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AI Growth: Finding the Constraint
Identify the part of the customer journey that is holding growth back. Compare an AI-assisted change with the current way of working, and keep it only if customer outcomes improve.
By Jake Bauman · Revised
Jake Bauman's working decision framework. It is a recommendation, not a measured law of growth.
Find the constraint
Map acquisition, activation, retention, and revenue. Choose one customer problem that you can observe. A faster content workflow matters only when it helps qualified people find, understand, or use the product.
Test before scaling
- Write down the baseline and the expected customer outcome.
- Run a small test against the existing process or a control.
- Record quality failures, human review time, and full operating cost.
- Keep the change only when the evidence supports it.
What the outside evidence says
Enterprise surveys show that agent adoption is growing and quality remains a barrier. They do not show that AI use alone causes growth. Treat them as context for the test, not proof of a return.
A one-week decision exercise
Pick the most visible customer friction and write a falsifiable hypothesis. For example, if buyers cannot understand what a product includes, test a clearer comparison page against the current version. Record qualified clicks and questions from buyers. The result might show that the offer, not the acquisition channel, is the constraint. Document both outcomes before changing the next part of the funnel.
Sources and context
These sources inform the framework. They do not validate the suggested actions for every business.
- LangChain, 2026 State of Agent Engineering: Survey of more than 1,300 respondents; findings describe respondents, not all businesses.
- Deloitte, 2026 State of AI in the Enterprise: Survey-based enterprise adoption findings, not evidence that a particular strategy will work.
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