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When AI gets stuck
Real situations management teams face
when ambition outpaces organization
Sound bites from the executive-rooms
AI rarely arrives as a clear problem. It shows up in conversations, hesitations and half-sentences. Below are real situations and remarks I regularly hear from management teams in small and mid-sized organizations - often just before AI initiatives start to stall.
01
"IT is doing the heavy lifting. The business is mostly watching."
The organization understands that AI should involve the business.
In practice, IT and data teams are doing the work. Meanwhile, business leaders observe from a distance.
Curious. Cautious. Ready to see whether AI succeeds — or quietly fails.
Ownership remains implicit. Decisions are postponed.
03
“CRM? We don’t really have one. So how are we supposed to benefit from AI?”
AI expectations are high.
The basics are not.
Without a shared customer model,
AI becomes a theoretical discussion instead of a practical tool.
The question shifts from “what can AI do?”
to “what do we need to organize first?”
02
"We want to use AI for sales and marketing — but we still run sales in spreadsheets."
There is a clear ambition to work more intelligently.
Personalization. Automation. Better customer insight. At the same time, the foundations are fragile;
AI sounds promising but without structure, it has nothing solid to build on - and data is not the only asset that needs restructuring...!
04
“We keep experimenting, but where is the value?”
Use-case are introduced.
New platforms appear.
People try them. Some are piloted.
Yet the ROI is not clear
Processes remain linear. Decisions stay manual. AI creates real business value when it reshapes how the organization operates. A new operating model is required when you pilots shift from use-case discovery to value-case redesign.
05
“Everyone expects results, but no one wants to own the risk.”
AI is discussed at management level.
Pressure grows to show progress.
At the same time, responsibility remains unclear: is this business? IT? innovation?
operations?
Without explicit ownership,
AI initiatives slow down and trust erodes quietly.
07
“We added MS Co-pilot, but impact remains limited.”
AI tools have been introduced into daily work. People are curious. Some adoption happens.
Still, the bigger question remains unanswered: How does this actually change the way we operate?
Without clear direction, AI becomes an add-on, not a capability that reshapes how work gets done.
06
“We know AI matters - we just don’t know where to start organizationally.”
Ideas are not the problem.
Technology is not the problem. What’s missing is clarity on: ownership, governance, value expectations, and how people are expected to work with AI
Without that clarity, every next step feels heavier than it should.
Facing one of these challenges?
A short conversation often helps to clarify the next step!