Cost
AI costs are growing, while its value is still hard to assess
AI integration audit
AI should cut costs, not create new ones
Spending on AI does not guarantee a return
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$186/mo
Estimated monthly cost of time lost per employee dealing with poor-quality AI output in a US survey
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42%
Of companies surveyed abandon most AI projects before production
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59%
Of leaders surveyed worry about measuring productivity gains from AI
AI is already being pulled into products, development, support and internal processes. I find costly and risky use cases and check quality, data and access.
The outcome is a clear plan: what to keep, stop, fix or scale.
Audit focus
Where the value is lost
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Quality
AI speeds up work, but people still have to check the results manually
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Risks
It is unclear which data AI can access and who is responsible for mistakes
What I do
I review AI in real work
I look at where AI saves time and where it creates more work, so you know what to keep, fix or stop
Case
Technical support for a large CDP
Moved the analysis of support requests and user interactions to Qwen
The company started spending fewer resources on technical support
Who is it for?
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Companies
AI is already used in your product, support or internal processes, but its value and total cost are still unclear
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Teams and startups
You are building a product with AI and want to check the idea, costs and risks before scaling development
Let’s look at your task
Let’s discuss how you use AI, what raises questions and where to start checking