Hey there,
A few weeks ago, I confessed I was having an identity crisis as a consultant. Everywhere I looked—LinkedIn posts, conference presentations, Harvard Business Review articles—I saw polished stories of AI transformation success. Companies showcasing seamless integrations, teams celebrating 10x productivity gains, leaders presenting sophisticated AI strategies with impressive metrics.
But the product leaders I was actually talking to were telling me something completely different. They described confusion, coordination problems, and strategic uncertainty. The gap between the public narrative of AI transformation success and the private reality of AI adoption struggle felt wide, and I wanted to understand what was really happening.
So I asked for your help. Sixteen product leaders responded to my reality check survey with refreshingly brutal honesty about what's actually happening inside their organizations.
The results validate my suspicion completely, but reveal something I didn't expect: Most companies are making opposite mistakes on two critical dimensions.
The Two-Dimensional Problem
What I discovered analyzing the responses is that AI success requires two completely different types of thinking, and most companies are getting both wrong.
Dimension 1: AI Product Integration requires strategic vision. You need to start with customer problems and competitive positioning, then evaluate whether AI creates new value propositions or defensive moats.
Dimension 2: AI Tool Adoption requires operational discipline. You need clear policies, defined use cases, security guidelines, and structured learning processes.
Interestingly, most companies are failing at both dimensions, but for different reasons:
They're treating product AI integration like technology adoption (looking for problems that AI can solve instead of starting with customer value)
They're treating AI tool adoption with no structure at all (letting everyone experiment individually instead of implementing basic operational discipline)
What the Survey Revealed
The survey responses paint a consistent picture of this dual dysfunction:
Product Integration Treated as Technology:
"We started with the commitment from leadership that we NEED to add AI to our product, regardless if there's real value to be had for our users." (200-1,000 employee company)
"Head of product wants to implement AI but only the generic term 'AI' and 'agents' are used." (1,000-5,000 employee company)
Tool Adoption Without Structure:
"It's the wild west. Try to use it but we don't have processes yet that leverage it." (5,000+ employee company)
"Bring your own tool, that's the culture in my company. Easy to get budget if the tool has 'AI.'" (200-1,000 employee company)
What we're witnessing is tactical thinking applied to strategic decisions (let's add AI to our product) plus no structured thinking applied to operational decisions (everyone figure out AI tools on your own).
The Successful Outliers
Only one company in my survey had genuinely figured this out—and they're an AI-first startup that built their entire value proposition around bringing AI to their industry. Of course they have strategic clarity about product integration; AI is their product strategy.
Everyone else is trying to retrofit AI into existing business models while simultaneously managing the operational chaos of unstructured tool adoption. They're playing a different game with different constraints, and the AI-first success stories aren't particularly relevant to their reality.
Here's where the plot thickens. This two-dimensional challenge is hitting product teams at precisely the moment when many have been gutted by layoffs justified by anticipated AI productivity gains.
The twisted logic works like this: "AI will make us more efficient, so we need fewer product managers." Layoffs happen."Now let's figure out how to implement AI strategically." Remaining skeleton crew drowns in operational chaos.
Companies cut product capacity expecting AI to fill the gap, but the remaining teams don't have bandwidth to implement AI strategically on either dimension. They're trapped in a vicious cycle—they need AI to succeed with fewer people, but they don't have enough people to implement AI successfully.
Meanwhile, their competitors who maintained or expanded product teams are building AI capabilities that create genuine competitive advantages. The companies treating AI as cost reduction are optimizing for efficiency while their competitors optimize for capability expansion. Guess who wins in three years?
The Opportunity Hiding in Plain Sight
But there is a counterintuitive opportunity hidden in here. The bar for AI differentiation is lower than most people think.
Even companies claiming "advanced/integrated usage" are mostly winging it. The survey revealed sophisticated technology experimentation happening alongside complete strategic confusion. Organizations with impressive AI tool portfolios operating without coherent vision about customer value creation.
This creates a significant opportunity for product leaders who can bring the right type of thinking to each dimension:
For AI Product Integration:
Start with customer problems, not AI capabilities
Ask "What new value can we create?" not "Where can we add AI?"
Think competitive positioning, not feature lists
Lead strategic conversations about business model implications
For AI Tool Adoption:
Implement basic operational discipline (use cases, security, shared learning)
Create simple guidelines about what goes into AI tools and what doesn't
Build structured processes for tool evaluation and team coordination
Focus on workflow integration, not individual experimentation
The Strategic Reality Check
To get AI right you need to understand what game you're actually playing.
If you're at a retrofit company, you're not competing with AI-first startups on their dimension. You're competing with other retrofit companies, most of whom are stuck in the same two-dimensional confusion you face. The competitive advantage goes to whoever figures out strategic thinking for product decisions and operational discipline for tool adoption.
If your company cut product teams expecting AI gains, you're playing with constraints that your competitors might not have. But you're also surrounded by companies making the same mistake, creating opportunities for relative advantage if you can solve the bandwidth problem more effectively than they can.
The companies "scrambling" aren't failing at AI adoption—they're succeeding at the wrong type of thinking on both dimensions. The solution isn't working harder; it's working more intentionally about which type of thinking to apply where.
Your Assignment This Week
Pick one dimension and fix your approach:
If you choose Product Integration: Stop asking "Where can we add AI?" Start asking "What customer problems could AI help us solve that we couldn't solve before?" Lead one strategic conversation about competitive positioning rather than feature possibilities.
If you choose Tool Adoption: Stop letting everyone figure it out individually. Start with a simple team assessment—which AI tools is your team actually using, for what tasks, and what's working versus what isn't? Create basic guidelines about appropriate use cases and data boundaries. Build one structured process for sharing discoveries and learnings across the team. (If you want a simple 4-question assessment to get started, just reply and I'll send it to you.)
The magic happens when you apply strategic rigor to product decisions and operational discipline to tool adoption. While your competitors are doing the opposite, you'll be building sustainable competitive advantage through clearer thinking rather than better technology.
The AI transformation narrative suggests you need to move faster. The reality suggests you need to think better. In a world where everyone is experimenting frantically, strategic clarity becomes your greatest differentiator.
Break a Pencil,
Michael
www.breakapencil.com
P.S. Ready to build systematic AI capabilities while maintaining strategic clarity? My next "Build an AI-Confident Product Team" cohort starts September 2. This is exactly the kind of intentional, framework-driven approach that separates sustainable competitive advantage from expensive experimentation. [Learn more here.]
P.P.S. Thank you to everyone who contributed to this reality check. Your honesty revealed something more valuable than transformation success stories: the actual challenge of building AI capabilities in the real world, with real constraints, under real pressure. The messy truth is more interesting and more actionable than the polished narrative.