S
Sara Faatz
Guest
Every organization is on its own AI journey. And when it comes to the digital experience, that journey can take many different paths.
For some organizations, the opportunity is helping teams create and optimize content more efficiently. For others, it’s rethinking how customers find answers across websites, documents and other sources of information. Still others are exploring personalization, conversational experiences or entirely new ways for people to interact with digital content.
Where you start (and where you go next) depends on what you’re trying to achieve, what you already have and what makes sense for your customers and your business.
In a technology landscape changing this quickly, the goal isn’t to predict every step of the journey. It’s to know what you’re trying to accomplish and build in a way that gives you room to adapt along the way.
With new models, agents and applications appearing constantly, it’s easy to start with the technology: Which model should we use? Where could we add an agent? What should we automate?
Those may be useful questions later. They aren’t the best place to start. A better question is: What is the customer trying to accomplish, and where are we making that harder than it needs to be?
Maybe customers struggle to find information buried across thousands of webpages and documents. Maybe they need help navigating complex products or services. Maybe content teams can’t create and optimize experiences quickly enough to keep pace with customer expectations. Start there. Then ask where AI can help.
Martech researcher Frans Riemersma has been making a similar argument through his work on value engineering. In a 2026 piece on the shift from features to outcomes in martech.org he wrote, “Customers were never paying for your features. They were paying for the outcomes that those features helped them achieve.” And that distinction matters. A model, agent or AI feature isn’t the outcome. A better customer experience is. AI simply gives us new ways to create it.
Most organizations already have a strong foundation for AI. They’ve invested in digital platforms, content, customer data, integrations and workflows that support how they engage customers and run their business. Those investments aren’t something AI needs to replace; they’re often what makes AI more useful.
Consider content. The webpages, product information, support documents, PDFs, videos and other assets an organization has created aren’t simply things to publish. Together, they represent knowledge that AI can help customers and employees access and use in new ways.
So instead of starting with “What AI technology should we buy?” try asking: “How could AI make what we already have more valuable to our customers?” That keeps the conversation where it belongs: on the problem you’re solving and the value you’re trying to create.
Once you know where AI can create value, there still may not be one right way to deliver it.
For some use cases, the simplest answer will be AI capabilities already built into the technology your teams use every day. Other experiences may call for connecting AI to trusted organizational content and data. And sometimes the right answer will be an external model, service or AI platform your organization has already selected. These approaches aren’t mutually exclusive. Most organizations will use a combination of them as their needs evolve.
The problem should determine the approach. Not the other way around.
Composability was a defining conversation in digital experience architecture well before generative AI arrived. A few years ago, much of that conversation centered on the freedom to assemble a technology stack from best-of-breed components. In the AI era, its greater value may be the freedom to change.
Models will change. AI services will change. Customer expectations will change. And the use cases that matter most to your business will change as you learn where AI creates value.
Riemersma recently described AI as pushing composability beyond software itself, with intelligence becoming a new building block that can “reason, plan, and work alongside SaaS systems and human teams.” That makes the ability to connect, evolve and recombine capabilities even more important.
And that may be where composability matters most in the AI era: giving organizations the flexibility to introduce new capabilities, change models or services and evolve the experience without rebuilding everything around them. That’s change-readiness.
For years, we’ve talked about future-proofing technology. AI makes the limitations of that idea particularly clear. We can’t know which models or capabilities we’ll need three years from now. We don’t need to predict the future. We need to make it easier to respond to it.
There is, of course, a risk in taking flexibility too far. When AI changes every week, waiting can feel prudent. Why make a decision today when a better tool or model may arrive tomorrow? Because it probably will. And something else will arrive after that.
Change-readiness isn’t about waiting for certainty. It’s about creating enough flexibility that you don’t need certainty before you act. Start with a customer problem worth solving. Build on the technology, content and data you already have where it makes sense. Choose the approach that fits the problem today. Measure whether it creates value. Then adapt.
That’s a more useful way to think about an AI journey: not as a fixed route to a known destination, but as a path guided by customer value and designed to change as you learn.
Every organization’s AI journey will look different because every organization’s customers, content, data, technology and priorities are different. At Progress, we believe the technology behind your digital experiences should reflect that reality. The Progress Sitefinity platform gives organizations multiple ways to put AI to work across the digital experience: from capabilities built directly into the platform to connecting trusted content and data with Progress Agentic RAG or integrating the AI technologies that fit their needs.
But the technology isn’t the starting point. The customer is.
Understand the experience you’re trying to improve. Identify where AI can create meaningful value. Choose the approach that helps you create it. And build your digital experience so you’re free to choose differently tomorrow.
Continue reading...
For some organizations, the opportunity is helping teams create and optimize content more efficiently. For others, it’s rethinking how customers find answers across websites, documents and other sources of information. Still others are exploring personalization, conversational experiences or entirely new ways for people to interact with digital content.
Where you start (and where you go next) depends on what you’re trying to achieve, what you already have and what makes sense for your customers and your business.
In a technology landscape changing this quickly, the goal isn’t to predict every step of the journey. It’s to know what you’re trying to accomplish and build in a way that gives you room to adapt along the way.
Start with Value, Not AI
With new models, agents and applications appearing constantly, it’s easy to start with the technology: Which model should we use? Where could we add an agent? What should we automate?
Those may be useful questions later. They aren’t the best place to start. A better question is: What is the customer trying to accomplish, and where are we making that harder than it needs to be?
Maybe customers struggle to find information buried across thousands of webpages and documents. Maybe they need help navigating complex products or services. Maybe content teams can’t create and optimize experiences quickly enough to keep pace with customer expectations. Start there. Then ask where AI can help.
Martech researcher Frans Riemersma has been making a similar argument through his work on value engineering. In a 2026 piece on the shift from features to outcomes in martech.org he wrote, “Customers were never paying for your features. They were paying for the outcomes that those features helped them achieve.” And that distinction matters. A model, agent or AI feature isn’t the outcome. A better customer experience is. AI simply gives us new ways to create it.
Build on the Value You Already Have
Most organizations already have a strong foundation for AI. They’ve invested in digital platforms, content, customer data, integrations and workflows that support how they engage customers and run their business. Those investments aren’t something AI needs to replace; they’re often what makes AI more useful.
Consider content. The webpages, product information, support documents, PDFs, videos and other assets an organization has created aren’t simply things to publish. Together, they represent knowledge that AI can help customers and employees access and use in new ways.
So instead of starting with “What AI technology should we buy?” try asking: “How could AI make what we already have more valuable to our customers?” That keeps the conversation where it belongs: on the problem you’re solving and the value you’re trying to create.
Choose the Approach That Fits
Once you know where AI can create value, there still may not be one right way to deliver it.
For some use cases, the simplest answer will be AI capabilities already built into the technology your teams use every day. Other experiences may call for connecting AI to trusted organizational content and data. And sometimes the right answer will be an external model, service or AI platform your organization has already selected. These approaches aren’t mutually exclusive. Most organizations will use a combination of them as their needs evolve.
The problem should determine the approach. Not the other way around.
Design for Change
Composability was a defining conversation in digital experience architecture well before generative AI arrived. A few years ago, much of that conversation centered on the freedom to assemble a technology stack from best-of-breed components. In the AI era, its greater value may be the freedom to change.
Models will change. AI services will change. Customer expectations will change. And the use cases that matter most to your business will change as you learn where AI creates value.
Riemersma recently described AI as pushing composability beyond software itself, with intelligence becoming a new building block that can “reason, plan, and work alongside SaaS systems and human teams.” That makes the ability to connect, evolve and recombine capabilities even more important.
And that may be where composability matters most in the AI era: giving organizations the flexibility to introduce new capabilities, change models or services and evolve the experience without rebuilding everything around them. That’s change-readiness.
For years, we’ve talked about future-proofing technology. AI makes the limitations of that idea particularly clear. We can’t know which models or capabilities we’ll need three years from now. We don’t need to predict the future. We need to make it easier to respond to it.
Keeping Your Options Open Doesn’t Mean Standing Still
There is, of course, a risk in taking flexibility too far. When AI changes every week, waiting can feel prudent. Why make a decision today when a better tool or model may arrive tomorrow? Because it probably will. And something else will arrive after that.
Change-readiness isn’t about waiting for certainty. It’s about creating enough flexibility that you don’t need certainty before you act. Start with a customer problem worth solving. Build on the technology, content and data you already have where it makes sense. Choose the approach that fits the problem today. Measure whether it creates value. Then adapt.
That’s a more useful way to think about an AI journey: not as a fixed route to a known destination, but as a path guided by customer value and designed to change as you learn.
AI Your Way
Every organization’s AI journey will look different because every organization’s customers, content, data, technology and priorities are different. At Progress, we believe the technology behind your digital experiences should reflect that reality. The Progress Sitefinity platform gives organizations multiple ways to put AI to work across the digital experience: from capabilities built directly into the platform to connecting trusted content and data with Progress Agentic RAG or integrating the AI technologies that fit their needs.
But the technology isn’t the starting point. The customer is.
Understand the experience you’re trying to improve. Identify where AI can create meaningful value. Choose the approach that helps you create it. And build your digital experience so you’re free to choose differently tomorrow.
Continue reading...