Logitech's Chief AI Officer hit 80% adoption across 7,000 people. Then he asked the harder question.
Eric Porres had the dashboard every AI leader wants.
Millions of AI interactions per month. Adoption climbing past 80% across a 7,000-person company. The kind of numbers that make board decks look great and earn you a speaking slot at the next conference.
Then he sat with it honestly and asked the harder question: has anything actually changed?
Same meetings. Same reports. Same five-day approval chains. Same Thursday. Logitech had gone from AI-curious to AI-competent, and bought a year of motion and called it progress.
That's the lesson Eric shared at AUTONOMOUS: OBSOLETE this month. And it's the one I think most companies need to hear right now. Usage is a vanity metric. It measures activity. It doesn't measure change.
If a team has used AI for six months and can't name one thing they've deleted, they don't have adoption. They have novelty.
So Logitech changed the question. They stopped asking "who is using AI" and started asking "what have you deleted?"
Deleted. The recurring meeting that's off the calendar. The report nobody builds by hand anymore. The approval chain that went from five days to two hours. Eric put a fine point on it: if a team has used AI for six months and can't name one thing they've deleted, they don't have adoption. They have novelty.
That distinction is critical and I think it's exactly right. And it leads to the question that actually compounds: what have you created for others? Not just for yourself. For your team. Finding a repeatable workflow, infusing it with AI, and making it something the whole group runs on.
Deleted and created. That's the system. At Logitech, they built an internal measurement called Pulse to track exactly this. Think of it like heart rate variability for your organization's AI fitness. A vital sign, not a scoreboard.
The bottleneck isn't where you think
Once you start measuring deletion, the real bottleneck shows up fast. And it's not where you'd expect. It's not the models. Eric said the models have been good enough for a while. It's not your frontline employees either. They're curious. They'll try anything.
It's the middle.
The middle manager isn't blocking out of fear of the technology. They're blocking because they're still measured on the old thing. The old report. The old process. Nobody told them they're allowed to stop doing it. Eric's framing: the constraint was never model quality. It's human permission quality.
I wrote about something connected to this in The Leader's Dilemma. My argument was that your people aren't resisting AI. They're resisting ambiguity. And when you don't tell them what's allowed to change, they assume nothing is. The middle manager who keeps running the five-day approval chain isn't being stubborn. They're being rational. They're optimizing for the metric they're measured on.
The sequence almost everyone runs backwards
Eric's answer to that is a sequence that I think almost everyone runs backwards. The instinct is that more training leads to more adoption. It doesn't. Here's what actually works: change the expectation first. Tell your team the report is now AI-drafted and the human edits. That's the job now. The changed expectation forces the workflow to change. The changed workflow produces visible time savings. And then training lands, because now people actually want it.
Expectation → Workflow → Savings → Training. In that order.
The second move is that embedded beats optional. If AI is just a tool people choose to open, usage is fragile. When you build it into the workflow itself, it stops being a choice and becomes infrastructure. It becomes just how work gets done.
The control plane
Eric was honest about the risk too. When execution gets cheap, value and risk concentrate in the same place. An agent that closes your books in an hour can close them wrong in an hour. You can't scale this without a control plane. Who can act on what data, with what audit trail, who reviews it. Logitech has agents running against real employee records. That means sandboxes and audit trails on every action.
It's slower at first. Especially in a landscape where every week brings a new model drop. It will feel slow. Eric's line on this one stuck with me: obsolescence without governance is just chaos with a demo.
What actually goes obsolete
And then he reframed what actually goes obsolete. Three things, and only one is software. The per-seat SaaS model, because when an agent does the work you don't buy 50 seats anymore. The usage dashboard, because the entire vanity layer of AI metrics becomes obsolete the moment you measure deletion instead. And the manager who measures activity instead of outcomes.
But here's where Eric landed that I think matters most. What goes obsolete is the small version of the job, so a bigger one can take its place. You won't lose your job to AI. But you might lose it to someone who cleared the old work off their plate while you were still doing it by hand.
That expansion is real, and it's not free. The price is paid in deletion. And here's the hardest part. We've all learned to give permission to fail. Fail fast. That grant is easy because it's about things that don't exist yet. Permission to delete is much harder. It means letting people eliminate what already exists. The report with an owner. The process the manager is measured on.
Permission to fail is about the future. Permission to delete is about the present. And it's the one this moment requires.
By the numbers
80%. AI adoption across 7,000 employees at Logitech. Millions of interactions per month. The dashboard looked great. The work underneath hadn't changed.
5 days → 2 hours. What happens to an approval chain when you actually give people permission to redesign it. Deleted and rebuilt from scratch.
45 years. How long Logitech has been operating. The greatest pushback wasn't technology. It was institutional muscle memory in a company older than most of its employees.
AUTONOMOUS: OBSOLETE · Logitech Chief AI Officer · May 2026
What to do this week
My advice: run Eric's delete test on your own team this week.
Ask every person who reports to you: name one thing you've deleted since you started using AI. Deleted. Gone from the calendar. Gone from the workflow. If they can't name one, that's your signal. You don't have an adoption problem. You have a permission problem.
Then ask yourself: what are you still measuring your middle managers on that AI has already made unnecessary? The weekly status report. The manual reconciliation. The five-step review. If those metrics are still on someone's scorecard, you're the bottleneck.
Permission to fail is about the future. Permission to delete is about the present. Grant the second one this week.
From the portfolio
The delete test surfaces where transformation is really stalling. And it's almost never at the technology layer. It's the organizational layer. The permission layer. The accountability structure.
Board of Innovation hosted AUTONOMOUS: OBSOLETE, the summit where Eric shared this. They're an AI Transformation Studio that helps mid-market and Fortune 500 companies redesign how they work when AI removes the assumptions their operating model was built on. When Eric talks about institutional muscle memory in a 45-year-old company, that's the exact inertia BOI helps organizations break through.
AlignAI is where the control plane Eric described meets enterprise reality. When he talks about agents running against real employee records and needing sandboxes and audit trails on every action, that's exactly the gap AlignAI closes. Structured refinement and approval so AI initiatives move from experiment to production without creating risk. They're also building an AI COE Collective in partnership with Slalom for leaders navigating this exact inflection point between adoption metrics and actual organizational change. If you're building or scaling AI in your enterprise, you shouldn't be doing it alone.
Permission to fail is about the future. Permission to delete is about the present. And it's the one this moment requires.
Talk Tuesday,
Jason Hauer
Founder & CEO, HauerX Holdings
jason@hauerX.com
Jason Hauer is the founder and CEO of HauerX Holdings, where he backs and builds a portfolio of AI-native companies that accelerate how businesses grow, operate, and compete. From mid-market to Fortune 500.
Frequently Asked Questions
What does Eric Porres mean by "what have you deleted?"
Deleted means gone. The recurring meeting that's off the calendar. The report nobody builds by hand anymore. The approval chain that went from five days to two hours. If a team has used AI for six months and can't name one thing they've deleted, they don't have adoption. They have novelty.
Where is the real bottleneck in enterprise AI adoption?
Not the models, they've been good enough for a while. Not the frontline employees, they're curious. It's the middle. Middle managers are blocking because they're still measured on the old thing. Eric's framing: the constraint was never model quality. It's human permission quality.
What's the correct sequence for driving AI adoption?
Most companies run it backwards. The instinct is that more training leads to more adoption. Eric's sequence: change the expectation first (the report is now AI-drafted and the human edits), which forces the workflow to change, which produces visible time savings, which then makes training land because people actually want it. Expectation → Workflow → Savings → Training.
Why does Logitech run sandboxes and audit trails on every agent action?
When execution gets cheap, value and risk concentrate in the same place. An agent that closes your books in an hour can close them wrong in an hour. You can't scale this without a control plane: who can act on what data, with what audit trail, who reviews it. Eric's line: obsolescence without governance is just chaos with a demo.
What are the three things going obsolete, according to Logitech's CAO?
The per-seat SaaS model, because when an agent does the work you don't buy 50 seats. The usage dashboard, because the entire vanity layer becomes obsolete the moment you measure deletion instead. And the manager who measures activity instead of outcomes. Only one of the three is software.
