Welcome to the 3rd issue of The Street Smart Edge, the free micro-newsletter of the Street Smart Product Manager to help you boost your confidence, stay focused, and reconnect with what really matters. For in-depth, proven actionable tactics that go beyond frameworks and transform how stakeholders perceive your value, become a paid subscriber or Inner Circle member.
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95% of enterprise AI pilots have failed. That is, if you want to believe a recent MIT study. Either way, this is excellent news for Product Management.
Here’s what MIT found:
Standard chatbots can't adapt to enterprise workflows
Trend-chasing
Misguided resource allocation
Shallow vs deep value
Paying the “verification tax” — humans spending time double-checking AI outputs
Lack of strategic focus
$30-40 billion invested. Only 5% actually delivered value.
Crikey.
This reinforces the need for solid Product Management. Here’s the opportunity for you as a product leader:
1. Shift from trend-chasing to strategic problem solving
Steer your organization toward opportunities grounded in a measurable strategy that solves a defined business problem.
2. Surface areas where the biggest ROIs are revealed
Opportunities in sales and marketing are easy to pitch and simple to imagine. But think about all the soulless customer support chatbots, the bland copy, the formulaic emails, and disengaging sales outreach.
Maybe they’ll get better. Meanwhile, real value may be in behind-the-scenes automation of back-office functions like procurement, finance, and operations.
3. Build vs buy decisions based on identifying specific use cases
Product Managers are experts in identifying profitable use cases. Use this skill to identify customer opportunities and process bottlenecks to select or develop AI solutions.
4. Think big, act small
It’s great to have a big vision. To prove out the investment, though, start small — pick a very specific pain point, define clear and realistic outcomes, execute well with a small and defined scope, and build from there based on results and learnings.
5. Tentatively right > confidently wrong
Encourage execs and ML teams to embrace a “tentatively right” mindset to avoid being "confidently wrong". This can help close the learning gap with LLMs and prevent paying the “verification tax” that erodes ROI.
This is exactly what I championed at my last AI company. We quantified the uncertainty to surface context gaps and built an accuracy flywheel so that our AI could retain feedback and improve over time.
6. Enterprise implementations are only as good as their workflows
Enterprise implementations are much more complex, by a factor of 10. Standalone apps rarely deliver. Like any software implementation, for an AI solution to succeed in enterprise, it must become a part of the business’s operating system.
At my last AI company, we learned the hard way that what our enterprise customers really valued much more than our LLM was its ability to dramatically and empirically improve their workflows.
This included enterprise integrations with 3rd party systems and data, and embedded, workflow-specific tools to make the AI work as a total solution.
Takeaway
AI transformation reminds me of the early days of digital transformation — lofty promises that in reality took a long time to produce the gains. Many of the same mistakes are being made with AI transformation.
Learn the lessons so we don't waste so much money, resources, and energy this time around to get the ROI that AI is promising to deliver.
And Product Management has the unique opportunity to be at the forefront of this transformation.
Have a joyful week, and, if you can, make it joyful for someone else too.
cheers,
shardul
Whenever you’re ready, there are 4 ways I can help you:
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Shardul Mehta
I ❤️ product managers
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