Stop Chasing AI Use Cases. Fix Your Operating Model First.

When we think about AI, we immediately think — what will be the best use cases for it in our organization? Companies spend a good amount of time building lists of potential AI applications for customer service, operations, HR, finance, marketing, and the list grows and grows.

But what if asking "What AI use cases should we pursue?" isn't quite enough to show actual impact and ROI?

What we've learned is that use cases don't create transformation. Operating models do.

Why pilots stall

Without clear decision-making, ownership, governance, prioritization, and adoption plans, even excellent AI solutions struggle to scale beyond isolated pilots.

We've seen organizations identify dozens of promising AI opportunities but deliver very little lasting business value, because every initiative was approached independently.

The questions we ask instead

So when we sit with our clients, after we listen to their most desired AI use cases, we ask them to uncover and answer some of the most crucial operational questions:

• How do you evaluate AI opportunities consistently?
• Who owns AI decisions?
• How do you measure business value?
• How do you manage risk without slowing progress?
• How do you help employees adopt new ways of working?

Once those foundations are in place, identifying and scaling high-value use cases becomes dramatically easier.

Having a strong operating model continues creating value regardless of which model or platform becomes popular next. Building the best system for continuously discovering, prioritizing, implementing, and scaling use cases is the key to successful AI strategy and implementation.

Are you struggling to identify or answer some of these operational challenges as you transition into AI?