Why Product People Can’t Coast Through the AI Transition.
While 95% of AI initiatives stall, the companies that succeed will need a new type of product professional.
MIT research shows 95% of GenAI pilots aren’t delivering revenue growth. Harvard Business Review found that only 26% of companies have working AI products, and just 4% are seeing significant returns. Gartner predicts that at least 30% of GenAI projects will be abandoned by the end of 2025.
But here’s what makes this different from previous tech hype cycles: the underlying capabilities are real.
Researchers at MIT and Chicago Booth point to familiar culprits - implementation lags, organizational change requirements, and the intangible capital needed to make new technology actually work in business contexts.
For product professionals, this gap represents something crucial: the companies that figure out AI implementation will need people who can navigate between technological capability and business reality.
The question is whether you'll be ready.
Skills that used to be enough.
The traditional product toolkit - user research, roadmap management, feature prioritization - remains foundational. But it’s no longer sufficient when your company is evaluating AI tools that could automate core workflows, when stakeholders are asking about integration strategies, or when competitive threats might emerge from AI-first startups in your space.
Companies are starting to realize they need people who can bridge the gap between AI capabilities and business implementation. Not external consultants who recommend generic tools, but internal professionals who understand both the technology landscape and their specific business constraints.
So what does expanding beyond the traditional toolkit actually look like in practice?
Where success is actually happening.
The organizations in that successful 5% aren't running flashy, transformational AI initiatives. They're focusing on specific, lower-level automations where the value is measurable and integration is straightforward.
Document processing instead of "AI-powered customer insights." Data extraction instead of "intelligent decision-making platforms." Workflow routing instead of "automated sales optimization."
Understanding this difference becomes crucial when you're evaluating tools, setting realistic timelines, or communicating with stakeholders about what's actually possible.
The product professionals who can consistently spot these realistic opportunities have developed a specific skill set that most are still missing.
What learning actually looks like.
This isn't about becoming a machine learning engineer. It's about developing fluency in how AI capabilities map to real business problems.
Think of it as building four core competencies that separate informed product professionals from those still caught up in AI hype:
Understand automation at a granular level. Which workflows in your domain are genuinely automatable versus which ones just look like they should be? Where are current AI tools actually succeeding versus where they're still mostly demos?
Grasp implementation complexity. Why do AI pilots stall when moving from proof-of-concept to production? What organizational factors determine success or failure?
Track competitive landscape shifts. What AI-enabled tools are emerging in your space? How close are they to replicating your core value proposition?
Master ROI measurement for AI initiatives. How do you instrument success beyond "the AI works"? What metrics actually matter for proving business value?
Developing the habit of asking these questions and pushing for concrete answers is how you build the fluency to evaluate AI initiatives based on business reality, not marketing pitches.
Your move.
Start by getting concrete about the workflows your product improves and researching what AI tools are targeting those specific processes. Learn enough about implementation challenges to have informed conversations about what’s realistic versus what’s marketing promises.
The goal isn’t to become an AI expert overnight. It’s to become someone your organization can rely on for practical, informed perspectives on how AI might impact your business.
The companies that figure out how to systematically turn AI experiments into business results will have a significant advantage. They need people who can navigate the gap between technological capability and organizational reality.
That’s either going to be external consultants with generic frameworks, or product professionals who’ve invested in understanding both the AI landscape and their business domain.
Until next week,
Mike @ Product Party
Want to connect? Send me a message on LinkedIn, Bluesky, Threads, or Instagram.
I'm starting to work with startups in a consulting capacity, helping founders navigate the specific product challenges that generic advice can't touch.
If you're dealing with scattered priorities, unclear customer needs, or reactive product decisions that feel uniquely messy, I'd love to hear about your situation.
Sometimes the most valuable conversation is the one tailored to your exact context, not general startup theory
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