Earlier this year I wrote about the AI Engineering Maturity Model — a five-level framework for understanding how engineers actually work with AI, from ad-hoc chatbot use all the way through to bounded autonomous workflows. This post is the first set of results.
We all feel that the use of AI in software engineering will be transformative, and there are a lot of enthusiastic evangelists around. Going behind the hype reveals a much more mixed picture of adoption. My goal was to get behind the great anecdotes and get an honest, org-wide picture of how engineers are really using these new tools.
For those who haven’t seen the original post: the AIMM is a five-level maturity scale scored from 1.0 to 4.0. L1 (Explorer) is where engineers are experimenting individually with AI tools. L2 (Tool Adopter) is active, habitual use — IDE plugins installed, regular AI interaction — but practice is still individual.
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My sails are full after an amazing day at Full Sail University in Orlando with the Boye & Company Digital Leaders group, where we were treated to an amazing tour by the incomparable Kassidy Pierce, the school’s community engagement manager.
From the cavernous Drone Innovation Center to the state-of-the-art Cyber Range (sponsored by Amazon Web Services (AWS), IBM, and Cloud Range), we were given VIP treatment, taking us behind the scenes to explore how this campus prepares students for the high-tech jobs of tomorrow.
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AI agents can still feel like others have already started figuring them out. You hear a few confident stories, see a few hands go up in the room, and suddenly the question shifts from what is this? to am I already behind?
This member call is for anyone who is curious, slightly uneasy, or ready to get more hands-on. It starts from the practical reality of daily work rather than a grand strategy. What happens when you stop waiting to understand everything first and simply begin experimenting?
In this conversation, we’ll explore what it looks like to learn by doing. Chris Weier set out with a simple question rather than a plan to build an AI-powered sales team.
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Sometimes a conversation leaves you with more questions than answers.
That's certainly how I felt after our recent Employee Experience Group meeting in Hamburg.
Whether it's true or not is almost beside the point. It captures something many people seem to be feeling right now.
Work is changing. Tools are changing. Some tasks that once took hours now take minutes. Others may disappear altogether.
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Last week I had the privilege of attending the Kentico MVP Summit 2026 at Kentico HQ in Brno, Czech Republic. As always, it was an intense week filled with product discussions, strategic conversations, hands-on workshops and valuable time with people across the Kentico ecosystem.
A few weeks earlier, I attended the Boye & Company CMS Summit in Frankfurt. Many of the themes discussed there resurfaced in Brno. Frankfurt highlighted where our industry is heading. Brno made that future feel much more concrete.
Across both events, the conversation moved beyond AI features and tools. The deeper question was how AI is changing the way organisations learn, coordinate and execute. It increasingly seems that competitive advantages are becoming shorter-lived as new tools and workflows spread rapidly across the industry.
Several sessions and discussions during the week offered different perspectives on that shift.
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Organisations know more than they publish.
Brand guidelines exist. Product knowledge exists. Audience insights exist. Editorial standards exist. Yet when AI generates content, much of that knowledge often remains out of reach.
The challenge is rarely a lack of information. More often, organisations struggle to make their knowledge available where it can be used.
That tension was visible throughout CMS Summit 26, held in May at Frankfurt's Museum für Kommunikation. The event brought together around 100 digital leaders to discuss content management, digital experience, and AI.
One of the highlights was the European CMS Idol competition, where CMS vendors demonstrate their platforms live before an audience of peers and practitioners. Representing Webiny, founder and CEO Sven Al Hamad demonstrated an approach to AI-assisted content creation
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Have you also asked your favourite AI assistant for travel advice?
I did. I asked mine a simple thing: get me from Rovinj to Malaga on 16 June.
The answer was completely wrong.
It gave me a ferry that did not run on that date, a bus to the wrong airport, and a flight from an airport that the bus did not serve. The timings overlapped. The whole thing read like an itinerary. None of it would get me to Spain.
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That statement came up in our Boye Digital Leadership peer group meeting in Hamburg this week.
I wrote it down immediately, partly because it was surprising and partly because it captured something broader. AI is moving from experimentation to operations, and organisations are starting to encounter the practical consequences.
I spent the day listening to peers share what is actually happening inside their organisations. Once again, I was reminded that some of the most useful signals about where digital organisations are heading rarely come from reports, keynotes, or vendor presentations. They emerge through conversations
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The web has a new audience: machines.
AI agents, search assistants, summarisers and retrieval systems are already reading, interpreting and re-presenting what organisations publish. Sometimes they get it right. Sometimes they guess. And when they guess badly, the wrong answer may still carry your brand.
In this member call, we’ll explore Machine Experience — MX — and what it means to design digital content and platforms so machines can read, trust and act on them reliably. The session draws on Tom Cranstoun’s new book….
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I said Atlanta was "HOTlanta," and this group really turned up the heat. We had a packed session at yesterday’s digital experience leaders group meeting, where Matthew McQueeny and I covered everything from the state of martech to the angles of agentic AI, with a through-line focus on open source CMS.
Building on a whirlwind global tour of insights that took us from New York to Frankfurt to the vibrant Poncey-Highland neighborhood of ATL, we were treated to some fantastic perspectives from a diverse international group of attendees.
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Day two in Gotham brought the gravitas at the CMS Experts NYC edition, held at Vimeo’s luxe Manhattan digs.
While AI visibility was still a recurring theme, we were treated to some fascinating and thought provoking presentations that spurred debate on everything from evolving revenue models to the role of AgenticOps.
Here’s what stood out
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In a New York minute, we scaled some skyscraping topics at the Boye & Company CMS Experts meeting in NYC, hosted at the hip offices of Vimeo in Manhattan. Matthew McQueeny joined me for a live rendition of our “Matt & Matt” show on the road, where AEO became a central topic of discussion.
What’s clear is that companies feel the urgency to act on Answer Engine Optimization - but there’s still a lot of confusion about the "what" and "how." From ai12z’s Bill Rogers to VShift’s Eric Feige, we were offered a plurality of insights through the vendor and agency lenses.
A couple of through-lines from day one….
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“I can’t join you in the morning, I have to work.”
“Sorry, I can only join from 5pm.”
I’ve heard versions of this for years.
I’m writing this on a Saturday morning, having travelled to an event I was invited to. Looking around, it brings back a familiar disconnect in how learning is treated across organisations.
In Scandinavia, learning is part of the working day.
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It took me a long time to realise this.
For years, I believed it was all about getting the right people in the room. Find the best speakers, bring together smart participants, and the rest would follow. That still matters. Yet over time, it has become clear that the room itself plays a more active role than we often admit.
The environment shapes the conversation.
You can have capable people, a strong agenda, and a clear purpose. Still, conversations never quite take off.
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What does it actually mean to take responsibility for the digital products we build?
Most teams already care about doing the right thing. The harder part is turning that intent into everyday decisions across design, data, platforms, and governance. Responsibility rarely sits in one place. It shows up in small choices, trade-offs, and blind spots across the entire product lifecycle.
In her work, Antonia Fedder breaks corporate digital responsibility down into seven practical aspects.
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