Product Leaders Lab Show: The AI adoption pressure
Leadership is pushing hard on AI. Your team is overwhelmed. In episode 1 of the Product Leaders Lab show, we see how two product leaders handled that exact moment in completely different ways.
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Leadership wants AI, now. Your team is overwhelmed and worried about their jobs. The LinkedIn noise is deafening. You have an all-hands tomorrow morning.
What do you do?
That's the scenario at the center of the very first episode of Product Leaders Lab — a new documentary-style podcast where I take one impossible leadership moment and show you how two different leaders actually handled it. Not just what they did. How they felt. What trade-offs they made. What actually happened.
For this episode, I talked to two senior product leaders who lived through this exact pressure: Joe King, Sr. Director of Product at Crucial Learning, and Greg Leach, Senior Director of Product at 7shifts. Same pressure. Two completely different solutions.
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The scenario nobody warned you about
It's January 2025. Your CEO sends a company-wide memo: AI is the top strategic priority. Product and Engineering will lead the way.
Within hours, Slack is blowing up. Your senior PMs are asking whether their jobs are going away. Your team doesn't know which tools to use or how to use them well. And everyone keeps seeing posts about other companies shipping AI features faster.
You have a team all-hands tomorrow morning. What does Maya — or you — do?
Here's what makes this moment genuinely hard. It's not one problem. It's three colliding at once.
Market pressure. As Joe described it, his company went from fear of AI two years ago — worried about IP protection — to now actively thinking about how to add AI features and create internal efficiencies. The market shifted fast, and companies that were cautious are scrambling to catch up.
Team anxiety. Greg put it plainly:
"I feel like across our employee base that people felt like they were falling behind. Like, who knows what my type of job will look like in three years, right? If I'm a marketer, I'm a salesperson, if I'm a product person, I'm an engineer."
The hype fog. On top of the real pressure, your team is scrolling LinkedIn and can't tell what's real. As Greg noted: "What is on social media is not real life — or sometimes it is. And I think it's very hard to judge on that right now."
That last part is key. Even experienced leaders are struggling to separate signal from noise. Your team isn't being irrational. The uncertainty is real.
Greg's approach — build the system
Greg chose to centralize AI adoption, but not by hiring a dedicated person or bringing in consultants. He built a cross-functional AI committee.
Here's how it worked: he asked for volunteers — people who actually wanted to work on this — and made sure every department had representation. Marketing, sales, engineering, product. The committee ended up being 15 to 17 people, and Greg volunteered to be the chair.
Their first job was to understand what was actually broken. They went deep on employee survey feedback and found three consistent problems.
The first was that there was no clear company viewpoint on AI. A document existed, but as Greg said: "It's like a document that no one had — maybe it had been shared in a Slack group, but you know how people forget." The fix sounds simple: document it, share it, make it consistently visible.
The second problem was tool access and budget. People didn't know how to get an AI tool approved. The approval process could take two and a half weeks, which was enough friction to kill experimentation entirely. The committee documented the process, streamlined it, and — crucially — created department-level budgets so teams could try tools without a lengthy approval chain.
The third problem was learning. People knew they needed to use AI but didn't know how to build workflows.
"Once you figure out a workflow and how to leverage AI to do that workflow, I'd say the biggest challenge is it sometimes takes four or five hours to set up that process."
Four or five hours. Most people give up before they get there. Greg's solution was to create ways for people to learn from each other rather than struggle alone.
The mechanism that worked best: AI demos. Bi-monthly sessions where anyone could volunteer to show how they were using AI — not just engineers, not just product, anyone who'd figured something out. The format was simple: here's the problem I had, here's what I built.
The results surprised even Greg:
"We had 100 to 120 people show up voluntarily. So that shows you the interest of like people that want to learn how to use this, right? We would just make a couple of posts in Slack — 'hey, come to this.'"
Out of 230 people, more than half showed up voluntarily. People walked away saying "I didn't know AI could do that." Greg called it the osmosis effect — and it's a good description. When people see real workflows from real colleagues solving real problems, it unlocks curiosity in a way that top-down mandates never can.
To keep the committee from dying a quiet death (it's everyone's side job, after all), Greg built in a forcing function: a monthly AI committee update at the company all-hands. Accountability built into the rhythm of the business.
Joe's approach — invest in leadership first
Joe took a completely different path. Instead of building a committee, his company made a concentrated investment: they sent four leaders to Kellogg School of Management for a two-part AI certification. His CPO, head of engineering, Joe himself, and an engineering lead.
Why Kellogg instead of a consultant or a cheaper course? Joe was direct about it:
"My company has this background in learning. We wanted to go to an institute that has history, is well-renowned, and has a professor who is kind of leading the charge on AI."
They wanted frameworks, not tactics. Something that would outlast whatever tools are popular this quarter.
The investment was around $12,800 total. Four leaders. Two courses — part one focused on AI transformation for the business, part two on AI product strategy. And four leaders out of the office for months.
But here's the part that made the investment work: they went together.
"I think that's the key — the shared experience, shared language. So often if you're not on the same page, it's gonna be hard to move things forward. But now the four of us can provide a shared recommendation and move forward."
Four leaders going through the same experience, building the same vocabulary, developing the same frameworks — and coming back already aligned. No translation needed when they returned.
The plan from there was to build a structured vision and cascade goals down to the broader team. As Joe put it:
"We have our ideas and now we can validate them against tested theories and frameworks before just jumping in and going. Which I don't think there's anything wrong with — I just think our business is more methodical in our approach."
That last line matters. Joe wasn't positioning their approach as universally correct. It fit their culture, their risk tolerance, and their need for alignment before action.
What you can learn from both
These two approaches aren't in conflict. They're optimized for different contexts.
Greg's committee approach works when:
- Your team has the energy and capacity to participate
- You want to build a culture of distributed learning
- You need to remove friction (process, budget, access) quickly
- You want momentum that doesn't depend on one person
Joe's leadership investment works when:
- You need leadership aligned before anything else moves
- Your organization is methodical and needs frameworks before tactics
- You have the budget to go deep rather than broad
- You can tolerate the team waiting while leadership learns
The trap most product leaders fall into is treating AI adoption like a feature launch — announce it, ship it, declare victory. Neither Greg nor Joe did that. They both recognized that adoption is a cultural and organizational challenge, not just a technical one.
Greg removed the barriers: the red tape, the unclear process, the "I don't know where to start." Joe removed the strategic uncertainty: the "we have ideas but no frameworks to validate them against."
Both were trying to answer the same underlying question their teams were asking: Can I trust that leadership has a real plan here?
Key takeaways
- The anxiety is real — acknowledge it before you strategize around it. Your team isn't being dramatic. They genuinely don't know what AI means for their role. Address that directly before you launch any adoption initiative.
- Voluntary participation beats mandates. Greg's 100+ person turnout at optional AI demos tells you something important: people want to learn. They just need a low-friction way to do it.
- Shared language matters as much as shared knowledge. Joe's biggest insight wasn't the course content — it was that four leaders went through it together. Alignment at the top cascades down.
- Remove the red tape first. If getting an AI tool approved takes two and a half weeks, adoption will stall. Solve the process problem before you solve the learning problem.
- Frameworks outlast tools. Whatever AI tools exist today will look different in 18 months. Leaders who invest in strategic frameworks for thinking about AI will outlast leaders who invested in learning specific tools.
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The bigger picture
What struck me most about both Joe and Greg's approaches is what they weren't doing. They weren't panicking. They weren't copying what they saw on LinkedIn. They weren't mandating AI use and hoping for the best.
They were leading. Thinking carefully about their specific teams, their cultures, their capacity. And making intentional choices about how to move forward.
That's the job. And it's harder than any AI tool will ever make it.
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