Adobe Experience Platform (AEP) and Customer Journey Analytics (CJA) continue to emerge as the preferred solutions for organizations seeking a unified, 360‑degree view of customer behavior.  For organizations requiring HIPAA compliance, AEP and CJA is a necessity.  Many organizations are now having discussions about whether they should retool or retire their legacy Adobe Analytics implementations.  The transition from Adobe Analytics to CJA is far more complex than simply disabling an old tool. Teams must carefully plan, perform detailed analysis, and develop a structured approach to ensure that reporting continuity, data integrity, and downstream dependencies remain intact.

Adobe Analytics remains a strong platform for organizations focused exclusively on web and mobile app measurement; however, enterprises that are prioritizing cross‑channel data activation, real‑time profiles, and detailed journey analysis should embrace AEP as the future. Of course, you won’t be maintaining two platforms after building out CJA so you must think about how to move on from Adobe Analytics.

Decommissioning Options and Key Considerations

You can approach decommissioning Adobe Analytics in several ways. Your options include: 1) disabling the extension; 2) adding an s.abort at the top of the AppMeasurement custom‑code block to prevent data from being sent to Adobe Analytics; 3) deleting all legacy rules; or 4) discarding Adobe Analytics entirely and creating a new Launch property for CJA. Although multiple paths exist, the best approach almost always involves preserving your data‑collection methods and keeping the historical Adobe Analytics data. You have likely collected that data for years, and you want it to remain meaningful after migration. Instead of wiping everything out, you can update Launch by removing rules you no longer need or by eliminating references to Adobe Analytics.

Recognizing the challenges involved in going through the data to make the right decisions during this process, I have developed a specialized tool – Analytics Decommissioner (AD) — designed to support organizations as they decommission Adobe Analytics and transition fully to AEP and CJA. The tool programmatically evaluates Adobe Platform Launch implementations using several Adobe API endpoints, enabling teams to quickly identify dependencies, references, and potential risks associated with disabling Adobe Analytics components.

Why Decommissioning Requires More Than a Simple Shutdown

One of the most significant obstacles in decommissioning Adobe Analytics is identifying where legacy tracking still exists and where removing Adobe Analytics could potentially break the website or cause errors. Over the years, many organizations accumulate layers of custom code, extensions, and tracking logic that reference Adobe Analytics variables—often in places that are not immediately obvious. These references may include s. object calls, hard‑coded AppMeasurement logic, or conditional rules created over the course of several years. Without a systematic way to surface dependencies, teams risk breaking critical data flows that feed CJA or AEP datasets.

Missing or outdated documentation makes the problem even harder. Many organizations fail to maintain complete or current solution design references (SDRs), especially for older implementations. As a result, teams rely on tribal knowledge, attempts to recall discussions from years ago, or a manual inspection of data collected to understand how the system collects data. This approach moves slowly, introduces errors, and cannot support large‑scale environments. When documentation lacks clarity, teams struggle to identify which rules, data elements, or custom scripts still matter and which they can safely remove. Now imagine repeating this process for every one of your Launch properties.

This is where Perficient and the AD tool provide significant value.
The AD tool programmatically scans Launch properties and uncovers dependencies that teams may have forgotten or never documented. A manual analysis might easily overlook these dependencies. AD also pinpoints where custom code still references Adobe Analytics variables, highlights rules that have been modified or disabled since deployment, and surfaces AppMeasurement usage that could inadvertently feed into CJA or AEP data ingestion. This level of visibility is essential for ensuring that the decommissioning process does not disrupt data collection or reporting.

How Analytics Decommissioner (AD) Works

The tool begins by scanning all Launch properties across your organization and asking the user to select a property. This is necessary because the decommissioning process must be done on each property individually.  This is the same way data is set for Adobe Analytics, one Launch property at a time.  Once a property is selected, the tool retrieves all production‑level data elements, rules, and rule components, including their revision histories.  The tool ignores rules and data element revisions that developers disabled or never published (placed in production).  The tool then performs a comprehensive search for AppMeasurement references and Adobe Analytics‑specific code patterns. These findings show teams exactly where legacy tracking persists and see what needs to be updated or modified and which items can be safely removed.  If no dependencies exist, AD can disable the rules and create a development library for testing.  When AD cannot confirm that a dependency exists, it reports the rule names and components where potential issues exist and depend on development experts to make the decision about the existence of a dependency.  The user always makes the final decisions.

This tool is especially valuable for large or complex implementations. In one recent engagement, a team used it to scan nearly 100 Launch properties. Some of those properties included more than 300 data elements and 125 active rules.  Attempting to review this level of complexity manually would have taken weeks and the risk would remain that critical dependencies are missed. Programmatic scanning ensures accuracy, completeness, and efficiency.  This allows teams to move forward with confidence.

A Key Component of a Recommended Decommissioning Approach

The AD tool and a comprehensive review are essential parts of a broader, recommended decommissioning framework. A structured approach typically includes:

• Inventory and Assessment – Identifying all Adobe Analytics dependencies across Launch, custom code, and environments.
• Mapping to AEP/CJA – Ensuring all required data is flowing into the appropriate schemas and datasets.
• Gap Analysis – Determining where additional configuration or migration work needs to be done.
• Remediation and Migration – Updating Launch rules, removing legacy code, and addressing undocumented dependencies.
• Validation and QA – Confirming that reporting remains accurate in CJA after removal of Launch rules and data elements created for Adobe Analytics.
• Sunset and Monitoring – Disabling AppMeasurement, removing Adobe Analytics extensions, and monitoring for errors.

Conclusion

Decommissioning Adobe Analytics is a strategic milestone in modernizing the digital data ecosystem. Using the right tools and having the right processes are essential.  The Analytics Decommissioner tool allows organizations to confidently transition to AEP and CJA. This approach to migration preserves data quality, reduces operational costs, and strengthens governance when teams execute it properly. By using the APIs and allowing the AD tool to handle the heavy lifting, teams ensure that they don’t overlook any dependencies.  This will enable a smooth and risk‑free transition with robust customer experience analytics.

At the Healthcare Interactive Conference (HCIC) last month, I got to talk to marketers who are very focused on results. They are also very focused on what will impact their marketing efforts and why. Every conversation came back to AI.

In my previous HCIC takeaway, I wrote about how AI is not a strategy—it’s a tool to solve real problems. Now I want to dig into a specific problem AI is creating for healthcare marketers: how we get found. We need to be thinking about all aspects of how AI can be used. In general, this breaks down into both impact and opportunity.

Impact: AI Search Is Transforming Healthcare Discovery

Several conference sessions alluded to this shift, but marketing experts Brittany Young and Gina Linville gave some deeper insight.

From a marketing perspective, the largest impact is one of being found. Think about how much time a typical hospital marketer puts into being found. I have had many conversations over the years about Search Engine Optimization (SEO) and the importance of having valuable content that the search engines view as unique and relevant.

AI impacts that in ways that are not at first obvious.

The New Reality of Patient Search

Think of how you typically use ChatGPT or how your search engine has evolved. AI now pulls the data and gives you a brief with information culled from multiple online sources. The good news is that the AI tool will typically reference a website it sources. The bad news is while AI typically credits source websites, patients get their answers without ever clicking through to your site.

The scale of this shift is staggering:

AI provides an overview for up to 84% of search queries when it comes to healthcare questions.

Healthcare leads nearly every sector in AI-powered search results—a trend that’s accelerating:

Strategic Response: Winning at AI Search in Healthcare

This shift demands a fundamental rethinking of content strategy. Two concepts are emerging as critical:

1) Answer Engine Optimization (AEO)

• Answer Engine Optimization (AEO) is the practice of structuring and optimizing content so that AI-powered systems, such as Google’s AI Overviews, ChatGPT, Perplexity AI, and voice assistants, can easily identify, extract, and cite it as a direct answer to user queries.

2) Generative Engine Optimization (GEO)

• Generative Engine Optimization (GEO) is a digital marketing technique designed to improve a brand’s visibility in results produced by generative artificial intelligence (GenAI) platforms. It involves adapting digital content and online presence to ensure that AI systems can accurately interpret, cite, and use the content when generating responses to user queries.

The imperative is clear: Organizations that don’t optimize for AI-powered discovery won’t just lose rankings—they’ll lose visibility entirely.

If you are not already thinking about how to orient your content to this then be aware that you will soon feel an impact.

Opportunity: Agentic AI and Productivity

On the flip side of the coin is the opportunity. While the impact above provides you with an opportunity provided you react appropriately, I want to focus on the productivity part of this. Specifically, think of what Agentic AI can do for your organization.

What Traditional Campaign Development Looks Like

Let me give you a few examples of common tasks and how long they typically take:

• Create a campaign brief: up to two weeks
• Create copy across multiple channels: 8-16 hours
• Create digital assets related to the campaign which fit your brand standards and work in each individual channel. Web site may allow for larger images. Paid search or paid social may have limited space: 40 hours
• Creation of the segment and pushing it to marketing automation tools: several hours

Now imagine specialized AI agents handling each component—not replacing human strategy and judgment, but accelerating execution while maintaining brand standards and compliance. Just getting one campaign going across multiple channels become a multi-person engagement over several weeks. While focused on that, you won’t focus on additional campaign or in honing your craft.

The AI Agent Team Your Marketing Organization Needs

The answer lies with Agentic AI. We believe that AI can cut down on the time necessary to complete these tasks and still keep humans in the loop. Here are a few examples of agents you might need in your organization:

Most importantly, this frees your marketing team to focus on what AI can’t do: strategic thinking, creative problem-solving, and understanding the nuanced needs of your community. 

The Path Forward: Integration, Not Replacement

The organizations winning in this new landscape aren’t choosing between human expertise and AI capabilities. They’re strategically integrating both.

Success requires more than technology. It needs an integrated approach:

1. Rethinking discoverability through AEO and GEO optimization
2. Deploying specialized AI agents for productivity acceleration
3. Maintaining human oversight for strategy, creativity, and judgment
4. Ensuring compliance at every step, particularly in heavily regulated healthcare
5. Measuring impact against business outcomes, not just operational metrics

Enabling Healthcare Organizations To Lead This Shift

HCIC reminded us that success in healthcare marketing isn’t about chasing technology for its own sake. As I shared in my first HCIC takeaway, AI is not a strategy—it’s a tool to solve real challenges that impact your organization’s ability to connect patients to care.

The search revolution is here. The productivity opportunity is real. The organizations that move quickly to optimize for AI-powered discovery while deploying strategic AI agents will gain a competitive advantage that compounds over time.

Start a conversation with our experts today.

Customer expectations are not just rising; they are skyrocketing. By 2026, the brands that create real customer loyalty won’t be the ones that send clever emails or spruce up their loyalty programs. They’ll be the ones who can almost read their customers’ minds, anticipating needs before customers have to ask. I think of it like this:

If Great CX in 2025 were a skilled short-order cook, it would have expertly filled every customer’s order as it came in. Great CX in 2026 is the master chef who knows your favorite dish before you even sit down, having already prepped the ingredients and timed everything perfectly for your arrival.

This leap from reacting to anticipating is already underway, and it is about to redefine what “Great CX” really means. With this post, I plan to explore what predictive engagement looks like, why it matters for your business, and how you can start cooking up this future today.

The Big Shift: From answering questions to reading minds

For ages, customer experience has been about responding. We’re answering questions, fixing hiccups, gently nudging conversions, and recommending next best actions. Predictive engagement changes everything. Instead of waiting for customers to reach out, we’ll use smart data and AI to understand what is coming and act with intention. Imagine a couple of predictive scenarios:

A customer’s delivery is running a bit late. Instead of them anxiously checking, tracking, or calling support, your system detects the delay, automatically offers alternative options, and sends a friendly, proactive update.

A customer is thinking about leaving for another brand. Predictive models can spot those early signals and trigger tailored, helpful offers before customers even consider shopping elsewhere. This proactive approach transforms customer relationships into something far more intuitive and supportive.

Four big ideas shaping CX in 2026

Given the ever-shifting nature of customer expectations, I tried to pinpoint what I think will cause the biggest changes for CX teams in 2026.

1. Hyper-Personalization Gets Real. Personalization still, oftentimes, means sending different messages to different defined segments. In 2026, it means real-time, adaptive journeys explicitly tailored to each person. Generative and predictive AI will power “next-best-experience” decisions, making every interaction feel incredibly relevant and perfectly timed.
2. Conversational AI Becomes Your Best Server. Chatbots are no longer just for FAQs. They are becoming intelligent assistants that can resolve issues, escalate with all the right context, and learn from every chat. The best experiences will combine smart automation with real human empathy, so customers always feel understood.
3. Service Fixes Itself. Predictive insights will flow into operations, allowing systems to fix problems before they even impact customers. Think of billing errors being corrected automatically or parts being replaced before they fail. This minimizes disruption and keeps things running smoothly.
4. Trust Becomes Your Brand’s Superpower. As AI takes center stage, transparency and ethics matter more than ever. Customers expect clear explanations of how their data is used and confidence that decisions are fair. Brands that build trust into every experience will truly shine.

Building your smart CX kitchen

Delivering predictive CX is not about adding just one more gadget. It is about creating a unified, event-driven ecosystem. This includes:

• A solid data foundation that brings together customer profiles and real-time signals.
• An AI and decisioning layer to predict intent and recommend the most helpful next steps.
• Experience orchestration to trigger proactive outreach across all your channels.
• Operational automation for those self-healing processes.
• Governance and trust controls for transparency and clear explanations.

How to start cooking up the future

• Map your most important customer journeys. Start by identifying those key signals that hint at intent, risk, or value.
• Build smart decision-making logic. Set clear boundaries for automation and when to bring in human help.
• Perfection is the enemy of progress. Pilot a few proactive ideas that move the needle from a CX perspective, things like helpful delivery updates or churn prevention, to get started.
• Scale your conversational AI to make sure customer context follows them across all channels.
• Measure what works and keep optimizing and experimenting.
• Make trust a core ingredient by using transparent notices and carefully overseeing your AI models.

Looking ahead – answering the question before it’s asked

Predictive CX is not just a nice-to-have for customers; it is a game-changer for your business. It boosts retention by catching churn risk early, reduces service costs by preventing avoidable contacts, and drives revenue by surfacing the right offer at the perfect moment. Plus, it makes your operations far more efficient by avoiding downstream issues.

Customer experience is shifting fast. In the next year, anticipation will be a customer expectation. The brands that win will combine data, predictive intelligence, and automation built on trust and transparency. Begin laying the groundwork today so you can deliver experiences that feel effortless and intuitive.

For years, the Drupal community has been engaged in a delicate balancing act. On one side, we have the “Structured Data Purists”—those who value Drupal for its rigid data modeling, fieldability, and API-first architecture. On the other side, we have the “Marketing Realists”—the content creators and site builders who look at tools like Squarespace, Wix, or Webflow and ask, “Why can’t my enterprise CMS be this easy to use?”

Historically, Drupal’s answer to this was a compromise. We gave you Layout Builder, which was powerful but often clunky. We gave you Paragraphs, which offered structure but lacked true visual editing. The result was often a disjointed experience where the backend power was unmatched, but the frontend authoring experience felt a decade behind.

That era effectively ends. With the release of Drupal Canvas 1.0 (formerly developed under the “Experience Builder” initiative), Drupal has finally delivered the missing piece of the puzzle. This is not just a new module; it is a fundamental reimagining of how we build, theme, and manage Drupal sites.

This guide will take you through exactly what Drupal Canvas is, what is included in the 1.0 release, and why it represents the single biggest leap forward for the platform in the modern web era.

What is Drupal Canvas?

Drupal Canvas is the new, default visual page builder for Drupal, designed to replace the aging Layout Builder and bridge the gap between “No-Code” ease of use and “Pro-Code” structural integrity.

It was born out of the Drupal Starshot initiative, which aims to make Drupal accessible to non-developers right out of the box. However, unlike previous attempts at “easy” site builders, Drupal Canvas does not sacrifice the underlying architecture. It is built on a modern React frontend that communicates seamlessly with Drupal’s core APIs.

The core philosophy of Drupal Canvas is “Visual Control with Architectural Safety.” It allows marketers to drag, drop, and style components in a true WYSIWYG (What You See Is What You Get) environment, while ensuring that every pixel they manipulate is backed by clean, reusable code defined by developers.

The End of the “Blank Screen” Problem

One of the biggest hurdles for new Drupal users has always been the “Blank Screen.” You install Drupal, and you get a plain white page. You have to build content types, configure views, and code a theme before you see anything.

Drupal Canvas flips this. When paired with the new Mercury theme (the default frontend for Drupal CMS v2) and Site Templates, users start with a fully visualized design system. They aren’t building from scratch; they are assembling experiences from a library of polished, brand-compliant components.

What’s Included in Drupal Canvas 1.0?

The 1.0 release is packed with features that target both the content editor’s need for speed and the developer’s need for order. Here is a detailed breakdown of the key components included in this release.

1. The React-Based Visual Editor

The heart of Drupal Canvas is a lightning-fast, React-based interface. Gone are the days of waiting for AJAX throbbers to spin while a block saves.

• True Drag-and-Drop: You can drag components from a sidebar directly onto the page canvas. The interface is smooth, mimicking the fluidity of SaaS site builders.
• Live Viewport Preview: The editor includes a native viewport switcher. You can instantly toggle between Desktop, Tablet, and Mobile views to see how your layout responds. This isn’t just a CSS trick; it simulates the rendering to ensure responsive integrity.
• Instant Property Editing: Click on any component—a button, a header, a card—and a sidebar opens with its properties. Change the text, swap an image, or adjust the alignment, and the canvas updates instantly.

2. Native Integration with Single Directory Components (SDC)

This is the “Architect” feature that makes Drupal Canvas revolutionary. In the past, site builders generated “magic code” that developers hated because it was hard to maintain.

Drupal Canvas is built entirely on Single Directory Components (SDC).

• One Source of Truth: A component (e.g., a “Hero Banner”) is defined in code with its Twig template, CSS, and JS all in one folder. Drupal Canvas simply discovers this component and exposes it to the UI.
• No Code Forking: When a developer updates the CSS for the “Hero Banner” in the code repository, every instance of that banner inside Drupal Canvas updates automatically. There is no separation between “Builder Components” and “Code Components.”

3. Props and Slots Architecture

Drupal Canvas introduces a standardized way to handle data, borrowed from modern frontend frameworks:

• Props (Properties): These are the settings of a component. For example, a “Card” component might have Props for Image, Title, Description, and Link. In the Canvas UI, these appear as simple form fields.
• Slots: These are “drop zones” inside a component. A “Grid” component might have four “Slots.” You can drag other components into these slots. This allows for nesting and complex layouts (e.g., putting a “Video Player” inside a “Modal” inside a “Grid”) without breaking the code.

4. In-Browser Code Editor

For the “Low-Code” developer, Drupal Canvas 1.0 includes a shocking amount of power directly in the browser.

• Edit Logic on the Fly: You can actually modify the CSS or lightweight logic of a component directly within the Canvas interface using a built-in code editor.
• JSON:API Client: The builder includes a client to fetch data dynamically. You can create a component that says “Show the latest 3 blog posts,” and configure the data fetching query right in the browser, bridging the gap between a static design and dynamic Drupal content.

5. The “Byte” Site Template & Mercury Theme

Drupal Canvas 1.0 doesn’t come empty-handed.

• The Mercury Theme: Replacing Olivero as the default face of Drupal CMS, Mercury is a modern, component-first theme designed specifically to work with Canvas. It is accessible (WCAG AA) and responsive by default.
• Byte Template: A pre-configured site template included in the release. It demonstrates the power of Canvas by providing a full “Agency” style website structure (Services, About, Contact, Portfolio) ready to be customized.

6. Multi-Step Undo/Redo & History

It sounds basic, but for enterprise teams, this is critical. Drupal Canvas maintains a robust history stack. If a content editor accidentally deletes a complex section, they can hit “Undo” (Command+Z) to restore it instantly. This safety net encourages experimentation without fear of breaking the site.

How Drupal Canvas Transforms the Workflow

The arrival of Drupal Canvas 1.0 fundamentally changes the lifecycle of a Drupal project. It solves the friction points that have historically slowed down enterprise deployments.

For the Content Marketer: “Independence Day”

The biggest winner here is the marketing team. In the pre-Canvas era, creating a new landing page usually meant filing a ticket with IT/Engineering. “We need a new layout for the Q3 campaign.”

• The Old Way: The developer builds a custom content type or template. Two weeks later, the marketer gets to enter text.
• The Canvas Way: The marketer opens Drupal Canvas. They drag a “Hero” component, a “Two-Column Text” component, and a “Sign-Up Form” component onto the page. They tweak the colors to match the campaign branding using the “Design Tokens” exposed in the UI. They hit publish.
• Result: Time-to-market drops from weeks to minutes.

For the Developer: “Governance Without Grunt Work”

Developers often fear page builders because they produce “spaghetti code” that is impossible to maintain. Drupal Canvas respects the developer’s craft.

• Focus on Components, Not Pages: Developers stop building “pages.” Instead, they build robust, accessible SDC components. Once a component is built (e.g., a complex “Pricing Calculator”), they push it to the library.
• Governance: The developer controls the constraints. They can define in the code that a “Testimonial Slider” allows for 3 to 5 slides, but no more. The Canvas UI enforces this. The marketer cannot break the layout because the code forbids it.
• Frontend Freedom: Because Canvas creates a clean separation via SDC, frontend developers can use modern tools (Tailwind, React, PostCSS) inside their components without worrying about Drupal’s legacy rendering pipeline interfering.

For the Agency/Enterprise: “Scalable Design Systems”

For large organizations managing hundreds of sites (Universities, Government, Multi-brand Corps), Canvas is a design system enabler.

• Brand Consistency: You can deploy a “Global Design System” module. Every site in the portfolio gets the same set of approved components in their Canvas library.
• Centralized Updates: If the brand color changes from Blue to Navy, you update the Design Token in the central theme. Every page built with Canvas across the ecosystem updates instantly.

The Strategic Edge – AI and Future Proofing

Drupal Canvas 1.0 is not just catching up to competitors; it is positioning Drupal to leapfrog them via the Starshot AI integration.

The AI Assistant

Demonstrated in the Driesnote and part of the roadmap for Drupal CMS v2, Canvas is designed to host AI agents.

• Generative Layouts: A user will be able to click a button in Canvas and type: “Build me a pricing page with a comparison table and an FAQ section.” The AI will select the correct components from your SDC library and assemble the page for you.
• Content Rewriting: Inside the Canvas properties panel, AI can assist in rewriting headlines for SEO or adjusting tone, directly within the visual flow.

Headless Readiness

Unlike many page builders that lock you into a specific rendering engine, the data saved by Drupal Canvas is structured. This means it can be decoupled. You can use Drupal Canvas to visually build a page, and then serve that layout data via JSON:API to a Next.js or React application on the frontend. It bridges the gap between “Visual Editing” and “Headless Architecture.”

Conclusion:

For many years, choosing a CMS was a choice between power and ease. You picked WordPress or Squarespace for ease, and you picked Drupal for power.

Drupal Canvas 1.0 eliminates the need to choose.

It provides the slick, intuitive authoring experience that modern web users demand, but it lays it on top of the most secure, scalable, and structured data engine in the world. It creates a workflow where developers can code rigorous standards and marketers can exercise creative freedom, simultaneously and harmoniously.

If you are currently running Drupal with Layout Builder, Paragraphs, or a legacy theme, the release of Drupal Canvas is your signal to start planning your evolution. The future of Drupal is not just about managing code; it’s about crafting experiences, and finally, we have the canvas to do it properly.

Salesforce Marketing Cloud + AI is revolutionizing marketing by combining advanced artificial intelligence with marketing automation to create hyper-personalized, data-driven campaigns that adapt in real time to customer behaviors and preferences. This fusion drives engagement, conversions, and revenue growth like never before.

Key AI Features of Salesforce Marketing Cloud

• Agentforce: An autonomous AI agent that helps marketers create dynamic, scalable campaigns with effortless automation and real-time optimization. It streamlines content creation, segmentation, and journey management through simple prompts and AI insights. Learn more at the Salesforce official site.

• Einstein AI: Powers predictive analytics, customized content generation, send-time optimization, and smart audience segmentation, ensuring the right message reaches the right customer at the optimal time.

• Generative AI: Using Einstein GPT, marketers can automatically generate email copy, subject lines, images, and landing pages, enhancing productivity while maintaining brand consistency.

• Marketing Cloud Personalization: Provides real-time behavioral data and AI-driven recommendations to deliver tailored experiences that boost customer loyalty and conversion rates.

• Unified Data Cloud Integration: Seamlessly connects live customer data for dynamic segmentation and activation, eliminating data silos.

• Multi-Channel Orchestration: Integrates deeply with platforms like WhatsApp, Slack, and LinkedIn to deliver personalized campaigns across all customer touchpoints.

Latest Trends & 2025 Updates

• With advanced artificial intelligence, marketing teams benefit from systems that independently manage and adjust their campaigns for optimal results.

• Real-time customer journey adaptations powered by live data.

• Enhanced collaboration via AI integration with Slack and other platforms.

• Automated paid media optimization and budget control with minimal manual intervention.

For detailed insights on AI and marketing automation trends, see this industry report.

Benefits of Combining Salesforce Marketing Cloud + AI

• Increased campaign efficiency and ROI through automation and predictive analytics.

• Hyper-personalized customer engagement at scale.

• Reduced manual effort with AI-assisted content and segmentation.

• Better decision-making powered by unified data and AI-driven insights.

• Greater marketing agility and responsiveness in a changing landscape.

Related Posts

• • Mastering Customer Journey Mapping with Salesforce

  • Top AI Tools for Marketing in 2025

Financial institutions are at a pivotal moment. As customer expectations evolve and AI reshapes digital engagement, leaders in marketing, CX, and IT must rethink how they deliver value.

Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” reveals that only 36% of the customer journey is currently personalized, despite 74% of executives acknowledging rising customer expectations. With transformation already underway, financial leaders face five imperatives that demand immediate action to drive relevance, trust, and growth.

1. Make Personalization More Meaningful

Personalization has long been a strategic focus, but today’s consumers expect more than basic segmentation or name-based greetings. They want real-time, omnichannel interactions that align with their financial goals, life stages, and behaviors.

To meet this demand, financial institutions must evolve from reactive personalization to predictive, intent-driven engagement. This means leveraging AI to anticipate needs, orchestrate journeys, and deliver content that resonates with individual context.

Perficient Adobe-consulting principal Ross Monaghan explains, “We are still dealing with disparate data and slow progression into a customer 360 source of truth view to provide effective personalization at scale. What many firms are overlooking is that this isn’t just a data issue. We’re dealing with both a people and process issue where teams need to adjust their operational process of typical campaign waterfall execution to trigger-based and journey personalization.”

His point underscores that personalization challenges go beyond technology. They require cultural and operational shifts to enable real-time, AI-driven engagement.

2. Redesign the Operating Model Around the Customer

Legacy structures often silo marketing, IT, and operations, creating friction in delivering cohesive customer experiences. To compete in a digital-first world, financial institutions must reorient their operating models around the customer, not the org chart.

This shift requires cross-functional collaboration, agile workflows, and shared KPIs that align teams around customer outcomes. It also demands a culture that embraces experimentation and continuous improvement.

Only 3% of financial services firms are structured around the customer journey, though 19% say it should be the ideal.

3. Build Content for AI-Powered Search

As AI-powered search becomes a primary interface for information discovery, the way content is created and structured must change. Traditional SEO strategies are no longer enough.

Customers now expect intelligent, personalized answers over static search results. To stay visible and trusted, financial institutions must create structured, metadata-rich content that performs in AI-powered environments. Content must reflect experience-expertise-authoritativeness-trustworthiness principles and be both machine-readable and human-relevant. Success depends on building discovery journeys that work across AI interfaces while earning customer confidence in moments that matter.

4. Unify Data and Platforms for Scalable Intelligence

Disconnected data and fragmented platforms limit the ability to generate insights and act on them at scale. To unlock the full potential of AI and automation, financial institutions must unify their data ecosystems.

This means integrating customer, behavioral, transactional, and operational data into a single source of truth that’s accessible across teams and systems. It also involves modernizing MarTech and CX platforms to support real-time decisioning and personalization.

But Ross points out, “Many digital experience and marketing platforms still want to own all data, which is just not realistic, both in reality and cost. The firms that develop their customer source of truth (typically cloud-based data platforms) and signal to other experience or service platforms will be the quickest to marketing execution maturity and success.”

His insight emphasizes that success depends not only on technology integration but also on adopting a federated approach that accelerates marketing execution and operational maturity.

5. Embed Guardrails Into GenAI Execution

As financial institutions explore GenAI use cases, from content generation to customer service automation, governance must be built in from the start. Trust is non-negotiable in financial services, and GenAI introduces new risks around accuracy, bias, and compliance.

Embedding guardrails means establishing clear policies, human-in-the-loop review processes, and robust monitoring systems. It also requires collaboration between legal, compliance, marketing, and IT to ensure responsible innovation.

At Perficient, we use our PACE (Policies, Advocacy, Controls, Enablement) Framework to holistically design tailored operational AI programs that empower business and technical stakeholders to innovate with confidence while mitigating risks and upholding ethical standards.

The Time to Lead is Now

The future of financial services will be defined by how intelligently and responsibly institutions engage in real time. These five imperatives offer a blueprint for action, each one grounded in data, urgency, and opportunity. Leaders who move now will be best positioned to earn trust, drive growth, and lead in the AI-powered era.

Learn About Perficient and Adobe’s Partnership

Are you looking for a partner to help you transform and modernize your technology strategy? Perficient and Adobe bring together deep industry expertise and powerful experience technologies to help financial institutions unify data, orchestrate journeys, and deliver customer-centric experiences that build trust and drive growth.

Get in Touch With Our Experts

I think about the early ATMs now and then. No one knew the “right” way to use them. I imagine a customer in the 1970s standing there, card in hand, squinting at this unfamiliar machine and hoping it would give something back; trying to decide if it really dispensed cash…or just ate cards for sport. That quick panic when the machine pulled the card in is an early version of the same confusion customers feel today in digital banking.

People were not afraid of machines. They were afraid of not understanding what the machine was doing with their money.

Banks solved it by teaching people how to trust the process. They added clear instructions, trained staff to guide customers, and repeated the same steps until the unfamiliar felt intuitive. 

However, the stakes and complexity are much higher now, and AI for financial product transparency is becoming essential to an optimized banking UX.

Today’s banking customer must navigate automated underwriting, digital identity checks, algorithmic risk models, hybrid blockchain components, and disclosures written in a language most people never use. Meanwhile, the average person is still struggling with basic money concepts.

FINRA reports that only 37% of U.S. adults can answer four out of five financial literacy questions (FINRA Foundation, 2022).

Pew Research finds that only about half of Americans understand key concepts like inflation and interest (Pew Research Center, 2024).

Financial institutions are starting to realize that clarity is not a content task or a customer service perk. It is structural. It affects conversion, compliance, risk, and trust. It shapes the entire digital experience. And AI is accelerating the pressure to treat clarity as infrastructure.

When customers don’t understand, they don’t convert. When they feel unsure, they abandon the flow. 

How AI is Improving UX in Banking (And Why Institutions Need it Now)

Financial institutions often assume customers will “figure it out.” They will Google a term, reread a disclosure, or call support if something is unclear. In reality, most customers simply exit the flow.

The CFPB shows that lower financial literacy leads to more mistakes, higher confusion, and weaker decision-making (CFPB, 2019). And when that confusion arises during a digital journey, customers quietly leave without resolving their questions.

This means every abandoned application costs money. Every misinterpreted term creates operational drag. Every unclear disclosure becomes a compliance liability. Institutions consistently point to misunderstanding as a major driver of complaints, errors, and churn (Lusardi et al., 2020).

Sometimes it feels like the industry built the digital bank faster than it built the explanation for it.

Where AI Makes the Difference

Many discussions about AI in financial services focus on automation or chatbots, but the real opportunity lies in real-time clarity. Clarity that improves financial product transparency and streamlines customer experience without creating extra steps.

In-context Explanations That Improve Understanding

Research in educational psychology shows people learn best when information appears the moment they need it. Mayer (2019) demonstrates that in-context explanations significantly boost comprehension. Instead of leaving the app to search unfamiliar terms, customers receive a clear, human explanation on the spot.

Consistency Across Channels

Language in banking is surprisingly inconsistent. Apps, websites, advisors, and support teams all use slightly different terms. Capgemini identifies cross-channel inconsistency as a major cause of digital frustration (Capgemini, 2023). A unified AI knowledge layer solves this by standardizing definitions across the system.

Predictive Clarity Powered by Behavioral Insight

Patterns like hesitation, backtracking, rapid clicking, or form abandonment often signal confusion. Behavioral economists note these patterns can predict drop-off before it happens (Loibl et al., 2021). AI can flag these friction points and help institutions fix them.

24/7 Clarity, Not 9–5 Support

Accenture reports that most digital banking interactions now occur outside of business hours (Accenture, 2023). AI allows institutions to provide accurate, transparent explanations anytime, without relying solely on support teams.

At its core, AI doesn’t simplify financial products. It translates them.

What Strong AI-Powered Customer Experience Looks Like

Onboarding that Explains Itself

• Mortgage flows with one-sentence escrow definitions.
• Credit card applications with visual explanations of usage.
  
• Hybrid products that show exactly what blockchain is doing behind the scenes. The CFPB shows that simpler, clearer formats directly improve decision quality (CFPB, 2020).

A Unified Dictionary Across Channels

The Federal Reserve emphasizes the importance of consistent terminology to help consumers make informed decisions (Federal Reserve Board, 2021). Some institutions now maintain a centralized term library that powers their entire ecosystem, creating a cohesive experience instead of fragmented messaging.

Personalization Based on User Behavior

Educational nudges, simplified paths, multilingual explanations. Research shows these interventions boost customer confidence (Kozup & Hogarth, 2008). 

Transparent Explanations for Hybrid or Blockchain-backed Products

Customers adopt new technology faster when they understand the mechanics behind it (University of Cambridge, 2021). AI can make complex automation and decentralized components understandable.

The Urgent Responsibilities That Come With This

GenAI can mislead customers without strong data governance and oversight. Poor training data, inconsistent terminology, or unmonitored AI systems create clarity gaps. That’s a problem because those gaps can become compliance issues. The Financial Stability Oversight Council warns that unmanaged AI introduces systemic risk (FSOC, 2023). The CFPB also emphasizes the need for compliant, accurate AI-generated content (CFPB, 2024).

Customers are also increasingly wary of data usage and privacy. Pew Research shows growing fear around how financial institutions use personal data (Pew Research Center, 2023). Trust requires transparency.

Clarity without governance is not clarity. It’s noise.

And institutions cannot afford noise.

What Institutions Should Build Right Now

To make clarity foundational to customer experience, financial institutions need to invest in:

• Modern data pipelines to improve accuracy
• Consistent terminology and UX layers across channels
• Responsible AI frameworks with human oversight
• Cross-functional collaboration between compliance, design, product, and analytics
• Scalable architecture for automated and decentralized product components
• Human-plus-AI support models that enhance, not replace, advisors

When clarity becomes structural, trust becomes scalable.

Why This Moment Matters

I keep coming back to the ATM because it perfectly shows what happens when technology outruns customer understanding. The machine wasn’t the problem. The knowledge gap was. Financial services are reliving that moment today.

Customers cannot trust what they do not understand.

And institutions cannot scale what customers do not trust.

GenAI gives financial organizations a second chance to rebuild the clarity layer the industry has lacked for decades, and not as marketing. Clarity, in this new landscape, truly is infrastructure.

Related Reading

• The Personalization Gap Is Hurting Financial Services, Here’s How to Close It: A discussion of how personalization shortfalls are holding back financial services from meeting customer expectations. 

• Closing the Gap Between Expectations and Experience in Financial Services: Analysis of rising customer expectations and why legacy systems struggle to keep up. 

• Trust Is the New Currency in Financial Services and Customers Are Setting the Terms: A look at how trust, speed, transparency, and control are reshaping what customers expect from their institutions. 

References 

• Accenture. (2023). Banking top trends 2023. https://www.accenture.com
• Capgemini. (2023). World retail banking report 2023. https://www.capgemini.com
• Consumer Financial Protection Bureau. (2019). Financial well-being in America. https://www.consumerfinance.gov
• Consumer Financial Protection Bureau. (2020). Improving the clarity of mortgage disclosures. https://www.consumerfinance.gov
• Consumer Financial Protection Bureau. (2024). Supervisory highlights: Issue 30. https://www.consumerfinance.gov
• Federal Reserve Board. (2021). Consumers and mobile financial services. https://www.federalreserve.gov
• FINRA Investor Education Foundation. (2022). National financial capability study. https://www.finrafoundation.org
• Financial Stability Oversight Council. (2023). Annual report. https://home.treasury.gov
• Kozup, J., & Hogarth, J. (2008). Financial literacy, public policy, and consumers’ self-protection. Journal of Consumer Affairs, 42(2), 263–270.
• Loibl, C., Grinstein-Weiss, M., & Koeninger, J. (2021). Consumer financial behavior in digital environments. Journal of Economic Psychology, 87, 102438.
• Lusardi, A., Mitchell, O. S., & Oggero, N. (2020). The changing face of financial literacy. University of Pennsylvania, Wharton School.
• Mayer, R. (2019). The Cambridge handbook of multimedia learning. Cambridge University Press.
• Pew Research Center. (2023). Americans and data privacy. https://www.pewresearch.org
• Pew Research Center. (2024). Americans and financial knowledge. https://www.pewresearch.org
• University of Cambridge. (2021). Global blockchain benchmarking study. https://www.jbs.cam.ac.uk

Content chaos is costing you more than you think

Every disconnected asset, every redundant workflow, every missed opportunity to reuse content adds up, not just in operational inefficiency, but in lost revenue, slower time-to-market, and diminished brand consistency. For many organizations, the content supply chain is broken, and the cracks show up everywhere: marketing campaigns delayed, creative teams overwhelmed, and customers receiving fragmented experiences.

Sitecore Content Hub can help solve this, but here’s the truth: technology alone won’t solve the problem. Success requires a strategic approach that aligns people, processes, and platforms. Over the years, I’ve seen one principle hold true: when you break the process into digestible steps, clarity emerges. Here’s the five-step framework I recommend for leaders who want to turn Content Hub into a competitive advantage. It’s what I wish I had before my first implementation. While Content Hub is extremely powerful for a Digital Asset Management (DAM) platform, and there could be entire books written on each configuration point, my hope in this post is to give someone new to the platform a mindset to have before beginning an implementation.

Step 1: Discover and Decode

Transformation starts with visibility. Before you configure anything, take a hard look at your current state. What assets do you have? How do they move through your organization, from creation to approval to archival? Who touches them, and where do bottlenecks occur?

This isn’t just an audit; it’s an opportunity to uncover inefficiencies and align stakeholders. Ask questions like:

• Are we duplicating content because teams don’t know what already exists?
• Where are the delays that slow down time-to-market?
• Which assets drive value and which are digital clutter?

Document these insights in a way that tells a story. When leadership sees the cost of inefficiency and the opportunity for improvement, alignment becomes easier. This step sets the foundation for governance, taxonomy, and integration decisions later. Skip it, and everything else wobbles.

Step 2: Design the Blueprint

Once you know where you are, define where you’re going. This is your architectural phase and the moment to design a system that scales.

Start with taxonomy. A well-structured taxonomy makes assets easy to find and reuse, while a poor one creates friction and frustration. Establish naming conventions and metadata standards that support searchability and personalization. Then, build a governance model that enforces consistency without stifling creativity.

Finally, map the flow of content across systems. Where is content coming from? Where does it need to go? These answers determine integration points and connectors. If you skip this step, you risk building silos inside your new system, which is a mistake that undermines the entire investment.

Step 3: Deploy the (Content) Hub

See what we did there?! With the blueprint in hand, it’s time to implement. Configure the environment, validate user roles, and migrate assets with care.

Deployment is more than a technical exercise. It’s a change management moment. How you roll out the platform will influence adoption. Consider a phased approach: start with a pilot group, gather feedback, and refine before scaling.

Testing is critical. Validate search functionality, user permissions, and workflows before you go live. A smooth deployment isn’t just about avoiding errors. It’s about building confidence across the organization.

Step 4: Drive Intelligent Delivery

Content Hub isn’t just a repository; it’s a strategic engine. This is where you unlock its full potential. Enable AI features to automate tagging and improve personalization. Create renditions and transformations that make omnichannel delivery seamless.

Think beyond efficiency. Intelligent delivery is about elevating the customer experience. When your content is enriched with metadata and optimized for every channel, you’re not just saving time. You’re driving engagement and revenue.

Governance plays a starring role here. Standards aren’t just rules. They’re the guardrails that keep your ecosystem healthy and scalable. Without them, even the smartest technology can devolve into chaos.

Step 5: Differentiate

This is where leaders separate themselves from the pack. Implementation is not the finish line—it’s the starting point for continuous improvement.

Differentiation begins with measurement. Build dashboards that show how content performs across channels and campaigns. Which assets drive conversions? Which formats resonate with your audience? These insights allow you to double down on what works and retire what doesn’t.

But don’t stop at performance metrics. Use audits to identify gaps in your content strategy. Are you missing assets for emerging channels? Are you over-investing in content that doesn’t move the needle? This level of visibility turns your content operation into a strategic lever for growth.

Finally, think about innovation. How can you use Content Hub to enable personalization at scale? How can AI-driven insights inform creative decisions? Leaders who embrace this mindset turn Content Hub from a tool into a competitive advantage.

Final Thoughts

Your current state may feel daunting, but clarity is within reach. By breaking the process into these five steps, you can transform chaos into a content strategy that drives real business outcomes. Sitecore Content Hub is powerful—but only if you implement it with intention.

Ready to start your journey? Begin with discovery. The rest will follow. If Perficient can help, reach out!

Sitecore has introduced the Content SDK for XM Cloud-now Sitecore AI to streamline the process of fetching content and rendering it on modern JavaScript front-end applications. If you’re building a website on Sitecore AI, the new Content SDK is the modern, recommended tool for your development team.

Think of it as a specialized, lightweight toolkit built for one specific job: getting content from Sitecore AI and displaying it on your modern frontend application (like a site built with Next.js).

Because it’s purpose-built for Sitecore AI, it’s fast, efficient, and doesn’t include a lot of extra baggage. It focuses purely on the essential “headless” task of fetching and rendering content.

What About the JSS SDK?
This is the original toolkit Sitecore created for headless development.

The key difference is that the JSS SDK was designed to be a one-size-fits-all solution. It had to support both the new, headless Sitecore AI and Sitecore’s older, all-in-one platform, Sitecore XP/XM.

To do this, it had to include extra code and dependencies to support older features, like the “Experience Editor”. This makes the JSS SDK “bulkier” and more complex. If you’re only using Sitecore AI, you’re carrying around a lot of extra weight you simply don’t need.

The Sitecore Content SDK is the modern, purpose-built toolkit for developers using Sitecore AI, providing seamless, out-of-the-box integration with the platform’s most powerful capabilities. This includes seamless visual editing that empowers marketers to build and edit pages in real-time, as well as built-in hooks for personalization and analytics that simplify the delivery and tracking of targeted user experiences. For developers, it provides GraphQL utilities to streamline data fetching and is deeply optimized for Next.js, enabling high-performance features like server-side rendering. Furthermore, with the recent introduction of App Router support (in beta), the SDK is evolving to give developers even more granular control over performance, SEO, bundle sizes, and security through a more modern, modular code structure.

What does the Content SDK offer?

1) App Router support (v1.2)

With version 1.2.0, Sitecore Content SDK introduces App Router support in beta. While the full fledged stable release is expected soon, developers can already start exploring its benefits and work flow with 1.2 version.
This isn’t just a minor update; it’s a huge step toward making your front-end development more flexible and highly optimized.

Why should you care? –
The App Router introduces a fantastic change to your starter application’s code structure and how routing works. Everything becomes more modular and declarative, aligning perfectly with modern architecture practices. This means defining routes and layouts is cleaner, content fetching is neatly separated from rendering, and integrating complex Next.js features like dynamic routes is easier than ever. Ultimately, this shift makes your applications much simpler to scale and maintain as they grow on Sitecore AI.

Performance: Developers can fine-tune route handling with nested layouts and more aggressive and granular caching to seriously boost overall performance, leading to faster load times.

Bundle Size: Smaller bundle size because it uses React Server Components (RSC) to render components. It help fetch and render component from server side without making the static files in bundle.

Security: It helps with security by giving improved control over access to specific routes and content.

With the starter kit applications, this is how app router routing structure looks like:

2) New configs – sitecore.config.ts & sitecore.cli.config.ts

The sitecore.config.ts file, located in the root of your application, acts as the central configuration point for Content SDK projects. It is replacement of the older temp/config file used by the JSS SDK. It contains properties that can be used throughout the application just by importing the file. It contains important properties like sitename, defaultLanguage, edge props like contextid. Starter templates include a very lightweight version containing only the mandatory parameters necessary to get started. Developers can easily extend this file as the project grows and requires more specific settings.

Key Aspects:

Environment Variable Support: This file is designed for deployment flexibility using a layered approach. Any configuration property present in this file can be sourced in three ways, listed in order of priority:

1. Explicitly defined in the configuration file itself.
2. Fallback to a corresponding environment variable (ideal for deployment pipelines).
3. Use a default value if neither of the above is provided.

This layered approach ensures flexibility and simplifies deployment across environments.

The sitecore.cli.config.ts file is dedicated to defining and configuring the commands and scripts used during the development and build phases of a Content SDK project.

Key Aspects:

CLI Command Configuration: It dictates the commands that execute as part of the build process, such as generateMetadata() and generateSites(), which are essential for generating Sitecore-related data and metadata for the front-end.

Component Map Generation: This file manages the configuration for the automatic component map generation. This process is crucial for telling Sitecore how your front-end components map to the content structure, allowing you to specify file paths to scan and define any files or folders to exclude. Explored further below.

Customization of Build Process: It allows developers to customize the Content SDK’s standard build process by adding their own custom commands or scripts to be executed during compilation.

While sitecore.config.ts handles the application’s runtime settings (like connection details to Sitecore AI), sitecore.cli.config.ts works in conjunction to handle the development-time configuration required to prepare the application for deployment.

3) Component map

In Sitecore Content SDK-based applications, every custom component must be manually registered in the .sitecore/component-map.ts file located in the app’s root. The component map is a registry that explicitly links Sitecore renderings to their corresponding frontend component implementations. The component map tells the Content SDK which frontend component to render for each component receives from Sitecore. When the rendering gets added to any page via presentation, component map tells which frontend rendering should be rendered at the place.

Key Aspects:

Unlike JSS implementations that automatically maps components, the Content SDK’s explicit component map enables better tree-shaking. Your final production bundle will only include the components you have actually registered and use, resulting in smaller, more efficient application sizes.

This is how it looks like: (Once you start creating custom component, you have to add the component name here to register.)

4) Import map

The import map is a tool used specifically by the Content SDK’s code generation feature. It manages the import paths of components that are generated or used during the build process. It acts as a guide for the code generation engine, ensuring that any new code it creates correctly references your existing components.
Where it is: It is a generated file, typically found at ./sitecore/import-map.ts, that serves as an internal manifest for the build process. You generally do not need to edit this file manually.
It simplifies the logic of code generation, guaranteeing that any newly created code correctly and consistently references your existing component modules.

The import map generation process is configurable via the sitecore.cli.config.ts file. This allows developers to customize the directories scanned for components.

5) defineMiddleware in the Sitecore Content SDK

defineMiddleware is a utility for composing a middleware chain in your Next.js app. It gives you a clean, declarative way to handle cross-cutting concerns like multi-site routing, personalization, redirects, and security all in one place. This centralization aligns perfectly with modern best practices for building scalable, maintainable functions.

The JSS SDK leverages a “middleware plugin” pattern. This system is effective for its time, allowing logic to be separated into distinct files. However, this separation often requires developers to manually manage the ordering and chaining of multiple files, which could become complex and less transparent as the application grew. The Content SDK streamlines this process by moving the composition logic into a single, highly readable utility which can customizable easily by extending Middleware

6) Debug Logging in Sitecore Content SDK

Debug logging helps you see what the SDK is doing under the hood. Super useful for troubleshooting layout/dictionary fetches, multisite routing, redirects, personalization, and more. The Content SDK uses the standard DEBUG environment variable pattern to enable logging by namespace. You can selectively turn on logging for only the areas you need to troubleshoot, such as: content-sdk:layout (for layout service details) or content-sdk:dictionary (for dictionary service details)
For all available namespaces and parameters, refer to sitecore doc – https://doc.sitecore.com/sai/en/developers/content-sdk/debug-logging-in-content-sdk-apps.html#namespaces 

7) Editing & Preview

In the context of Sitecore’s development platform, editing and preview render optimization with the Content SDK involves leveraging middleware, architecture, and framework-specific features to improve the performance of rendering content in editing and preview modes. The primary goal is to provide a fast and responsive editing experience for marketers using tools like Sitecore AI Pages and the Design Library. EditingRenderMiddleware: The Content SDK for Next.js includes optimized middleware for editing scenarios. Instead of a multi-step process involving redirects, the optimized middleware performs an internal, server-side request to return the HTML directly. This reduces overhead and speeds up rendering significantly.
This feature Works out of the box in most environments: Local container, Vercel / Netlify, SitecoreAI (defaults to localhost as configured)

For custom setups, override the internal host with: SITECORE_INTERNAL_EDITING_HOST_URL=https://host
This leverages a Integration with XM Cloud/Sitecore AI Pages for visual editing and testing of components.

8) SitecoreClient

The SitecoreClient class in the Sitecore Content SDK is a centralized data-fetching service that simplifies communication with your Sitecore content backend typically with Experience Edge or preview endpoint via GraphQL endpoints.
Instead of calling multiple services separately, SitecoreClient lets you make one organized request to fetch everything needed for a page layout, dictionary, redirects, personalization, and more.

Key Aspect:

Unified API: One client to access layout, dictionary, sitemap, robots.txt, redirects, error pages, multi-site, and personalization.
To understand all key methods supported, please refer to sitecore documentation: https://doc.sitecore.com/sai/en/developers/content-sdk/the-sitecoreclient-api.html#key-methods

9) Built-In Capabilities for Modern Web Experiences

GraphQL Utilities: Easily fetch content, layout, dictionary entries, and site info from Sitecore AI’s Edge and Preview endpoints.
Personalization & A/B/n Testing: Deploy multiple page or component variants to different audience segments (e.g., by time zone or language) with no custom code.
Multi-site Support: Seamlessly manage and serve content across multiple independent sites from a single Sitecore AI instance.
Analytics & Event Tracking: Integrated support via the Sitecore Cloud SDK for capturing user behavior and performance metrics.
Framework-Specific Features: Includes Next.js locale-based routing for internationalization, and supports both SSR and SSG for flexible rendering strategies.

10) Cursor for AI development

Starting with Content SDK version 1.1, Sitecore has provided comprehensive “Cursor rules” to facilitate AI-powered development.
The integration provides Cursor with sufficient context about the Content SDK ecosystem and Sitecore development patterns. These set of rules and context helps to accelerate the development. The cursor rules are created for contentsdk with starter application under .cursor folder. This enables the AI to better assist developers with tasks specific to building headless Sitecore components, leading to improved development consistency and speed following same patterns just by providing few commands in generic terms. Example given in below screenshot for Hero component which can act as a pattern to create another similar component by cursor.

11) Starter Templates and Example Applications

To accelerate development and reduce setup time, the Sitecore Content SDK includes a set of starter templates and example applications designed for different use cases and development styles.
The SDK provides a Next.js JavaScript starter template that enables rapid integration with Sitecore AI. This template is optimized for performance, scalability, and best practices in modern front-end development.
Starter Applications in examples

basic-nextjs -A minimal Next.js application showcasing how to fetch and render content from Sitecore AI using the Content SDK. Ideal for SSR/SSG use cases and developers looking to build scalable, production-ready apps.

basic-spa -A single-page application (SPA) example that demonstrates client-side rendering and dynamic content loading. Useful for lightweight apps or scenarios where SSR is not required.

Other demo site to showcase Sitecore AI capabilities using the Content SDK:

kit-nextjs-article-starter

kit-nextjs-location-starter

kit-nextjs-product-starter

kit-nextjs-skate-park

Final Thoughts

The Sitecore Content SDK represents a major leap forward for developers building on Sitecore AI. Unlike the older JSS SDK, which carried legacy dependencies, the Content SDK is purpose-built for modern headless architectures—lightweight, efficient, and deeply optimized for frameworks like Next.js. With features like App Router support, runtime and CLI configuration flexibility, and explicit component mapping, it empowers teams to create scalable, high-performance applications while maintaining clean, modular code structures.

Healthcare leaders face a critical mandate: deliver seamless, patient-centered experiences while boosting efficiency and measurable outcomes. Lasting transformation happens when strategy, data, technology, and experience converge—and Rochester Regional Health’s recent recognition proves what’s possible.

We’re proud to share that our work with Rochester Regional Health earned two 2025 eHealthcare Leadership Awards and the Sitecore Digital Impact Award for Business Impact, underscoring the power of strategic digital investments in healthcare.

Why This Matters for Healthcare Leaders

Patients expect frictionless access to care, personalized experiences, and real-time engagement. Our recent Access to Care research outlines how these priorities drive competitive advantage for healthcare organizations. More than 50% of respondents who encountered friction when scheduling an appointment took their care elsewhere. That’s not just lost revenue—it’s lost continuity, lost data, and lost trust. To deliver on consumers’ expectations, leaders need a unified digital strategy that connects systems, streamlines workflows, and gives consumers simple, reliable ways to find and schedule care.

Rochester Regional Health and Perficient embraced this challenge, consolidating dozens of disparate websites into one seamless experience and implementing a mobile-first design that mirrors the simplicity of modern commerce. The results speak volumes.

Business Impact

Sitecore Digital Impact Award: Business Impact | Recognized for removing friction and focusing on experience, this award demonstrates how digital transformation accelerates growth and improves care access. Sitecore shares, “The Business Impact winners remind us that digital transformation only matters when it delivers real results for people and the business. Rochester Regional Health and Perficient turned 24 disconnected websites into one seamless experience, helping patients get to the care they need faster.… These stories show what happens when great brands remove friction, focus on experience, and grow because of it.” | Learn more about this award

Best Mobile Experience

eHealthcare Leadership Award, Gold, Healthcare System | A mobile-first redesign delivers intuitive navigation, regional personalization, and real-time appointment scheduling, boosting accessibility, engagement, and conversions. This award recognizes the best examples of healthcare mobile experience, whether via installed app or mobile website via a browser. Judges evaluated usability, design, branding, quality of content, clarity of purpose and consumer ratings. | Learn more about this award

Best Use of Artificial Intelligence in Healthcare Marketing

eHealthcare Leadership Award, Distinction, Healthcare System | Rochester Regional’s new site offers smart search, dynamic filters, and real-time booking making it easy for patients and their caregivers to discover and schedule care that best supports patient needs. It drove a 26% boost in appointment scheduling and $79K+ monthly saving in call center costs. This category awarded the successful application of AI and Machine Learning (ML) to achieve marketing goals, including customer acquisition and retention, online content personalization, digital experience, understanding user intent, physician search, call center optimization, and more. | Learn more about this award

What This Signals for 2026

The next phase of digital priorities will focus on scalable personalization, AI-driven operational efficiency, and connected ecosystems that extend beyond the hospital walls. Leaders are investing in platforms that integrate clinical, financial, and consumer data to deliver proactive care and predictive insights. Digital-first models, intelligent scheduling, and automation will become standard. Organizations that build flexible, cloud-based architectures now and leverage AI for personalization and resource optimization position themselves to improve access, reduce costs, and strengthen patient loyalty in a competitive market.

Explore the full case study to see how Rochester Regional Health partnered with Perficient to make this vision a reality.

Reimagine Access to Care with Confidence

These awards validate the impact of our approach and reinforce the urgency of digital innovation as a strategic imperative for healthcare leaders.

More importantly, it reflects what we’re hearing across the industry: the need to prioritize consumer-centric transformation is accelerating. Leaders are looking for solutions that improve access, personalize engagement, and deliver measurable outcomes for both patients and the business.

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 health care consumers, streamline operations, and improve the cost, quality, and equity of care.

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

We are trusted by leading technology partners, mentioned by analysts, and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

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

In financial services, trust has always been foundational. But today, it’s being redefined, not by brand reputation or policy language, but by how customers experience speed, control, and transparency in real time. 

According to Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” 96% of financial services executives say customers value privacy and data protection, and 63% say they expect transparent pricing. These have become operational expectations, and they’re shaping how trust is built moment by moment.

Trust Is Built in the Details Customers Can See

A face-ID login. A real-time transaction alert. A personalized financial nudge. These micro-moments now carry more weight than any static privacy policy. Customers judge trustworthiness by how responsive and secure their digital experiences feel—especially when managing sensitive tasks like wire transfers, credit approvals, or investment decisions. 

In this new landscape, trust is engineered, not assumed. 

Designing for Trust Means Designing for the Customer

Customers today expect more than digital convenience. They want to feel in control of their money, identity, and digital footprint and engage with institutions that respect their time, values, and privacy. Trust is no longer built solely through face-to-face interactions or legacy brand reputation. Trust is earned through every digital touchpoint.

To meet these expectations, financial institutions must deliver on three critical fronts:

1. Mobile-First Journeys With Instant Authentication

Customers expect secure access anytime, anywhere. A mobile-first design can enable frictionless, secure interactions that reinforce a sense of control and safety. Biometric authentication, real-time alerts, and intuitive navigation all contribute to a trustworthy experience.

2. Personalized Recommendations That Reflect Their Financial Goals

Trust grows when customers feel understood. Using AI and data responsibly to deliver tailored insights, whether it’s budgeting tips, investment opportunities, or credit alerts, shows that the institution is aligned with the customer’s financial well-being. Transparency in how data is used is key to maintaining that trust.

3. Seamless, Omnichannel Experiences That Feel Consistent and Secure

Whether a customer is engaging via app, website, call center, or in-branch, the experience should feel unified and secure. Consistency in branding, messaging, and service quality reinforces reliability, while secure data handling across channels ensures peace of mind.

Institutions that fail to deliver these experiences risk losing not just attention but loyalty. In a competitive landscape where switching providers is easier than ever, trust becomes a differentiator and a strategic imperative.

From Compliance Output to Design Input

Trust has become a core design principle. Instead of treating it as the outcome of compliance, financial institutions are embedding it into the very fabric of the customer experience. This shift reflects a broader understanding: trust is emotional, experiential, and earned in moments, not just mandated in policies.

That means:

Aligning products, security, and experience teams.

Trustworthy experiences require collaboration across silos. When product managers, cybersecurity experts, and UX designers work together, they can create solutions that are not only secure but also intuitive and empathetic. This alignment ensures that security features enhance, not hinder, the user experience.

Ensuring Personalization respects boundaries and data use is clearly communicated.

Customers want tailored experiences, but also want to know their data is safe. Leading institutions are adopting privacy-by-design principles, making it easy for users to understand how their data is used and giving them control over personalization settings. Transparency builds confidence; ambiguity erodes it.

Embedding transparency and predictability into every screen and interaction.

From clear language in disclosures to consistent UI patterns, every detail matters. Predictable flows, upfront information, and visible security cues (like encryption badges or session timers) help users feel safe and informed. These micro-moments of clarity add up to a macro-impact on trust.

This evolution requires cross-functional collaboration and a deep understanding of customer expectations.

Ready to Build Trust Through Experience Design?

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Artificial Intelligence (AI) is everywhere. Every month brings new features promising “deeper thinking” and “agentic processes.” Tech titans are locked in trillion-dollar battles. Headlines scream about business, economic, and societal concerns. Skim the news and you’re left excited and terrified!

Here’s the thing: we’re still human – virtues, flaws, quirks, and all. We’ve always had our agency, collectively shaping our future. Even now, while embracing AI, we need to keep building for us.

We Fear What We Do Not Know

“AI this… AI that…” Even tech leaders admit they don’t fully understand it. Sci-fi stories warn us with cautionary tales. News cycles fuel anxiety about job loss, disconnected human relationships, and cognitive decline.

Luckily, this round of innovation is surprisingly transparent. You can read the Attention is All You Need paper (2017) that started it all. You can even build your own AI if you want! This isn’t locked behind a walled garden. That’s a good thing.

What the Past Can Tell Us

I like to look at the past to gauge what we can expect from the future. Humans have feared every major invention and technological breakthrough. We expect the worst, but most have proven to improve life.

We’ve always had distractions from books, movies, games, to TikTok brain-rot. Some get addicted and go too deep, while others thrive. People favor entertainment and leisure activities – this is nothing new – so I don’t feel like cognitive decline is anything to worry about. Humanity has overcome all of it before and will continue to do so.

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Humans are Simple (and Complicated) Creatures

We look for simplicity and speed. Easy to understand, easy to look at, easy to interact with, easy to buy from. We skim read, we skip video segments, we miss that big red CTA button. The TL;DR culture rules. Even so, I don’t think we’re at risk of the future from Idiocracy (2006).

That’s not to say that we don’t overcomplicate things. The Gods Must Be Crazy movie (1980) has a line that resonates, “The more [we] improved [our] surroundings to make life easier, the more complicated [we] made it.” We bury our users (our customers) in detail when they just want to skim, skip, and bounce.

Building for Computers

The computer revolution (1950s-1980s) started with machines serving humans. Then came automation. And eventually, systems talking to systems.

Fast-forward to the 2010s, where marketers gamed the algorithms to win at SEO, SEM, and social networking. Content was created for computers, not humans. Now we have the dead internet theory. We were building without humans in mind.

We will still have to build for systems to talk to systems. That won’t change. APIs are more important than ever, and agentic AI relies on them. Because of this, it is crucial to make sure what you are building “plays well with others”. But AIs and APIs are tools, not the audience.

Building for Humans

Google used to tell us all to build what people want, as opposed to gaming their systems. I love that advice. However, at first it felt unrealistic…gaming the system worked. Then after many updates, for a short bit, it felt like Google was getting there! Then it got worse and feels like pay-to-play recently.

Now AI is reshaping search and everything else. You can notice the gap between search results and AI recommendations. They don’t match. AI assistants aim to please humans, which is great, until it inevitably changes.

Digital teams must build for AI ingestion, but if you neglect the human aspect and the end user experience, then you will only see short-term wins.

Examples of Building for Humans

• Make it intuitive and easy. Simple for end users means a lot of work for builders, but it is worth it! Reduce their cognitive load.
• Build with empathy. Appeal to real people, not just personas and bots. Include feedback loops so they can feel heard.
• Get to the point. Don’t overwhelm users, instead help them take action! Delight your customers by saving them time.
• Add humor when appropriate. Don’t be afraid to be funny, weird, or real…it connects on a human level.
• Consider human bias. Unlike bots and crawlers, humans aren’t always logical. Design for human biases.
• Watch your users. Focus groups or digital tracking tools are great for observing. Learn from real users and iterate.

Conclusion

Building for humans never goes out of style. Whatever comes after AI will still need to serve people. So as tech evolves, let’s keep honing systems that work with and around our human nature.

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If you are looking for that extra human touch (built with AI), reach out to your Perficient account manager or use our contact form to begin a conversation.