Decide Where the Time Saved by AI Goes

When AI helps a team finish work faster, leaders still have a decision to make. Where should that time go?

It can become better customer follow-up, more thoughtful decisions, or room to fix a problem everyone has been working around. It can also disappear into extra reporting, more requests, and another round of revisions. A faster task doesn't automatically create a better working week.

Jabra's new workplace research gives leaders a timely reason to examine that gap. The practical question is whether your team has a shared plan for the capacity AI creates.

What Jabra's new research tells us

On October 13, 2026, Jabra released WorkLife 2026, based on a survey of more than 6,000 knowledge workers in six countries. Toluna conducted the research in May 2026, so the publication is new this week while the responses describe an earlier period.

According to Jabra's announcement and methodology, 85% of respondents reported more work in at least one area compared with a year earlier. Nearly half, 49%, said they spent more time on administrative work. Among frequent AI users, 64% reported more collaboration with colleagues, compared with 25% of less-frequent users.

These are self-reported associations from vendor-sponsored research. They don't establish that AI caused workloads to increase. They also don't tell you whether a particular business is getting better results.

They do raise a useful management question. If individual tasks are getting easier, what is happening to the work around them?

Account for the whole task

Start by looking at the work from the initial request to an accepted result. Include preparation, checking, corrections, coordination, and the person who uses the output next.

Consider a hypothetical team that spends ten hours each week preparing recurring updates. AI reduces preparation to four hours, and review takes another two. That leaves four hours to allocate. If the team immediately doubles its reporting frequency, the apparent saving may disappear.

That might be a reasonable choice if customers need more frequent updates. It deserves an explicit decision and a reason to believe the extra reporting helps.

The same applies when a faster first draft creates more versions for someone else to review. Ask the downstream team what changed before counting the improvement. Useful measures include total time to completion, corrections required, and whether the recipient can act on the result.

Give the saved time a purpose

Before expanding an AI-supported workflow, name the priority the freed capacity will serve. Be specific enough that the team can recognize progress.

Good choices might include:

• Following up with customers whose questions have remained unresolved

• Removing a recurring source of rework

• Giving managers time for coaching that keeps getting postponed

• Improving a service handoff that regularly causes delays

• Reducing avoidable overtime during a sustained busy period

Choose with the people doing the work. They can identify constraints that aren't obvious from a leadership dashboard, including tasks that arrive informally or corrections nobody records.

Also distinguish available time from usable time. Saving five minutes across several scattered tasks may reduce frustration without creating an uninterrupted hour for planning. Combining work differently or changing a meeting schedule may be necessary before the benefit becomes usable.

For teams developing agentic workflows, include this capacity decision before expanding the work an agent handles.

Make room by retiring work

An AI rollout can make it easier to create things the business no longer needs. Before generating a daily report, ask who reads the weekly version and which decision it supports.

Bring one recurring activity to the next team meeting. Ask when it last changed a decision, whether another source provides the same information, and what would happen if you stopped producing it for a month.

Some activities must stay because of customer commitments or other requirements. Others can be shortened, combined, or removed with the right approval.

Give managers permission to make those choices within clear boundaries. A team asked to adopt new tools while maintaining every existing deliverable has little room to improve how it works.

Run a four-week capacity review

Use a small review to test whether the change helps the team and the business. Keep the measurement lightweight enough that it doesn't become another project.

1. Choose one recurring workflow. Start with work that happens often enough to compare, such as preparing customer updates. Describe an acceptable result and record a baseline for completion time and rework.

2. Agree on the intended benefit. Decide where any freed capacity will go. For example, the team might use it to resolve outstanding customer questions within an agreed response window.

3. Track what moved. Each week, review preparation time, checking time, corrections, and new requests. Ask the next person in the workflow whether their workload changed. Use estimates where exact tracking would create unnecessary overhead.

4. Protect the chosen priority. Put the intended use of the time into the schedule. If an urgent request displaces it, name the tradeoff rather than silently adding both commitments.

5. Decide what to change. At the end of four weeks, compare quality, completion time, and the intended customer or team outcome. Keep the improvement, adjust the workflow, or pause the experiment if the extra work outweighs the benefit.

Use this review to improve the workflow. Avoid turning it into a ranking of individual employees by AI usage. People should be able to report slowdowns, mistakes, and unhelpful outputs without feeling they are failing an adoption target.

Keep the decision with leadership

Managers need more than a target to use AI. They need the authority to change priorities, remove unnecessary work, and explain where the benefit is going.

A fractional CAIO can help connect those decisions across teams when the work lacks a clear executive owner. The day-to-day choices still need to involve the people responsible for delivery.

At your next leadership meeting, choose one workflow where AI appears to be saving time. Ask the team what became easier, what became harder, and which important work gained room as a result.

Then make one visible decision about where that capacity belongs. Your team should be able to point to the improvement in its working week and the result it supports.