There has rarely been a more convenient time to write OKRs. Feed a language model a short description of your team and what you want to achieve, and within seconds you have an objective and a few key results that read like they came out of a textbook. The structure is clean, the wording is tight, and the metrics sound credible. For the formulation part, AI has quietly done a lot of the heavy lifting.
The catch is that writing OKRs was never really the hard part. The hard part is the thinking that should sit underneath the wording: deciding what matters most this cycle, agreeing on what success actually looks like, and committing to it together as a team. That thinking is what gives OKRs their value, and it is also what AI is least equipped to do for you.
This article is a practical look at how to use AI in OKR writing in a way that supports the team rather than replaces it. We will go through where AI fits in the process, what to delegate and what to keep human, how to set it up as a sparring partner instead of a ghostwriter, and what to look out for so that the goals end up owned by the people who will be working with them.
Where AI fits in the OKR writing process
A common instinct, when a team sits down to write OKRs, is to start with AI. Describe the team, paste in some strategy notes, ask the model to produce a first draft, and refine from there. It is a quick way to fill a page, and the output usually looks reasonable.
The problem is that the page filling itself is not the bottleneck. What the team most needs early in the process is its own conversation: what are we actually trying to move this cycle, what would success look like, where are we likely to disagree, and what are we leaving for later. That conversation generates the substance the rest of the work depends on. If AI is in the room before that conversation has happened, the team often skips it and quietly accepts the model’s framing instead.
A more useful place for AI is later, once the team has done the bulk of its thinking, and the questions become specific and narrow. By that point the team usually knows what it wants to achieve and roughly how, and gets stuck on the wording, the measurability of a particular key result, or whether an objective is doing enough work. That is where AI can genuinely help. A useful prompt at that stage sounds more like “what could be an underlying result behind this activity” or “can we sharpen the wording of this objective without losing what we mean” than “write us a set of OKRs for next quarter.”
So a good rule of thumb is to bring AI in late, when the team is stuck on a specific question, rather than early, when the team is still working out what it cares about.
What to delegate to AI, and what to keep human
Some parts of the OKR writing process are reasonable to delegate to AI. Collecting and organising information is the clearest example. If your AI setup has access to the strategic OKRs, the work other teams have committed to this cycle, or how your own team did last quarter, asking it to surface that picture is a sensible use of the tool. It saves time, it keeps everyone working from the same context, and it does not require ownership of any judgement.
What AI cannot do is drive the discussion about what matters. The interpretation of strategy down into the results the team should focus on right now needs the human context and the discussion to go with it. There are two reasons for this.
The first is context. AI does not know which team members have capacity this quarter, which skills sit with which individuals, or what other commitments the team has taken on outside of OKRs. A great deal of what shapes a realistic set of goals lives outside any document and inside the heads of the people in the room.
The second is commitment. Without the discussion you do not get the engagement. If the team does not take the burdensome step of arguing through priorities and trade-offs, what you end up with is not a goal the team is committed to, but a reporting target that lives in a document. The thinking itself is part of how OKRs work, and skipping it leaves you with the format but not the value.
Using AI as a coach, not a writer
A more interesting use of AI is to instruct it to challenge the team rather than write for it. In that role it is unusually good at asking the questions a team might be too tired, too aligned, or too close to the work to ask itself. Is this key result actually measurable, or just numeric. Does this objective describe an outcome, or an activity dressed up as one. Is the ambition real, or safe wording with a stretch number on top. These are the kinds of questions you would also work through with the OKR writing checklist, and AI can run a team through them quickly when the energy in the room has started to dip.
To get this from an AI tool, you have to set it up explicitly. The model will not default to coaching mode just because you paste in a draft. It needs to be told what kind of response you are looking for, what its role is, and what good looks like in your organisation. Some of that comes from the prompt itself, and some of it should come from context attached to the tool, either through an OKR-aware product or by uploading a short playbook of how your organisation thinks about OKRs (for example how ambitious goals are expected to be, whether non-result key results are acceptable, and how you measure success).
A simple prompt that follows this approach looks like this:
You are an OKR coach for our team. I am going to share a draft objective
and a few key results. Do not rewrite them for me. Your job is to
challenge my thinking.
For each key result, ask one or two pointed questions: is there a clear
data source, am I describing an activity instead of a result, is the
ambition real or just safe wording, does this key result actually
contribute to the objective.
Where something looks weak, do not give me the fix. Help me see what is
missing so the team can work it out together.
Context: [paste a short description of how your organisation works with
OKRs, including the expected level of ambition and any local rules.]
Used this way, AI becomes a sparring partner that sharpens the team’s thinking. The team still writes the words, still owns the discussion, and still makes the trade-offs. The output of the exchange feeds back into the conversation rather than replacing it.
A practical example of this in action comes from a service support team that had got stuck during their OKR session. Their draft had ended up looking like a project plan, with key results that were all activities the team had committed to doing. Rather than ask the model to rewrite the OKRs from scratch, they took one of their proposed key results into a coaching prompt and asked what underlying results the activity could plausibly drive. The model suggested several different outcomes, the team discussed which of those was actually the one they wanted, and from there worked back to whether the original activity was still the right one to drive it. The OKR they ended up with was their own, but they would not have got there without the AI prompting them out of the activity-first mindset.
A small but important warning here. When you work backwards from an activity to find a result, an activity can plausibly drive five different results. It is tempting to pick the first plausible one and move on, because anything is better than an activity-shaped key result. The discipline to apply is to first decide what the team actually wants to achieve, and only then check whether the original activity is the right one to drive it. AI can help generate the options, but the team has to choose.
When AI has crossed the line from coach to author
The clearest sign that AI has stopped supporting and started authoring is that the discussion goes quiet. AI is very good at the methodology and the formulation, often better than most teams left to their own devices. What it cannot supply is the context inside the team. When the team accepts an AI-written goal without working through what it really means and why it sits in this set rather than another, the goal might read perfectly but it does not yet belong to anyone.
A useful check at this stage is to ask the team, in their own words, what each OKR actually means and why it is important. If two team members give meaningfully different answers, or if nobody can quite say why this objective is on the list and not another, the OKRs have not yet been internalised. The fix is rarely to rewrite the wording. The fix is to go back into discussion.
A team-owned OKR with rough edges almost always outperforms a polished OKR that the team did not have to argue about
The polish is easy to get from a model. The ownership only comes from working through the trade-offs together.
A related check is what you might call the generic test. Look at your draft OKRs and ask: could these be the OKRs of any marketing team, of any sales team, of any development team. Or are these inherently our OKRs, shaped by the way we talk about our work and the choices we have actually made. AI has a strong tendency, especially when given thin prompts, to produce great formulations that could fit almost any company. The objective sounds inspiring, the key results sound measurable, and yet there is nothing in there that pinpoints the specific challenge your team is dealing with this cycle. If your OKRs would not be recognisable to your team without the team name on top, they have probably drifted too far towards AI’s centre of gravity.
How to use AI depending on your team’s experience
AI is most useful for teams that already understand the value of the conversation around their goals. A team that has run a few OKR cycles, struggled with measurability, debated ambition, and made the trade-offs around focus has the instincts to recognise when an AI suggestion sharpens the thinking and when it papers over a missing decision. For these teams, AI can be a clear accelerator.
For a team that is new to OKRs, the picture is more complicated. AI can offer a shortcut around the parts of the learning curve that are uncomfortable but valuable: noticing that your key result is really an activity, sitting with the discomfort of admitting that you do not have a measurement for what you care about, debating whether 80 per cent is a stretch or a soft target. These are the moments where the craft of OKR writing is actually learned, and where many of the most common OKR writing mistakes get caught and corrected. If AI steps in and resolves them too quickly, the team gets a tidy document without the underlying understanding, and the next cycle they will need AI again to produce something equally tidy.
The way through is not to ban AI for new teams, but to let them experience the process with less support first, and to invite AI in deliberately once they know what they are looking for. As with most things, the value of the tool grows once the team has the judgement to use it well.
The facilitator’s job when AI is in the process
If you are running an OKR setting workshop, AI is going to be in the room whether you have addressed it or not. Someone will have prepared with it, someone else will bring it up mid-session, and a third person will quietly paste the team’s notes into a model under the table. It is not realistic to treat this as the elephant in the room, and pretending it is not happening tends to produce the worst version of it, where someone walks in with a fully formed set of OKRs they have not had to defend.
The facilitator’s job is to talk about it explicitly, before the workshop or at the start of it. What use of AI do we expect in this process. Where is it helpful, and where do we want the team to work without it. A practical default is that AI can support information gathering before the session and specific, narrow questions during the session, but should not be used to produce candidate OKRs the group is then asked to react to. Without that kind of framing, individuals do not really know what a good use of AI looks like in OKR writing, and they will reach for whatever feels useful in the moment, which is rarely the right thing for the team’s goals.
The pattern across all of this is that AI in OKR writing is genuinely useful, but only when the team knows what it is doing with it. The formulation work AI can take off your plate has never really been where OKRs fail. The interpretation, the trade-offs, the discussion, and the commitment have. The teams that benefit most from AI in their OKR writing process are the ones that are intentional about where it enters, what they ask of it, and what they refuse to delegate. Be deliberate about that, and AI becomes a real support. Skip the question, and you get tidy goals that nobody really owns.