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Why I measure AI’s value by time, not by productivity

Much of the conversation about AI integration right now is centered around productivity. And for good reason — about 42% of jobs include workloads that could be cut in half by AI automation, according to recent data analyzed by the Penn Wharton School of Business. That’s an estimate that has many business owners zoning in on how they could maximize worktime in ways that were previously unimaginable. There are now, in other words, 36 or more potential hours in a day. The applications feel limitless.

What’s more, the most automatable tasks are the ones that every business needs. Penn Wharton estimated that office and administrative work could be 75% automated, business and financial operations, 68%, and sales 60%.

But with all this focus on productivity, I think we’re missing something that we shouldn’t pass by. AI integration isn’t just about getting more productivity — it’s about reclaiming time.

Productivity looked good on paper but felt empty in practice

When I first rolled out AI across my workflows, I tried to justify it the usual way: with productivity metrics. I tracked hours saved, tasks automated, cycle time reductions.

This was the way I would have evaluated any operational initiative. Those inputs generated glossy charts, but they turned AI into a tool efficiency story rather than a human capacity story.

On paper, I was “succeeding.” I had three parallel projects moving, an onboarding backlog clearing, a dozen half-formed ideas captured in notes. AI gave me a veneer of multitasking. In reality, I wasn’t present. I was productive but scattered. The busier I became, the thinner my attention stretched. With ADHD in the mix, the churn was relentless and the clarity was gone.

So I tried a different approach. I delegated mental clutter to AI. I offloaded transcription, call summaries and scaffolding for the next day. The following morning I sat down and felt something I hadn’t felt in months: a clear mind, space for deep work, and room to make better decisions. That was the break in the dam. I stopped measuring only the time AI took off the clock and started measuring the time and the parts of my life that it gave me back. 

And it created a paradigm shift for me. Productivity-tracking asks, “How efficiently can I move pieces on the board?” But time-tracking asks, “How well can I see the board at all?”

Here’s a concrete example. I used to spend hours refining executive interview transcripts manually — just one of those small but essential tasks that most of us grudgingly accept, even though it’s a timesuck. I may have even told myself there was a benefit to doing it manually, whether or not that was true. When I integrated AI, the same work took roughly fifteen minutes. If I had stayed fixated on productivity, the takeaway would have been, “I saved 1-2 hours.” But the real value was what came next. With the reclaimed time, I designed a new client onboarding flow that doubled retention. More time and productivity were byproducts of the change, sure. But the real prize was that I got to zoom out and create an innovation that led to measurable increases in my bottom line. It was the kind of top-level thinking that those small tasks were stealing from me, along with my time.

Why time resonates with leaders

The shift in point of view changed how I speak to stakeholders, too. When I led with productivity claims, I saw polite nods. When I led with time, people leaned in. Most leaders aren’t craving more productivity. They’re at capacity already. They want breathing room for strategy and better decisions.

There’s a human truth here. Productivity is abstract. It belongs to the org chart. Time is universal. Everyone feels it slipping. Framing AI as a time-returner opened doors that a 20% efficiency slide never did. It moved the conversation from tools and outputs to leadership, judgment and quality of life.

In large organizations I work with, knowledge workers are adopting AI quickly, often on their own, precisely because it buys back minutes that compound into hours across a week. Research in 2024 found the majority of knowledge workers are already using AI and that usage doubled over a short window. The headline isn’t “more output.” The headline is “less drag,” which is why the message lands.

As I embraced this framing, I started pointing peers to bodies of work on attention and deep work. The empirical case matters, but honestly, it’s the lived feeling that wins people over. You don’t need a dashboard to know what it feels like to get an hour of your life back.

The moments that matter most cannot be automated

The strongest validation for measuring time came at home. When I built my first agentic workflow for executive onboarding, the work that once took six or seven hours dropped to under one. That first week, I wrapped before 5 p.m. Instead of squeezing in “one more task,” I went upstairs and danced with my daughter before her bath. It became a nightly tradition.

AI didn’t just save me time. It gave me back the time I had been trading away without noticing. There isn’t a KPI for spinning in the living room, and there shouldn’t be, but that moment changed how I approach every task in my business.

Today, I evaluate tools with this question: “Does this buy me an hour of deep work, or does it simply make me busier, faster?”

There’s also a broader health context leaders should acknowledge. The line between productive and punishing can blur fast, and it carries real costs. The World Health Organization has reported that sustained long working hours are associated with higher risks of stroke and heart disease. Time is not just a performance variable. It’s a health and sustainability variable.

Time has become my North Star metric

This isn’t just about AI anymore. Time has become my North Star. The philosophy has expanded across my stack, from comms systems to scheduling software. If an investment doesn’t buy back meaningful time or mental energy, I don’t take it down the discovery path.

Here’s how that plays out in practice:

  • Prioritization: I fund initiatives that reduce cognitive load across the team. Shorter status cycles, fewer context switches and clearer handoffs. These reduce the invisible taxes on everyone that compound over months.
  • Design: I default to systems that make the first draft automatic and the edit cycle human. I want people using their human skills in ways that matter, not for something that AI can do.
  • Metrics: I still track productivity, just not as the headline. The headline is time returned to deep work, manager one-on-ones, customer listening and strategic planning. Productivity compounds effort. Time compounds value.

It also changes how I communicate roadmaps. If I tell an executive a program improves productivity by 20%, it lands like a quarterly report. If I tell them it gives them two hours back each day,  it lands like a life choice that supports their humanity and a competitive advantage.

I don’t think productivity has a finish line. But time does. At 39, I’m realizing it’s finite, and it’s the only metric we all share. The longer I lead, the more obvious that becomes.

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Category: NewsNovember 19, 2025
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