The days of measuring marketing success by impressions and engagement are over, especially in financial services. Today, marketing leaders are being asked to do more than build brand awareness. They’re expected to drive top-line growth. 

According to Adobe’s report, “State of Customer Experience in Financial Services in an AI-Driven World,” 90% of financial services marketing leaders say they’re now expected to directly contribute to revenue. And 96% are being asked to become more efficient while doing so. 

This new mandate requires not only a change in metrics but a mindset transformation as well. 

Marketing is Now a Growth Engine

Modern financial institutions are retooling their marketing functions to prioritize: 

• Pipeline creation 

• Product adoption 

• Customer lifetime value

Campaigns are no longer judged by vanity KPIs. Success is measured by conversion lift, wallet share, and ROI. That means marketing must operate with the same precision and accountability as sales and finance. 

Performance-Driven Marketing Requires New Infrastructure

To meet these expectations, marketing teams need: 

• Attribution models that tie spend to outcomes 

• Automation platforms that enable real-time optimization 

• Journey tracking that connects every touchpoint to business impact 

Along with the right tools, financial services marketers will also need to build a culture of continuous improvement and commercial fluency. 

Business Fluency is the New Financial Services Marketing Skillset

To lead in this environment, marketers must speak the language of finance. That means understanding: 

• Unit economics 

• Acquisition cost 

• Profitability metrics 

Winning teams are breaking down silos between marketing, sales, and product to drive aligned, data-informed execution. Financial services marketing is moving beyond a support function to a strategic partner in growth. 

Precision, Accountability, and Impact

By embracing data-driven strategies, building the right infrastructure, and fostering commercial fluency, marketing teams can move from a support function to a strategic driver of revenue. The organizations that succeed will be those that align marketing with business outcomes and lead with precision, accountability, and agility. 

Download the full Adobe report to learn more about the top insights shaping financial services marketing and the industry as a whole. 

How Perficient and Adobe Help Financial Services Marketers Lead

We help financial services firms modernize their marketing operations from journey orchestration to performance measurement. Together with Adobe, we’re enabling marketing teams to become growth architects, not just brand custodians. 

Let’s connect and uncover new ways to drive measurable impact together.

What developers need to know about the architectural changes that launched on November 10th

Last week at Sitecore Symposium 2025 was one of those rare industry events that reminded me why this community is so special. I got to reconnect with former colleagues I hadn’t seen in years, finally meet current team members face-to-face who had only been voices on video calls, and form genuine new relationships with peers across the ecosystem. Beyond the professional connections, we spent time with current customers and had fascinating conversations with potential new ones about their challenges and aspirations. And let’s be honest—the epic Universal Studios party that capped off the event didn’t hurt either.

Now that we’re settling back into routine work, it’s time to unpack everything that was announced. The best part? As of today, November 10th, it’s all live. When you log into the platform, you can see and experience everything that was demonstrated on stage.

After a decade of Sitecore development, I’ve learned to separate marketing announcements from actual technical changes. This one’s different: SitecoreAI represents a genuine architectural shift toward AI-first design that changes how we approach development.

Here’s what developers need to know about the platform evolution that launched today.

Architecture Changes That Matter

Cloud-Native Foundation with New Deployment Model

SitecoreAI maintains XM Cloud’s Azure-hosted foundation while introducing four connected environments:

• Agentic Studio – where marketers and AI collaborate to plan, create, and personalize experiences
• App Studio – dedicated space for custom application development
• Sitecore Connect – for integrations
• Marketplace – for sharing and discovering solutions

If you’re already on XM Cloud, your existing implementations transition without breaking changes. That’s genuinely good news—no major refactoring required. The platform adds enhanced governance with enterprise deployment controls without sacrificing the SaaS agility we’ve come to expect. There’s also a dedicated App Studio environment specifically for custom application development.

The entire platform is API-first, with RESTful APIs for all platform functions, including AI agent interaction. The key difference from traditional on-premises complexity is that you get cloud-native scaling with enterprise-grade governance built right in.

Unified Architecture vs. Integration Complexity

The biggest architectural change is having unified content, customer data, personalization, and AI in a single platform. This fundamentally changes how we think about integrations.

Instead of connecting separate CMS, CDP, personalization, and AI tools, everything operates within one data model. Your external system integrations change from multi-platform orchestration to single API framework connections. There are trade-offs here—you gain architectural simplicity but need to evaluate vendor lock-in versus best-of-breed flexibility for your specific requirements.

The Development Paradigm Shift: AI Agents

The most significant change for developers is the introduction of autonomous AI agents as a platform primitive. They’ve gone ahead and built this functionality right into the platform, so we’re not trying to bolt it on as an addon. This feels like it’s going to be big.

What AI Agents Mean for Developers

AI agents operate within the platform to handle marketing workflows autonomously—content generation, A/B testing, personalization optimization. They’re not replacing custom code; they’re handling repeatable marketing tasks.

As developers, our responsibilities shift to designing the underlying data models that agents consume, creating integration patterns for agent-external system interactions, building governance frameworks that define agent operational boundaries, and handling complex customizations that exceed agent capabilities.

Marketers can configure basic agents without developer involvement, but custom data models, security frameworks, and complex integrations still require development expertise. So our role evolves rather than disappears.

New Skillset Requirements

Working with AI agents requires understanding several new concepts. You need to know how to design secure, compliant boundaries for agent operations and governed AI frameworks. You’ll also need to structure data so agents can operate effectively, understand how agents learn and improve from configuration and usage, and know when to use agents versus traditional custom development.

This combines traditional technical architecture with AI workflow design.  A new skillset that bridges development and intelligent automation.

Migration Path from XM Cloud

What “Seamless Transition” Actually Means

For XM Cloud customers, the upgrade path is genuinely straightforward. There are no breaking changes.  Existing customizations, integrations, and content work without modification. AI capabilities layer on top of current functionality, and the transition can happen immediately.  When you log in today it’ll all be there waiting for you, no actions needed.

Legacy Platform Migrations

For developers migrating from older Sitecore implementations or other platforms, SitecoreAI provides SitecoreAI Pathway tooling that claims 70% faster migration timelines. The tooling includes automated content conversion with intelligent mapping of existing content structures, schema translation with automated data model conversion and manual review points, and workflow recreation tools to either replicate existing processes or redesign them with AI agent capabilities.

Migration Planning Approach

Based on what I’ve seen, successful migrations follow a clear pattern. Start with an assessment phase to catalog existing customizations, integrations, and workflows. Then make strategy decisions about whether to replicate each component exactly or reimagine it with AI agents. Use a phased implementation that starts with core functionality and gradually add AI-enhanced workflows. Don’t forget team training to educate developers on agent architecture and governance patterns.

The key architectural question becomes: which processes should remain as traditional custom code versus be reimagined as AI agent workflows?

Integration Strategy Considerations

API Framework and Connectivity

SitecoreAI’s unified architecture changes integration patterns significantly. You get native ecosystem integration with direct connectivity to Sitecore XP, Search, CDP, and Personalize without separate integration layers. Third-party integration happens through a single API framework with webhook support for real-time external system connectivity. Authentication is unified across all platform functions.

Data Flow Changes

The unified customer data model affects how you architect integrations. You now have a single customer profile across content, behavior, and AI operations. Real-time data synchronization happens without ETL complexity, and there’s centralized data governance for AI agent operations.

One important note: existing integrations that rely on separate CDP or personalization APIs may need updates to leverage the unified data model.

What This Means for Your Development Team

Immediate Action Items

If you’re currently on XM Cloud, start by documenting your existing custom components for compatibility assessment. Review your integrations to evaluate which external system connections could benefit from unified architecture. Look for repetitive marketing workflows that could be handled by agents.

If you’re planning a migration, use this as an opportunity to modernize rather than just lift-and-shift. Evaluate whether SitecoreAI Pathway’s claimed time savings match your migration complexity. Factor in the learning curve for AI agent architecture when planning team skills development.

Skills to Develop

You’ll want to focus on AI workflow design and understand how to structure processes for agent automation. Learn about building secure, compliant boundaries for autonomous operations. Get comfortable designing for a single customer data model versus traditional integration patterns. Become proficient working in the five-environment Studio model.

Developer’s Bottom Line

For XM Cloud developers, this is evolutionary, not revolutionary. Your existing skills remain relevant while the platform adds AI agent capabilities that reduce routine customization work.

For legacy Sitecore developers, the migration path provides an opportunity to modernize architecture while gaining AI automation capabilities but requires learning cloud-native development patterns.

The strategic shift is clear: development work shifts from building everything custom to designing frameworks where AI agents can operate effectively. You’re architecting for intelligent automation, not just content management.

The platform launched today. For developers, the key question isn’t whether AI will change digital platforms, it’s whether you want to learn agent-based architecture now or catch up later.  The future is here and I’m for it.

Coming Up: I’ll be writing follow-up posts on AI agent development patterns, integration architecture deep dives, and migration playbooks.

In today’s AI activated world, there are ample number of AI related tools that organizations can use to tackle diverse business challenges. In line with this, Amazon has it’s set of Amazon Web Services for AI and ML, to address the real-world needs.

This blog provides details on AWS services, but by understanding this writeup you can also get to know how AI and ML capabilities can be used to address various business challenges. To illustrate how these services can be leveraged, I have used a few simple and straightforward use cases and mapped the AWS solutions to them.

AI Use Cases : Using AWS Services

1. Employee On boarding process

Any employee onboarding process has its own challenges which can be improved by better information discovery, shortening the onboarding timelines, providing more flexibility to the new hire, option for learning and re-visiting the learning multiple times and enhancing both the security and personalization of the induction experience.

Using natural language queries, the AWS AI service – Amazon Kendra, enables new hires to easily find HR manuals, IT instructions, leave policies, and company guidelines, without needing to know exact file names or bookmark multiple URLs.

Amazon Kendra uses Semantic Search which understands the user’s intent and contextual meaning. Semantic search relies on Vector embeddings, Vector search, Pattern matching and Natural Language Processing.

Real-time data retrieval through Retrieval-augmented Generation (RAG) in Amazon Kendra empowers employees to access up-to-date content securely and efficiently.

Following are examples of few prompts a new hire can use to retrieve information:

• How can I access my email on my laptop and on my phone.
• How do I contact the IT support.
• How can I apply for a leave and who do I reach out to for approvals.
• How do I submit my timesheet.
• Where can I find the company training portal.
• ….etcetera.

Data Security

To protect organizational data and ensure compliance with enterprise security standards, Amazon Kendra supports robust data security measures, including encryption in transit and at rest, and seamless integration with AWS Identity and Access Management (IAM).

Role-based access ensures that sensitive information is only visible to authorized personnel.

Thus, in the Onboarding process, the HR team can provide the personalized touch, and the AI agent ensures the employees have easy, anytime access to the right information throughout their on-boarding journey.

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2. Healthcare: Unlocking Insights from Unstructured Clinical Data

Healthcare providers always need to extract critical patient information and support timely decision-making. They face the challenge of rapidly analyzing vast amounts of unstructured medical records, such as physician notes, discharge summaries, and clinical reports.

From a data perspective two key features are required, namely, Entity Recognition and Attribute detection. Medical entities include symptoms, medications, diagnoses, and treatment plans. Similarly Attribute detection includes identifying the dosage, frequency and severity associated with these entities.

Amazon provides the service, Amazon Comprehend Medical which uses NLP and ML models for extracting such information from unstructured data available with healthcare organizations.

One of the crucial aspects in healthcare is to handle Security and compliance related to patient’s health data. AWS has Amazon Macie as a security related service which employs machine learning & pattern matching to discover, classify, and protect Protected Health Information (PHI) within Amazon S3 bucket. Such a service helps organizations maintain HIPAA compliance through automated data governance.

3. Enterprise data insights

Any large enterprise has data spread across various tools like SharePoint, Salesforce, Leave management portals or some accounting applications.

From these data sets, executives can extract great insights, evaluate what-if scenarios, check on some key performance indicators, and utilize all this for decision making.

We can use AWS AI service, Amazon Q business for this very purpose using various plugins, connectors to DBs, and Retrieval Augmented Generation for up-to-date information.

The user can use natural language to query the system and Amazon Q performs Semantic search to return back contextually appropriate information. It also uses Knowledge Grounding which eventually helps in providing accurate answers not relying solely on training data sets.

To ensure that AI-generated responses adhere strictly to approved enterprise protocols, provide accurate and relevant information, we can define built-in guardrails within Amazon Q, such as Global Controls and Topic blocking.

4. Retail company use cases

a) Reading receipts and invoices

The company wants to automate the financial auditing process. In order to achieve this we can use Amazon Textract to read receipts and invoices as it uses machine learning algorithms to accurately identify and extract key information like product names, prices, and reviews.

b) Analyse customer purchasing patterns

The company intends to analyse customer purchasing patterns to predict future sales trends from their large datasets of historical sales data. For these analyses the company wants to build, train, and deploy machine learning models quickly and efficiently.

Amazon SageMaker is the ideal service for such a development.

c) Customer support Bot

The firm receives thousands of customer calls daily. In order to smoothen the process, the firm is looking to create a conversational AI bot which can take text inputs and voice commands.

We can use Amazon Bedrock to create a custom AI application from a dataset of ready to use Foundation models. These models can process large volumes of customer data, generate personalized responses and integrate with other AWS services like Amazon SageMaker for additional processing and analytics.

We can use Amazon Lex to create the bot, and Amazon Polly for text to speech purposes.

d) Image analyses

The company might want to identify and categorize their products based on the images uploaded. To implement this, we can use Amazon S3 and Amazon Rekognition to analyze images as soon as the new product image is uploaded into the storage service.

AWS Services for Compliance & Regulations

AWS Services for Compliance & Regulations

In order to manage complex customer requirements and handling large volumes of sensitive data it becomes essential for us to adhere to various regulations.

Key AWS services supporting these compliance and governance needs include:

1. AWS Config
   Continuously monitors and records resource configurations to help assess compliance.
2. AWS Artifact
   Centralized repository for on-demand access to AWS compliance reports and agreements.
3. AWS CloudTrail
   Logs and tracks all user activity and API calls within your AWS environment for audit purposes.
4. AWS Inspector
   Automated security assessment service that identifies vulnerabilities and deviations from best practices.
5. AWS Audit Manager
   Simplifies audit preparation by automating evidence collection and compliance reporting.
6. AWS Trusted Advisor
   Provides real-time recommendations to optimize security, performance, and cost efficiency.

Security and Privacy risks: Vulnerabilities in LLMs

Vulnerabilities in LLMs

While dealing with LLMs there are ways available to attack the prompts, however there are various safeguards also against them. Keeping in view the attacks I am noting down some vulnerabilities which are useful to understand the risks around your LLMs.

Can you co-relate the above use cases with any of your challenges at hand? Have you been able to use any of the AWS services or other AI platforms for dealing with such challenges?

References:

https://aws.amazon.com/ai/services/
https://www.udemy.com/share/10bvuD/

In my previous blogs, I outlined key considerations for planning a Sitecore migration and shared strategies for executing it effectively. The next critical step is to understand how your business and technical requirements align with the broader Sitecore ecosystem.
Before providing careful recommendations to a customer, it’s essential to map your goals—content management, personalization, multi-site delivery, analytics, and future scalability onto Sitecore’s composable and cloud-native offerings. This ensures that migration and implementation decisions are not only feasible but optimized for long-term value.
To revisit the foundational steps and execution strategies, check out these two helpful resources:
•  Planning Sitecore Migration: Things to Consider
•  Executing a Sitecore Migration: Development, Performance, and Beyond

Sitecore is not just a CMS; it’s a comprehensive digital experience platform.
Before making recommendations to a customer, it’s crucial to clearly define what is truly needed and to have a deep understanding of how powerful Sitecore is. Its Digital Experience Platform (DXP) capabilities, including personalization, marketing automation, and analytics—combined with cloud-native SaaS delivery, enable organizations to scale efficiently, innovate rapidly, and deliver highly engaging digital experiences.
By carefully aligning customer requirements with these capabilities, you can design solutions that not only meet technical and business needs but also maximize ROI, streamline operations, and deliver long-term value.

In this blog, I’ll summarize Sitecore’s Digital Experience Platform (DXP) offerings to explore how each can be effectively utilized to meet evolving business and technical needs.

1. Sitecore XM Cloud

Sitecore Experience Manager Cloud (XM Cloud) is a cloud-native, SaaS, hybrid headless CMS designed to help businesses create and deliver personalized, multi-channel digital experiences across websites and applications. It combines the flexibility of modern headless architecture with robust authoring tools, enabling teams to strike a balance between developer agility and marketer control.

Key Capabilities

• Cloud-native: XM Cloud is built for the cloud, providing a secure, reliable, scalable, and enterprise-ready system. Its architecture ensures high availability and global reach without the complexity of traditional on-premises systems.
• SaaS Delivery: Sitecore hosts, maintains, and updates XM Cloud regularly. Organizations benefit from automatic updates, new features, and security enhancements without the need for costly installations or manual upgrades. This ensures that teams always work with the latest technologies while reducing operational overhead.
• Hybrid Headless: XM Cloud separates content and presentation, enabling developers to build custom front-end experiences using modern frameworks, while marketers utilize visual editing tools like the Page Builder to make real-time changes. This allows routine updates to be handled without developer intervention, maintaining speed and agility.
• Developer Productivity: Developers can model content with data templates, design reusable components, and assign content through data sources. Sitecore offers SDKs like the Content SDK for building personalized Next.js apps, the ASP.NET Core SDK for .NET integrations, and the Cloud SDK for extending DXP capabilities into Content SDK and JSS applications connected to XM Cloud. Starter kits are provided for setting up the code base.
• Global Content Delivery: With Experience Edge, XM Cloud provides scalable GraphQL endpoints to deliver content rapidly across geographies, ensuring consistent user experiences worldwide.
• Extensibility & AI Integration: XM Cloud integrates with apps from the Sitecore Marketplace and leverages Sitecore Stream for advanced AI-powered content generation and optimization. This accelerates content creation while maintaining brand consistency.
• Continuous Updates & Security: XM Cloud includes multiple interfaces, such as Portal, Deploy, Page Builder, Explorer, Forms, and Analytics, which are regularly updated. Deploy app to deploy to XM Cloud projects.

XM Cloud is ideal for organizations seeking a scalable, flexible, and future-proof content platform, allowing teams to focus on delivering compelling digital experiences rather than managing infrastructure.

2. Experience Platform (XP)

Sitecore Experience Platform (XP) is like an all-in-one powerhouse—it’s a complete box packed with everything you need for delivering personalized, data-driven digital experiences. While Experience Management (XM) handles content delivery, XP adds layers of personalization, marketing automation, and deep analytics, ensuring every interaction is contextually relevant and optimized for each visitor.

Key Capabilities

• Content Creation & Management: The Content Editor and Experience Editor allow marketers and content authors to create, structure, and manage website content efficiently, supporting collaboration across teams.
• Digital Marketing Tools: Built-in marketing tools enable the creation and management of campaigns, automating triggers and workflows to deliver personalized experiences across multiple channels.
• Experience Analytics: XP provides detailed insights into website performance, visitor behavior, and campaign effectiveness. This includes metrics like page performance, conversions, and user engagement patterns.
• Experience Optimization: Using analytics data, XP allows you to refine content and campaigns to achieve better results. A/B testing and multivariate testing help determine the most effective variations.
• Path Analyzer: This tool enables you to analyze how visitors navigate through your site, helping you identify bottlenecks, drop-offs, and opportunities to enhance the user experience.
  By combining these capabilities, XP bridges content and marketing intelligence, enabling teams to deliver data-driven, personalized experiences while continuously refining and improving digital engagement.

By combining these capabilities, XP bridges content and marketing intelligence, enabling teams to deliver data-driven, personalized experiences while continuously refining and improving digital engagement.

3. Sitecore Content Hub

Sitecore Content Hub unifies content planning, creation, curation, and asset management into a single platform, enabling teams to collaborate efficiently and maintain control across the entire content lifecycle and digital channels.

Key Capabilities

• Digital Asset Management (DAM): Content Hub organizes and manages images, videos, documents, and other digital assets. Assets can be tagged, annotated, searched, and shared efficiently, supporting teams in building engaging experiences without losing control over asset usage or consistency.
• Campaign & Content Planning: Teams can plan campaigns, manage editorial calendars, and assign tasks to ensure smooth collaboration between marketing, creative, and operational teams. Structured workflows enforce version control, approvals, and accountability, ensuring that content moves systematically to the end user.
• AI-Powered Enhancements: Advanced AI capabilities accelerate content operations. These intelligent features reduce manual effort, increase productivity, and help teams maintain brand consistency at scale.
• Microservice Architecture & Integration & Multi-Channel Delivery: Content Hub is built on a microservice-based architecture, allowing flexible integration with external systems, headless CMS, and cloud development pipelines. Developers can extend capabilities or connect Content Hub to other platforms without disrupting core operations. Content Hub ensures that teams can deliver consistent, high-quality experiences across websites, social media, commerce, and other digital channels.

Sitecore Content Hub empowers organizations to manage content as a strategic asset, streamlining operations, enabling global collaboration, and providing the technical flexibility developers need to build integrated, scalable solutions.

strong>4. Sitecore Customer Data Platform (CDP)

Sitecore Customer Data Platform (CDP) enables organizations to collect customer data across all digital channels, providing a single, unified view of every user. By centralizing behavioral and transactional data, CDP allows businesses to deliver personalized experiences and data-driven marketing at scale.

Key Capabilities

• Real-Time Data Collection: The Stream API captures live behavioral and transactional data from your applications and sends it to Sitecore CDP in real time. This ensures that customer profiles are always up-to-date and that personalization can be applied dynamically as users interact with your digital properties.
• Batch Data Upload: For larger datasets, including guest data or offline orders, the Batch API efficiently uploads bulk information into CDP, keeping your customer data repository comprehensive and synchronized.
• CRUD Operations: Sitecore CDP offers REST APIs for retrieving, creating, updating, and deleting customer data. This enables developers to integrate external systems, enrich profiles, or synchronize data between multiple platforms with ease.
• Data Lake Export: With the Data Lake Export Service, all organizational data can be accessed from Amazon S3, allowing it to be downloaded locally or transferred to another S3 bucket for analysis, reporting, or integration with external systems.
• SDK Integrations (Cloud SDK & Engage SDK): Developers can leverage Sitecore’s Cloud SDK and Engage SDK to streamline data collection, manage user information, and integrate CDP capabilities directly into applications. These SDKs simplify the process of connecting applications to XM Cloud and other services to CDP, enabling real-time engagement and seamless data synchronization.

Sitecore CDP captures behavioral and transactional interactions across channels, creating a unified, real-time profile for each customer. These profiles can be used for advanced segmentation, targeting, and personalization, which in turn informs marketing strategies and customer engagement initiatives.
By integrating CDP with other components of the Sitecore ecosystem—such as DXP, XM Cloud, and Content Hub —organizations can efficiently orchestrate personalized, data-driven experiences across websites, apps, and other digital touchpoints.

5. Sitecore Personalize

Sitecore Personalize enables organizations to deliver seamless, consistent, and highly relevant experiences across websites, mobile apps, and other digital channels. By leveraging real-time customer data, predictive insights, and AI-driven decisioning, it ensures that the right content, offers, and messages get delivered to the target customer/audience.

Key Capabilities

• Personalized Experiences: Deliver tailored content and offers based on real-time user behavior, predictive analytics, and unified customer profiles. Personalization can be applied across web interactions, server-side experiences, and triggered channels, such as email or SMS, ensuring every interaction is timely and relevant.
• Testing and Optimization: Conduct A/B/n tests and evaluate which variations perform best based on actual customer behavior. This enables continuous optimization of content, campaigns, and personalization strategies.
• Performance Analytics: Track user interactions and measure campaign outcomes to gain actionable insights. Analytics support data-driven refinement of personalization, ensuring experiences remain effective and relevant.
• Experiences and Experiments: Helps to create a tailored experience for each user depending on interaction and any other relevant user data.
• AI-Driven Assistance: The built-in Code Assistant can turn natural language prompts into JavaScript, allowing developers to quickly create custom conditions, session traits, and programmable personalization scenarios without writing code from scratch.

By combining real-time data from CDP, content from XM Cloud and Content Hub, and AI-driven decisioning, Sitecore Personalize allows organizations to orchestrate truly unified, intelligent, and adaptive customer experiences. This empowers marketers and developers to respond dynamically to signals, test strategies, and deliver interactions that drive engagement and value, along with a unique experience for users.

6. Sitecore Send

Sitecore Send is a cloud-based email marketing platform that enables organizations to create, manage, and optimize email campaigns. By combining automation, advanced analytics, and AI-driven capabilities, marketing teams can design, execute, and optimize email campaigns efficiently without relying heavily on IT support.

Key Capabilities

• Campaign Creation & Management: Sitecore Send offers a no-code campaign editor that enables users to design campaigns through drag-and-drop and pre-built templates. Marketers can create campaigns quickly, trigger messages automatically, and also perform batch sends.
• A/B Testing & Optimization: Campaigns can be A/B tested to determine which version resonates best with the target audience, helping improve open rates, click-through rates, and overall engagement.
• AI-Powered Insights: Built-in AI capabilities help optimize send times, segment audiences, and predict engagement trends, ensuring messages are timely, relevant, and impactful.
• API Integration: The Sitecore Send API enables developers to integrate email marketing functionality directly into applications. It supports tasks such as:
  • Creating and managing email lists
  • Sending campaigns programmatically
  • Retrieving real-time analytics
  • Automating repetitive tasks
  • This API-driven approach allows teams to streamline operations, accelerate campaign delivery, and leverage programmatic control over their marketing initiatives.

Sitecore Send integrates seamlessly with the broader Sitecore ecosystem, using real-time data from CDP and leveraging content from XM Cloud or Content Hub. Combined with personalization capabilities, it ensures that email communications are targeted, dynamic, and aligned with overall customer experience strategies.
By centralizing email marketing and providing programmatic access, Sitecore Send empowers organizations to deliver scalable, data-driven campaigns while maintaining full control over creative execution and performance tracking.

7. Sitecore Search

Sitecore Search is a headless search and discovery platform that delivers fast, relevant, and personalized results across content and products. It enables organizations to create predictive, AI-powered, intent-driven experiences that drive engagement, conversions, and deeper customer insights.

Key Capabilities

• Personalized Search & Recommendations: Uses visitor interaction tracking and AI/ML algorithms to deliver tailored search results and product/content recommendations in real time.
• Headless Architecture: Decouples search and discovery from presentation, enabling seamless integration across websites, apps, and other digital channels.
• Analytics & Optimization: Provides rich insights into visitor behavior, search performance, and business impact, allowing continuous improvement of search relevance and engagement.
• AI & Machine Learning Core: Sophisticated algorithms analyze large datasets—including visitor location, preferences, interactions, and purchase history to deliver predictive, personalized experiences.

With Sitecore Search, organizations can provide highly relevant, omnichannel experiences powered by AI-driven insights and advanced analytics.

8. Sitecore Discover

Sitecore Discover is an AI-driven product search similar to sitecore search, but this is more product and commerce-centric. It enables merchandisers and marketers to deliver personalized shopping experiences across websites and apps. By tracking user interactions, it generates targeted recommendations using AI recipes, such as similar products and items bought together, which helps increase engagement and conversions. Merchandisers can configure pages and widgets via the Customer Engagement Console (CEC) to create tailored, data-driven experiences without developer intervention.

Search vs. Discover

• Sitecore Search: Broad content/product discovery, developer-driven, AI/ML-powered relevance, ideal for general omnichannel search. Optimized for content and product discovery.
• Sitecore Discover: Commerce-focused product recommendations, merchandiser-controlled, AI-driven personalization for buying experiences. Optimized for commerce personalization and merchandising.

9. Sitecore Connect

Sitecore Connect is an integration tool that enables seamless connections between Sitecore products and other applications in your ecosystem, creating end-to-end, connected experiences for websites and users.

Key Capabilities

• Architecture: Built around recipes and connectors, Sitecore Connect offers a flexible and scalable framework for integrations.
• Recipes: Automated workflows that define triggers (events occurring in applications) and actions (tasks executed when specific events occur), enabling process automation across systems.
• Connectors: Manage connectivity and interactivity between applications, enabling seamless data exchange and coordinated workflows without requiring complex custom coding.

With Sitecore Connect, organizations can orchestrate cross-system processes, synchronize data, and deliver seamless experiences across digital touchpoints, all while reducing manual effort and integration complexity.

10. OrderCloud

OrderCloud is a cloud-based, API-first, headless commerce and marketplace platform designed for B2B, B2C, and B2X scenarios. It provides a flexible, scalable, and fully customizable eCommerce architecture that supports complex business models and distributed operations.

Key Capabilities

• Headless & API-First: Acts as the backbone of commerce operations, allowing businesses to build and connect multiple experiences such as buyer storefronts, supplier portals, or admin dashboards—on top of a single commerce platform.
• Customizable Commerce Solutions: Supports large and complex workflows beyond traditional shopping carts, enabling tailored solutions for distributed organizations.
• Marketplace & Supply Chain Support: Facilitates selling across extended networks, including suppliers, franchises, and partners, while centralizing order management and commerce operations.

OrderCloud empowers organizations to scale commerce operations, extend digital selling capabilities, and create fully customized eCommerce experiences, all while leveraging a modern, API-first headless architecture.

Final Thoughts

Sitecore’s composable DXP products and its suite of SDKs empower organizations to build scalable, personalized, and future-ready digital experiences. By understanding how each component fits into your architecture and aligns with your  business goals, you can make informed decisions that drive long-term value. Whether you’re modernizing legacy systems or starting fresh in the cloud, aligning your strategy with Sitecore’s capabilities ensures a smoother migration and a more impactful digital transformation.

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.

……

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.

In previous blog, the strategic and architectural considerations that set the foundation for a successful Sitecore migration is explored. Once the groundwork is ready, it’s time to move from planning to execution, where the real complexity begins. The development phase of a Sitecore migration demands precision, speed, and scalability. From choosing the right development environment and branching strategy to optimizing templates, caching, and performance, every decision directly impacts the stability and maintainability of your new platform.

This blog dives into the practical side of migration, covering setup best practices, developer tooling (IDE and CI/CD), coding standards, content model alignment, and performance tuning techniques to help ensure that your transition to Sitecore’s modern architecture is both seamless and future-ready.Title (suggested): Executing a Successful Sitecore Migration: Development, Performance, and Beyond

1. Component and Code Standards Over Blind Reuse

• In any Sitecore migration, one of the biggest mistakes teams make is lifting and shifting old components into the new environment. While this may feel faster in the short term, it creates long-term problems.
• Missed product offerings: Old components were often built around constraints of an earlier Sitecore version. Reusing them as-is means you can’t take advantage of new product features like improved personalization, headless capabilities, SaaS integrations, and modern analytics.
• Outdated standards: Legacy code usually does not meet current coding, security, and performance standards. This can introduce vulnerabilities and inefficiencies into your new platform.
  Accessibility gaps: Many older components don’t align with WCAG and ADA accessibility standards — missing ARIA roles, semantic HTML, or proper alt text. Reusing them will carry accessibility debt into your fresh build.
• Maintainability issues: Old code often has tight coupling, minimal test coverage, and obsolete dependencies. Keeping it will slow down future upgrades and maintenance.

Best practice: Treat the migration as an opportunity to raise your standards. Audit old components for patterns and ideas, but don’t copy-paste them. Rebuild them using modern frameworks, Sitecore best practices, security guidelines, and accessibility compliance. This ensures the new solution is future-proof and aligned with the latest Sitecore roadmap.

2. Template Creation and Best Practices

• Templates define the foundation of your content structure, so designing them carefully is critical.
• Analyze before creating: Study existing data models, pages, and business requirements before building templates.
• Use base templates: Group common fields (e.g., Meta, SEO, audit info) into base templates and reuse them across multiple content types.
• Leverage branch templates: Standardize complex structures (like a landing page with modules) by creating branch templates for consistency and speed.
• Follow naming and hierarchy conventions: Clear naming and logical organization make maintenance much easier.

3. Development Practices and Tools

A clean, standards-driven development process ensures the migration is efficient, maintainable, and future-proof. It’s not just about using the right IDEs but also about building code that is consistent, compliant, and friendly for content authors.

• IDEs & Tools
  • Use Visual Studio or VS Code with Sitecore- and frontend-specific extensions for productivity.
  • Set up linting, code analysis, and formatting tools (ESLint, Prettier in case of JSS code, StyleCop) to enforce consistency.
  • Use AI assistance (GitHub Copilot, Codeium, etc.) to speed up development, but always review outputs for compliance and quality. There are many different AI tools available in market that can even change the design/prototypes into specified code language.

• Coding Standards & Governance
  • Follow SOLID principles and keep components modular and reusable.
  • Ensure secure coding standards: sanitize inputs, validate data, avoid secrets in code.
  • Write accessible code: semantic HTML, proper ARIA roles, alt text, and keyboard navigation.
  • Document best practices and enforce them with pull request reviews and automated checks.

• Package & Dependency Management
  • Select npm/.NET packages carefully: prefer well-maintained, community-backed, and security-reviewed ones.
  • Avoid large, unnecessary dependencies that bloat the project.
  • Run dependency scanning tools to catch vulnerabilities.
  •  Keep lockfiles for environment consistency.

• Rendering Variants & Parameters
  • Leverage rendering variants (SXA/headless) to give flexibility without requiring code changes.
  • Add parameters so content authors can adjust layouts, backgrounds, or alignment safely.
  • Always provide sensible defaults to protect design consistency.

• Content Author Experience

Build with the content author in mind:

• • Use clear, meaningful field names and help text.
  • Avoid unnecessary complexity: fewer, well-designed fields are better.
  • Create modular components that authors can configure and reuse.
  • Validate with content author UAT to ensure the system is intuitive for day-to-day content updates.

Strong development practices not only speed up migration but also set the stage for easier maintenance, happier authors, and a longer-lasting Sitecore solution.

4. Data Migration & Validation

Migrating data is not just about “moving items.” It’s about translating old content into a new structure that aligns with modern Sitecore best practices.

• Migration tools
  Sitecore does provides migration tools to shift data like XM to XM Cloud. Leverage these tools for data that needs to be copied.
• PowerShell for Migration
  • Use Sitecore PowerShell Extensions (SPE) to script the migration of data from the old system that does not need to be as is but in different places and field from old system.
  • Automate bulk operations like item creation, field population, media linking, and handling of multiple language versions.
  • PowerShell scripts can be run iteratively, making them ideal as content continues to change during development.
  • Always include logging and reporting so migrated items can be tracked, validated, and corrected if needed.

• Migration Best Practices

• • Field Mapping First: Analyze old templates and decide what maps directly, what needs transformation, and what should be deprecated.
  • Iterative Migration: Run migration scripts in stages, validate results, and refine before final cutover.
  • Content Cleanup: Remove outdated, duplicate, or unused content instead of carrying it forward.
  • SEO Awareness: Ensure titles, descriptions, alt text, and canonical fields are migrated correctly.
  • Audit & Validation:
    • Use PowerShell reports to check item counts, empty fields, or broken links.
    • Crawl both old and new sites with tools like Screaming Frog to compare URLs, metadata, and page structures.

5. SEO Data Handling

SEO is one of the most critical success factors in any migration — if it’s missed, rankings and traffic can drop overnight.

• Metadata: Preserve titles, descriptions, alt text, and Open Graph tags. Missing these leads to immediate SEO losses.
• Redirects: Map old URLs with 301 redirects (avoid chains). Broken redirects = lost link equity.
• Structured Data: Add/update schema (FAQ, Product, Article, VideoObject). This improves visibility in SERPs and AI-generated results.
• Core Web Vitals: Ensure the new site is fast, stable, and mobile-first. Poor performance = lower rankings.
• Emerging SEO: Optimize for AI/Answer Engine results, focus on E-E-A-T (author, trust, freshness), and create natural Q&A content for voice/conversational search.
• Validation: Crawl the site before and after migration with tools like Screaming Frog or Siteimprove to confirm nothing is missed.

Strong SEO handling ensures the new Sitecore build doesn’t just look modern — it retains rankings, grows traffic, and is ready for AI-powered search.

6. Serialization & Item Deployment

Serialization is at the heart of a smooth migration and ongoing Sitecore development. Without the right approach, environments drift, unexpected items get deployed, or critical templates are missed.

• Best Practices
  • Choose the Right Tool: Sitecore Content Serialization (SCS), Unicorn, or TDS — select based on your project needs.
  • Scope Carefully: Serialize only what is required (templates, renderings, branches, base content). Avoid unnecessary content items.
  • Organize by Modules: Structure serialization so items are grouped logically (feature, foundation, project layers). This keeps deployments clean and modular.
  • Version Control: Store serialization files in source control (Git/Azure devops) to track changes and allow safe rollbacks.
  • Environment Consistency: Automate deployment pipelines so serialized items are promoted consistently from dev → QA → UAT → Prod.
  • Validation: Always test deployments in lower environments first to ensure no accidental overwrites or missing dependencies.

Properly managed serialization ensures clean deployments, consistent environments, and fewer surprises during migration and beyond.

7. Forms & Submissions

In Sitecore XM Cloud, forms require careful planning to ensure smooth data capture and migration.

•  XM Cloud Forms (Webhook-based): Submit form data via webhooks to CRM, backend, or marketing platforms. Configure payloads properly and ensure validation, spam protection, and compliance.
• Third-Party Forms: HubSpot, Marketo, Salesforce, etc., can be integrated via APIs for advanced workflows, analytics, and CRM connectivity.
• Create New Forms: Rebuild forms with modern UX, accessibility, and responsive design.
• Migrate Old Submission Data: Extract and import previous form submissions into the new system or CRM, keeping field mapping and timestamps intact.
• Best Practices: Track submissions in analytics, test end-to-end, and make forms configurable for content authors.

This approach ensures new forms work seamlessly while historical data is preserved.

8. Personalization & Experimentation

Migrating personalization and experimentation requires careful planning to preserve engagement and insights.

• Export & Rebuild: Export existing rules, personas, and goals. Review them thoroughly and recreate only what aligns with current business requirements.
• A/B Testing: Identify active experiments, migrate if relevant, and rerun them in the new environment to validate performance.
• Sitecore Personalize Implementation:
  • Plan data flow into the CDP and configure event tracking.
  • Implement personalization via Sitecore Personalize Cloud or Engage SDK for xm cloud implementation, depending on requirements.

Best Practices:

• Ensure content authors can manage personalization rules and experiments without developer intervention.
• Test personalized experiences end-to-end and monitor KPIs post-migration.

A structured approach to personalization ensures targeted experiences, actionable insights, and a smooth transition to the new Sitecore environment.

9. Accessibility

Ensuring accessibility is essential for compliance, usability, and SEO.

• Follow WCAG standards: proper color contrast, semantic HTML, ARIA roles, and keyboard navigation.
• Validate content with accessibility tools and manual checks before migration cutover.
• Accessible components improve user experience for all audiences and reduce legal risk.

10. Performance, Caching & Lazy Loading

Optimizing performance is critical during a migration to ensure fast page loads, better user experience, and improved SEO.

• Caching Strategies:
  • Use Sitecore output caching and data caching for frequently accessed components.
  • Implement CDN caching for media assets to reduce server load and improve global performance.
  • Apply cache invalidation rules carefully to avoid stale content.
• Lazy Loading:
  • Load images, videos, and heavy components only when they enter the viewport.
  • Improves perceived page speed and reduces initial payload.
• Performance Best Practices:
  • Optimize images and media (WebP/AVIF).
  • Minimize JavaScript and CSS bundle size, and use tree-shaking where possible.
  • Monitor Core Web Vitals (LCP, CLS, FID) post-migration.
  • Test performance across devices and regions before go-live.
  • Content Author Consideration:
  • Ensure caching and lazy loading do not break dynamic components or personalization.
  • Provide guidance to authors on content that might impact performance (e.g., large images or embeds).

Proper caching and lazy loading ensure a fast, responsive, and scalable Sitecore experience, preserving SEO and user satisfaction after migration.

11. CI/CD, Monitoring & Automated Testing

A well-defined deployment and monitoring strategy ensures reliability, faster releases, and smooth migrations.

• CI/CD Pipelines:
  • Set up automated builds and deployments according to your hosting platform: Azure, Vercel, Netlify, or on-premise.
  • Ensure deployments promote items consistently across Dev → QA → UAT → Prod.
  • Include code linting, static analysis, and unit/integration tests in the pipeline.
• Monitoring & Alerts:
  • Track website uptime, server health, and performance metrics.
  • Configure timely alerts for downtime or abnormal behavior to prevent business impact.
• Automated Testing:
  • Implement end-to-end, regression, and smoke tests for different environments.
  • Include automated validation for content, forms, personalization, and integrations.
  • Integrate testing into CI/CD pipelines to catch issues early.
• Best Practices:
  • Ensure environment consistency to prevent drift.
  • Use logs and dashboards for real-time monitoring.
  • Align testing and deployment strategy with business-critical flows.

A robust CI/CD, monitoring, and automated testing strategy ensures reliable deployments, reduced downtime, and faster feedback cycles across all environments.

12. Governance, Licensing & Cutover

A successful migration is not just technical — it requires planning, training, and governance to ensure smooth adoption and compliance.

• License Validation: Compare the current Sitecore license with what the new setup requires. Ensure coverage for all modules, environments. Validate and provide accurate rights to users and roles.
• Content Author & Marketer Readiness:
  • Train teams on the new workflows, tools, and interface.
  • Provide documentation, demos, and sandbox environments to accelerate adoption.
• Backup & Disaster Recovery:
  • Plan regular backups and ensure recovery procedures are tested.
  • Define RTO (Recovery Time Objective) and RPO (Recovery Point Objective) for critical data.
• Workflow, Roles & Permissions:
  • Recreate workflows, roles, and permissions in the new environment.
  • Implement custom workflows if required.
  • Governance gaps can lead to compliance and security risks — audit thoroughly.
• Cutover & Post-Go-Live Support:
  • Plan the migration cutover carefully to minimize downtime.
  • Prepare a support plan for immediate issue resolution after go-live.
  • Monitor KPIs, SEO, forms, personalization, and integrations to ensure smooth operation.

Proper governance, training, and cutover planning ensures the new Sitecore environment is compliant, adopted by users, and fully operational from day one.

13. Training & Documentation

Proper training ensures smooth adoption and reduces post-migration support issues.

• Content Authors & Marketers: Train on new workflows, forms, personalization, and content editing.
• Developers & IT Teams: Provide guidance on deployment processes, CI/CD, and monitoring.
• Documentation: Maintain runbooks, SOPs, and troubleshooting guides for ongoing operations.
• Encourage hands-on sessions and sandbox practice to accelerate adoption.

Summary:

Sitecore migrations are complex, and success often depends on the small decisions made throughout development, performance tuning, SEO handling, and governance. This blog brings together practical approaches and lessons learned from real-world implementations — aiming to help teams build scalable, accessible, and future-ready Sitecore solutions.

While every project is different, the hope is that these shared practices offer a useful starting point for others navigating similar journeys. The Sitecore ecosystem continues to evolve, and so do the ways we build within it.

In today’s financial landscape, personalization is no longer a luxury; it’s a customer expectation. Yet, according to Adobe’s latest State of Customer Experience in Financial Services in an AI-Driven World report, only 36% of the customer journey is currently personalized, despite 74% of financial services executives acknowledging that their customers expect tailored interactions.

This gap isn’t just a missed opportunity; it’s a trust breaker.

Why Personalization Matters More Than Ever

Financial decisions are deeply personal. Whether a customer is exploring mortgage options, planning for retirement, or managing small business finances, they expect advice and experiences that reflect their unique goals and life stage. Generic nudges and one-size-fits-all messaging simply don’t cut it anymore.

Early-stage interactions—like product discovery or financial education—are especially critical. These are high-value moments where relevance builds trust and guides decision-making. Yet many institutions fall short, lacking the orchestration needed to deliver personalized engagement across these initial touchpoints.

What’s Holding Institutions Back?

The report highlights several barriers:

• Fragmented data systems that prevent a unified view of the customer
• Legacy operating models that prioritize product silos over customer journeys
• Compliance concerns that limit personalization efforts, even when customers expect it

These challenges are compounded by the rise of AI-powered experiences, which demand real-time, context-aware personalization across channels.

The Path Forward: Adaptive, Lifecycle Personalization

To close the gap, financial institutions must evolve from episodic personalization to adaptive, full-lifecycle engagement. That means:

• Investing in unified customer profiles and behavioral insights
• Building real-time content engines that respond to customer signals
• Designing personalization strategies that grow with the relationship and not just the transaction

Download the full Adobe report to explore the top 10 insights shaping the future of financial services, and discover how your organization can lead with intelligence, responsibility, and trust.

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 services organizations unify data, orchestrate journeys, and deliver customer-centric experiences that build trust and drive growth.

Get in Touch With Our Experts

Perficient is proud to be included in The Organizational Change Management Services Landscape, Q4 2025 by Forrester. We believe this recognition reflects our continued momentum in helping enterprises navigate complexity, accelerate transformation, and deliver measurable outcomes through strategic change. 

In the report, Perficient is listed as a consultancy with a geographic focus across North America, EMEA, APAC, and LATAM, with an industry focus in financial services, healthcare, and utilities.  

Forrester asked each provider included in the Landscape to select the top business scenarios for which clients select them and from there determined which are the extended business scenarios that highlight differentiation among the providers.  Perficient is shown in the report for having selected extended business scenarios including Declining Performance, Process Improvement/Engineering, and Volatility as top reasons clients work with them out of those business scenarios. Perficient believes these are areas where change is not optional, but essential. 

Built to Lead with Change 

Forrester defines organizational change management (OCM) as: 

“A method that companies use to evolve their capabilities via people, process, and technology changes. OCM’s success rests on the organization’s ability to continuously sense and respond to changing requirements in order to generate the scale of change at strategic, operational, and leadership levels.” (The Organizational Change Management Landscape, Q4 2025, Forrester) 

This aligns with our approach at Perficient. We integrate strategy, execution, and innovation to help clients build adaptive organizations that thrive amid disruption. Our change strategies are designed to scale, sustain, and deliver impact across the enterprise. 

AI Is Reshaping Change—and How It’s Managed 

AI is transforming how work gets done and how change is delivered. At Perficient, we take an AI-first approach to change. We’ve helped clients launch AI-powered research platforms, deploy virtual assistants for over 30,000 employees, and define governance frameworks to accelerate the responsible adoption of AI. Our proprietary platform, Envision, connects strategy to execution. Using intelligent tools to assess readiness, identify capability gaps, and prioritize high-impact initiatives. 

We also use AI to streamline how we deliver change. From avatar-based training to multilingual narration, our team leverages AI to move faster and reach broader audiences with precision. 

Human-Centered, Outcome-Focused 

Change fatigue is real. So is the cost of poor execution. That’s why our approach is grounded in empathy, data, and measurable outcomes.  

We help leaders prepare their people to lead in an AI-powered world by: 

• Aligning change strategy with business ambition 

• Assessing impact and readiness across the organization 

• Designing enablement programs that drive adoption 

• Engaging stakeholders with clarity and purpose 

• Communicating change with precision and relevance 

Whether you’re facing declining performance, reengineering processes, or navigating volatility, Perficient is ready to help you lead through change, with purpose and precision. 

Explore the full report to see how we believe Perficient and other providers are shaping the future of organizational change: The Organizational Change Management Services Landscape, Q4 2025

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here . 

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.

Introduction:

Custom code in Talend offers a powerful way to enhance batch processing efficiently by allowing developers to implement specialized logic that is not available through Talend’s standard components. This can involve data transformations, custom code as per use case and integration with flat files as per specific project needs. By leveraging custom code, users can optimize performance, improve data quality, and streamline complex batch workflows within their Talend jobs.

Talend Components:

Key components for batch processing as mention below:

• tDBConnection: Establish and manage database connections within a job & allow configuration with single connection to reuse within Talend job.
• tFileInputDelimited: For reading data from flat files.
• tFileRowCount: Reads file row by row to calculate the number of rows.
• tLoop: Executes a task automatically, based on a loop size.
• tHashInput, tHashOutput: For high-speed data transfer and processing within a job. tHashOutput writes data to cache memory, while tHashInput reads from that cached data.
• tFilterRow: For filtering rows from a dataset based on specified.
• tMap: Data transformation allows you to map input data with output data and enables you to perform data filtering, complex data manipulation, typecasting, and multiple input source joins.
• tJavaRow: It can be used as an intermediate component, and we are able to access the input flow and transform the data using custom Java code.
• tJava: It has no input or output data flow & can be used independently to Integrate custom Java code.
• tPreJob, tPostJob: PreJob start the execution before the job & PostJob at the end of the job.
• tDBOutput: Supports wide range of databases & used to write data to various databases.
• tDBCommit:It retains and verifies the alterations applied to a connected database throughout a Talend job, guaranteeing that it permanently records the data changes.
• tDBClose:  It explicitly close a database connection that was opened by a tDBConnection component.
• tLogCatcher: It is used in error handling within Talend job for adding runtime logging information. It catches all the exceptions and warnings raised by tWarn and tDie components during Talend job execution.
• tLogRow: It is employed in error handling to display data or keep track of processed data in the run console.
• tDie: We can stop the job execution explicitly if it fails. In addition, we can create a customized warning message and exit code.

Workflow with example:

To process the bulk of data in Talend, we can implement batch processing to efficiently process flat file data within a minimal execution time. We can read the flat file data & after the execution, we can process it to insert it into MySQL database table as a target & we can achieve this without batch processing. But this data flow will take quite a longer time to execute. If we use batch processing using the custom code, it takes minimal execution time to write the entire source file data into batch of records into MySQL database table at the target location.

Talend Job Design 

Solution:

• Establish the database connection at the start of the execution so that we can reuse.
• Read the number of rows in the source flat file using tFileRowCount component.
• To determine the batch size, subtract the header count from the total row count and then divide the number by the total batch size. Take the whole number nearby which indicates the total number of batch or chunk.
  

  Calculate the batch size from total row count

• Now use tFileInputDelimited component to read the source file content. In the tMap component, utilize the sequence Talend function to generate row numbers for your data mapping and transformation tasks. Then, load all of the data into the tHashOutput component, which stores the data into a cache.
• Iterate the loop based on the calculated whole number using tLoop
• Retrieve all the data from tHashInput component.
• Filter the dataset retrieved from tHashInput component based on the rowNo column in the schema using tFilterRow

Filter the dataset using tFilterRow

• If First Iteration is in progress & batch size is 100 then rowNo range will be as 1 to 100.
  If Third Iteration is in progress & batch size is 100 then rowNo range will be as 201 to 300.
  For example, if the value of current iteration is 3 then [(3-1=2)* 100]+1 = 201 & [3*100=300]. So final dataset range for the 3^rd iteration will be 201 to 300.
• Finally extract the dataset range between the rowNo column & write the batch data MySQL database table using tDBOutput
• The system uses the tLogCatcher component for error management by capturing runtime logging details, including warning or exception messages, and employs tLogRow to display the information in the execution console.
• Regarding performance tuning, tMap component that maps source data to output data, allows for complex data transformation, and offers unique join, first join, and all other join options for looking up data within the tMap component.
• The temporary data that the tHashInput & tHashOutput components store in cache memory enhances runtime performance.
• At the end of the job execution, we are committing the database modification & closing the connection to release the database resource.

Advantages of Batch Processing:

• Batch processing can efficiently handle large datasets.
• It takes minimal time to process the data even after data transformation.
• By grouping records from a large dataset and processing them as a single unit, it can be highly beneficial for improving performance.
• With the batch processing, it can easily scale to accommodate growing data volumes.
• It is particularly useful for operations like generating reports, performing data integration, and executing complex transformations on large datasets.

For more details: Get-started-talend-open-studio-data-integration

Note: Efficient Batch Processing in Talend-Part 1

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. 

Subscribe Where You Listen

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

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.