Managers Are Struggling to Keep Up with the AI Productivity Boom

“Every 30 minutes, someone creates something I have to look at,” a manager recently told us.

As AI accelerates individual execution, a new bottleneck to progress is emerging: managers. Traditional management was designed for a world where execution took time. You could delegate a task and then comfortably check in the following week.

But AI has collapsed that timeline. Increasingly, the pace of progress is limited by how quickly managers can offer feedback. “If I discuss a project with a direct report in a 1:1,” a manager shared in an interview we conducted for this article, “It’s already shipped and running the next day.”

Research from Atlassian, where Liz works, shows that 89% of leaders agree that AI has accelerated the speed of work, creating an always-on review environment. As one manager told us, “I feel perpetually behind.” When we asked another how they were handling their team’s increased output, they just laughed. “I sleep a lot less.”

The answer isn’t to slow the team down. Every manager we spoke with for this piece was also excited by what their teams could now accomplish. But they all described the same paradox: AI has created abundance, meaning it allows teams to pursue more opportunities and ideas, but has also skyrocketed feelings of scarcity, as the new pace strains time, energy, and attention. Managers need to be across more work and make faster calls without the luxury of deep exploration they’ve been used to.

To keep up with your teams’ pace, you can no longer lead like it’s 2022. Your role has to shift from editor-in-chief to strategic guide. Here are five changes to make based on our work, research, and experience helping organizations develop high-performing managers.

Focus on where, not what.

The Atlassian research shows that 87% of knowledge workers say that with everyone in execution mode, teams lack the time or capacity to coordinate. That makes it even more crucial for managers to ensure their teams are aligned.

You no longer need to assign specific, sequential tasks. AI can execute the “what” in minutes; your focus should be on articulating “where” your team is headed. That means clarifying your mission, objectives, and key results. As one leader told us, “My team members all manage AI agents, so I’m operating like a manager of managers. I focus a lot more on defining the change we’re trying to drive.”

To make your “where” actionable, anchor on a metric. Think: reducing churn by 50%, or boosting brand authority by 20%. Tracking specific numbers also helps you figure out where you need to intervene. Reigan Combs, VP of Marketing, North America at Adyen, notes, “Driving accountability for metrics has been the single most useful principle for ensuring that my teams stay focused on the right work.”

Before moving forward, verify that every one of your directs can answer four questions:

• What is the core problem we are solving?

• What specific change are we driving?

• How will we measure success?

• Who is responsible?

Once your team understands the bigger picture, they can use AI to navigate the tactical steps without needing permission at each one.

Make managing up a core skill.

According to a recent BetterUp survey, 54% of managers report receiving AI workslop, content that appears polished but lacks real substance. To avoid this, clarify what good looks like. “I have a rule that my team cannot send me something AI-generated that they didn’t read or edit,” said Hamed Faquiryan, Executive Director of Quantitative Research at MSCI. “Because then, I might as well talk to the LLM myself. We need to stand behind anything we submit, even if it was partially AI generated.”

Help your team understand the altitude at which you want to review specific work, and which decisions you actually need to be a part of. For example, you might say, “Before you work on a full draft, I’d like to review a half-page outline to ensure we’re on the right path,” or “I need to approve the final version of anything executive-facing.”

Deploy AI as an EQ coach.

When you’re time-constrained, it’s easy to optimize for information density and forget about tonal cues. But messages that are intended to be efficient often read as curt or dismissive. Fernando Garcia Valenzuela, Head of Engineering, Atlassian Cloud Storage, built an agent that scans his DMs with his direct reports. Every two weeks, it generates a summary flagging poor tone, missed recognition, and relationship-building opportunities.

“What I didn’t expect was how consistent the small things were,” Garcia Valenzuela told us. “Single-word confirmations or reassurance without anything concrete to anchor it. None of these are dramatic failures. But across hundreds of messages and dozens of relationships, they add up.”

For example, when a direct report shared a well-researched project pivot, Valenzuela replied in three quick messages:

“Ok.”

“That’s unfortunate.”

“Good thinking, glad we failed fast.”

The agent flagged the exchange. While the final message was supportive, the first two risked reading as indifference. The agent’s coaching was simple: Lead with the recognition.

Use AI to filter, not flatten.

Managers need broad context, but they also need depth on the work that matters most. Relying too heavily on AI summaries can be a trap. “I’ve found AI summaries to be useless in most contexts,” Dr. Stefanie Tignor, Head of Data Science at Superhuman, told us, “They flatten everything: slop is summarized the same as high-quality work, and the result is something too general and broad to really act on or inform me of anything new.”

Instead of solely asking AI to condense information, use it to understand which work merits more careful review. Dr. Tignor automated a weekly digest that picks up on signals across channels to surface links to important projects. “I have AI find me the five individual, specific nuggets that I know will be useful for me to go deep in.” Signals that indicate high-impact work to Dr. Tignor include a statistically significant metric movement, a project shared on Slack that receives a lot of comments, a data point that appears in multiple executive decks, and work that is discussed at length in meeting notes.

Restructure your check-in cadence.

Because AI accelerates execution, you may want to adapt both the content and frequency of meetings:

• Automate status updates to elevate 1:1s. Dr. Caribay Garcia Marquez, Principal Manager & Head of People Science R&D at Microsoft Viva, relies on AI-generated status updates so she can spend 1:1s on conceptual coaching: discussing how to approach complex decisions, offering richer context around specific feedback, and connecting daily work to broader business goals.

• Increase frequency to course-correct faster. “We can prototype things much faster,” says MSCI’s Faquiryan. “But that means people can waste real money on tokens or lose enormous amounts of time running far in the wrong direction.” To prevent this, especially with more junior reports, supplement or replace weekly check-ins with frequent, 15-minute ad-hoc syncs to keep everyone on the same page. Chris Gomes, VP of Product at Conveyer, had his teams stop creating weekly reports and hosting biweekly product reviews. “It was all too slow. Instead, I told my managers they should be meeting with their teams daily.”

Work isn’t going to slow anytime soon. Managing the old way is a one-way ticket to burnout and disengagement.

The most effective managers will be the ones who realize their job is no longer to police the work, but to clear the path. By setting direction, trusting your team to drive, and demanding synthesized communication, you can accelerate your team’s progress; not throw sand in the gears.

Copyright 2026 Harvard Business School Publishing Corporation. Distributed by The New York Times Syndicate.

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