Turn on CNBC during almost any trading day and you’ll see and hear plenty of AI buzz that sounds great, and may look great in a deck, but falls short in regulated industries. For financial services firms, AI must do two things at once: unlock genuine business value and satisfy strict compliance, privacy, and audit requirements. Salesforce’s AI stack — led by Einstein GPT, Data Cloud, and integrated with MuleSoft, Slack, and robust security controls — is engineered to meet that dual mandate. Here’s a practical look at what Salesforce AI delivers for banks, insurers, credit unions, wealth managers, and capital markets firms, and how to extract measurable value without trading off controls and/or governance.

What Salesforce AI actually is (and why it matters for Financial Services)

Salesforce is widely adopted by financial services firms, with over 150,000 companies worldwide using its CRM, including a significant portion of the U.S. market, where 83% of businesses opt for its Financial Services Cloud (“FSC”). Major financial institutions like Wells Fargo, Bank of America Merrill Lynch and The Bank of New York are among its users, demonstrating its strong presence within the industry. Salesforce has combined together generative AI, predictive models, and enterprise data plumbing into a single ecosystem. Key capabilities include:

• Einstein GPT: Generative AI tailored for CRM workflows — draft client communications, summarize notes, and surface contextual insights using your internal data.
• Data Cloud: A real-time customer data platform that ingests, unifies, and models customer profiles at scale, enabling AI to operate on a trusted single source of truth.
• Tableau + CRM Analytics: Visualize model outcomes, monitor performance, and create operational dashboards that align AI outputs with business KPIs.
• MuleSoft: Connectors and APIs to bring core banking, trading, and ledger systems into the loop securely.
• Slack & Flow (and Flow Orchestrator): Operationalize AI outputs into workflows, approvals, and human-in-the-loop processes.

For financial services, that integration matters more than flashy demos: accuracy, traceability, and context are non-negotiable. Salesforce’s ecosystem lets you apply AI where it impacts revenue, risk, and customer retention — and keep audit trails for everything.

High-value financial services use cases

Here are the pragmatic use cases where Salesforce AI delivers measurable ROI:

Client advisory and personalization

Generate personalized portfolio reviews, client outreach, or renewal communications using Einstein GPT combined with up-to-date holdings and risk profiles from Data Cloud. The result: more relevant outreach and higher conversion rates with less advisor time.

Wealth management — scalable advice and relationship mining

AI-driven summarization of client meetings, automated risk-tolerance classifiers, and opportunity scoring help advisors prioritize high-value clients and surface cross-sell opportunities without manual data wrangling.

Commercial lending — faster decisioning and better risk controls

Combine predictive credit risk models with document ingestion (via MuleSoft and integrated OCR) to auto-populate loan applications, flag exceptions, and route for human review where model confidence is low.

Fraud, AML, and compliance augmentation

Use real-time customer profiles and anomaly detection to surface suspicious behaviors. AI can triage alerts and summarize evidence for investigators, improving throughput while preserving explainability for regulators. AI can also reduce the volume of false alerts, which is the bane of every compliance officer ever.

Customer support and claims

RAG-enabled virtual assistants (Einstein + Data Cloud) pull from policy language, transaction history, and client notes to answer common questions or auto-draft claims responses — reducing service time and improving consistency. The virtual assistants can also interact in multiple languages, which helps reduce customer turnover for non-English writing clients.

Sales and pipeline acceleration

Predictive lead scoring, propensity-to-buy models, and AI-suggested next-best actions increase win rates and shorten sales cycles. Integrated workflows push suggestions to reps in Slack or the Salesforce console, making adoption frictionless.

Why Salesforce’s integrated approach reduces risk

Financial firms can’t treat AI as a separate experiment. Salesforce’s value proposition is that AI is embedded into systems that already handle customer interactions, security, and governance. That produces the following practical advantages:

Single source of truth

Data Cloud reduces conflicting customer records and stale insights, which directly lowers the risk of AI producing inappropriate or inaccurate outputs.

Controlled model access and hosting options

Enterprises can choose where data and model inference occur, including private or managed-cloud options, helping meet residency and confidentiality requirements.

Explainability and audit trails

Salesforce logs user interactions, AI-generated outputs, and data lineage into the platform. That creates the documentation regulators ask for and lets financial services executives investigate where models made decisions.

Human-in-the-loop and confidence thresholds

Workflows can be configured so that high-risk or low-confidence outputs require human approval. That’s essential for credit decisions, compliance actions, and investment advice.

Implementation considerations for regulated firms

To assist in your planned deployment of Salesforce AI in financial services, here’s a checklist of practical guardrails and steps:

Start with business outcomes, not models

• Identify high-frequency, low-risk tasks for pilots (e.g., document summarization, inquiry triage) and measure lift on KPIs like turnaround time, containment rate, and advisor productivity.

Clean and govern your data

Invest in customer identity resolution, canonicalization, and metadata tagging in Data Cloud. Garbage in, garbage out is especially painful when compliance hangs on a model’s output.

Create conservative guardrails

Hard-block actions that have material customer impact (e.g., account closure, fund transfers) from automated flows. Use AI to assist drafting and recommendation, not to execute high-risk transactions autonomously.

Establish model testing and monitoring

Implement A/B tests, accuracy benchmarks, and drift detection. Integrate monitoring into Tableau dashboards and set alerts for performance degradation or unusual patterns.

Document everything for auditors and regulators

Maintain clear logs of training data sources, prompt templates, model versions, and human overrides. Salesforce’s native logging plus orchestration records from Flow help with this.

Train users and change-manage

Advisors, compliance officers, and client service reps should be part of prompt tuning and feedback loops. Incentivize flagging bad outputs — their corrections will dramatically improve model behavior.

Measurable outcomes to expect

When implemented with discipline, financial services firms typically see improvements including:

• Reduced average handling time and faster loan turnaround
• Higher client engagement and improved cross-sell conversion
• Fewer false positives and faster investigator resolution times
• Better advisor productivity via automated notes and suggested actions

Those outcomes translate into cost savings, improved regulatory posture, and revenue lift — the hard metrics CFOs, CROs, and CCOs require.

Final thoughts — pragmatic AI adoption

Salesforce gives financial institutions a practical path to embed AI into customer-facing and operational workflows without ripping up existing systems. The power isn’t just in the model; it’s in the combination of unified data (Data Cloud), generative assistance (Einstein GPT), secure connectors (MuleSoft), and operationalization (Flows and Slack). If you treat governance, monitoring, and human oversight as first-class citizens, AI becomes an accelerant — not a liability.

To help financial services firms either install or expand their Salesforce capability, Perficient has a 360-degree strategic partnership with Salesforce. While Salesforce itself is the provider of the platform and technology, as a global digital consultancy firm Perficient partners with Salesforce to offer its expertise in implementation, customization, and optimization of Salesforce solutions, leveraging Salesforce’s AI-first technologies and platform to deliver consulting, implementation, and integration services. Working together, Salesforce and Perficient’s partnership helps mutual clients build customer-centric solutions and operate as “agentic enterprises”.  

The Rise of AI in Banking

AI adoption in banking has accelerated dramatically. Predictive analytics, generative AI, and autonomous agentic systems are now embedded in core banking functions such as loan underwriting, compliance including fraud detection and AML, and customer engagement. 

A recent White Paper by Perficient affiliate Virtusa Agentic Architecture in Banking – White Paper | Virtusa documented that when designed with modularity, composability, Human-in-the-Loop (HITL), and governance, agentic AI agents empower a more responsive, data-driven, and human-aligned approach in financial services.

However, the rollout of agentic and generative AI tools without proper controls poses significant risks. Without a unified strategy and governance structure, Strategically Important Financial Institutions (“SIFIs”) risk deploying AI in ways that are opaque, biased, or non-compliant. As AI becomes the engine of next-generation banking, institutions must move beyond experimentation and establish enterprise-wide controls.

Key Components of AI Data Governance

Modern AI data governance in banking encompasses several critical components:

1. Data Quality and Lineage: Banks must ensure that the data feeding AI models is accurate, complete, and traceable.

Please refer to Perficient’s recent blog on this topic here:

AI-Driven Data Lineage for Financial Services Firms: A Practical Roadmap for CDOs / Blogs / Perficient

2. Model Risk Management: AI models must be rigorously tested for fairness, accuracy, and robustness. It has been documented many times in lending decision-making software that the bias of coders can result in biased lending decisions.

3. Third-Party Risk Oversight: Governance frameworks now include vendor assessments and continuous monitoring. Large financial institutions do not have to develop AI technology solutions themselves (Buy vs Build) but they do need to monitor the risks of having key technology infrastructure owned and/or controlled by third parties.

4. Explainability and Accountability: Banks are investing in explainable AI (XAI) techniques. Not everyone is a tech expert, and models need to be easily explainable to auditors, regulators, and when required, customers.

5. Privacy and Security Controls: Encryption, access controls, and anomaly detection are essential. These are all done already in legacy systems and extending it to the AI environment, whether it is narrow AI, machine learning, or more advanced agentic and/or generative AI it is natural to ensure these proven controls are extended to the new platforms. 

Industry Collaboration and Standards

The FINOS Common Controls for AI Services initiative is a collaborative, cross-industry effort led by the FINtech Open-Source Foundation (FINOS) to develop open-source, technology-neutral baseline controls for safe, compliant, and trustworthy AI adoption in financial services. By pooling resources from major banks, cloud providers, and technology vendors, the initiative creates standardized, open-source technology-neutral controls, peer-reviewed governance frameworks, and real-time validation mechanisms to help financial institutions meet complex regulatory requirements for AI. 

Key participants of FINOS include financial institutions such as BMO, Citibank, Morgan Stanley, and RBC, and key Technology & Cloud Providers include Perficient’s technology partners including Microsoft, Google Cloud, and Amazon Web Services (AWS). The FINOS Common Controls for AI Services initiative aims to create vendor-neutral standards for secure AI adoption in financial services.

At Perficient, we have seen leading financial institutions, including some of the largest SIFIs, establishing formal governance structures to oversee AI initiatives. Broadly, these governance structures typically include:

– Executive Steering Committees at the legal entity level
– Working Groups, at the legal entity as well as the divisional, regional and product levels
– Real-Time Dashboards that allow customizable reporting for boards, executives, and auditors

This multi-tiered governance model promotes transparency, agility, and accountability across the organization.

Regulatory Landscape in 2025

Regulators worldwide are intensifying scrutiny of Artificial Intelligence in banking. The EU AI Act, the U.S. SEC’s cybersecurity disclosure rules, and the National Insititute of Standards and Technology (“NIST”) AI Risk Management Framework are shaping how financial institutions must govern AI systems.

Key regulatory expectations include:

– Risk-Based Classification
– Human Oversight
– Auditability
– Bias Mitigation

Some of these, and other regulatory regimes have been documented and summarized by Perficient at the following links:

AI Regulations for Financial Services: Federal Reserve / Blogs / Perficient

AI Regulations for Financial Services: European Union / Blogs / Perficient 

The Road Ahead

As AI becomes integral to banking operations, data governance will be the linchpin of responsible innovation. Banks must evolve from reactive compliance to proactive risk management, embedding governance into every stage of the AI lifecycle.

The journey begins with data—clean, secure, and well-managed. From there, institutions must build scalable frameworks that support ethical AI development, align with regulatory mandates, and deliver tangible business value.

Readers are urged to read the links contained in this blog and then contact Perficient, a global AI-first digital consultancy to discuss how partnering with Perficient can help run a tailored assessment and pilot design that maps directly to your audit and governance priorities and ensure all new tools are rolled out in a well-designed data governance environment.

Introduction

Imagine just as you’re sipping your Monday morning coffee and looking forward to a hopefully quiet week in the office, your Outlook dings and you see that your bank’s primary federal regulator is demanding the full input – regulatory report lineage for dozens of numbers on both sides of the balance sheet and the income statement for your latest financial report filed with the regulator. The full first day letter responses are due next Monday, and as your headache starts you remember that the spreadsheet owner is on leave; the ETL developer is debugging a separate pipeline; and your overworked and understaffed reporting team has three different ad hoc diagrams that neither match nor reconcile.

If you can relate to that scenario, or your back starts to tighten in empathy, you’re not alone. Artificial Intelligence (“AI”) driven data lineage for banks is no longer a nice-to-have. We at Perficient working with our clients in banking, insurance, credit unions, and asset managers find that it’s the practical answer to audit pressure, model risk (remember Lehman Brothers and Bear Stearns), and the brittle manual processes that create blind spots. This blog post explains what AI-driven lineage actually delivers, why it matters for banks today, and a phased roadmap Chief Data Officers (“CDOs”) can use to get from pilot to production.

Why AI-driven data lineage for banks matters today

Regulatory pressure and real-world consequences

Regulators and supervisors emphasize demonstrable lineage, timely reconciliation, and governance evidence. In practice, financial services firms must show not just who touched data, but what data enrichment and/or transformations happened, why decisions used specific fields, and how controls were applied—especially under BCBS 239 guidance and evolving supervisory expectations.

In addition, as a former Risk Manager, the author knows that he would have wanted and has spoken to a plethora of financial services executives who want to know that the decisions they’re making on liquidity funding, investments, recording P&L, and hedging trades are based on the correct numbers. This is especially challenging at global firms that operate in in a transaction heavy environment with constantly changing political, interest rate, foreign exchange and credit risk environment.

Operational risks that keep CDOs up at night

Manual lineage—spreadsheets, tribal knowledge, and siloed code—creates slow audits, delayed incident response, and fragile model governance. AI-driven lineage automates discovery and keeps lineage living and queryable, turning reactive fire drills into documented, repeatable processes that will greatly shorten the time QA tickets are closed and reduce compensation costs for misdirected funds. It also provides a scalable foundation for governed data practices without sacrificing traceability.

What AI-driven lineage and controls actually do (written by and for non-tech staff)

At its core, AI-driven data lineage combines automated scanning of code, SQL, ETL jobs, APIs, and metadata with semantic analysis that links technical fields to business concepts. Instead of a static map, executives using AI-driven data lineage get a living graph that shows data provenance at the field level: where a value originated, which transformations touched it, and which reports, models, or downstream services consume it.

AI adds value by surfacing hidden links. Natural language processing reads table descriptions, SQL comments, and even README files (yes they do still exist out there) to suggest business-term mappings that close the business-IT gap. That semantic layer is what turns a technical lineage graph into audit-ready evidence that regulators or auditors can understand.

How AI fixes the pain points keeping CDOs up at night

Faster audits: As a consultant at Perficient, I have seen AI-driven lineage that after implementation allowed executives to answer traceability questions in hours rather than weeks. Automated evidence packages—exportable lineage views and transformation logs—provide auditors with a reproducible trail.
Root-cause and incident response: When a report or model spikes, impact analysis highlights which datasets and pipelines are involved, highlighting responsibility and accountability, speeding remediation and alleviating downstream impact.
Model safety and feature provenance: Lineage that includes training datasets and feature transformations enables validation of model inputs, reproducibility of training data, and enforcement of data controls—supporting explainability and governance requirements. That allows your P&L to be more R&S. (a slogan used by a client that used R&S P&L to mean rock solid profit and loss.)

Tooling, architecture, and vendor considerations

When evaluating vendors, demand field-level lineage, semantic parsing (NLP across SQL, code, and docs), auditable diagram exports, and policy enforcement hooks that integrate with data protection tools. Deployment choices matter in regulated banking environments; hybrid architectures that keep sensitive metadata on-prem while leveraging cloud analytics often strike a pragmatic balance.

A practical, phased roadmap for CDOs

Phase 0 — Align leadership and define success: Engage CRO, COO, and Head of Model Risk. Define 3–5 KPIs (e.g., lineage coverage, evidence time, mean time to root cause) and what “good” will look like. This is often done during a evidence gathering phase by Perficient with clients who are just starting their Artificial Intelligence journey.
Phase 1 — Inventory and quick wins: Target a high-risk area such as regulatory reporting, a few production models, or a critical data domain. Validate inventory manually to establish baseline credibility.
Phase 2 — Pilot AI lineage and controls: Run automated discovery, measure accuracy and false positives, and quantify time savings. Expect iterations as the model improves with curated mappings.
Phase 1 and 2 are usually done by Perficient with clients as a Proof-of-Concept phase to show that the key feeds into and out of existing technology platforms can be done.
Phase 3 — Operationalize and scale: Integrate lineage into release workflows, assign lineage stewards, set SLAs, and connect with ticketing and monitoring systems to embed lineage into day-to-day operations.
Phase 4 — Measure, refine, expand: Track KPIs, adjust models and rules, and broaden scope to additional reports, pipelines, and models as confidence grows.

Risks, human oversight, and governance guardrails

AI reduces toil but does not remove accountability. Executives, auditors and regulators either do or should require deterministic evidence and human-reviewed lineage. Treat AI outputs as recommendations subject to curator approval. This will avoid what many financial services executives are dealing with what is now known as AI Hallucinations.

Guardrails include the establishment of exception processing workflows for disputed outputs and toll gates to ensure security and privacy are baked into design—DSPM, masking, and appropriate IAM controls should be integral, not afterthoughts.

Conclusion and next steps

AI data lineage for banks is a pragmatic control that directly addresses regulatory expectations, speeds audits, and reduces model and reporting risk. Start small, prove value with a focused pilot, and embed lineage into standard data stewardship processes. If you’re a CDO looking to move quickly with minimal risk, contact Perficient to run a tailored assessment and pilot design that maps directly to your audit and governance priorities. We’ll help translate proof into firm-wide control and confidence.

In this episode of “What If? So What?”, Jim Hertzfeld sits down with Wally Burchfield, former senior executive at GM, Nissan, and Nissan United, to explore what’s driving transformation in the automotive industry and beyond. 

 Wally’s perspective is clear: in a world obsessed with automation and data, the companies that win will be the ones that stay human. 

 From “Build and Sell” to “Know and Serve” 

 The old model was simple: build a car, sell a car, repeat. But as Wally explains it, that formula no longer works in a world where customer expectations are shaped by digital platforms and instant personalization. “It’s not just about selling a product,” he said. “It’s about retaining the customer through a high-quality experience one that feels personal, respectful, and effortless.” Every interaction matters, and every brand is in the experience business. 

 Data Alone Doesn’t Build Loyalty – Trust Does 

 It’s true that organizations have more data than ever before. But as Wally points out, it’s not how much data you have, it’s what you do with it. The real differentiator is how responsibly, transparently, and effectively you use that data to improve the customer experience. 

 “You can have a truckload of data but if it doesn’t help you deliver value or build trust, it’s wasted,” Wally said. 

 When used carelessly, data can feel manipulative. When used well, it creates clarity, relevance, and long-term relationships. 

 AI Should Remove Friction, Not Feeling 

 Wally’s take on AI is refreshingly grounded. He sees it as a tool to reduce friction, not replace human connection. Whether it’s scheduling service appointments via SMS or filtering billions of digital signals, the best AI is invisible, working quietly in the background to make the customer feel understood. 

 Want to Win? Listen Better and Faster 

 At the end of the day, the brands that thrive won’t be the ones with the biggest data sets; they’re the ones that move fast, use data responsibly, and never lose sight of the customer at the center. 

 Listen to the full conversation with Wally Burchfield for more on how trust, data, and AI can work together to build lasting customer relationships—and why the best strategies are still the most human. 

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Meet our Guest – Wally Burchfield

Wally Burchfield is a veteran automotive executive with deep experience across retail, OEM operations, marketing, aftersales, dealer networks, and HR. 

He spent 20 years at General Motors before joining Nissan, where he held multiple VP roles across regional operations, aftersales, and HR. He later served as COO of Nissan United (TBWA), leading Tier 2/3 advertising and field marketing programs to support dealer and field team performance. Today, Wally runs a successful consulting practice helping OEMs, partners, and dealer groups solve complex challenges and drive results. A true “dealer guy”, he’s passionate about improving customer experience, strengthening OEM-dealer partnerships, and challenging the status quo to unlock growth. 

Follow Wally on LinkedIn  

Learn More about Wally Burchfield

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

The headlines are troubling but predictable. The Trump administration will launch a program next year to find out how much money an artificial intelligence algorithm could save the federal government by denying care to Medicare patients. Meanwhile, a survey of physicians published by the American Medical Association in February found that 61% think AI is “increasing prior authorization denials, exacerbating avoidable patient harms and escalating unnecessary waste now and into the future.”

We’re witnessing the healthcare industry’s narrow vision of AI in action: algorithms designed to say “no” faster and more efficiently than ever before. But what if we’re missing the bigger opportunity?

The Current AI Problem: Built to Deny, Not to Help

The recent expansion of AI-powered prior authorization reveals a fundamental flaw in how we’re approaching healthcare technology. “The more expensive it is, the more likely it is to be denied,” said Jennifer Oliva, a professor at the Maurer School of Law at Indiana University-Bloomington, whose work focuses on AI regulation and health coverage.

This approach creates a vicious cycle: patients don’t understand their benefits, seek inappropriate or unnecessary care, trigger costly prior authorization processes, face denials, appeal those denials, and ultimately either give up or create even more administrative burden for everyone involved.

The human cost is real. Nearly three-quarters of respondents thought prior authorization was a “major” problem in a July poll published by KFF, and we’ve seen how public displeasure with insurance denials dominated the news in December, when the shooting death of UnitedHealthcare’s CEO led many to anoint his alleged killer as a folk hero.

A Better Vision: The AI Concierge Approach

What if instead of using AI to deny care more efficiently, we used it to help patients access the right care more effectively? This is where the AI Concierge concept transforms the entire equation.

An AI Concierge doesn’t wait for a claim to be submitted to make a decision. Instead, it proactively:

• Educates patients about their benefits before they need care
• Guides them to appropriate providers within their network
• Explains coverage limitations in plain language before appointments
• Suggests preventive alternatives that could avoid more expensive interventions
• Streamlines pre-authorization by ensuring patients have the right documentation upfront

The Quantified Business Case

The financial argument for AI Concierge services is compelling:

Star Ratings Revenue Impact: A half-star increase in Medicare Star Ratings is valued at approximately $500 per member. For a 75,000-member plan, that translates to $37.5 million in additional funding. An AI Concierge directly improves patient satisfaction scores that drive these ratings.

Operational Efficiency Gains: Healthcare providers implementing AI-powered patient engagement systems report 15-20% boosts in clinic revenue and 10-20% reductions in overall operational costs. Clinics using AI tools see 15-25% increases in patient retention rates.

Cost Avoidance Through Prevention: Utilizing AI to help patients access appropriate care could save up to 50% on treatment costs while improving health outcomes by up to 40%. This happens by preventing more expensive interventions through proper preventive care utilization.

The HEDIS Connection

HEDIS measures provide the perfect framework for demonstrating AI Concierge value. With 235 million people enrolled in plans that report HEDIS results, improving these scores directly impacts revenue through bonus payments and competitive positioning.

An AI Concierge naturally improves HEDIS performance in:

• Preventive Care Measures: Proactive guidance increases screening and immunization rates
• Care Gap Closure: Identifies and addresses gaps before they become expensive problems
• Patient Engagement: Improves medication adherence and chronic disease management

Beyond the Pilot Programs

While government initiatives like the WISeR pilot program focus on “Wasteful and Inappropriate Service Reduction” through AI-powered denials, forward-thinking healthcare organizations have an opportunity to differentiate themselves with AI-powered patient empowerment.

The math is simple: preventing a $50,000 hospitalization through proactive care coordination delivers better ROI than efficiently denying the claim after it’s submitted.

AI Healthcare Concierge Implementation Strategy

For healthcare leaders considering AI Concierge implementation:

• Phase 1: Deploy AI-powered benefit explanation tools that reduce call center volume and improve patient understanding
• Phase 2: Integrate predictive analytics to identify patients at risk for expensive interventions and guide them to preventive alternatives
• Phase 3: Expand to comprehensive care navigation that optimizes both patient outcomes and organizational performance

The Competitive Advantage

While competitors invest in AI to process denials faster, organizations implementing AI Concierge services are investing in:

• Member satisfaction and retention (15-25% improvement rates)
• Star rating improvements ($500 per member value per half-star)
• Operational cost reduction (10-20% typical savings)
• Revenue protection through better member experience

Conclusion: Choose Your AI Future

The current trajectory of AI in healthcare—focused on denial optimization—represents a massive missed opportunity. As one physician noted about the Medicare pilot: “I will always, always err on the side that doctors know what’s best for their patients.”

AI Healthcare Concierge services align with this principle by empowering both patients and providers with better information, earlier intervention, and more effective care coordination. The technology exists. The business case is proven. The patient need is urgent.

The question isn’t whether AI will transform healthcare—it’s whether we’ll use it to build walls or bridges between patients and the care they need.

The choice is ours. Let’s choose wisely.

We’re proud to share that Perficient’s What If? So What? podcast has been named a Gold Stevie® Award winner in the Technology Podcast category at the 22nd Annual International Business Awards®. These awards are among the world’s top honors for business achievement, celebrating innovation, impact, and excellence across industries.

Winners were selected by more than 250 executives worldwide, whose feedback praised the podcast’s ability to translate complex digital trends into practical, high-impact strategies for business and technology leaders.

Hosted by Jim Hertzfeld, Perficient’s AVP of Strategy, the podcast explores the business impact of digital transformation, AI, and disruption. With guests like Mark Cuban, Neil Hoyne (Google), May Habib (WRITER), Brian Solis (ServiceNow), and Chris Duffey (Adobe), we dive into the possibilities of What If?, the practical impact of So What?, and the actions leaders can take with Now What?

The Stevie judges called out what makes the show stand out:

• “What If? So What? Podcast invites experts from different industries, which is important to make sure that audiences are listening and gaining valuable information.”
• “A sharp, forward-thinking podcast that effectively translates complex digital trends into actionable insights.”
• “With standout guests like Mark Cuban, Brian Solis, and Google’s Neil Hoyne, the podcast demonstrates exceptional reach, relevance, and editorial curation.”

In other words, we’re not just talking about technology for technology’s sake. We’re focused on real business impact, helping leaders make smarter, faster decisions in a rapidly changing digital world.

We’re honored by this recognition and grateful to our listeners, guests, and production team who make each episode possible.

If you haven’t tuned in yet, now’s the perfect time to hear why the judges called What If? So What? a “high-quality, future-forward show that raises the standard for business podcasts.”

Catch the latest episodes here: What If? So What? Podcast

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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

Drupal 11’s AI Features: What They Actually Mean for Your Team

If you’ve been following the Drupal community lately, you’ve probably heard about the excitement with AI in Drupal 11 and the new Drupal AI Initiative. With over $100,000 in funding and 290+ AI modules already available, this will be a game changer.

But here’s the thing, AI in Drupal isn’t about replacing your team. It’s about making everyone more effective at what they already do best. Let’s talk through some of these new capabilities and what they mean for different teams in your organization.

Content Teams: Finally, An Assistant That Actually Helps

Creating quality content quickly has always been a challenge, but Drupal 11’s AI features tackle this head-on. The AI CKEditor integration gives content creators real-time assistance right in the editing interface, things like spelling corrections, translations, and contextual suggestions as you type.

The AI Content module is where things get interesting. It can automatically adjust your content’s tone for different audiences, summarize long content, and even suggest relevant taxonomy terms. For marketing teams juggling multiple campaigns, this means maintaining brand consistency without the usual back-and-forth reviews.

One feature that’s already saving teams hours is the AI Image Alt Text module. Instead of manually writing alt text for accessibility compliance, it generates descriptions automatically. The AI Translate feature is another game-changer for organizations with global reach—one-click multilingual content creation that actually understands context.

The bottom line? Your content team can focus on strategy and creativity instead of getting bogged down in routine tasks.

Developers: Natural Language Site Building

Here’s where Drupal 11 gets really exciting for a dev team. The AI Agents module introduces something we haven’t seen before, text-to-action capabilities. Developers can now modify Drupal configurations, create content types, and manage taxonomies just by describing what they need in spoken english.

Instead of clicking through admin interfaces, you can literally tell Drupal what you want, “Create a content type for product reviews with fields for rating, pros, cons, and reviewer information.” The system understands and executes these commands.

The AI module ecosystem supports over 21 major providers, OpenAI, Claude, AWS Bedrock, Google Vertex, and more. This means you’re not locked into any single AI provider and can choose the best model for specific tasks. The AI Explorer gives you a testing ground to experiment with prompts before pushing anything live.

For complex workflows, AI Automators let you chain multiple AI systems together. Think automated content transformation, field population, and business logic handling with minimal custom code.

The other great aspect of Drupal AI, is the open source backbone of Drupal, allows you to extend, add and build upon these agents in any way your dev team sees fit.

Marketing Teams: Data-Driven Campaign Planning

Marketing teams might be the biggest winners here. The AI Content Strategy module analyzes your existing content and provides recommendations for what to create next based on actual data, not guesswork. It identifies gaps in your content strategy and suggests targeted content based on audience behavior and industry trends.

The AI Search functionality means visitors can find content quickly, no more keyword guessing games. The integrated chatbot framework provides intelligent customer service that can access your site’s content to give accurate responses.

For SEO, the AI SEO module generates reports with user recommendations, reviewing content and metadata automatically. This reduces the need for separate SEO tools while giving insights right where you can act on them.

Why This Matters Right Now

The Drupal AI Initiative represents something more than just new features. With dedicated teams from leading agencies and serious funding behind it, this is Drupal positioning itself as the go-to platform for AI-powered content management.

For IT executives evaluating CMS options, Drupal 11’s approach is a great fit. You maintain complete control over your data and AI interactions while getting enterprise-grade governance with approval workflows and audit trails. It’s AI augmentation rather than AI replacement.

The practical benefits are clear: faster campaign launches, consistent brand voice across all content, and teams freed from manual tasks to focus on strategic work. In today’s competitive landscape, that kind of operational efficiency can make the difference between leading your market and playing catch-up.

The Reality Check

We all know, no technology is perfect. The success of these AI features, especially within the open source community, depends heavily on implementation and team adoption. You’ll need to spend time in training and process development to see real benefits. Like any new technology, there will be a learning curve as your team figures out the best ways to leverage these new features.

Based on what we are seeing within groups that have done early adoption of the AI features, they are seeing a good ROI on improvement of team efficiency, marketing time as well as reduced SEO churn.

If you’re considering how Drupal 11’s AI features might fit your organization, it’s worth having a conversation with an experienced implementation partner like Perficient. We can help you navigate the options and develop an AI strategy that makes sense for your specific situation.

Empathy, Resilience, Innovation, and Speed: The Blueprint for Intelligent Healthcare Transformation

Forrester’s recent report, Becoming An Intelligent Healthcare Organization Is An Attainable Goal, Not A Lost Cause, confirms what healthcare executives already know: transformation is no longer optional.

Perficient is proud to be quoted in this research, which outlines a pragmatic framework for becoming an intelligent healthcare organization (IHO)—one that scales innovation, strengthens clinical and operational performance, and delivers measurable impact across the enterprise and the populations it serves.

Why Intelligent Healthcare Is No Longer Optional

Healthcare leaders are under pressure to deliver better outcomes, reduce costs, and modernize operations, all while navigating fragmented systems and siloed departments. The journey to transformation requires more than technology; it demands strategic clarity, operational alignment, and a commitment to continuous improvement.

Forrester reports, “Among business and technology professionals at large US healthcare firms, only 63% agree that their IT organization can readily reallocate people and technologies to serve the newest business priority; 65% say they have enterprise architecture that can quickly and efficiently support major changes in business strategy and execution.”

Despite widespread investment in digital tools, many healthcare organizations struggle to translate those investments into enterprise-wide impact. Misaligned priorities, inconsistent progress across departments, and legacy systems often create bottlenecks that stall innovation and dilute momentum.

Breaking Through Transformation Barriers

These challenges aren’t just technical or organizational. They’re strategic. Enterprise leaders can no longer sit on the sidelines and play the “wait and see” game. They must shift from reactive IT management to proactive digital orchestration, where technology, talent, and transformation are aligned to business outcomes.

Business transformation is not a fleeting trend. It’s an essential strategy for healthcare organizations that want to remain competitive as the marketplace evolves.

Forrester’s report identifies four hallmarks of intelligent healthcare organizations, emphasizing that transformation is not a destination but a continuous practice.

Four Hallmarks of An Intelligent Healthcare Organization (IHO)

To overcome transformation barriers, healthcare organizations must align consumer expectations, digital infrastructure, clinical workflows, and data governance with strategic business goals.

1. Empathy At Scale: Human-Centered, Trust-Enhancing Experiences

A defining trait of intelligent healthcare organizations is a commitment to human-centered experiences.

• Driven By: Continuous understanding of consumer needs
• Supported By: Strategic technology investments that enable timely, personalized interventions and touchpoints

As Forrester notes, “The most intelligent organizations excel at empathetic, swift, and resilient innovation to continuously deliver new value for customers and stay ahead of the competition.”

Empathy is a performance driver. Organizations that prioritize human-centered care see higher engagement, better adherence, and stronger loyalty.

Our experts help clients reimagine care journeys using journey sciences, predictive analytics, integrated CRM and CDP platforms, and cloud-native architectures that support scalable personalization. But personalization without protection is a risk. That’s why empathy must extend beyond experience design to include ethical, secure, and responsible AI adoption.

Healthcare organizations face unique constraints, including HIPAA, PHI, and PII regulations that limit the utility of plug-and-play AI solutions. To meet these challenges, we apply our PACE framework—Policies, Advocacy, Controls, and Enablement—to ensure AI is not only innovative but also rooted in trust.

• Policies establish clear boundaries for acceptable AI usage, tailored to healthcare’s regulatory landscape.
• Advocacy builds cross-functional understanding and adoption through education and collaboration.
• Controls implement oversight, auditing, and risk mitigation to protect patient data and ensure model integrity.
• Enablement equips teams with the tools and environments needed to innovate confidently and securely.

This approach ensures AI is deployed with purpose, aligned to business goals, and embedded with safeguards that protect consumers and care teams alike. It also supports the creation of reusable architectures that blend scalable services with real-time monitoring, which is critical for delivering fast, reliable, and compliant AI applications.

Responsible AI isn’t a checkbox. It’s a continuous practice. And in healthcare, it’s the difference between innovation that inspires trust and innovation that invites scrutiny.

2. Designing for Disruption: Resilience as a Competitive Advantage

Patient-led experiences must be grounded in a clear-eyed understanding that market disruption isn’t simply looming. It’s already here. To thrive, healthcare leaders must architect systems that flex under pressure and evolve with purpose. Resilience is more than operational; it’s also behavioral, cultural, and strategic.

Perficient’s Access to Care research reveals that friction in the care journey directly impacts health outcomes, loyalty, and revenue:

• More than 50% of consumers who experienced scheduling friction took their care elsewhere, resulting in lost revenue, trust, and care continuity
• 33% of respondents acted as caregivers, yet this persona is often overlooked in digital strategies
• Nearly 1 in 4 respondents who experienced difficulty scheduling an appointment stated that the friction led to delayed care, and they believed their health declined as a result
• More than 45% of consumers aged 18–64 have used digital-first care instead of their regular provider, and 92% of them believe the quality is equal or better

This sentiment should be a wakeup call for leaders. It clearly signals that consumers expect healthcare to meet both foundational needs (cost, access) and lifestyle standards (convenience, personalization, digital ease). When systems fail to deliver, patients disengage. And when caregivers—who often manage care for entire households—encounter barriers, the ripple effect is exponential.

To build resilience that drives retention and revenue, leaders must design systems that anticipate needs and remove barriers before they impact care. Resilient operations must therefore be designed to:

• Reduce friction across the care journey, especially in scheduling and follow-up
• Support caregivers with multi-profile tools, shared access, and streamlined coordination
• Enable digital-first engagement that mirrors the ease of consumer platforms like Amazon and Uber

Consumers are blending survival needs with lifestyle demands. Intelligent healthcare organizations address both simultaneously.

Resilience also means preparing for the unexpected. Whether it’s regulatory shifts, staffing shortages, or competitive disruption, IHOs must be able to pivot quickly. That requires leaders to reimagine patient (and member) access as a strategic lever and prioritize digital transformation that eases the path to care.

3. Unified Innovation: Aligning Strategy, Tech, and Teams

Innovation without enterprise alignment is just noise—activity without impact. When digital initiatives are disconnected from business strategy, consumer needs, or operational realities, they create confusion, dilute resources, and fail to deliver meaningful outcomes. Fragmented innovation may look impressive in isolation, but without coordination, it lacks the momentum to drive true transformation.

To deliver real results, healthcare leaders must connect strategy, execution, and change readiness. In Forrester’s report, a quote from an interview with Priyal Patel emphasizes the importance of a shared strategic vision:

“Today’s decisions should be guided by long-term thinking, envisioning your organization’s business needs five to 10 years into the future.” — Priyal Patel, Director, Perficient

Our approach begins with strategic clarity. Using our Envision Framework, we help healthcare organizations rapidly identify opportunities, define a consumer-centric vision, and develop a prioritized roadmap that aligns with business goals and stakeholder expectations. This framework blends real-world insights with pragmatic planning, ensuring that innovation is both visionary and executable.

We also recognize that transformation is not just technical—it’s human. Organizational change management (OCM) ensures that teams are ready, willing, and able to adopt new ways of working. Through structured engagement, training, and sustainment, we help clients navigate the behavioral shifts required to scale innovation across departments and disciplines.

This strategic rigor is especially critical in healthcare, where innovation must be resilient, compliant, and deeply empathetic. As highlighted in our 2025 Digital Healthcare Trends report, successful organizations are those that align innovation with measurable business outcomes, ethical AI adoption, and consumer trust.

Perficient’s strategy and transformation services connect vision to execution, ensuring that innovation is sustainable. We partner with healthcare leaders to identify friction points and quick wins, build a culture of continuous improvement, and empower change agents across the enterprise.

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4. Speed With Purpose and Strategic Precision

The ability to pivot, scale, and deliver quickly is becoming a defining trait of tomorrow’s healthcare leaders. The way forward requires a comprehensive digital strategy that builds the capabilities, agility, and alignment to stay ahead of evolving demands and deliver meaningful impact.

IHOs act quickly without sacrificing quality. But speed alone isn’t enough. Perficient’s strategic position emphasizes speed with purpose—where every acceleration is grounded in business value, ethical AI adoption, and measurable health outcomes.

Our experts help healthcare organizations move fast by:

• Designing for agility with composable architectures, MVP+ delivery models, and intelligent automation that compliantly accelerates time-to-value while maintaining clinical integrity
• Aligning digital transformation with value-based care goals, enabling organizations to shift from volume to value through strategic investments that integrate cloud platforms, interoperable systems, and advanced analytics
• Operationalizing AI responsibly to support equitable, secure, compliant, and scalable AI adoption

This approach supports the Quintuple Aim: better outcomes, lower costs, improved experiences, clinician well-being, and health equity. It also ensures that innovation is not just fast. It’s focused, ethical, and sustainable.

Speed with purpose means:

• Rapid prototyping that validates ideas before scaling
• Real-time data visibility to inform decisions and interventions
• Cross-functional collaboration that breaks down silos and accelerates execution
• Outcome-driven KPIs that measure impact, not just activity

Healthcare leaders don’t need more tools. They need a strategy that connects business imperatives, consumer demands, and an empowered workforce to drive transformation forward. Perficient equips organizations to move with confidence, clarity, and control.

Collaborating to Build Intelligent Healthcare Organizations

We believe our inclusion in Forrester’s report underscores our role as a trusted advisor in intelligent healthcare transformation. From insight to impact, our healthcare expertise equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of healthcare consumers, streamline operations, and improve the cost, quality, and equity of care.

We have been trusted by the 10 largest health systems and the 10 largest health insurers in the U.S., and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Our strategic partnerships with industry-leading technology innovators—including AWS, Microsoft, Salesforce, Adobe, and more—accelerate healthcare organizations’ ability to modernize infrastructure, integrate data, and deliver intelligent experiences. Together, we shatter boundaries so you have the AI-native solutions you need to boldly advance business.

Ready to advance your journey as an intelligent healthcare organization?

We’re here to help you move beyond disconnected systems and toward a unified, data-driven future—one that delivers better experiences for patients, caregivers, and communities. Let’s connect and explore how you can lead with empathy, intelligence, and impact.

Perficient has officially announced a groundbreaking partnership with WRITER, the leader in agentic AI for the enterprise. This 360-degree collaboration marks a pivotal moment in our AI-first journey, combining WRITER’s powerful end-to-end agent platform with Perficient’s deep consulting expertise to deliver scalable, secure, and transformative AI solutions to the Global 2000. 

To explore the significance of this partnership, I sat down with Bill Davis, Perficient’s Senior Vice President and Head of Partners and Ecosystem, to discuss what this means for our clients, our colleagues, and the future of enterprise AI. 

Connor Stieferman: Bill, what makes this partnership with WRITER so significant for Perficient and the broader market? 

Bill Davis: This partnership represents a major milestone not just for Perficient and WRITER, but for the enterprise AI landscape as a whole. WRITER is gaining serious momentum in the market, and their agentic AI platform is redefining how organizations think about productivity, automation, and intelligence at scale. By combining WRITER’s cutting-edge technology with Perficient’s deep industry expertise and global implementation capabilities, we’re creating a force multiplier for enterprise transformation. Together, we’re enabling organizations to move beyond isolated AI experiments and into scalable, secure, and measurable deployments.  

To put it simply, this partnership sets a new standard for how AI can be adopted and operationalized across industries. 

Connor: What makes Perficient a strong partner for a company like WRITER? 

Bill: WRITER is leading the way in agentic AI, and companies at that level need partners who can match their pace and deliver enterprise-grade execution. Perficient brings deep industry expertise, a global delivery model, and a strong track record of helping large organizations adopt emerging technologies at scale. We understand how to translate innovation into business outcomes with speed and precision. We also add a critical strategic layer, helping clients identify where agentic AI can drive the most value, designing tailored solutions, and ensuring successful adoption. By jointly going to market with WRITER, we’re co-developing best-in-class, industry-specific agentic solutions that deliver real outcomes for enterprise customers.  

Connor: How does this partnership reflect Perficient’s AI-first strategy? 

Bill: Our AI-first strategy is about embedding intelligence into everything we do — from internal operations to client solutions. By broadly deploying WRITER agents across our own enterprise, we’re demonstrating a top-down and bottom-up commitment to transformation. We’re not just advising clients on AI; we’re living it. This partnership allows us to build and deploy custom agents that automate our own workflows, generate contextual content, and deliver insights, showcasing what’s possible when AI is fully integrated into a business. 

Connor: What’s the significance of WRITER being Perficient’s first 360-degree partner? 

Bill: It’s a testament to the depth of our collaboration. Our relationship extends well beyond jointly going to market together. Each organization is deeply committed to the other’s success. The alignment extends across our executive, sales, marketing, and technology teams and reflects the strength of our shared vision. This level of partnership is rare — and it positions us to lead the market in agentic AI adoption. 

Connor: What kind of value can clients expect from this collaboration? 

Bill: Clients will see accelerated time-to-value through rapid deployment of tailored AI agents. They’ll benefit from embedded intelligence that integrates seamlessly with their existing systems, along with strategic guidance from our AI experts to ensure adoption and ROI. Plus, WRITER’s platform offers enterprise-grade security and governance, which is critical for large-scale deployments. Together, we’re helping clients cut through the noise and focus on fast, secure outcomes. 

Connor: What excites you most about what’s ahead? 

Bill: Honestly, it’s the opportunity to help our clients become agent builders themselves. We’re not just delivering tools — we’re enabling transformation. And we’re doing it alongside the exceptional team at WRITER. They’re agile, collaborative, and deeply committed to moving fast and getting things done right. Partnerships work best when both sides are aligned, and WRITER brings the same obsession over client outcomes that we value at Perficient. Together, we’re empowering organizations to reinvent how they work, innovate, and grow. The future of enterprise AI is agentic, and Perficient and WRITER are at the forefront of making that future real. 

Final Thoughts 

Perficient’s partnership with WRITER is a bold step forward in our mission to transform enterprises through AI. By combining cutting-edge technology with deep consulting expertise, we’re helping clients unlock the full potential of agentic AI. 

Stay tuned for more updates as we roll out new solutions, launch innovation labs, and continue to lead the way in enterprise AI transformation. 

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. 

Listen to the full conversation

Subscribe Where You Listen

Apple | Spotify | Amazon | Overcast | Watch the full video episode on YouTube

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 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

Feature	Native Commerce	Adobe Real-Time CDP
Segmentation	Static, rule-based	Real-time, AI-powered
Data Sources	Commerce-only	Omnichannel (web, CRM, etc.)
Personalization	Session-based	Cross-channel, predictive
Identity Graph	No Identity Graph	Cross-device customer data
Activation	Limited to Commerce	Activate across systems

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.   

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.