AI Strategy
AI strategy is about changing what the business can do.
The strongest AI programs improve cycle time, capacity, cost structure, service levels, and the speed of decision-making. They move valuable capabilities into production and redesign the surrounding process so those capabilities create measurable operating results. The articles here focus on how companies turn AI into operating leverage and durable business value.
4 articles
The Token Tax
The Hidden Cost of Giving AI the Wrong Responsibility
AUG 24, 2026
Two Kinds of AI. Only One Belongs on Your Ledger.
Is it safe to automate your close with AI? Only if it follows rules instead of guessing.
JUL 30, 2026
AI Is Breaking the Channel's Oldest Rule
AI is severing the link between revenue and headcount. The partners who rebuild pricing, talent, and delivery around it will protect their margin.
JUL 23, 2026
300 Approved. 34 Running.
The most useful thing any executive said about AI this year came from a bank CEO telling shareholders his AI program would not lift margins.
JUL 16, 2026
About this topic
Where the value is created
The largest gains come from redesigning work around capabilities that were previously too slow, expensive, or difficult to scale. AI can reduce manual review, accelerate decisions, increase throughput, and allow teams to handle more complexity without proportional growth in headcount.
A successful deployment should change something meaningful about the business. It should lower the cost to deliver, increase capacity, shorten time to revenue, improve customer response, or make previously impractical opportunities economically viable.
Build for production impact
A strong AI strategy starts with the business outcome and works backward. Identify the operating constraint, determine where AI can materially change performance, and then design the process, controls, roles, and systems required to make that improvement durable.
Pilots should answer a production question: can this capability improve the economics or performance of the process enough to justify changing how the organization operates? When the evidence supports that conclusion, the next step is deployment and scale.
How to use these articles
Start with the operating constraint. Identify where manual review, fragmented information, slow decisions, high service costs, or headcount-dependent processes are limiting performance. Then determine where AI can materially change the economics, speed, or capacity of that work.
Define the outcome you want, the process changes required to achieve it, and the evidence that will prove the improvement is real. Focus on production capabilities that improve performance, expand capacity, and create new options for the business.