What you are probably seeing
You look at your workflow and see that vendors claim their agents will cut costs or boost output, but the claims lack specific numbers tied to your actual work.
Your own data shows a gap between the projected benefits in sales decks and the messy reality of where you actually lose time.
Why it happens
The market relies on general narratives about efficiency because it is hard to isolate the specific variable of an AI agent within a complex system.
Buyers often focus on technology features rather than operational outcomes, so vendors rarely publish rigorous before-and-after performance data.
What changed recently
Step by step
1. Define the specific task and the baseline metric you currently use to measure success or failure before introducing the agent.
2. Request or design a test where the agent performs the task in parallel with your current method for a defined period.
3. Compare the results using your chosen metric to see if the agent provides a measurable improvement or just a different outcome.
4. Analyze the cost of error in the agent’s output to determine if the time saved outweighs the risk of mistakes.
5. Document the observed difference in performance to create a clear evidence base for future scaling or vendor negotiations.
Ways to go further
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Other problems readers raise
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