“Data is everywhere—so how do we make it work for us?” 

Imagine walking into your favorite store, and before you even reach the counter, your phone buzzes with a personalized discount for an item you’ve been eyeing. This isn’t the future—it’s happening now. Adobe and Perficient are revolutionizing digital experiences, making personalization smarter, faster, and more intuitive.

Ross Monaghan, Principal of Perficient’s Adobe practice, breaks it down: “There’s a lot of different parts of a customer that are left in a multitude of places. Some individuals may have multiple devices, in-store experiences—there’s a lot of data to stitch together.” The challenge? Unify this scattered data into a single, actionable profile. The solution? Adobe’s AI-powered ecosystem, coupled with Perficient’s expertise, turns raw data into seamless, hyper-personalized experiences.

Why Data Readiness is the Key to Success 

Most businesses have enormous amounts of data but struggle to use it effectively. Often, IT controls the data, leaving marketers without the access they need. Perficient helps bridge this gap through its Adobe Data Factory Jumpstart, allowing marketing and IT teams to collaborate, unify attributes, and act on real-time customer insights.

“We bring all those different sources of data together and unify them within a profile that’s constantly enriched,” Ross explains. This means that every new interaction—whether online, in-store, or via mobile—enhances the customer’s profile, allowing brands to communicate with precision, relevance, and consistency.

The Future is AI-Driven & Dynamic 

Adobe Experience Cloud is a game-changer, enabling businesses to leverage AI-powered automation for content creation, audience segmentation, and journey optimization. Instead of relying on manual A/B testing, companies can now dynamically generate and deploy personalized ads, website experiences, and messaging in real time.

“Leveraging Adobe’s GenStudio and Firefly, we can dynamically create new imagery and messaging at scale,” says Ross. “This isn’t about replacing creativity—it’s about amplifying it.” The result? Highly relevant, on-brand experiences that resonate with customers and drive conversion.

Personalization That Delivers Results 

True personalization is about being in the right place, at the right time, with the right message. With Adobe Journey Optimizer, brands can send contextual notifications based on real-time triggers. Picture this: A loyal customer enters your store, and their phone alerts them to use their $20 loyalty reward—right at the perfect moment.

Ross highlights the power of this, “These are highly relevant contextual experiences that matter, and Adobe’s experience platform really allows you to satisfy that end-to-end.” By leveraging AI-driven insights and real-time customer profiles, businesses can enhance loyalty, increase engagement, and maximize revenue.

 The Perficient Advantage: Turning Data into Actionable Personalization 

Implementing personalization at scale isn’t easy. Data readiness, AI adoption, and seamless customer journeys require the right strategy and execution. As an Adobe Platinum Partner, Perficient has the expertise to help businesses unlock their full digital potential.

“These are complex topics, but Perficient is ready to help you take your digital marketing to the next level,” Ross assures. Whether you’re looking to optimize your content strategy, enhance your customer journey, or scale your personalization efforts, our team is ready to bring your vision to life. Let’s unlock the full potential of your digital marketing strategy together.

Get in touch with us today and take the next step toward digital excellence. 

Salesforce has been giving us a ‘No Code’ way to have Data Cloud notify Sales Cloud of changes through Data Actions and Flows.   But did you know you can go the other direction too?

The Data Cloud Ingestion API allows us to setup a ‘No Code’ way of sending changes in Sales Cloud to Data Cloud.

Why would you want to do this with the Ingestion API?

1. You are right that we could surely setup a ‘normal’ Salesforce CRM Data Stream to pull data from Sales Cloud into Data Cloud.  This is also a ‘No Code’ way to integrate the two.  But maybe you want to do some complex filtering or logic before sending the data onto Sales Cloud where a Flow could really help.
2. CRM Data Streams only run on a schedule with every 10 minutes.  With the Ingestion API we can send to Data Cloud immediately, we just need to wait until the Ingestion API can run for that specific request.  The current wait time for the Ingestion API to run is 3 minutes, but I have seen it run faster at times.  It is not ‘real-time’, so do not use this for ‘real-time’ use cases.  But this is faster than CRM Data Streams for incremental and smaller syncs that need better control.
3. You could also ingest data into Data Cloud easily through an Amazon S3 bucket.  But again, here we have data in Sales Cloud that we want to get to Data Cloud with no code.
4. We can do very cool integrations by leveraging the Ingestion API outside of Salesforce like in this video, but we want a way to use Flows (No Code!) to send data to Data Cloud.

Use Case:

You have Sales Cloud, Data Cloud and Marketing Cloud Engagement.  As a Marketing Campaign Manager you want to send an email through Marketing Cloud Engagement when a Lead fills out a certain form.

You only want to send the email if the Lead is from a certain state like ‘Minnesota’ and that Email address has ordered a certain product in the past.  The historical product data lives in Data Cloud only.  This email could come out a few minutes later and does not need to be real-time.

Solution A:

If you need to do this in near real-time, I would suggest to not use the Ingestion API.  We can query the Data Cloud product data in a Flow and then update your Lead or other record in a way that triggers a ‘Journey Builder Salesforce Data Event‘ in Marketing Cloud Engagement.

Solution B:

But our above requirements do not require real-time so let’s solve this with the Ingestion API.  Since we are sending data to Data Cloud we will have some more power with the Salesforce Data Action to reference more Data Cloud data and not use the Flow ‘Get Records’ for all data needs.

We can build an Ingestion API Data Stream that we can use in a Salesforce Flow.  The flow can check to make sure that the Lead is from a certain state like ‘Minnesota’.  The Ingestion API can be triggered from within the flow.  Once the data lands in the DMO object in Data Cloud we can then use a ‘Data Action’ to listen for that data change, check if that Lead has purchased a certain product before and then use a ‘Data Action Target’ to push to a Journey in Marketing Cloud Engagement.  All that should occur within a couple of minutes.

Sales Cloud to Data Cloud with No Code!  Let’s do this!

Here is the base Salesforce post sharing that this is possible through Flows, but let’s go deeper for you!

The following are those deeper steps of getting the data to Data Cloud from Sales Cloud.  In my screen shots you will see data moving between a VIN (Vehicle Identification Number) custom object to a VIN DLO/DMO in Data Cloud, but the same process could be used for our ‘Lead’ Use Case above.

1. Create a YAML file that we will use to define the fields in the Data Lake Object (DLO).  I put an example YAML structure at the bottom of this post.
2. Go to Setup, Data Cloud, External Integrations, Ingestion API.   Click on ‘New’
   
   1. Give your new Ingestion API Source a Name.  Click on Save.
      
   2. In the Schema section click on the ‘Upload Files’ link to upload your YAML file.
      
   3. You will see a screen to preview your Schema.  Click on Save.
      
   4. After that is complete you will see your new Schema Object
      
   5. Note that at this point there is no Data Lake Object created yet.
3. Create a new ‘Ingestion API’ Data Stream.  Go to the ‘Data Steams’ tab and click on ‘New’.   Click on the ‘Ingestion API’ box and click on ‘Next’.
   
   1. Select the Ingestion API that was created in Step 2 above.  Select the Schema object that is associated to it.  Click Next.
      
   2. Configure your new Data Lake Object by setting the Category, Primary Key and Record Modified Fields
      
   3. Set any Filters you want with the ‘Set Filters’ link and click on ‘Deploy’ to create your new Data Stream and the associated Data Lake Object.
      
   4. If you want to also create a Data Model Object (DMO) you can do that and then use the ‘Review’ button in the ‘Data Mapping’ section on the Data Stream detail page to do that mapping.  You do need a DMO to use the ‘Data Action’ feature in Data Cloud.
4. Now we are ready to use this new Ingestion API Source in our Flow!  Yeah!
5. Create a new ‘Start from Scratch’, ‘Record-Triggered Flow’ on the Standard or Custom object you want to use to send data to Data Cloud.
6. Configure an Asynchronous path.  We cannot connect to this ‘Ingestion API’ from the ‘Run Immediately’ part of the Flow because this Action will be making an API to Data Cloud.  This is similar to how we have to use a ‘Future’ call with an Apex Trigger.
   
7. Once you have configured your base Flow, add the ‘Action’ to the ‘Run Asynchronously’ part of the Flow.    Select the ‘Send to Data Cloud’ Action and then map your fields to the Ingestion API inputs that are available for that ‘Ingestion API’ Data Stream you created.
   
8. Save and Activate your Flow.
9. To test, update your record in a way that will trigger your Flow to run.
10. Go into Data Cloud and see your data has made it there by using the ‘Data Explorer’ tab.
11. The standard Salesforce Debug Logs will show the details of your Flow steps if you need to troubleshoot something.

Congrats!

You have sent data from Sales Cloud to Data Cloud with ‘No Code’ using the Ingestion API!

Setting up the Data Action and connecting to Marketing Cloud Journey Builder is documented here to round out the use case.

Here is the base Ingestion API Documentation.

At Perficient we have experts in Sales Cloud, Data Cloud and Marketing Cloud Engagement.  Please reach out and let’s work together to reach your business goals on these platforms and others.

Example YAML Structure:

openapi: 3.0.3
components:
schemas:
VIN_DC:
type: object
properties:
VIN_Number:
type: string
Description:
type: string
Make:
type: string
Model:
type: string
Year:
type: number
created:
type: string
format: date-time

This week, the Perficient team had an amazing time at the Salesforce Manufacturing Summit in Atlanta. The event was filled with energy and innovation, and we were excited to be a part of it. With over 800 attendees, more than 40 sessions, and 20 product demonstrations, the summit provided a fantastic opportunity to connect with industry leaders, peers, and customers.

Event Overview 

The Manufacturing Cloud Summit 2025 brought together industry leaders, peers, and customers to explore the latest advancements and trends in the manufacturing sector. With a strong focus on innovation and technology, the summit provided a platform for meaningful connections and insightful discussions. 

Key Highlights 

Generative AI Takes Center Stage 

One of the most exciting aspects of the summit was the emphasis on Generative AI, particularly through Salesforce’s Agentforce and Data Cloud. These technologies are set to revolutionize the manufacturing industry by enhancing efficiency and customer satisfaction. Salesforce is actively building a library of Agent “skills” designed to work seamlessly with users, partners, and customers, reducing friction and improving overall experiences. 

AI Engagement in Manufacturing 

According to the Salesforce State of Manufacturing Survey 2024, over 80% of manufacturers are now engaged in Artificial Intelligence. This statistic underscores the growing importance of AI in driving innovation and competitiveness in the manufacturing sector. 

Manufacturing Cloud Goals 

Salesforce’s Manufacturing Cloud aims to unify digital experiences across the value chain with data. Here are the primary goals outlined at the summit: 

1. Modernize Commercial Operations: The Manufacturing Cloud enables businesses to manage their entire book of business, from sales opportunities to order fulfillment, streamlining operations and improving efficiency. 

1. Simplify Partner Engagement: By enhancing visibility, engagement, and performance of suppliers and channel partners, the Manufacturing Cloud fosters stronger and more productive partnerships. 

1. Transform Service Experience: The Manufacturing Cloud optimizes service experiences from contact centers to field service and customer interactions, ensuring faster and smarter asset-centric service. 

Announcements & Showcases 

The summit also featured several exciting announcements and showcases, including: 

• Integration with Revenue Cloud: The ability to extend Manufacturing Cloud functionalities with Revenue Cloud was a significant highlight, offering enhanced capabilities for managing revenue streams. 

• Agent-first Field Service: New capabilities for field service operations were introduced, emphasizing an agent-first approach to improve service delivery. 

• New Manufacturing Skills for Agentforce: Salesforce unveiled new manufacturing-specific skills for Agentforce, further enhancing its utility and effectiveness in the industry. 

Additional Resources 

For those interested in exploring the trends and insights discussed at the summit in more detail, the Global Trends Report is a must-read. This report offers a comprehensive overview of the current state and future direction of the manufacturing industry. 

Salesforce Agentforce Readiness Assessment 

Evaluate your AI readiness with Perficient’s comprehensive assessment. Identify key use cases, ensure data security, and develop a tailored roadmap for deploying Agentforce. 

Transform with Agentforce 

Discover tailored AI solutions and industry-specific use cases. Leverage pre-built templates and secure data access to enhance productivity across various business departments. 

Salesforce Data Cloud Readiness Assessment 

Assess your infrastructure and data readiness for Data Cloud adoption. Highlight areas for improvement and create a solid migration plan to ensure a smooth transition to the cloud. 

These resources provide valuable insights and practical steps to help businesses successfully implement and leverage Agentforce and Data Cloud solutions. 

Ready to start your Agentforce journey? Contact us today to learn more and get started! 

Artificial Intelligence (AI) is revolutionizing B2B ecommerce, enabling capabilities such as personalized product recommendations, dynamic pricing, and predictive analytics. However, the effectiveness of these AI-driven solutions depends heavily on the quality of the underlying data. Despite AI’s potential, poor data governance remains a significant challenge in the industry. A recent Statista survey revealed that 25% of B2B ecommerce companies in the United States have fully implemented AI technologies, while 56% are experimenting with them.

As AI adoption grows, B2B companies must address data quality issues to leverage AI’s benefits fully. Anyone who has spent time in the B2B industry will acknowledge that quality data is often a struggle. This article explores the critical importance of clean data in AI applications and offers strategies for improving data governance in the B2B ecommerce sector.

Common Symptoms of Bad Data Governance

Bad data governance is a pervasive issue in the B2B ecommerce landscape, particularly in industries like manufacturing, where complex supply chains and product catalogs create unique challenges. Here are some of the most common symptoms:

1. Duplicate Records: Customer and product data often contain duplicate entries due to inconsistent data entry processes or a lack of validation protocols. For example, a single customer might appear in the database multiple times with slight variations in name or contact information, leading to inefficiencies in communication and order processing.
2. Inconsistent Formatting: Manufacturing and distribution often involve extensive product catalogs, and inconsistencies in SKU formats, product descriptions, or units of measurement can disrupt operations. For instance, some entries might use “kg” while others use “kilograms,” confusing systems and causing inventory management and procurement errors.
3. Outdated or Missing Data: Stale data, such as outdated pricing, obsolete product details, or inactive customer accounts, can lead to misinformed decisions. Missing data, like incomplete shipping addresses or contact details, can result in delayed deliveries or lost opportunities.
4. Siloed Data Systems: Many B2B companies, especially in manufacturing, rely on disparate systems that don’t communicate effectively. A lack of integration between ERP systems, CRMs, and ecommerce platforms leads to fragmented data and manual reconciliation efforts, increasing the risk of errors.
5. Unreliable Vendor and Supplier Information: Manufacturing businesses often deal with a large network of suppliers, each with varying formats for invoices, contracts, and delivery schedules. Poorly managed supplier data can result in delayed production, stockouts, or overordering.

Why is Bad Data Governance So Prevalent in B2B Manufacturing?

Unlike B2C industries, where streamlined data processes are often a core focus, manufacturing businesses face unique challenges due to their operations’ complexity, reliance on legacy systems, and decentralized structures. Understanding why these problems are so prevalent is key to addressing the underlying causes and fostering long-term improvements.

1. Complexity of Operations: Manufacturing involves numerous moving parts—raw materials, suppliers, distributors, and customers—making data governance inherently more challenging. The sheer volume of data generated across the supply chain increases the likelihood of inconsistencies.
2. Legacy Systems: Many B2B manufacturing companies rely on outdated legacy systems not designed for modern ecommerce integration. These systems often lack robust data validation and cleaning mechanisms, perpetuating bad data practices.
3. Decentralized Operations: Manufacturing companies frequently operate in multiple locations, each with its own systems, processes, and data entry standards. This decentralization contributes to a lack of standardization across the organization.
4. Focus on Production Over Data: In traditional manufacturing mindsets, operational efficiency and production output take precedence over data accuracy. Thus, data governance investments may be considered a lower priority than equipment upgrades or workforce training.
5. Limited Awareness of the Impact: Many B2B organizations underestimate the long-term impact of bad data on their operations, customer satisfaction, and AI-driven initiatives. The focus often shifts to immediate problem-solving rather than addressing root causes through improved governance.

By recognizing these symptoms and understanding the reasons behind poor data governance, B2B manufacturing companies can take the first steps toward addressing these issues. This foundation is critical for leveraging AI and other technologies to their fullest potential in ecommerce.

Why Clean Data Governance is Non-Negotiable in the AI Era

AI thrives on data—structured, accurate, and relevant data. For B2B ecommerce, where AI powers everything from dynamic pricing to predictive inventory, clean data isn’t just a nice-to-have; it’s the foundation for success. Without clean data governance, AI systems struggle to provide reliable insights, leading to poor decisions and diminished trust in the technology.

As the B2B commerce world embraces AI, those who recognize and prioritize addressing a systemic industry problem of bad data will quickly move to the front of the pack. Garbage in, garbage out. Implementing AI tools with bad data will be doomed to failure as the tools will be ineffective. Meanwhile, those who take the time to ensure they have a good foundation for AI support will overtake the competition. It’s a watershed moment for the B2B industry where those who recognize how to get the most value out of AI while those who refuse to alter their own internal workflows because “that’s the way it’s always been done” will see their market share diminish.

1. Accuracy and Relevance: AI models rely on historical and real-time data to make predictions and recommendations. If the data is inaccurate or inconsistent, the AI outputs become unreliable, directly impacting decision-making and customer experiences.
2. Scalability and Growth: In an era where B2B companies are scaling rapidly to meet global demands, clean data ensures that AI systems can grow alongside the business. Bad data governance introduces bottlenecks, stifling the scalability of AI-driven solutions.
3. Customer Experience: AI-powered personalized recommendations, accurate delivery timelines, and responsive customer service are critical to building customer trust and loyalty. These benefits rely on clean, well-governed data. A single misstep, like recommending the wrong product or misquoting delivery times, can damage a company’s reputation.
4. AI Amplifies Data Issues: Unlike traditional systems, AI doesn’t just process data—it learns from it. Bad data doesn’t just result in poor outputs; it trains AI systems to make flawed assumptions over time, compounding errors and reducing the ROI of AI investments.
5. Competitive Advantage: Clean data governance can be a differentiator in a competitive B2B market. Companies with well-maintained data are better positioned to leverage AI for faster decision-making, improved customer service, and operational efficiencies, giving them a significant edge.

Ignoring data governance in the AI era isn’t just a missed opportunity—it’s a liability. Poor data practices lead to inefficient AI models, frustrated customers, and, ultimately, lost revenue. Moreover, as competitors invest in clean data and AI, companies with bad data governance risk falling irreparably behind.

Clean data governance is no longer optional; it’s a strategic imperative in the AI-driven B2B ecommerce landscape. By prioritizing data accuracy and consistency, companies can unlock AI’s full potential and position themselves for long-term success.

How B2B Companies Can Address Bad Data Governance

Tackling bad data governance is no small feat, but it’s a journey worth undertaking for B2B companies striving to unlock AI’s full potential. The solution involves strategic planning, technological investment, and cultural change. Here are actionable steps businesses can take to clean up their data and ensure it stays that way:

1. Conduct a Comprehensive Data Audit
2. Standardize the Data Entry Process
3. Implement Master Data Management (MDM)
4. Leverage Technology for Data Cleaning and Enrichment
5. Break Down Silos with Integration
6. Foster a Culture of Data Ownership
7. Commit to Continuous Improvement

The first step is conducting a thorough data audit—think of it as a spring cleaning for your databases. By identifying gaps, redundancies, and inaccuracies, businesses can reveal the full extent of their data issues. This process isn’t just about finding errors; it’s about creating a baseline understanding of the company’s data health. Regular audits prevent these issues from snowballing into more significant, costly problems.

Once the audit is complete, it’s time to set some ground rules. Standardizing data entry processes is critical for ensuring consistency. Clear guidelines for formatting SKUs, recording customer details, and storing supplier information can prevent the chaos of mismatched or incomplete records. Employees should be trained on these standards, and tools like automated forms or validation rules can make compliance seamless.

Of course, even the best data entry standards won’t help if different systems across the organization aren’t communicating. That’s where Master Data Management (MDM) comes in. By centralizing data into a single source of truth, companies ensure that updates in one system are automatically reflected across all others. With MDM in place, teams can work confidently, knowing that their data is accurate and consistent.

But standardizing and centralizing aren’t enough if you’re already sitting on a mountain of messy data. Performing this step by hand is significantly time-intensive. Enter data cleaning and enrichment tools. AI-powered solutions can quickly identify and correct errors, deduplicate records and fill in missing fields. These tools don’t just clean up the past; they automate routine processes to keep data clean moving forward.

For many B2B companies, fragmentation is one of the biggest hurdles to clean data. Silos between ERP systems, CRM platforms, and ecommerce tools create inconsistencies that ripple across the business. Breaking down these silos through system integration ensures a unified flow of data, improving collaboration and decision-making across departments. This requires a thoughtful integration strategy, often with the help of IT experts, but the payoff is well worth the effort.

Clean data isn’t just a technical problem—it’s a cultural one. Companies must foster a culture of data ownership, where employees understand the importance of the data they handle and feel accountable for its accuracy. Assigning clear responsibilities, such as appointing a Chief Data Officer (CDO) or similar role, can ensure that data governance remains a priority.

Finally, data governance isn’t a one-and-done project. Continuous improvement is essential. Regular review of data policies and feedback from team members help refine processes over time. Establishing KPIs for data quality can also provide measurable insights into the success of these efforts.

By taking these steps, B2B companies can move from reactive problem-solving to proactive data management. Clean, well-governed data isn’t just the backbone of AI success—it’s a strategic asset that drives better decisions, smoother operations, and stronger customer relationships. In an increasingly data-driven world, those who master their data will lead the way.

Conclusion: Turn Your Data into a Competitive Advantage in the AI Era

In the rapidly evolving landscape of B2B ecommerce, integrating AI technologies offers unprecedented opportunities for growth and efficiency. However, as we’ve explored, the effectiveness of AI is intrinsically linked to the quality of the underlying data. Companies risk undermining their AI initiatives without robust data governance, leading to inaccurate insights and missed opportunities.

Perficient stands at the forefront of addressing these challenges. With extensive experience in implementing comprehensive data governance frameworks, we empower B2B organizations to harness the full potential of their data. Our expertise encompasses:

• Product Information Management (PIM): We assist in managing all aspects of your product data—from SKUs and descriptions to stock levels and pricing—ensuring consistency and accuracy across all platforms.
• Digital Asset Management (DAM): Our solutions help organize and distribute digital assets related to your products, such as photos and videos, enhancing the efficiency of your operations.
• Data Integration and Standardization: We streamline your data processes, breaking down silos and ensuring seamless communication between systems, which is crucial for effective AI implementation.

Investing in clean data governance is not just a technical necessity but a strategic imperative. With Perficient’s expertise, you can transform your data into a powerful asset, driving informed decision-making and sustainable growth in the AI era.

In the face of electrification, evolving consumer behavior and expectations, sustainability initiatives, regulatory pressures, and geopolitical volatility, oil and gas companies are being challenged to shift their approach and innovate to stay competitive. While there’s a continued focus on the digital experience for customers especially in the downstream sector, companies are also pressured to address ESG policies and reporting from production to transport and sale of their products. The development of plans to utilize emerging technologies with data-driven approaches remains integral, however, they’re executing on these all while weaving through one merger and acquisition after another.

We are excited to announce that Perficient was recently recognized by a leading global technology research and advisory firm’s report highlighting notable oil and gas industry consultancies in the U.S. and Europe. Perficient experts have worked closely with organizations within the industry to overcome challenges and gain a competitive advantage with digital transformation.

“A key differentiator for Perficient is approaching each challenge with a deep understanding of the oil and gas industry while also tapping into innovative solutions that have secured real results in other industries.” – John Latham, GM, Houston

By keeping a pulse on the ever-changing trends and pain points within the industry, maintaining cutting-edge capabilities in technology, and conducting first-party research to inform strategy, we deliver results-driven solutions that our partners are seeking.

Data Analytics and App Development for Improved Worker Safety

Like in many industries, oil and gas companies are not immune to siloed and inaccessible data. We help these organizations access, consolidate, and manage that information easily. We’ve completed numerous projects in app development, such as shift handover applications and integrated many worker safety programs, including a system to monitor gas within trucks without the need to open lids and send personnel onto dangerous catwalks.

Streamlined Transitions Throughout Mergers and Acquisitions

Over the years, we’ve helped oil and gas companies navigate the growing number of mergers and acquisitions in the industry. When one company acquires another, they want system integration as quickly as possible. Post-merger integration, supply chain and logistics, supplier management, and standardizing systems across processes are playbooks we’ve written for not just oil and gas, but every industry we’ve worked in. Further, the abundance of data that occurs due to mergers is something we expertly handle to prevent further siloing.

Cross-Industry Solutions in Oil and Gas

Oil and gas companies are stretching beyond their role as service providers to act as retailers and manufacturers. They are beginning to delve into solutions like loyalty programs and hiring executives from Target and other big box retail environments. Gas stations are now mini supermarkets striving to increase foot traffic and the size of customer baskets.  Further, all eyes are on the automotive industry as energy companies are attempting to predict the demand for gasoline and what it would look like to provide electric vehicle charging stations.

Our work across industries has made us a trusted partner and resource for these organizations hoping to build on strategies and insights from other markets. Our inclusion in this report reflects the countless hours devoted to our partnerships and understanding the work that matters to them so that we deliver real results.

Learn more about Perficient’s energy and utilities expertise.

In the world of AI, we often hear about “Responsible AI.” However, if you ask ten people what it actually means, you might get ten different answers. Most will focus on ethical standards: fairness, transparency, and social good. But is that the end of responsibility? Many of our AI solutions are built by enterprise organizations who aim to meet both ethical standards AND business objectives. To whom are we responsible, and what kind of responsibility do we really owe? Let’s dive into what “Responsible AI” could mean with a broader scope. 

Ethical Responsibility: The Foundation of Responsible AI 

Ethical responsibility is often our go-to definition for Responsible AI. We’re talking about fairness in algorithms, transparency in data use, and minimizing harm, especially in areas like bias and discrimination. It’s crucial and non-negotiable, but ethics alone don’t cover the full range of responsibilities we have as business and technology leaders. As powerful as ethical guidelines are, they only address one part of the responsibility puzzle. So, let’s step out of this comfort zone a bit to dive deeper. 

Operational Responsibility: Keeping an Eye on Costs 

At its core, AI tools are a resource-intensive technology. When we deploy AI, we’re not just pushing lines of code into the world; we’re managing data infrastructure, compute power, and – let’s face it – a budget that often feels like it’s getting away from us.  

This brings up a question we don’t always want to ask: is it responsible to use up cloud resources so that the AI can write a sonnet? 

Of course, some use cases justify high costs, but we need to weigh the value of specific applications. Responsible AI isn’t just about can we do something; it’s about should we do it, and whether it’s appropriate to pour resources into every whimsical or niche application. 

 Operational responsibility means asking tough questions about costs and sustainability—and, yes, learning to say “no” to AI haikus. 

Responsibility to Employees: Making AI Usable and Sustainable 

If we only think about responsibility in terms of what AI produces, we miss a huge part of the equation: the people behind it. Building Responsible AI isn’t just about protecting the end user; it’s about ensuring that developers, data scientists, and support teams innovating AI systems have the tools and support they need.  

Imagine the mental gymnastics required for an employee navigating overly-complex, high-stakes AI projects without proper support. Not fun. Frankly, it’s an environment where burnout, inefficiency, and mistakes become inevitable. Responsible AI also means being responsible to our employees by prioritizing usability, reducing friction, and creating workflows to make their jobs easier, not more complicated. Employees who are empowered to build reliable, ethical, and efficient AI solutions ultimately deliver better results.  

User Responsibility: Guardrails to Keep AI on Task 

Users love pushing AI to its limits—asking it quirky questions, testing its boundaries, and sometimes just letting it meander into irrelevant tangents. While AI should offer flexibility, there’s a balance to be struck. One of the responsibilities we carry is to guide users with tailored guardrails, ensuring the AI is not only useful but also used in productive, appropriate ways.  

That doesn’t mean policing users, but it does mean setting up intelligent limits to keep AI applications focused on their intended tasks. If the AI’s purpose is to help with research, maybe it doesn’t need to compose a 19th-century-style romance novel (as entertaining as that might be). Guardrails help direct users toward outcomes that are meaningful, keeping both the users and the AI on track. 

Balancing Responsibilities: A Holistic View of Responsible AI 

Responsible AI encompasses a variety of key areas, including ethics, operational efficiency, employee support, and user guidance. Each one adds an additional layer of responsibility, and while these layers can occasionally conflict, they’re all necessary to create AI that truly upholds ethical and practical standards. Taking a holistic approach requires us to evaluate trade-offs carefully. We may sometimes prioritize user needs over operational costs or support employees over certain ethics constraints, but ultimately, the goal is to balance these responsibilities thoughtfully. 

Expanding the scope of “Responsible AI” means going beyond traditional ethics. It’s about asking uncomfortable questions, like “Is this AI task worth the cloud bill?” and considering how we support the  people who are building and using AI. If we want AI to be truly beneficial, we need to be responsible not only to society at large but also to our internal teams and budgets. 

Our dedicated team of AI and digital transformation experts are committed to helping the largest organizations drive real business outcomes. For more information on how Perficient can implement your dream digital experiences, contact Perficient to start your journey.

Intelligence is the new currency of business: the ability to transform raw data into actionable insights isn’t just a competitive advantage, it’s a strategic imperative. Organizations are rapidly discovering that enterprise intelligence is the key to unlocking unprecedented business value, driving decision-making, and creating transformative experiences.

IDC Market Glance: Enterprise Intelligence Services, 3Q24

We’re proud to be included in the IT Services Providers with Enterprise Intelligence Services offerings category for the IDC Market Glance: Enterprise Intelligence Services report (doc #US51423524, September 2024). IDC defines Enterprise Intelligence as “an organization’s capacity to learn combined with its ability to synthesize the information it needs in order to learn and to apply the resulting insights at scale by establishing a strong data culture”.

Our enterprise intelligence expertise and proven ability to execute comprehensive technology transformations demonstrate why we believe we’ve been included in this report. Our consistent commitment to delivering solutions helps harness untapped potential and drive measurable business results for our clients.

Turning Data into Strategic Advantage

Our approach goes beyond traditional data services. We don’t just implement technology—we create intelligent ecosystems that breathe life into your data strategy and accelerate transformation. Our Data and Intelligence practice encompasses all enterprise intelligence facets and focuses on technical strategy, implementation, integration, and support of cutting-edge, end-to-end intelligence technologies that transform how businesses operate.

The evolution of enterprise intelligence is about more than collecting data—it’s about creating a living, breathing intelligence framework that adapts, learns, and drives strategic decision-making. We understand that today’s IT leaders are tasked with more than technical execution; they’re architects of business transformation.

Beyond Technology: A Holistic Data Approach

We understand that insights alone aren’t enough – they must be paired with a comprehensive strategy, actionable automation, and fully scalable solutions. Our expertise spans the digital transformation landscape, where we combine AI-powered architectures with advanced analytics to reduce manual workloads and create self-optimizing systems. We don’t just implement solutions; we create intelligent strategies that align technology with your critical business objectives.

By embedding intelligent technologies into platforms, products, and experiences, we help transform data from a passive resource into an active, strategic asset. Our solutions operationalize insights into a continuous cycle of improvement, enhancing employee experiences and delivering measurable business outcomes.

To discover how Perficient can help you harness the power of enterprise intelligence and stay ahead of digital disruption, visit Data + Intelligence/Perficient.

Now more than ever, we’re witnessing a significant shift from simple AI capabilities to action-driven AI Agents that promise to revolutionize how we approach knowledge work, product development, and business processes. Drawing insights from Perficient’s industry experts, we’re constantly exploring Generative AI, the emerging world of agentic frameworks, and their potential to reshape organizational capabilities. 

Beyond Chatbots: The Evolution of AI Agents 

For the past few years, many organizations have been deploying AI within their organizations via generative AI chatbots – tools that take prompts, access a knowledge base, and generate responses. While these were once groundbreaking tools to improve business functions, they are essentially one-dimensional: they could provide information but couldn’t take meaningful action. 

While AI chatbots can respond to user input, AI Agents can take action and perform tasks within defined parameters. 

AI Agents are rapidly expanding across multiple domains including virtual assistance, complex task management, social media content, product development, and more.  

But what makes an AI Agent truly revolutionary? It’s about creating a more nuanced, human-like intelligence. An AI Agent is characterized by: 

1. Knowledge Base: Similar to chatbots, but augmented with information that supports outputs, standards, or historical content.
2. Role Definition: A clear, contextual understanding of its purpose and role often within a team.
3. Skills and Cooperation: The ability to make decisions and take action (within defined parameters) while providing and taking feedback within a team of other Agents and humans.

Navigating the AI Agent Implementation Journey 

Imagine transforming your organization’s potential, not through a massive overhaul, but through iterative, strategic steps. Successful AI Agent implementation is less about a revolutionary leap and more about a thoughtful, incremental progression. 

Like most transformations, planning where to provide value is critical. It’s important to identify pain points that can be delegated to an Agent, rather than distracting your most talented people away from valuable work.  

Perficient’s AI Accelerated Modeling Process (AMP) can help you implement Agentic AI quickly and responsibly. AI AMP is a short, focused four- to six- week initiative with the goal of developing an interactive model that demonstrates how your organization can leverage machine learning, natural language processing, and cognitive computing to jump start Al adoption. 

Organizations are discovering AI Agents aren’t just theoretical – they’re practical problem-solvers across multiple domains: 

• An Agentic team that can reverse engineer legacy software, documenting business requirements and how the software currently works. 

• Supplementing your Product Owners by checking the quality of backlog artifacts across multiple teams, providing feedback and enriching those requirements. 

• Creating synthetic data that can provide greater test coverage at scale. 

• A social media team with a writer, reviewer, and editor that have knowledge of the brand and previous posts and can critique the writing based on the research. 

• An Agentic CX team that can merge research, gather initial insights and draft presentations so the human team can focus on the deep insights and recommendations. 

• Automatic routing of emails based on the content and context of customer service requests. 

AI Agents Aren’t Just About Capability – It’s About Responsibility.  

Security isn’t an afterthought; it’s the foundation. Perficient’s PACE Framework is a holistic approach to designing tailored operational AI programs that empower business and technical stakeholders to innovate with confidence while mitigating risks and upholding ethical standards. 

Our comprehensive engagement model evaluates your organization against the PACE framework, tailoring programs and processes to effectively and responsibly integrate AI capabilities across your organization. 

The Future of Work 

The transformative potential of AI agents extends far beyond traditional chatbots, representing a strategic pathway for organizations to augment human capabilities intelligently and responsibly.  

To explore how your enterprise can benefit from Agentic AI, reach out to Perficient’s team of experts today. The next wave of AI is about creating intelligent, collaborative agentic systems that augment and transform our capabilities, one specialized Agent at a time. 

Imagine a world where digital interfaces anticipate your needs, understand your preferences, and adapt in real-time to enhance your experience. This is not a futuristic daydream, but the promise of generative interfaces. 

Generative interfaces represent a new paradigm in user experience design, moving beyond static layouts to create highly personalized and adaptive interactions. These interfaces are powered by generative AI technologies that respond to each user’s unique needs, behaviors, and context. The result is a fluid, intuitive experience—a digital environment that transforms, adapts, and grows with its users. 

The Evolution of User Interaction 

Traditional digital interfaces have long relied on predefined structures and user journeys. While these methods have served us well, they fall short of delivering truly personalized experiences. 

Generative interfaces, on the other hand, redefine personalization and interactivity at the level of individual interactions. They have the capability to bring data and components directly to users from multiple systems, seamlessly integrating them into a cohesive user experience.  

Users can perform tasks without switching applications as generative systems dynamically render necessary components within the interface, such as images, interactive components, and data visualizations. 

This adaptability means that generative interfaces continually evolve based on users’ inputs, preferences, and behaviors, creating a more connected and fluid experience. Instead of users adapting to software, the software adapts to them, enhancing productivity, reducing friction, and making digital interactions feel natural. 

Adaptive Design Principles 

At the heart of generative interfaces lies the principle of adaptability. This adaptability is more than just personalization—it’s about creating an interface that is in constant dialogue with its user. Unlike conventional systems that rely on rules and configurations set during development, generative interfaces leverage machine learning and user data to generate real-time responses. This not only makes the experience dynamic but also inherently human-centered. 

For instance, a digital assistant that supports a knowledge worker doesn’t just answer questions—it understands the context of the work, anticipates upcoming needs, and interacts in a way that aligns with the user’s goals. Generative interfaces are proactive and responsive, driven by the understanding that user needs can change from moment to moment. 

Envisioning the Future 

Generative interfaces hold the promise of reshaping not just individual applications, but entire categories of digital interaction—from productivity tools to entertainment platforms. Imagine entertainment systems that automatically adjust content suggestions based on your mood, or collaboration platforms that adapt their layouts and tools depending on whether you are brainstorming or executing a task. 

This is why data privacy and security considerations must be built into every aspect of the system, from data collection and storage to processing and output generation.  Without control of the experience, you risk low-quality outputs that can do more harm than good. 

As organizations deploy generative interfaces, robust governance frameworks become essential for managing risks and ensuring responsible AI use.  

Embracing Generative Interfaces

The shift towards generative interfaces is a step towards making technology more human-centric. As we embrace these adaptive designs, we create an opportunity to redefine our digital experiences, making them more intuitive, enjoyable, and impactful. At Perficient, we are pushing the boundaries of how technology can adapt to users rather than forcing users to adapt to technology. 

The impact of these interfaces goes beyond just convenience; they are capable of crafting meaningful digital experiences that feel personal and fulfilling. As generative AI continues to advance, I envision a future where technology fades into the background, seamlessly blending into our lives and intuitively enhancing everything from work to leisure. 

Metadata Extraction: IDMC vs. PowerCenter

When we talk about metadata extraction, IDMC (Intelligent Data Management Cloud) can be trickier than PowerCenter. Let’s see why.
In PowerCenter, all metadata is stored in a local database. This setup lets us use SQL queries to get data quickly and easily. It’s simple and efficient.
In contrast, IDMC relies on the IICS Cloud Repository for metadata storage. This means we have to use APIs to get the data we need. While this method works well, it can be more complicated. The data comes back in JSON format. JSON is flexible, but it can be hard to read at first glance.
To make it easier to understand, we convert the JSON data into a table format. We use a tool called jq to help with this. jq allows us to change JSON data into CSV or table formats. This makes the data clearer and easier to analyze.

In this section, we will explore jq. jq is a command-line tool that helps you work with JSON data easily. It lets you parse, filter, and change JSON in a simple and clear way. With jq, you can quickly access specific parts of a JSON file, making it easier to work with large datasets. This tool is particularly useful for developers and data analysts who need to process JSON data from APIs or other sources, as it simplifies complex data structures into manageable formats.

For instance, if the requirement is to gather Succeeded Taskflow details, this involves two main processes. First, you’ll run the IICS APIs to gather the necessary data. Once you have that data, the next step is to execute a jq query to pull out the specific results. Let’s explore two methods in detail.

Extracting Metadata via Postman and jq:-

Step 1:

To begin, utilize the IICS APIs to extract the necessary data from the cloud repository. After successfully retrieving the data, ensure that you save the file in JSON format, which is ideal for structured data representation.

Step 2:
Construct a jq query to extract the specific details from the JSON file. This will allow you to filter and manipulate the data effectively.

Windows:-
(echo Taskflow_Name,Start_Time,End_Time & jq -r ".[] | [.assetName, .startTime, .endTime] | @csv" C:\Users\christon.rameshjason\Documents\Reference_Documents\POC.json) > C:\Users\christon.rameshjason\Documents\Reference_Documents\Final_results.csv

Linux:-
jq -r '["Taskflow_Name","Start_Time","End_Time"],(.[] | [.assetName, .startTime, .endTime]) | @csv' /opt/informatica/test/POC.json > /opt/informatica/test/Final_results.csv

Step 3:
To proceed, run the jq query in the Command Prompt or Terminal. Upon successful execution, the results will be saved in CSV file format, providing a structured way to analyze the data.

Extracting Metadata via Command Prompt and jq:-

Step 1:
Formulate a cURL command that utilizes IICS APIs to access metadata from the IICS Cloud repository. This command will allow you to access essential information stored in the cloud.

Windows and Linux:-
curl -s -L -X GET -u USER_NAME:PASSWORD "https://<BASE_URL>/active-bpel/services/tf/status?runStatus=Success" -H "Accept: application/json"

Step 2:
Develop a jq query along with cURL to extract the required details from the JSON file. This query will help you isolate the specific data points necessary for your project.

Windows:
(curl -s -L -X GET -u USER_NAME:PASSWORD "https://<BASE_URL>/active-bpel/services/tf/status?runStatus=Success" -H "Accept: application/json") | (echo Taskflow_Name,Start_Time,End_Time & jq -r ".[] | [.assetName, .startTime, .endTime] | @csv" C:\Users\christon.rameshjason\Documents\Reference_Documents\POC.json) > C:\Users\christon.rameshjason\Documents\Reference_Documents\Final_results.csv

Linux:
curl -s -L -X GET -u USER_NAME:PASSWORD "https://<BASE_URL>/active-bpel/services/tf/status?runStatus=Success" -H "Accept: application/json" | jq -r '["Taskflow_Name","Start_Time","End_Time"],(.[] | [.assetName, .startTime, .endTime]) | @csv' /opt/informatica/test/POC.json > /opt/informatica/test/Final_results.csv

Step 3:
Launch the Command Prompt and run the cURL command that includes the jq query. Upon running the query, the results will be saved in CSV format, which is widely used for data handling and can be easily imported into various applications for analysis.

Conclusion
To wrap up, the methods outlined for extracting workflow metadata from IDMC are designed to streamline your workflow, minimizing manual tasks and maximizing productivity. By automating these processes, you can dedicate more energy to strategic analysis rather than tedious data collection. If you need further details about IDMC APIs or jq queries, feel free to drop a comment below!

Reference Links:-

IICS Data Integration REST API – Monitoring taskflow status with the status resource API

jq Download Link – Jq_Download

In the PC-IDMC conversion process, it can be challenging to gather detailed information about workflows. Specifically, we often need to determine:

• The number of transformations used in each mapping.
• The number of sessions utilized within the workflow.
• Whether any parameters or variables are being employed in the mappings.
• The count of reusable versus non-reusable sessions used in the workflow etc.

To obtain these details, we currently have to open each workflow individually, which is time-consuming. Alternatively, we could use complex queries to extract this information from the PowerCenter metadata in the database tables.

This section focuses on XQuery, a versatile language designed for querying and extracting information from XML files. When workflows are exported from the PowerCenter repository or Workflow Manager, the data is generated in XML format. By employing XQuery, we can effectively retrieve the specific details and data associated with the workflow from this XML file.

Step-by-Step Guide to Extracting Workflow Details Using XQuery: –

For instance, if the requirement is to retrieve all reusable and non-reusable sessions for a particular workflow or a set of workflows, we can utilize XQuery to extract this data efficiently.

Step 1:
Begin by exporting the workflows from either the PowerCenter Repository Manager or the Workflow Manager. You have the option to export multiple workflows together as one XML file, or you can export a single workflow and save it as an individual XML file.

Step 2:-
Develop the XQuery based on our specific requirements. In this case, we need to fetch all the reusable and non-reusable sessions from the workflows.

let $header := "Folder_Name,Workflow_Name,Session_Name,Mapping_Name"
let $dt := (let $data := 
    ((for $f in POWERMART/REPOSITORY/FOLDER
    let $fn:= data($f/@NAME)
    return
        for $w in $f/WORKFLOW
        let $wn:= data($w/@NAME)
        return
            for $s in $w/SESSION
            let $sn:= data($s/@NAME)
            let $mn:= data($s/@MAPPINGNAME)
            return
                <Names>
                    {
                        $fn ,
                        "," ,
                        $wn ,
                        "," ,
                        $sn ,
                        "," ,
                        $mn
                    }
                </Names>)
    |           
    (for $f in POWERMART/REPOSITORY/FOLDER
    let $fn:= data($f/@NAME)
    return          
        for $s in $f/SESSION
        let $sn:= data($s/@NAME)
        let $mn:= data($s/@MAPPINGNAME)
        return
            for $w in $f/WORKFLOW
            let $wn:= data($w/@NAME)
            let $wtn:= data($w/TASKINSTANCE/@TASKNAME)
            where $sn = $wtn
            return
                <Names>
                    {
                        $fn ,
                        "," ,
                        $wn ,
                        "," ,
                        $sn ,
                        "," ,
                        $mn
                    }
                </Names>))
       for $test in $data
          return
            replace($test/text()," ",""))
      return
 string-join(($header,$dt), "
")

Step 3:
Select the necessary third-party tools to execute the XQuery or opt for online tools if preferred. For example, you can use BaseX, Altova XMLSpy, and others. In this instance, we are using Basex, which is an open-source tool.

Create a database in Basex to run the XQuery.

Step 4: Enter the created XQuery into the third-party tool or online tool to run it and retrieve the results.

Step 5:
Export the results in the necessary file extensions.

Conclusion:
These simple techniques allow you to extract workflow details effectively, aiding in the planning and early detection of complex manual conversion workflows. Many queries exist to fetch different kinds of data. If you need more XQueries, just leave a comment below!

Data isn’t just an asset—it’s the lifeblood of most organizations. As businesses continue to amass vast amounts of information, the exposure to potential breaches grows exponentially. According to IBM, the global cost of data breaches continues to rise, with the average incident now costing companies $4.88 million in 2024, up 10% from the previous year.

Yet surprisingly, security often remains an afterthought in data management strategies, creating vulnerabilities that can prove costly.

The Growing Threat of Data Breaches

Data breaches represent far more than security incidents—they’re existential threats that can unravel years of carefully built revenue streams and customer trust. As organizations’ digital footprints expand and data volumes grow exponentially, the stakes continue to rise. Companies face mounting pressure to protect sensitive information while maintaining operational efficiency, a balance that becomes increasingly precarious as traditional data-sharing methods evolve and regulatory frameworks grow more complex. The potential impact of these breaches scales with our growing reliance on digital systems, making them one of the most significant risks facing modern organizations.

Why Data is a Prime Target

1. Value: Personal and corporate data can be sold on the dark web or used for identity theft.

2. Ransom: Cybercriminals can encrypt data and demand payment for its release.

3. Competitive Advantage: Stolen intellectual property can give competitors an unfair edge.

4. Political Motives: State-sponsored attacks may target sensitive government or infrastructure data.

The Challenge of Modern Data Sharing

Many organizations still rely on physical data sharing methods, creating unnecessary risks in an increasingly complex digital world. Modern approaches offer more sophisticated solutions, including the ability to quickly revoke user access and remove shared data—a crucial feature for modern, enterprise-scale organizations. The modern data sharing capabilities offer greater security, stronger governance and enable effective data sharing needs in real-time. However, companies often struggle with the “how” of implementing these solutions, particularly from a business process perspective.

Building a Security-First Framework

At Perficient, we understand that effective data security isn’t inherited—it’s built through careful planning and regular evaluation. Our approach centers on three key principles:

1. Tactical Assessment and Planning: We specialize in quickly assessing an organization’s current security posture and developing actionable plans for improvement. No two companies are identical, so this starts with understanding where you are and creating a custom roadmap to where you need to be.
2. Relationship-Driven Implementation: Success in data security isn’t just about the technology—it’s also about people and processes surrounding it. We work closely with key constituents across your organization, recognizing the industry-specific requirements and regulations that often drive security needs.
3. Principle of Least Privilege: We advocate for and implement the practice of providing users only the minimum access necessary for their roles, significantly reducing potential exposure points. This is incredibly important not only in creation of roles and user accounts but constantly reapplied on a routine basis.

Taking Action

Organizations looking to strengthen their data security posture should start by:

• Evaluating current data sharing processes and identifying potential vulnerabilities
• Implementing modern data-sharing solutions that offer greater control and visibility
• Developing clear protocols for access management and regular security assessments
• Create industry-specific frameworks that align with regulatory requirements
• Constantly reevaluate the security posture to realign to business needs
• Conduct regular cybersecurity training to educate employees about phishing scams, password security, and recognizing suspicious activity. Employees are often the first line of defense against breaches caused by human error.

Moving Forward

As data continues to grow in volume and importance, organizations can’t afford to treat security as an afterthought. By taking a proactive approach to data security and working with experienced partners, businesses can better protect their most valuable asset while maintaining the efficiency they need to compete in today’s market.

Security isn’t a one-time implementation—it’s an ongoing process requiring regular evaluation and adjustment. Every data implementation, regardless of its primary purpose, has security implications that need to be carefully considered. This is why we emphasize the importance of building frameworks that incorporate more secure steps from the ground up.

Remember: data security isn’t just about preventing breaches—it’s about building a foundation for sustainable business success. Learn how Perficient can support your data security needs.