Organizations don't fail at AI because of technology. They fail because they solve before they understand.
A Lesson from My GenAI Conversations
As a newly minted Certified Professional in Managing AI (PMI-CPMAI), I have a new appreciation for AI solution development — and an even greater appreciation for my ongoing chats with my GenAI friends, ChatGPT and Claude. I now understand why my friends sometimes forget earlier parts of our conversations. That ol' context window is real.
So when I don't get the response I was hoping for, I've started asking a different question: Why did I ask for help in the first place? Our conversations take many twists and turns, even after they politely ask, "What are you working on today, Nina?"
Now I pause and consider whether my prompt was clear enough, whether I defined the objective, and what I could have asked differently to get the result I actually needed.
It turns out those opening greetings aren't just pleasantries. They're an invitation to establish intent before moving forward. No more skipping the salutations for me because clarity at the start determines the value of everything that follows.
The Organizational Parallel
This same lesson applies when organizations turn to AI solutions.
On a much larger scale, organizations face the same challenge when implementing AI. Leaders understandably focus on tools, models, and technical capability. But AI implementation is a pivotal organizational moment, one that begins well before development starts.
Before introducing AI, leadership teams must pause to examine the current state of the business, the problem they are truly trying to solve, and whether AI is the right solution at all.
At this stage, three reflection questions become especially valuable:
- What is working well today?
- What problems persist despite current efforts?
- What should be done differently before moving forward with AI?
Reflection creates value only when insight translates into coordinated action. Clear problem definition must quickly become aligned decisions, implementation priorities, and structured execution that guide AI development, adoption, and operational use across the organization.
I introduced this reflection practice in my first From the Lab piece — the connection between pivotal moments and what must happen next.
Why the First Reflection Matters Most
Reflection will continue throughout the AI lifecycle, from business understanding through model operationalization, as teams iterate and refine. But the first reflection matters most.
In those cases, the technology didn't fail. The starting point did.
Organizations that successfully realize value from AI do more than select the right technology. They establish clarity early, align stakeholders around intended outcomes, and reinforce adoption through disciplined change and execution management as the AI model moves into operation.
What This Looks Like in Practice
Individual clarity before engaging generative AI mirrors the organizational clarity leadership teams need before pursuing AI solutions. Organizations that realize value from AI are not simply moving faster to implementation. They pause first to ensure the solution aligns to the problem — and then execute with intention.
The CROSSING CHANGE Connection
The same discipline applies beyond AI initiatives. Through CROSSING CHANGE, leaders and their teams pause at pivotal moments to examine what the organization is learning as they determine what should happen next. Structured Lessons Learned Labs™ help leadership teams clarify the underlying issues, align on what must change, and translate those lessons into clear action plans that guide execution across the organization.