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.

.

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.

Introduction

In this blog, we’ll explore how to connect OpenAI’s API with a Spring Boot application, step by step.

We’ll cover the setup process, walk through the implementation with a practical example.

By integrating OpenAI with Spring Boot, you can create solutions that are not only powerful but also scalable and reliable.

Prerequisites

• Java 17+
• Maven
• Spring Boot (3.x recommended)
• OpenAI API Key (get it from platform.openai.com)
• Basic knowledge of REST APIs

OpenAI’s platform helps developers to understand how to prompt a models to generate meaningful text. It’s basically a cheat sheet for how to communicate to AI so it gives you smart and useful answers by providing prompts. 

Implementation in Spring Boot

To integrate OpenAI’s GPT-4o-mini model into a Spring Boot application, we analyzed the structure of a typical curl request and response provided by OpenAI.

API docs reference:

https://platform.openai.com/docs/overview

https://docs.spring.io/spring-boot/index.html

Curl Request

<html>
curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {"role": "assistant", "content": "Hello"},
      {"role": "user", "content": "Hi"}
    ]
  }'
</html>

Note-

“role”: “user” – Represents the end-user interacting with the assistant

“role”: “assistant” – Represents the assistant’s response.

The response generated from the model and it looks like this:

{
  "id": "chatcmpl-B9MBs8CjcvOU2jLn4n570S5qMJKcT",
  "object": "chat.completion",
  "created": 1741569952,
  "model": "gpt-4o-mini-2025-04-14",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I assist you today?",
        "refusal": null,
        "annotations": []
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 19,
    "completion_tokens": 10,
    "total_tokens": 29,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "service_tier": "default"
}

Controller Class:

In below snippet, we will explore a simple spring boot controller to interact with Open AI’s API. When end user sends a prompt to that url (e.g /bot/chat?prompt=what is spring boot), the controller reads the model name and API url from applocation.properties file. It then creates a request using prompt provided and sends it to Open AI using rest call(RestTemplate). After verifying the request, OpenAI sends back a response.

@RestController
@RequestMapping("/bot")
public class GenAiController {

    @Value("${openai.model}")
    private String model;

    @Value(("${openai.api.url}"))
    private String apiURL;

    @Autowired
    private RestTemplate template;

    @GetMapping("/chat")
    public String chat(@RequestParam("prompt") String prompt) {
        GenAiRequest request = new GenAiRequest(model, prompt);
        System.out.println("Request: " + request );
        GenAIResponse genAIResponse = template.postForObject(apiURL, request, GenAIResponse.class);
        return genAIResponse.getChoices().get(0).getMessage().getContent();
    }

Configuration Class:

Annotated with @Configuration, this class defines beans and settings for the application context. Pulling the Open API key from properties file and the a customized RestTemplate is created and configured to include the Authorization Bearer <API_KEY> header in all requests. This setup ensures that every call to OpenAI’s API is authenticated without manually adding headers in each request.

@Configuration
public class OpenAIAPIConfiguration {

    @Value("${openai.api.key}")
     private String openaiApiKey;

    @Bean
    public RestTemplate template(){
        RestTemplate restTemplate=new RestTemplate();
        restTemplate.getInterceptors().add((request, body, execution) -> {
            request.getHeaders().add("Authorization", "Bearer " + openaiApiKey);
            return execution.execute(request, body);
        });
        return restTemplate;
    }
    
}

Require getters and setters for request and response classes:

Based on the Curl structure and response, we generated the corresponding request and response java classes with appropriate getters and setters with selected attributes to repsesent request and response object. These getter/setter classes help turn JSON data into objects we can use in code, and also turn our code’s data back into JSON when interacting to the OpenAI API. We implemented a bot using the gpt-4o-mini model, integrating it with a REST controller and also handled the authentication via the API key.

//Request
@Data
public class GenAiRequest {

    private String model;
    private List<GenAIMessage> messages;

    public List<GenAIMessage> getMessages() {
        return messages;
    }

    public GenAiRequest(String model, String prompt) {
        this.model = model;
        this.messages = new ArrayList<>();
        this.messages.add(new GenAIMessage("user",prompt));
    }
}

@Data
@AllArgsConstructor
@NoArgsConstructor
public class GenAIMessage {

    private String role;
    private String content;   
    
    public String getContent() {
        return content;
    }
    public void setContent(String content) {
        this.content = content;
    }
}

//Response
@Data
@AllArgsConstructor
@NoArgsConstructor
public class GenAIResponse {

    private List<Choice> choices;

    public List<Choice> getChoices() {
        return choices;
    }

    @Data
    @AllArgsConstructor
    @NoArgsConstructor
    public static class Choice {

        private int index;
        private GenAIMessage message;
        public GenAIMessage getMessage() {
            return message;
        }
        public void setMessage(GenAIMessage message) {
            this.message = message;
        }

    }

}

Essential Configuration for OpenAI Integration in Spring Boot

To connect your Spring Boot application with OpenAI’s API, you need to define a few key properties in your application.properties or application.yml file:

• server.port: Specifies the port on which your Spring Boot application will run. You can set it to any available port like 8080, 9090, etc. (The default port for a Spring Boot application is 8080)
• openai.model: Defines the OpenAI model to be used. In this case, gpt-4o-mini is selected for lightweight and efficient responses.
• openai.api.key: Your secret API key from OpenAI. This is used to authenticate requests. Make sure to keep it secure and never expose it publicly.
• openai.api.url: The endpoint URL for OpenAI’s chat completion API. (This is where your application sends prompts and receives responses)

server.port=<add server port>
openai.model=gpt-4o-mini
openai.api.key=	XXXXXXXXXXXXXXXXXXXXXXXXXXXX
openai.api.url=https://api.openai.com/v1/chat/completions

Postman Collection:

GET API: http://localhost:<port>/bot/chat?prompt=What is spring boot used for ?

Content-Type: application/json

Usage of Spring Boot + OpenAI Integration

• AI-Powered Chatbots: Build intelligent assistants for customer support, internal helpdesks, or onboarding systems.
• Content Generation Tools: Automate blog writing, email drafting, product descriptions, or documentation, generate personalized content based on user input.
• Code Assistance & Review: Create tools that help developers write, refactor, or review code using AI, Integrate with IDEs or CI/CD pipelines for smart suggestions.
• Data Analysis & Insights: Use AI to interpret data, generate summaries, answer questions about datasets combine with Spring Boot APIs to serve insights to dashboards or reports.
• Search Enhancement: Implement semantic search or question-answering systems over documents or databases, use embeddings and GPT to improve relevance and accuracy.
• Learning & Training Platforms: Provide personalized tutoring, quizzes, and explanations using AI & adapt content based on user performance and feedback.
• Email & Communication Automation: Draft, summarize, or translate emails and messages, integrate with enterprise communication tools.
• Custom usages: In a business-to-business context, usage can be customized according to specific client requirements.

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

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

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

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

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

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

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

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

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

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

Subscribe Where You Listen

APPLE PODCASTS | SPOTIFY | AMAZON MUSIC | OTHER PLATFORMS 

Watch Full Video Episodes on YouTube

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Final Thoughts 

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

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

Balancing AI Strategy With Human Wisdom 

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

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

When (and When Not) to Automate 

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

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

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

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

Who sets the goals? Who ensures alignment?  

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

Smarter AI Strategies, Not Just More AI 

Susan’s key takeaway is clear:  

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

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

Keep People at the Center of Your AI Strategy 

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

Listen to the full conversation

Subscribe Where You Listen

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

Meet our Guest – Susan Etlinger

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

Follow Susan on LinkedIn  

Learn More about Susan Etlinger

Meet our Host

Jim Hertzfeld is Area Vice President, Strategy for Perficient.

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

Connect with Jim:

LinkedIn | Perficient

The world of digital experiences is evolving more quickly than ever before, and let’s be honest, artificial intelligence (AI) is more than just a trendy term these days. It’s becoming a business necessity.

AI is no longer a “nice to have” for companies that use Sitecore as their Digital Experience Platform (DXP). It’s turning into the difference between falling behind and meeting customer expectations.

In this blog, we’ll explore:

• How AI is transforming Sitecore
• Current AI tools and integrations
• Real-world use cases
• What’s next for Sitecore and AI

Let’s get started!

Why AI Matters in Sitecore Digital Experience Platform?

Sitecore has long been known for managing content, personalization, commerce, and customer data at scale.

However, as digital complexity grows, traditional rule-based systems start to struggle, especially when:

• Audience segments are too granular
• Data is too vast to process manually
• Real-time personalization is required

This is where AI makes a real difference.

Fact Check:

Gartner’s 2024 Magic Quadrant for DXP reports:

“AI-based personalization increases customer engagement compared to rule-based systems.”

Source: Gartner DXP Report 2024

Current AI Capabilities in Sitecore

Sitecore Stream: Enterprise-Grade AI Across the Stack

Sitecore Stream, launched in 2024–2025, is Sitecore’s newest AI-powered platform.

It brings smart, brand-aware copilots, automated workflows, and secure content management – all designed to help teams work faster and deliver better digital experiences.

Key Capabilities:

• Brand-Aware AI: Upload brand guidelines and style references so AI generates only on-brand content
• AI Copilots: Assist in writing, summarizing, ideating content, and setting up campaigns directly inside Experience Hub and Content Hub
• Agentic Workflows: Multi-step campaign orchestration with autonomous task execution (e.g., campaign brief → draft → assign → publish)
• Grounded AI via RAG (Retrieval-Augmented Generation) on Azure OpenAI for enterprise-grade security and control

All thanks to Mahima Patel for laying out this detailed overview in her excellent blog post “Why AI-Led Experiences Are the Future – And How Sitecore Stream Delivers Them”.

Sitecore Stream brings AI capabilities to Sitecore products, transforming how marketers work in today’s fast-paced digital landscape.

Source: Sitecore Stream

Sitecore Personalize: AI-Powered Real-Time Personalization

Sitecore Personalize leverages advanced AI and machine learning to deliver real-time individualized experiences across channels.

Key Features:

• AI-driven experimentation (A/B & multivariate testing)
• Predictive personalization using behavioral data
• Real-time decisioning & context-aware content delivery
• Built-in Code Assistant (2025): Helps non-technical users write JavaScript/SQL snippets for:
  • Personalization conditions
  • Session traits
  • Audience exports
  • Experiment logic

Sitecore Personalize uses AI/ML models to predict visitor actions based on historical data and real-time interactions.

Sources: Sitecore Documentation – Personalize AI Models & Dylan Young Blog – First Look at Sitecore Personalize Code Assistant (2025)

Sitecore Content Hub: AI-Generated Content

Sitecore Content Hub integrates directly with OpenAI (ChatGPT) and other generative AI providers.

This streamlines content creation, editing, and distribution workflows.

Use Cases:

• Automated content drafts for blogs, emails, and campaigns
• Product descriptions and metadata generation
• SEO-focused content suggestions
• Social media copywriting
• Translation assistant (2025): Auto-translates components/pages using AI

In 2023, Sitecore announced direct integration with generative AI for Content Hub.

Sources: Sitecore Press Release – AI & Content Hub Integration (2023)

AI-Powered Search & Recommendations

Sitecore partners with Coveo, SearchStax, and Azure Cognitive Search to offer intelligent, personalized discovery experiences across websites and commerce platforms.

• Semantic search using NLP
• AI-powered relevance tuning based on user behavior
• Personalized recommendations for content, products, and CTAs
• Predictive search and autocomplete

Coveo for Sitecore uses machine learning to adjust search relevance automatically based on user behavior.

Sources: Coveo for Sitecore Documentation – Get started with Coveo Machine Learning in Sitecore

AI in Sitecore XM Cloud: The SaaS Evolution (2025)

Sitecore XM Cloud is evolving fast and AI is at the heart of it.

Whether you’re building pages or analyzing performance, AI helps you work smarter, not harder.

• Suggest Page Layouts: Get smart layout ideas while editing pages, based on your goals.
• Improve Components: AI recommends tweaks to improve SEO, conversions, or accessibility.
• Predict What Works: Built-in insights tell you how your content is performing—and what to test next.
• Help Developers Too: From faster component setup to AI-generated code and test helpers, dev’s get a boost too.

These features are part of Sitecore’s ongoing investment in AI, highlighted at Sitecore Symposium 2024 and expanded throughout 2025 with Sitecore Stream.

Sources: Sitecore Symposium Keynote 2024 – Roadmap XM Cloud Developer Experience

Generative AI for Sitecore Development Teams

AI isn’t just for marketers – it’s transforming Sitecore development workflows too.

Developer Use Cases:

• AI-assisted code generation & scaffolding
• Automated testing with Copilot & ChatGPT plugins
• AI-based Sitecore log summarization

Challenges & Considerations

AI in Sitecore brings opportunities – but also some challenges:

• Data Privacy: GDPR, CCPA compliance is crucial
• Bias in AI Models: Requires careful monitoring
• Integration Complexity: AI tools need thoughtful orchestration
• Vendor Lock-In: Cloud service dependencies (OpenAI, Azure, Coveo)

What’s Next? The Future of AI in Sitecore

Here’s what’s coming in the next wave of Sitecore AI innovation:

• AI-based Content Performance Prediction
• AI-driven Brand Compliance & Tone Checking
• Conversational Interfaces for Commerce (ChatGPT Plugins)
• Hyper-Personalization via AI CDP (Customer Data Platform)

Conclusion

AI is no longer a “nice-to-have” in Sitecore – it’s essential.

From content creation to personalization and commerce optimization, AI is enhancing every layer of the Sitecore ecosystem.

If you’re in Sitecore development, marketing, or digital strategy, now is the time to embrace AI to future-proof your digital experiences.

References & Further Reading

• Sitecore Personalize Documentation
• Sitecore Content Hub AI Integration Announcement
• Coveo for Sitecore AI Search
• Gartner Magic Quadrant for DXP 2024
• Sitecore Symposium 2024 Keynote Highlights
• Sitecore Stream
• Dylan Young Blog – Sitecore Personalize Code Assistant

Customer experience (CX) continues to be a defining factor in business success. In a digital-first world, even a single poor interaction can drive customers to competitors, contributing to an estimated $1.6 trillion in annual losses in the U.S. alone. On the other hand, exceptional omnichannel experiences build trust, deepen loyalty, and turn customers into lifelong advocates.

Perficient included in IDC Market Glance: Customer Experience Services, 2025

We’re proud to share that Perficient has once again been included in the category of IT Services Providers in the IDC Market Glance: Customer Experience Services, 2Q25 report (doc #US52469525, June 2025).

According to IDC, “Agentic AI and GenAI are working their way into marketing and sales technologies and services, beginning with a pragmatic focus on automating, improving and scaling existing business processes and offerings. New AI-based business models have yet to emerge, but AI is already putting existing CX services under pressure to change.”

Embracing an AI-First Future

As part of our AI-first company mission, Perficient is committed to helping organizations harness the power of artificial intelligence to revolutionize customer experiences. From the use of generative AI in content creation and virtual agents, to intelligent automation and predictive analytics, we’re enabling businesses to unlock new levels of personalization, efficiency, and growth.

Strategy Meets Innovation

Our strategists use Journey Science, a core component of our Envision Framework, to help clients identify opportunities, define a customer-centric vision, and build a prioritized roadmap for transformation. This approach ensures that every touchpoint is optimized to deliver seamless, personalized, and measurable experiences.

Operationalizing CX with Data and AI

The future of CX is rooted in customer obsession—and we help you execute that vision. By combining deep customer insights with AI-powered tools and data-driven strategies, we enable organizations to deliver extraordinary value at every stage of the customer journey.

Ready to elevate your customer experience strategy? Explore how Perficient’s AI-first approach and CX expertise can help you drive measurable results: Customer Experience + Digital Marketing Services | Perficient

In today’s fast-paced digital landscape, businesses often find themselves in a peculiar situation: they’re brimming with valuable data, specialized tools, and incredibly talented teams, yet connecting these pieces efficiently can feel like navigating a complex maze. Individual departments might develop ingenious solutions, but these often remain isolated, creating fragmented processes and slowing down the adoption of true innovation.

This isn’t just about finding information; it’s about action. Imagine trying to streamline a critical business process, only to realize the necessary data is scattered across multiple systems, or the expert who knows how to unlock its full potential are not available. This fragmentation isn’t just inefficient; it’s a productivity bottleneck that prevents organizations from truly leveraging the power of AI.

The Power of Protocol: Connecting AI to Your Business Needs

At Perficient, we’re building a future where AI isn’t just a collection of disparate tools, but a unified, intelligent ecosystem that seamlessly integrates with your existing operations. We’re doing this by embracing a powerful concept: standardized protocols for AI applications, particularly the MCP (Model Context Protocol).

Think of it like the USB-C port for AI. Just as USB-C provides a universal way to connect devices, we’re advocating for a standardized method that allows AI models to effortlessly “plug into” diverse data sources, specialized tools, and even other advanced AI capabilities. This eliminates the traditional hurdles of custom integrations and siloed solutions, opening up a world of possibilities.

This approach means that highly specialized AI solutions can be developed, managed, and then easily integrated into a broader intelligent framework. Imagine a sales enablement AI that can instantly tap into your extensive product documentation, personalized customer histories, and market insights to generate hyper-relevant proposals. Or consider a project management AI that guides your teams through best practices, automatically surfaces relevant resources, and even identifies the ideal subject matter expert for a tricky client challenge – all while adhering to your company’s unique workflows.

Intelligent Automation: From Data to Dynamic Action

What truly sets this approach apart is the capability for these integrated AI solutions to not just provide information, but to orchestrate and execute intelligent automation. This means your AI can go beyond answering questions; it can trigger sophisticated, multi-step processes. Need to perform deep market research that requires gathering data from various internal reports, external databases, and even analyzing competitive landscapes? A well-integrated AI can orchestrate that entire process, delivering a synthesized analysis or even taking proactive steps, like scheduling follow-up meetings based on its findings.

This is where AI transitions from a helpful assistant to a true force multiplier for your business. It allows you to automate complex workflows, streamline decision-making, and unlock new levels of efficiency.

Driving Unified Innovation for Your Business

At Perficient, our internal Generative AI system, Scarlett, is a testament to this vision. Scarlett acts as a unified front-end, demonstrating how a standardized approach to AI can centralize efforts, streamline access to specialized intelligence, and accelerate solution adoption across diverse teams. This internal capability reflects the same principles we apply to help our clients achieve:

• Atomization of Effort: Break down large, complex problems into manageable, AI-powered components that can be rapidly developed and deployed.
• Centralized Access & Governance: Provide a single, intuitive interface for all your AI-driven capabilities, ensuring consistency, security, and scalability.
• Accelerated Progress & Adoption: When AI is easy to access and seamlessly integrated, your teams can adopt new solutions faster, democratize specialized knowledge, and drive innovation across the entire organization.

Ultimately, this unified approach to AI is about empowering your business to operate smarter, faster, and more cohesively. It’s about transforming how you deliver value to your customers and achieve your strategic objectives in an increasingly AI-driven world.

In Adobe’s 2025 AI and Digital Trends report, one message rings loud and clear: the convergence of marketing and IT is essential to digital success. As AI becomes increasingly embedded in customer experience strategies, marketing and IT leaders must collaborate closely to unlock its full potential.

The Rise of Agentic AI

One of the most transformative ideas in the report is the rise of agentic AI, autonomous systems that collaborate across platforms to deliver hyper-personalized, real-time experiences. For marketing and IT leaders, this represents a major shift. These aren’t just tools, but strategic partners capable of transforming how content is created, optimized, and delivered at scale.

This shift is already being realized in the field, as industry leaders begin to harness the power of Agentic AI to streamline operations and enhance customer outcomes. For example, Perficient’s Adobe services delivery team is leveraging the technology to make AEM as a Cloud Service migrations faster.

“The emergence of Agentic AI is revolutionizing our service delivery, delivering significant time and effort savings for our customers. Take the move to AEM Cloud, for instance. We’re leveraging agents to handle tasks like code remediation and complex mapping for advanced content migrations into AEM,” says Robert Sumner, principal in Perficient’s Adobe practice.

As organizations explore these capabilities, the collaboration between marketing and IT becomes even more critical. IT leaders must ensure the infrastructure is in place to support real-time data flow and AI orchestration, while marketers must rethink workflows to fully leverage AI’s creative and analytical potential.

Nearly two-thirds (65%) of senior executives identify leveraging AI and predictive analytics as primary contributors to growth in 2025. (Adobe 2025 Digital Trends report)

Seamless, Personalized Experiences

The report also highlights a growing emphasis on predictive analytics. Businesses are moving beyond reactive strategies to proactively anticipate customer needs. This shift is enabling more meaningful, real-time interactions across the customer journey.

For marketing leaders, this means moving beyond historical performance metrics to real-time, forward-looking insights. For IT, it means ensuring the data infrastructure is robust, integrated, and accessible across teams. However, many organizations still struggle with siloed systems and fragmented data, which hinder their ability to deliver seamless experiences.

Ross Monaghan, principal in Perficient’s Adobe practice, underscores this point by highlighting how leading organizations are turning data into action through AI-powered strategies.

“We don’t have a shortage of data. The challenge lies in consuming it, unifying it, and activating it in real time to deliver personalized, one-to-one experiences that meet the demands of consumers. Organizations that are winning are already leveraging predictive bidding, dynamic budget allocation, and channel mix modelling to optimize media buying. They’re also developing AI-driven audience clusters based on behavior, intent, or psychographics to improve targeting precision. By training models on past customer behavior, they can predict which leads are most likely to convert. The possibilities are endless if you embrace the power of what AI can bring to the table. AI isn’t here to replace us, it’s here to eliminate the thousands of hours spent on manual tasks, chasing insights that may or may not exist in fragmented data,” says Monaghan.

Efficient, accurate, and personalized experiences all start with secure and reliable data. Learn More

Breaking Down Silos to Boost Adoption

The customer journey is increasingly owned by a mix of marketers, technologists, and CX specialists. And while this diversity brings valuable perspectives, it can also lead to friction and inefficiencies. Marketing needs IT to deliver scalable, secure, and flexible platforms. IT needs marketing to define the customer vision and drive innovation. The report suggests that aligning these teams around shared goals and metrics is essential for success and the ability to deliver seamless, personalized experiences.

Monaghan says, “Organizations must continue to view everything through the lens of the customer. That means breaking down internal silos, politics, and bureaucracy. Customers don’t care who owns the data or whether it sits with IT or marketing. What matters is that we come together to identify key audiences, mapping core personas and journeys, and developing dynamic use cases that guide each user through a personalized experience. We then can analyze the effectiveness of content, offers, and sentiment to drive customer lifetime value at scale.”

Ensuring Responsible AI Deployment

The report also addresses the ethical dimensions of AI. Issues like data privacy, algorithmic bias, and transparency are top of mind for both marketing and IT leaders. Building trust with customers requires more than compliance. It demands intentional governance. IT leaders must implement frameworks for responsible AI use, while marketers must ensure that personalization efforts respect user boundaries and values.

The Time to Act is Now

Adobe’s 2025 report is both a roadmap and a call to action for marketing and IT leaders. The future belongs to organizations that can harness AI not just as a tool, but as a strategic enabler, bridging creativity and technology to deliver exceptional customer experiences.

In Part 1 we’ve talked about why we urgently need to make sure AI is used responsibly and has clear rules. We looked at the real dangers of AI that isn’t checked, like how it can make existing biases worse, invade our privacy, create tricky legal problems around who owns what, and slowly make people lose trust. It’s pretty clear: if we don’t handle the amazing power of Generative AI carefully and proactively, it could easily go off track and cause a lot of harm instead of bringing good things. 

But just pointing out the problems isn’t enough. The next important step is to figure out how we can actually deal with these challenges. How do we go from knowing why to actually doing something? This is where the idea of Responsible AI becomes not just a theory, but something we absolutely must put into practice. To build a future where AI helps humanity achieve its best, we need to design it carefully, manage it well, and keep a close eye on it all the time. 

How Do We Implement Responsible AI? A Blueprint for Action 

The challenges are formidable, but so too is the potential of Generative AI to benefit humanity. To realize this potential responsibly, we cannot afford to let innovation outpace governance. We need a concerted, collaborative effort involving governments, industry, academia, civil society, and the public. Here’s a blueprint for action: 

1. Ethical Principles as a Guiding Star

Every stage of AI development and deployment must be anchored by strong ethical principles. These principles should include: 

• Fairness: Ensuring AI systems do not perpetuate or amplify biases and treat all individuals and groups equitably. This means actively identifying and mitigating discriminatory outcomes. 

• Accountability: Establishing clear lines of responsibility for AI system actions and outcomes, allowing for redress when harm occurs. Someone, or some entity, must always be answerable. 

• Transparency & Explainability: Designing AI systems that are understandable in their operation and provide insights into their decision-making processes, especially in high-stakes applications. The “black box” needs to become a glass box. 

• Privacy & Security: Protecting personal data throughout the AI lifecycle and safeguarding systems from malicious attacks. Data must be handled with the utmost care and integrity. 

• Safety & Reliability: Ensuring AI systems operate dependably, predictably, and without causing unintended harm. They must be robust and resilient. 

• Human Oversight & Control: Maintaining meaningful human control over AI systems, especially in critical decision-making contexts. The ultimate decision-making power must remain with humans. 

These principles shouldn’t just be abstract concepts; they need to be translated into actionable guidelines and best practices that developers, deployers, and users can understand and apply. 

2. Prioritizing Data Quality and Governance

The adage “garbage in, garbage out” has never been more relevant than with AI. Responsible AI begins with meticulously curated and ethically sourced data. This means: 

• Diverse and Representative Datasets: Actively working to build datasets that accurately reflect the diversity of the world, reducing the risk of bias. This is a continuous effort, not a one-time fix. 

• Data Auditing: Regularly auditing training data for biases, inaccuracies, and sensitive information. This proactive step helps catch problems before they propagate. 

• Robust Data Governance: Implementing clear policies and procedures for data collection, storage, processing, and usage, ensuring compliance with privacy regulations. This builds a strong foundation of trust. 

• Synthetic Data Generation: Exploring the use of high-quality synthetic data where appropriate to mitigate privacy risks and diversify datasets, offering a privacy-preserving alternative. 

3. Emphasizing Transparency and Explainability 

The “black box” nature of many advanced AI models is a significant hurdle to responsible deployment. We need to push for: 

• Model Documentation: Comprehensive documentation of AI models, including their intended purpose, training data characteristics, known limitations, and performance metrics. This is akin to an engineering blueprint for AI. 

• Explainable AI (XAI) Techniques: Developing and integrating methods that allow humans to understand the reasoning behind AI decisions, rather than just observing the output. This is crucial for debugging, auditing, and building confidence. 

• “AI Nutrition Labels”: Standardized disclosures that provide users with clear, understandable information about an AI system’s capabilities, limitations, and data usage. Just as we read food labels, we should understand our AI. 

4. Upholding Consent and Compliance

In a world increasingly interacting with AI, respecting individual autonomy is paramount. This means: 

• Informed Consent: Obtaining clear, informed consent from individuals when their data is used to train AI models, particularly for sensitive applications. Consent must be truly informed, not buried in legalese. 

• Adherence to Regulations: Rigorous compliance with existing and emerging data protection and AI-specific regulations (e.g., GDPR, EU AI Act, and future national laws). Compliance is non-negotiable. 

• User Rights: Empowering users with rights regarding their data used by AI systems, including the right to access, correct, and delete their information. Users should have agency over their digital footprint. 

5. Continuous Monitoring and Improvement

Responsible AI is not a one-time achievement; it’s an ongoing process. The dynamic nature of AI models and the evolving world they operate in demand constant vigilance. This requires: 

• Post-Deployment Monitoring: Continuously monitoring AI systems in real-world environments for performance degradation, emergent biases, unintended consequences, and security vulnerabilities. AI systems are not static. 

• Feedback Loops: Establishing mechanisms for users and stakeholders to provide feedback on AI system performance and identify issues. Their real-world experiences are invaluable. 

• Iterative Development: Adopting an agile, iterative approach to AI development that allows for rapid identification and remediation of problems based on monitoring and feedback. 

• Performance Audits: Regular, independent audits of AI systems to assess their adherence to ethical principles and regulatory requirements. External validation builds greater trust. 

6. Maintaining Human in the Loop (HITL) 

While AI is powerful, human judgment and oversight remain indispensable, especially for high-stakes decisions. This involves: 

• Meaningful Human Review: Designing AI systems where critical decisions are reviewed or approved by humans, particularly in areas like medical diagnosis, judicial rulings, or autonomous weapon systems. Human oversight is the ultimate safeguard. 

• Human-AI Collaboration: Fostering systems where AI augments human capabilities rather than replacing them entirely, allowing humans to leverage AI insights while retaining ultimate control. It’s about synergy, not substitution. 

• Training and Education: Equipping individuals with the skills and knowledge to effectively interact with and oversee AI systems. An AI-literate workforce is essential for responsible deployment. 

Conclusion: A Collaborative Future for AI 

The implementation of responsible AI is a grand, multifaceted challenge, demanding nothing short of global cooperation and a shared commitment to ethical development. While regional efforts like the EU AI Act are commendable first steps, a truly effective framework will require international dialogues, harmonized principles, and mechanisms for interoperability to avoid a fragmented regulatory landscape that stifles innovation or creates regulatory arbitrage. 

The goal is not to stifle the incredible innovation that Generative AI offers, but to channel it responsibly, ensuring it serves humanity’s highest aspirations. By embedding ethical principles from conception to deployment, by prioritizing data quality and transparency, by building in continuous monitoring and human oversight, and by establishing clear accountability, we can cultivate a future where AI is a force for good. 

The journey to responsible and regulated AI will be complex, iterative, and require continuous adaptation as the technology evolves. But it is a journey we must embark upon with urgency and unwavering commitment, for the sake of our shared future. The generative power of AI must be met with the generative power of human wisdom and collective responsibility. It is our collective duty to ensure that this transformative technology builds a better world for all, not just a more automated one.