I’m thrilled to announce new research, Customer Zero Programs Are A New Trust Test For Autonomous Execution: Why “Prove You Can Run It” Has Become The Gating Factor.
I’m passionate about “customer zero” programs because they embody the authentic experience of an innovator that survived contact with reality and, as a result, has earned the right to guide others. At this early stage of agentic AI, enterprises should “buy parachutes from the person who just landed safely, not the one still sketching designs behind a desk.”
The research matters. The story of how I got there also matters …
In late 2025, I had just published The Forrester Wave™: Digital Experience Platforms, Q4 2025. Every vendor told me about their strategy to embed agent orchestration into their software. Some had it. Some didn’t yet. They all pointed to the same proof point: generating marketing copy at scale. And yet, across all my customer interviews, only one had actually used a vendor’s AI agents. This was three years after ChatGPT. How could that be possible?
Later, I was at a vendor conference catching up with marketing and analyst relations teams, asking for their take on slow customer adoption. These were behind-the-scenes conversations — nothing public. We talked about how AI agents were reshaping how investors valued SaaS companies. 🧠 LinkedIn was packed 📦 with emoji-filled headers and ✅ bullet lists. Doomsayers warned of job loss. Yet workforce productivity stayed flat. How could that be possible?
Some people believe access to resources is the determining factor of success, in life and in business. I know some executives who disagree — DJs, too. I’ve spent most of my life doing tricks on a BMX bike, and I’ve yet to meet someone who could out‑trick me by spending more money instead of putting in the time.
The difference — whether it’s a sword, a set of turntables, or a bike — is skill: skill built through disciplined practice; skill grounded in principles, learned from a master or discovered through structured experimentation.
In my world, discipline is the difference. That was my opening line when I sat down for lunch with Kian Gohar of GeoLab. Kian built his latest business on teaching people how to think with AI — which I found interesting, because so many marketing and sales and even operations professionals spend their days teaching people what to think about AI.
“You’re close,” he replied. Microlearning, yes. Structured problem decomposition, yes. Thinking differently, yes. He paused. “The mistake people make,” he said, “is they think AI is the hard part.” He shook his head. “AI is easy. Human beings are hard.”
That was the moment I knew I was speaking to someone who understood something others didn’t. Changing how you think and behave at a human level requires sustained effort, repetition, and structure. This echoes what Katy Tynan teaches. It’s hard to be a human changing your mind about a better way to get work done.
Up until then, I had been asking the wrong questions. Why wasn’t AI delivering results? Why were productivity gains stuck in the low single digits? Why weren’t agents showing up in production? The right question is “Why are humans still thinking the same way they did before AI existed?”
Kian walked me through how he works with teams. They don’t start with the tool. They don’t start with the interface. They don’t start with the task. They start with the problem. The version they examine is the one that emerges after assumptions are surfaced, challenged, and expanded.
He described a session with an energy company: experts in the room, years of experience, and well-defined constraints. They believed they understood the problem. Then they brought AI into the process to interrogate their assumptions.
The system surfaced variables they hadn’t considered. It exposed dependencies they hadn’t modeled. It expanded the problem space from five constraints to eight — three entirely new. That’s when the real work began.
That’s when I understood what was happening: The companies struggling with AI were operating without the cognitive discipline required to use it effectively. Kian’s model is simple to describe and difficult to execute, like doing tricks on a bike. People build capability through repetition.
Ten minutes a day, one concept at a time, applied to real work. In one vendor pilot, that meant a two-week microlearning program: daily prompts, short demos, applied practice. Confidence increased by 15% in 10 days. The improvement came from changes in behavior and interaction patterns.
Most companies approach AI adoption as a rollout. Kian approaches it as a behavioral system: asynchronous learning paired with coaching; immediate application inside real workflows; peer learning that makes new behaviors visible and repeatable. He retrains how people think. That’s when the connection to customer zero became clear. Kian now trains digital experience platform (DXP) vendors.
The vendors making real progress are transforming how their own teams think, work, and operate under AI. Customer zero is where that transformation is proven in practice. The companies that once only sold software now learn (and teach) a way of working, grounded in discipline, shaped by experience, and tested inside their own operations before anyone else is asked to trust it.
Customer zero programs are not vendors drinking their own champagne in celebration. Customer zero programs are vendors becoming the adaptive digital samurai and showing you how to become one, too.
It’s about time. If you need someone to guide you on where to spend it — and how to think with AI about your operations — let’s talk.
Schedule an inquiry or guidance session with me to understand how to think with your agentic DXP. In a world of abundant technology, I offer a refreshed perspective on how to drive the selection of your digital experience providers that aligns with your strategic commitment to take control of your AI voyage.
Forrester clients can read Customer Zero Programs Are A New Trust Test For Autonomous Execution.
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