AI tools are no longer something your team might encounter in the future — they are already there. Microsoft Copilot is embedded in Office 365. Writing assistants, meeting summarisers, coding tools, data analysis platforms and AI-powered project management tools are in daily use across most knowledge-work teams. As a new manager, your job is not to decide whether your team works alongside AI — it is to decide how to make that work well.
1. Understand what AI actually does in your team
Before you can manage AI-human collaboration effectively, you need a clear picture of which tools your team is using and what they are actually doing. This sounds obvious, but in most organisations AI adoption has outpaced formal policy — people are using tools independently, in different ways, with varying levels of critical oversight.
AI tools in knowledge-work settings typically handle one or more of the following:
- Content generation: drafting emails, documents, reports, presentations, code
- Information processing: summarising meetings, analysing data, extracting insights from large documents
- Task automation: scheduling, routing requests, triggering workflows, updating records
- Decision support: surfacing options, flagging risks, generating recommendations
Understanding which category a given tool falls into tells you a lot about where human oversight is most important — and where AI genuinely just saves time without meaningful risk.
Have a brief team conversation specifically about AI tool use: which tools are people using, what for, and how are they evaluating the outputs? You will likely find both gaps in awareness and informal best practices worth sharing more widely. This conversation also signals that you take AI governance seriously without being heavy-handed about it.
2. Address the human response to AI honestly
The introduction of AI tools into a team rarely lands neutrally. Some people are excited and adopt quickly. Others are anxious — about job security, about whether they will be able to keep up, about whether the tool will make them look less capable if it does their work better or faster than they do. Both responses are understandable, and both need active management.
The most common mistake managers make is trying to bypass the anxiety by being relentlessly positive about AI — focusing on efficiency gains and opportunity while never acknowledging the genuine discomfort. This backfires. People feel their concerns are not being heard, which increases rather than reduces resistance.
When introducing an AI tool, create space for honest reactions before you make the case for the tool. Ask people what their initial reaction is and what concerns they have. Acknowledge the ones that are legitimate. The teams that integrate AI most successfully are those where people feel their perspective has genuinely been heard — not just managed.
3. Build genuine AI literacy in your team
There is a significant difference between a team that uses AI tools and a team that uses them well. Using them well requires understanding not just how to operate a tool but how to evaluate its outputs critically — knowing when to trust the result and when to question it, how to provide better inputs to get better outputs, and how to identify the characteristic failure modes of a given system.
This is what prompt literacy means in practice: not a technical skill but a critical thinking skill applied to a new kind of tool. As a manager, you are responsible for ensuring your team develops this — both because it makes them more effective and because uncritical AI use creates real quality and reputational risks.
Run a brief team session where everyone tries using an AI tool on a real work task, then compares and discusses the outputs. What worked well? What needed significant editing? What did the AI miss that a human would have caught? This kind of shared experience builds practical literacy faster than any training document.
Leading effectively in an AI-enabled workplace is one of the skills covered in the LeadWise Emerging Leaders Program — alongside the fundamentals of building trust, developing people and driving team performance.
Explore the program — €2994. Set clear boundaries for AI use
One of the most practical things you can do as a manager is establish clear, simple guidelines for how AI tools should and should not be used in your team's work. Without this, you get inconsistency — some people over-relying on AI outputs without review, others avoiding tools entirely out of uncertainty about what is acceptable.
Useful dimensions to address:
- Review requirements: which types of AI-generated output must always be reviewed by a human before use, and by whom
- Data boundaries: what information should not be entered into external AI tools — customer data, confidential documents, personal information
- Attribution: when and how to disclose that AI was used in producing work, particularly for client-facing or published material
- Quality standards: the expectation that AI output is a starting point, not a final product
Keep your AI use guidelines short and practical — one page at most. The goal is to give people a clear frame to work within, not to create a compliance document no one reads. Revisit and update them as tools and experience evolve; treating them as a living document rather than a fixed policy makes them far more likely to be followed.
5. Maintain human accountability at every decision point
As AI becomes more capable and more integrated into workflows, there is a natural tendency for accountability to blur. When an AI tool generates a recommendation, drafts a communication or screens a candidate, who is responsible for the outcome? The answer must always be: the human who acted on it.
This is not just an ethical principle — it is a practical one. AI systems make mistakes. They can reflect biases present in their training data, miss contextual nuance that a human would catch and produce plausible-sounding outputs that are factually wrong. In any situation where an AI output informs a decision that affects people — performance assessments, hiring, workload allocation, customer communications — human review is not optional.
Make human review of AI outputs a visible, named part of your team's workflow rather than an implicit expectation. When people know they are the responsible reviewer — not just someone who glances at what AI produced — the quality of that review is significantly higher. Accountability requires clarity about who holds it.
6. Keep investing in the skills AI cannot replicate
The clearest risk of working extensively alongside AI is the gradual atrophy of the skills it handles. If AI writes most of your team's first drafts, do people stay sharp at writing? If AI summarises every meeting, does anyone develop the skill of active listening and synthesis? These are not hypothetical concerns — they are real dynamics that managers need to watch for and counteract deliberately.
The skills that genuinely cannot be replicated by current AI — empathy, nuanced judgement, relationship-building, creative synthesis, ethical reasoning — are exactly the skills that define excellent management and excellent team members. Invest in them explicitly and continuously.
Periodically rotate which tasks are done with AI assistance and which are done independently. This keeps the underlying skills alive and also gives your team a clearer sense of where AI is genuinely adding value versus where it is becoming a crutch. Teams that can work both ways are more resilient than those that have become dependent on a single mode.
7. Evaluate and iterate regularly
AI tool adoption is not a one-time implementation project — it is an ongoing process of learning what works, what does not and how to get better. Build a simple feedback loop: regularly ask your team which tools are actually saving time and improving quality, which have turned out to be more trouble than they are worth, and what new opportunities have emerged that you have not yet explored.
Include AI tool effectiveness in your team retrospectives — a monthly or quarterly moment to assess what is working and what to change. Treating AI adoption as an ongoing experiment rather than a settled question keeps your team adaptive and signals that their experience and judgement matter in how the team uses these tools.
The bottom line
Managing a team that works alongside AI is not fundamentally different from managing a team that does not — it still comes down to clarity, trust, good judgement and genuine care for the people you lead. What changes is the specific things you need to be clear about, the new failure modes you need to watch for and the importance of keeping your team's distinctly human skills sharp in a context that can gradually erode them.
The managers who navigate this best will not be those who know the most about AI technology. They will be those who ask the best questions about how it is affecting their team, who create the most honest space for people to share their experience of it, and who remain stubbornly anchored to the human dimensions of leadership even as the tools around them keep changing.