Your AI Investment Needs an Exit Rule
October 19, 2026 · Keith Pasley
Every AI initiative starts with a business case.
How many have a documented reason to stop?
Gartner released new research today showing that only 13% of CIOs report significant or transformational value from AI.
Another finding deserves executive attention.
Only 17% have guidelines for abandoning AI initiatives that are already in production and delivering insufficient value.
Organizations with those guidelines were 2.6 times more likely to belong to Gartner’s higher-performing CIO group.
I think this highlights a weakness in how companies manage AI investments.
We spend considerable time evaluating which AI projects deserve funding.
But what happens after deployment?
Costs continue. Business expectations change. Technology improves. Some applications never achieve their original objectives.
Yet the investment continues.
I’d require three decisions before approving an AI initiative.
1. What measurable business outcome justifies the investment?
Establish the baseline, target improvement, and financial value.
2. What is the complete cost of delivering that outcome?
Include infrastructure, software, implementation, support, governance, and ongoing operations.
3. What results would cause us to stop, redesign, or redirect the investment?
Set measurable thresholds, review dates, and clear accountability.
For technology partners, this should become part of the AI discovery and business planning process.
Help customers define successful outcomes and the evidence required to continue investing.
That creates stronger business cases and more credible long-term relationships.
Here’s the question I’d put in front of the executive team:
How many AI initiatives operating in production today have a measurable business objective and a documented exit decision?
Executive take away
Require every AI investment to have measurable success criteria, scheduled performance reviews, and predefined conditions for continuing, redesigning, or terminating the initiative. This strengthens capital allocation discipline and reduces spending on AI that fails to deliver sufficient business value.
Technology doesn’t create business value. Better decisions do.