Copilot will automatically generate the function for you!

• Write part of the code, and Copilot completes the rest
  • Start writing a “\for\” loop or an API call, and Copilot will suggest the following lines.
• Cycle through suggestions
  • Press Tab to accept a suggestion, or use Alt + [ / Alt + ] to browse different options.

Real-Life Use Cases

Here’s how I personally use Copilot in my day-to-day coding:

Tips to Get the Most Out of Copilot

1. Write clear comments:
   • Copilot works better when you describe what you want.
2. Don’t blindly trust the output:
   • It’s smart, but not always correct.
   • Review the suggestions carefully, especially for security-sensitive code.
3. Pair it with documentation:
   • Use Copilot for assistance, but keep the official docs open.
   • Copilot is great, but it doesn’t replace your understanding of the framework.
4. Use Copilot Labs (Optional):
   • If you want more experimental features like code explanation or refactoring suggestions, try Copilot Labs.

Is Copilot Replacing Developers?

Short answer? No.

Copilot is a tool, not a replacement for developers.
It speeds up the boring parts, but:

• Critical thinking? Still you.
• Architecture decisions? Still you.
• Debugging complex issues? Yes, still you.

Think of Copilot as an assistant, not a boss. It helps you code faster, but you’re still in charge of the logic and creativity.

Pros and Cons of Copilot

Pros

• Saves time on repetitive coding tasks
• Reduces context-switching to StackOverflow or Google
• Helps you learn new syntaxes quickly
• Available right inside VS Code

Cons

• Requires an active subscription after the free trial
• Sometimes generates incorrect or outdated code
• Can make you over-rely on suggestions if you’re not careful

Final Thoughts: Is Copilot Worth It?

If you’re someone who:

• Codes daily
• Works across multiple languages or frameworks
• Wants to focus on the “what” and less on the “how”

Then GitHub Copilot is absolutely worth trying out.

Personally, I’ve found it to be a game-changer for productivity. It doesn’t write all my code, but it takes away the mental fatigue of boilerplate so I can focus on solving real problems.

Useful Links

• GitHub Copilot Official Page
• Copilot VS Code Marketplace Extension
• Copilot Pricing Details

Whether you’re building embedded software for next-gen diagnostics, modernizing lab systems, or scaling user-facing platforms, the pressure to innovate is universal, and AI is becoming a key differentiator. When embedded into the software development lifecycle (SDLC), AI offers a path to reduce costs, accelerate timelines, and equip the enterprise to scale with confidence. 

But AI doesn’t implement itself. It requires a team that understands the nuance of regulated software, SDLC complexities, and the strategic levers that drive growth. Our experts are helping MedTech leaders move beyond experimentation and into execution, embedding AI into the core of product development, testing, and regulatory readiness. 

“AI is being used to reduce manual effort and improve accuracy in documentation, testing, and validation.” – Reuters MedTech Report, 2025 

Whether it’s generating test cases from requirements, automating hazard analysis, or accelerating documentation, we help clients turn AI into a strategic accelerator. 

AI-Accelerated Regulatory Documentation 

Outcome: Faster time to submission, reduced manual burden, improved compliance confidence 

Regulatory documentation remains one of the most resource-intensive phases of medical device development.  

• Risk classification automation: AI can analyze product attributes and applicable standards to suggest classification and required documentation. 
• Drafting and validation: Generative AI can produce up to 75% of required documentation, which is then refined and validated by human experts. 
• AI-assisted review: Post-editing, AI can re-analyze content to flag gaps or inconsistencies, acting as a second set of eyes before submission. 

AI won’t replace regulatory experts, but it will eliminate the grind. That’s where the value lies. 

For regulatory affairs leaders and product teams, this means faster submissions, reduced rework, and greater confidence in compliance, all while freeing up resources to focus on innovation. 

Agentic AI in the SDLC 

Outcome: Increased development velocity, reduced error rates, scalable automation 

Agentic AI—systems of multiple AI agents working in coordination—is emerging as a force multiplier in software development. 

• Task decomposition: Complex development tasks are broken into smaller units, each handled by specialized agents, reducing hallucinations and improving accuracy. 

• Peer review by AI: One agent can validate the output of another, creating a self-checking system that mirrors human code reviews. 

• Digital workforce augmentation: Repetitive, labor-intensive tasks (e.g., documentation scaffolding, test case generation) are offloaded to AI, freeing teams to focus on innovation. This is especially impactful for engineering and product teams looking to scale development without compromising quality or compliance. 

• Guardrails and oversight mechanisms: Our balanced implementation approach maintains security, compliance, and appropriate human supervision to deliver immediate operational gains and builds a foundation for continuous, iterative improvement. 

Agentic AI can surface vulnerabilities early and propose mitigations faster than traditional methods. This isn’t about replacing engineers. It’s about giving them a smarter co-pilot. 

AI-Enabled Quality Assurance and Testing 

Outcome: Higher product reliability, faster regression cycles, better user experiences 

AI is transforming QA from a bottleneck into a strategic advantage. 

• Smart regression testing: AI frameworks run automated test suites across releases, identifying regressions with minimal human input. 
• Synthetic test data generation: AI creates high-fidelity, privacy-safe test data in minutes—data that once took weeks to prepare. 
• GenAI-powered visual testing: AI evaluates UI consistency and accessibility, flagging issues that traditional automation often misses. 
• Chatbot validation: AI tools now test AI-powered support interfaces, ensuring they provide accurate, compliant responses. 

We’re not just testing functionality—we’re testing intelligence. That requires a new kind of QA.

For organizations managing complex software portfolios, this transforms QA from a bottleneck into a strategic enabler of faster, safer releases. 

AI-Enabled, Scalable Talent Solutions 

Outcome: Scalable expertise without long onboarding cycles 

AI tools are only as effective as the teams that deploy them. We provide specialized talent—regulatory technologists, QA engineers, data scientists—that bring both domain knowledge and AI fluency. 

• Accelerate proof-of-concept execution: Our teams integrate quickly into existing workflows, leveraging Agile and SAFe methodologies to deliver iterative value and maintain velocity. 
• Reduce internal training burden: AI-fluent professionals bring immediate impact, minimizing ramp-up time and aligning with sprint-based development cycles. 
• Ensure compliance alignment from day one: Specialists understand regulated environments and embed quality and traceability into every phase of the SDLC, consistent with Agile governance models. 

Whether you’re a CIO scaling digital health initiatives or a VP of Software managing multiple product lines, our AI-fluent teams integrate seamlessly to accelerate delivery and reduce risk. 

Proof of Concept Today, Scalable Solution Tomorrow 

Outcome: Informed investment decisions, future-ready capabilities 

Many of the AI capabilities discussed are already in early deployment or active pilot phases. Others are in proof-of-concept, with clear paths to scale. 

We understand that every organization is on a unique AI journey. Whether you’re starting from scratch, experimenting with pilots, or scaling AI across your enterprise, we meet you where you are. Our structured approach delivers value at every stage, helping you turn AI from an idea into a business advantage. 

As you evaluate your innovation and investment priorities across the SDLC, consider these questions: 

1. Are we spending too much time on manual documentation?
2. Do we have visibility into risk classification and mitigation?
3. Can our QA processes scale with product complexity?
4. How are we building responsible AI governance?
5. Do we have the right partner to operationalize AI?

Final Thought: AI Demands a Partner, Not Just a Platform 

AI isn’t the new compliance partner. It’s the next competitive edge, but only when guided by the right strategy. For MedTech leaders, AI’s real opportunity comes by adopting and scaling it with precision, speed, and confidence. That kind of transformation can be accelerated by a partner who understands the regulatory terrain, the complexity of the SDLC, and the business outcomes that matter most. 

No matter where you sit — on the engineering team, in the lab, in business leadership, or in patient care — AI is reshaping how MedTech companies build, test, and deliver value. 

From insight to impact, our industry, platform, data, and AI expertise help organizations modernize systems, personalize engagement, and scale innovation. We deliver AI-powered transformation that drives engagement, efficiency, and loyalty throughout the lifecycle—from product development to commercial success. 

• Business Transformation: Deepen collaboration, integration, and support throughout the value chain, including channel sales, providers, and patients. 
• Modernization: Streamline legacy systems to drive greater connectivity, reduce duplication, and enhance employee and consumer experiences. 
• Data + Analytics: Harness real-time data to support business success and to impact health outcomes. 
• Consumer Experience: Support patient and consumer decision making, product usage, and outcomes through tailored digital experiences. 

Ready to move from AI potential to performance? Let’s talk about how we can accelerate your roadmap with the right talent, tools, and strategy. Contact us to get started. 

In today’s data-driven landscape, Organizations are not just collecting the Data, they are striving to understand, trust, and maximize its value. One of the critical capabilities that helps achieve the goal is data enrichment, especially when implemented through enterprise-grade governance tools like Collibra.

In this blog, we will explore how Collibra enables data enrichment, why it is essential for effective data governance, and how organizations can leverage it to drive better decision-making.

What is Data Enrichment in Collibra?

Data enrichment enhances the dataset within the Collibra data governance tool by adding business context, metadata, correcting inaccuracies, and governance attributes that help users to understand the data’s meaning, usage, quality, and lineage.

Rather than simply documenting tables/columns, data enrichment enables organizations to transform technical metadata into meaningful, actionable insights, in which this enriched context empowers business and technical users alike to trust the data they are working with and use it confidently for analysis, reporting, and compliance.

How does Data Enrichment work in Collibra?

How We Use Data Enrichment in the Banking Domain

In today’s digital landscape, banks manage various data formats (such as CSV, JSON, XML, and tables) with vast volumes of data originating from internal and external sources like file systems, cloud platforms, and databases. Collibra automatically catalogs these data assets and generates metadata.

But simply cataloging data isn’t enough. The next step is data enrichment, where we link technical metadata with business-glossary terms to give metadata meaningful business context and ensure consistent description and understanding across the organization. Business terms clarify what each data element represents from a business perspective, making it accessible not just to IT teams but also to business users.

In addition, each data asset is tagged with data classification labels such as PCI (Payment Card Information), PII (Personally Identifiable Information), and confidential. This classification plays a critical role in data security, compliance, and risk management, especially in a regulated industry like banking.

To further enhance the trustworthiness of data, Collibra integrates data profiling capabilities. This process analyzes the actual content of datasets to assess their structure and quality. Based on profiling results, we link data to data‑quality rules that monitor completeness, accuracy, and conformity. These rules help enforce high-quality standards and ensure that the data aligns with both internal expectations and external regulatory requirements.

An essential feature in Collibra is data lineage, which provides a visual representation of the data flow from its source to its destination. This visibility helps stakeholders understand how data transforms and moves through various systems, which is essential for impact analysis, audits, and regulatory reporting.

Finally, the enriched metadata undergoes a structured workflow-driven review process. This involves all relevant stakeholders, including data owners, application owners, and technical managers. The workflow ensures that we not only produce accurate and complete metadata but also review and approve it before publishing or using it for decision-making.

Example: Enriching the customer data table

• Database: Vertica Datalake
• Table: Customer_Details
• Column: Customer_MailID
• Business Term: Customer Mail Identification
• Classification:P II (Personally Identifiable Information)
• Quality rule: There are no null values in Customer mailID. (Completeness)
• Linked Polity: GDPR policy for the EU Region
• Lineage: Salesforce à ETL pipeline à Vertica

Data enrichment in Collibra is a cornerstone of a mature Data Governance Framework; it helps transform raw technical metadata into a living knowledge asset, fueling trust, compliance, and business value. By investing time in enriching your data assets, you are not just cataloging them; you are empowering your organization to make smarter, faster, and more compliant data-driven decisions.