As production gets easier, choosing the work becomes more consequential.
A team can use AI to produce more analyses, proposals, summaries, and plans. Each item can look useful in isolation. Together, they can create a growing demand on everyone else's attention. Someone still has to read, judge, connect, and act on the work.
This is the part of the future-of-work conversation I find most interesting. Expanding what a team can produce also expands the set of things it can choose to do. Leadership has to make that choice more deliberately.
Consider an illustrative weekly reporting process. AI reduces preparation time dramatically. The natural response is to make the report more detailed or produce it more often. Before doing either, I would ask who uses it and which decision it changes.
If the report primarily reassures people that work is happening, faster production may preserve a weak process. If it helps someone allocate scarce capacity, the opportunity might be to surface the few changes that require a decision. The useful redesign depends on the purpose.
This calls for looking at the portfolio of work, including the small recurring obligations that rarely appear in a strategic plan. Meetings, status updates, reconciliations, internal presentations, and requests for information all consume attention. Making them cheaper to produce can increase the volume unless someone changes the demand.
There is a practical question worth asking before automation: if we stopped this activity, what would become worse, for whom, and how soon would we know?
Some answers will reveal essential work whose value has been underappreciated. Others will expose a report without a reader, an approval without a decision, or a meeting that exists because information is difficult to find. AI can help us investigate those patterns, but the choice about what deserves to continue remains a leadership responsibility.
I also want to protect space for a more ambitious question: what have we not attempted because the effort seemed disproportionate to the opportunity?
In my recent writing, I have been exploring AI for efficiency and AI for opportunity. The first lens examines existing work. The second examines possibilities that a changed constraint brings within reach. Both belong in the conversation.
Imagine a professional community with years of useful discussions that are difficult to navigate. An efficiency approach might summarize each new session more quickly. An opportunity approach might test whether members can explore the accumulated knowledge around their own challenges, with clear sources and a route to human discussion.
The second idea introduces work the organization was not previously doing. Its value would need to be tested: do people find relevant knowledge, apply it, and make better decisions? The point is to give that possibility a fair hearing before every available hour is absorbed by existing routines.
That makes released capacity a strategic resource. A leader can explicitly allocate some of it to service improvement, learning, or experiments. Without that choice, the organization may simply add more assignments to the same people and call the result transformation.
There is also a question about professional development. Much expertise grows through doing work, receiving feedback, and recognizing patterns over time. If AI takes over parts of that work, leaders should examine how people will continue to develop the judgment needed to supervise it.
For example, a junior colleague who always receives a polished analysis may have fewer opportunities to notice how an ambiguous question was framed. A redesigned learning path could ask them to make an initial assessment, compare it with the AI-assisted version, and explain the differences. The work changes, and the learning design should change with it.
Performance expectations need attention too. If employees are rewarded primarily for volume, making production easier can encourage more output regardless of its usefulness. Measures of customer impact, decision quality, resolved problems, and reduced downstream effort give a different signal about what the organization values.
This does not mean every activity needs an immediate financial return. Exploration, relationship-building, and professional learning can be worthwhile before their effects are measurable. Leaders should describe why that work matters and what evidence would justify continuing, changing, or ending it.
A practical starting point is a work review alongside the technology review. Select one team and examine what should continue, what should change, what can stop, and what newly possible work deserves a test. Invite the people receiving the team's output, because they can often explain its value more clearly than the people producing it.
I would expect that conversation to be uncomfortable in useful ways. It may expose an executive request that no longer serves a purpose. It may reveal that the most valuable use of AI is to make space for work leadership has repeatedly postponed. It may show that a slower, more thoughtful exchange deserves to remain human.
If your team could produce twice as much next quarter, which work would you deliberately decline to expand—and what would you finally make room to try?