Discussions of AI in the workplace tend to focus heavily on which specific tasks might be automated — a narrow, if important, part of a much broader shift. The more consequential change, for anyone managing people, is less about which tasks disappear and more about how the role of management itself needs to adapt as AI tools become embedded in ordinary workflows.
Why This Is a Management Question, Not Just a Technology Question
It’s tempting to treat AI adoption as primarily an IT decision — which tools to license, which processes to automate. In practice, the harder and more consequential questions sit squarely in management’s domain: how does a manager evaluate work when part of it was produced with AI assistance? How does a team maintain genuine skill development when a tool can shortcut some of the learning that used to happen through repetition? How does trust get built when it’s not always obvious which parts of a piece of work reflect a person’s own judgement versus a tool’s output? None of these are technology questions. They’re management questions that happen to have been created by new technology.
What’s Genuinely Changing
What counts as valuable individual contribution. As AI tools take on more of the routine execution that used to demonstrate someone’s competence, the specifically human contribution — judgement, framing the right question, knowing when a tool’s output is actually wrong — becomes relatively more valuable, and managers need to learn to recognise and evaluate it directly, rather than relying on older proxies for competence that assumed all the execution was done manually.
How skill development happens. A significant amount of professional skill has traditionally been built through repeated, hands-on execution of tasks that are now, at least partially, automatable. If a tool increasingly handles that execution, the learning that used to happen incidentally through doing the work needs to be built deliberately instead, rather than assumed to happen automatically.
What managers need to understand technically. A manager doesn’t need to become a technical expert in the specific tools their team uses, but a reasonable working understanding — what the tools are actually good at, where they reliably fail, what kind of oversight their output genuinely needs — is increasingly necessary to make good decisions about how the team should use them.
The pace of decisions. Tools that can quickly generate options, analysis, or drafts compress the time available for a decision, which shifts more of a manager’s value toward quickly evaluating quality and making a sound judgement call, and relatively less toward the patient, extended deliberation that used to be more available by default.
What genuine trust in a team member’s work looks like. When it’s unclear how much of a given piece of work reflects a person’s own judgement versus a tool’s assistance, evaluating quality and giving useful feedback both become somewhat more complicated, and managers need new, more explicit conversations about this rather than assuming the old assumptions still apply.
How Managers Actually Adapt
Build genuine, working familiarity with the tools your team actually uses. This doesn’t require deep technical expertise, but enough hands-on familiarity to have an informed opinion about where a given tool adds genuine value and where its output needs real scrutiny.
Redefine what “good work” looks like, explicitly. If the process of producing a piece of work has changed, the standard for evaluating it likely needs to change too — being explicit about this with your team, rather than leaving it ambiguous, prevents confusion about what’s actually being assessed.
Build deliberate skill development back into the process. If a tool is shortcutting some of the repetition that used to build capability, consider what deliberate practice or stretch assignments might need to replace that incidental learning, rather than assuming skill will develop on its own the way it used to.
Have direct conversations about how AI assistance is being used. Rather than treating it as an implicit, unspoken issue, ask directly how team members are using available tools, and what judgement they’re applying to the tools’ output — this builds a shared, explicit understanding rather than leaving it to individual assumption.
Stay genuinely curious rather than either uncritically enthusiastic or reflexively resistant. Both extremes miss useful information — uncritical enthusiasm risks over-trusting tool output without adequate scrutiny, while reflexive resistance risks missing genuine efficiency gains. A grounded, curious middle ground serves a team better than either.
A Practical Scenario
A manager notices that the quality of a particular type of deliverable from her team has become noticeably more consistent since a new AI-assisted tool was adopted — but she also notices that a newer team member, who’s leaned heavily on the tool from the start, seems to struggle more than expected when asked to handle a similar task without it, or to catch an error the tool missed.
Rather than banning the tool or ignoring the gap, she has a direct conversation about it, and deliberately builds in periodic exercises where the newer team member works through a task without the tool’s assistance, specifically to build the underlying judgement the tool had been quietly substituting for. The tool remains part of the regular workflow, but the manager has explicitly ensured that genuine skill development is still happening underneath it, rather than assuming it would happen automatically the way it once did.
Common Mistakes
Treating AI adoption as purely a technology decision. The genuinely difficult questions — evaluation, skill development, trust — sit in management’s domain, not IT’s alone.
Assuming skill development will happen automatically despite a changed process. If a tool is handling some of what used to build capability through repetition, deliberate steps are often needed to make sure that learning still happens.
Leaving expectations about AI use ambiguous. Without an explicit, shared understanding, team members are left guessing at what’s actually expected, which produces inconsistent practices across a team.
Reacting with either uncritical enthusiasm or reflexive resistance. Both extremes miss useful information that a more grounded, curious stance would pick up.
Action Steps
- Build some genuine, hands-on familiarity with a tool your team currently uses, rather than relying entirely on secondhand understanding.
- Have an explicit conversation with your team about how AI-assisted tools are currently being used, and what standard of oversight their output receives.
- Identify one area where a tool might be quietly substituting for skill development, and consider what deliberate practice could restore that learning.
- Revisit what “good work” actually means for a specific deliverable that’s changed as a result of new tools, and communicate that standard explicitly.
- Notice your own instinctive reaction to a new AI tool — is it uncritical enthusiasm, reflexive resistance, or genuine, grounded curiosity?
Key Takeaways
- AI’s effect on management goes well beyond which specific tasks get automated — it changes evaluation, skill development, and trust.
- A working, if not deeply technical, familiarity with relevant tools is increasingly necessary for good managerial decision-making.
- Skill development that used to happen incidentally through repeated execution may need to be built deliberately as tools take on more of that execution.
- Explicit conversations about how AI assistance is being used prevent ambiguity and inconsistent practice across a team.
- A grounded, curious stance serves managers better than either uncritical enthusiasm or reflexive resistance toward new tools.
Conclusion
The most consequential effects of AI on management aren’t really about which tasks get automated — they’re about how evaluation, skill development, and trust need to adapt as the process behind a piece of work changes. Managers who build genuine familiarity with relevant tools, stay explicit about evolving standards, and deliberately protect the skill development that tools might otherwise quietly shortcut will navigate this shift considerably better than those who treat it as someone else’s problem to solve.
Frequently Asked Questions
Do managers need to become technical experts in AI tools to lead effectively?
Not at an expert level, but a reasonable, hands-on working understanding of what relevant tools do well and where they reliably fail is increasingly important for good decision-making.
How can a manager tell if a team member’s skill development is being quietly undermined by AI tools?
Watch for a gap between a team member’s output with the tool and their performance on a similar task without it — a significant gap can signal that the tool is substituting for skill rather than supporting it.
Should managers evaluate AI-assisted work differently from fully manual work?
Often yes, at least in terms of what’s being assessed — being explicit with your team about the evolving standard prevents confusion and inconsistent expectations.
Is it appropriate to ask team members directly how they’re using AI tools in their work?
Yes — an open, direct conversation builds a shared, explicit understanding, which tends to work better than leaving expectations ambiguous and assumed.
How can a manager stay current with a fast-moving area like AI tools without it becoming a full-time undertaking?
Focus on the specific tools your team actually uses, building practical, hands-on familiarity rather than trying to track the entire field comprehensively.
Is resistance to adopting new AI tools ever a reasonable managerial stance?
Genuine, informed scepticism about a specific tool’s reliability or fit is reasonable; reflexive resistance to the category as a whole risks missing real efficiency gains worth taking seriously.