AI Strategy Starts with Questions, Not Answers
Before you can build an AI strategy, you need to know what questions to ask. Here are the five that matter most.
The strategy trap
When boards ask for an "AI strategy," most executives respond with answers: We'll implement a chatbot. We'll use AI for customer segmentation. We'll automate invoice processing.
These might be good ideas. They might be terrible ones. The problem is that jumping to solutions before understanding the landscape almost always leads to wasted effort.
The five questions that matter
1. What problems are we actually trying to solve?
"Implement AI" is not a problem statement. "Reduce customer churn by identifying at-risk accounts earlier" is.
2. Where does AI create genuine advantage?
AI excels at pattern recognition in large datasets, automation of repetitive cognitive tasks, and prediction when historical data is relevant.
3. What's the cost of getting it wrong?
For low-stakes applications (email drafts), this is fine. For high-stakes decisions (credit approvals), you need much more scrutiny.
4. What do we need to believe for this to work?
Make your assumptions explicit. Challenge them. The ones you can't validate are your biggest risks.
5. What's our path to learning?
What will you learn from this project even if it fails?
Strategy is about choices
An AI strategy isn't a list of projects. It's a clear articulation of where you will and won't invest.
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