“Transform your business” is a big promise. It leaves enterprise buyers with practical questions like: What improves? Under what conditions? And who checks the results? The credibility gap opens when an AI story moves beyond what a company can demonstrate.
Research illustrates why precision matters. In a 2023 experiment involving 758 Boston Consulting Group consultants, the AI users completed tasks within the technology's capabilities about 25% faster. But on a task beyond those capabilities, AI users were 19 percentage points less likely to reach the correct answer. The intriguing finding: the same technology could improve performance or undermine it, depending on the task.
Specificity gives buyers something to believe
Morgan Stanley’s 2024 launch of AI @ Morgan Stanley Debrief offers a concrete example. With client consent, the tool generates meeting notes, identifies action items and drafts follow-up emails for advisors to edit and send at their discretion. The announcement describes a recognizable workflow and makes the advisor’s role explicit.
For technology communicators, that specificity is useful. Buyers can understand what the tool does, where it fits and where human judgment remains involved.
A credible AI story doesn't just make a clear promise; it shows where that promise ends.
Naming those boundaries gives customers something to evaluate. A claim about faster document review becomes more useful when it explains which documents were tested, how accuracy was assessed and what still required expert attention. The detail makes the benefit tangible.
What comes next
Before your next announcement, review each AI claim with your product team. Identify the task, the measured result, the testing conditions and the remaining limitations. Distinguish customer results from internal tests, and available capabilities from future plans.
Then build the story around your strongest supported outcome. Give executives, sales teams and spokespeople the same evidence and clear language about human oversight.
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