The Barriers to AI Value Aren't Technical. They're Organizational—and They're Also Personal
- mbhirsch
- 16 minutes ago
- 4 min read
There's a CPO at a $4B company who built her own analytics connection to Snowflake. She created an agent system that triages her email, analyzes her calendar, and preps her for meetings. She's rewriting her team's career ladder to include AI capability expectations.
She didn't file a ticket with IT. She didn't wait for HR to figure out AI-era career development. She built what she needed.
There's a senior PM at a martech company who invested in her own development—courses, experimentation, building in public. She developed her own strategic framework for evaluating AI opportunities. She became the most vocal AI advocate in her organization—not because anyone asked her to, but because she'd done the work to have informed opinions. She's now leading AI development and presenting roadmaps to Fortune 500 clients.
She didn't wait for her company to create an "AI Leader" role. She built the capability, demonstrated it publicly, and the recognition followed.
Neither waited for their organization to build the infrastructure they needed.
The conversation about AI and careers has been stuck on the wrong axis.
"Will AI take my job?" versus "How do I use AI better?" both miss the point.
The real question is: are you building, or are you waiting for someone else to build for you?
New research on AI adoption keeps confirming the same uncomfortable pattern. As Misha Sulpovar writes in The AI Executive's Handbook, a few power users bend the curve while most see little or nothing. AI's ROI is lumpy, unpredictable, and unequally distributed.
The standard explanation is skill gaps. Some people are better at prompting, more technically comfortable, earlier adopters.
That's not what's actually happening.
The power users aren't just better at AI. They're doing something structurally different. They're building context layers, systematic workflows, personal knowledge infrastructure. Everyone else is using AI for one-off tasks within workflows someone else designed.
That's not a skill gap. That's an ownership gap.
I've written recently about the Capability Core—the organizational infrastructure that makes AI actually useful. Context engineering. Workflow discovery. Learning systems. The configuration layer that most organizations haven't built.
What I didn't say explicitly was that you don't have to wait for them to build it.
The Capability Core is organizational infrastructure. But you can build a personal version.
Your own context layer. The accumulated knowledge, documented decisions, and strategic context that makes AI useful for your specific work. Instead of re-explaining your product strategy, customer segments, and competitive dynamics every single prompt—you build it once and maintain it.
Your own workflows. Not prompts you copy from LinkedIn, but systematic approaches you've developed, tested, and refined for your recurring decisions. The CPO I mentioned built agents for calendar analysis, email triage, meeting prep. You might not have her technical setup, but you can build systematic approaches for your own recurring work.
Your own learning system. How do you extract what's working and build on it? Most people try something, get a result, move on. The ones capturing disproportionate value are documenting what works, understanding why, and compounding their capability over time.
None of this requires executive authority. It requires deciding you're not going to wait.
When I work with aspiring product leaders, one of the first pieces of advice I give them is to start acting like a product leader before they have the title. Provide coaching, mentoring, and guidance even without formal authority. Position yourself as a leader by doing the work of leadership.
The same logic applies here.
Start building the decision infrastructure before you have the authority to mandate it.
Not because you're going to single-handedly transform your organization. But because your own effectiveness depends on it—whether or not your org provides it.
And there's a compounding effect most people miss. When you're using AI to work faster within workflows someone else designed, you're extracting value now. Real value. But when the tools change, when the organizational context shifts, when someone redesigns the process—you're back to learning mode. The person who built the workflow extracted value and developed the capability to build the next one. Their value compounds. Yours resets.
"The person who built the workflow extracted value and developed the capability to build the next one. Their value compounds. Yours resets."
There's a part no one talks about: when you build your own capability infrastructure, you get ahead of your organization.
You start seeing gaps they haven't identified. You develop opinions about how work should flow before they've formed a committee to discuss it. You become the person asking uncomfortable questions in meetings—not because you're difficult, but because you've done the work to know what questions to ask.
That's uncomfortable. For you, because you're operating ahead of the support structure. For your organization, because you're surfacing things they're not ready to address.
But no one gets ahead by making sure things stay comfortable. Building your own Capability Core is a bet on yourself—a statement that your development isn't contingent on whether your company figures this out first.
"No one gets ahead by making sure things stay comfortable. Building your own Capability Core is a bet on yourself."
The senior PM I mentioned didn't wait for her company to train her on strategic AI thinking. She invested in her own development, became vocal about what she was learning, and positioned herself as someone who'd already figured it out. When they needed someone to lead AI development, she wasn't competing for the role. She'd already been doing it.
That's how AI champions are made. Not appointed. Made—by the people who started building before anyone gave them permission.
The barriers to AI value aren't primarily technical.
We know they're organizational. Most companies haven't built the systematic infrastructure for AI capability development. They've bought tools and hoped adoption would follow. It didn't.
But the barriers are also personal.
If you're waiting for your company to build the Capability Core, you might wait forever. If you're waiting for better tools, you're optimizing the wrong variable. If you're waiting for someone to hand you the context layer, the workflows, the learning systems you need—you're choosing to be a passenger in your own career.
The people leading AI capability in their organizations aren't leading because someone gave them authority. They're leading because they started building.
The question is whether you're building or waiting.
If you're a product leader who wants to build systematic AI capability in your team—not just individual tool proficiency, but the organizational infrastructure that compounds—I'd welcome a conversation. [Schedule time here.]
Break a Pencil,
Michael
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