Use AI to improve goal drafts, test measures and identify missing dependencies without outsourcing the final agreement. This practical guide to AI goal setting brings together a step-by-step approach, illustrative examples, a reusable worksheet and answers to common questions. Start with the section closest to your current challenge, then use the working session to turn the guidance into a clear next action.
Start with the team’s actual priority
AI-assisted goal setting can help turn a rough priority into questions, alternative wording or possible measures. It cannot know the organisation’s true priorities or capacity unless that context is supplied and checked. Begin with the problem, the intended beneficiary and the reason the work matters.
Use an approved tool and appropriate information. A goal draft may reveal confidential projects or employee details. Keep inputs limited to what the task needs and avoid treating a general model suggestion as an approved organisational target.
Ask for options and missing information
Give the assistant the agreed context and ask it to separate outcomes from activities. Request candidate measures and the information needed to assess them. It should identify missing baselines or dependencies rather than invent figures to make a goal look complete.
For example, a prompt might ask for three ways to assess whether a handover is clearer, along with the limitations of each measure. The manager and employee then decide which evidence is relevant and available at a reasonable cost.
Test the draft against real work
Check whether the proposed outcome belongs to the role, whether the person can influence it and what support is required. Review the time frame against actual work cycles. A well-formed sentence can still describe an impossible commitment.
Look for incentives that undermine the objective. A target to close requests faster may encourage premature closure. Pair a speed measure with an appropriate quality check and define how unusual cases are handled. AI can suggest questions, but the team must examine the operational consequences.
Worked example: improving onboarding
Illustrative scenario: the initial prompt says “make onboarding more efficient.” The assistant proposes a percentage reduction without a baseline. The reviewer rejects the invented target and asks what evidence would describe the current experience.
The team chooses to examine access delays and whether starters can complete a first meaningful task with appropriate support. It establishes the starting point before agreeing a target. The final goal records owners and dependencies, while the activity plan describes changes intended to support the outcome.
Goal-draft review worksheet
- Priority: What approved problem or opportunity does the goal address?
- Outcome: What should change, and for whom?
- Evidence: Which source and definition make the result assessable?
- Baseline: What is known now, and what must be measured first?
- Influence: What can the employee control and what depends on others?
- Quality: Could the measure reward the wrong behaviour?
- Agreement: Have the employee and manager accepted the wording, support and review date?
Record the final human agreement rather than keeping several competing drafts with unclear status.
Keep goals current after drafting
Review the goal when priorities, dependencies or role scope change. Preserve the reason for a revision so later assessments use the expectations that actually applied. Do not generate a new target retrospectively simply to make an outcome appear successful.
Compare the quality of discussions and clarity of commitments with the previous method. The SMART goals guide and OKR guide provide the underlying frameworks; the AI prompting guide helps keep the drafting task bounded and reviewable.
Put the guide into practice
Set aside a working session with the people who own this process and one or two people who experience it. Use a fictional or appropriately authorised case, so the discussion can be specific without sharing unnecessary personal information. The purpose is to leave with a usable decision or document, not just agreement that the topic matters.
Prepare the case
Choose a fictional team objective such as improving the reliability of a support handoff. Provide the current problem, the intended customer outcome and the team’s actual decision authority. Leave any unknown baseline explicitly unknown. Ask for three draft goals and require the model to label assumptions instead of inventing targets, dates or measures.
Write the starting assumptions down before discussing solutions. If the group disagrees on what happened, identify the information needed to resolve that difference rather than building a plan on an untested story.
Work through the decision
Compare the drafts against a simple checklist: a clear outcome, an owner, an evidence source, a realistic review point and dependencies. Reject any target that looks precise but lacks a basis. Ask the employee who would own the goal to explain how they would influence it and what support they would need from other people.
Ask each participant to explain the proposed decision in their own words. Differences in interpretation often reveal an unclear criterion, a missing responsibility or an instruction that will be difficult to follow.
Test an exception
Introduce a change in the team’s workload or a missing data source. Ask whether the goal still measures useful progress. Revise the draft manually before asking the model for clearer wording. This keeps the decision with the people responsible for the work and prevents a polished sentence from disguising a target that no longer makes sense.
Record what changes in this situation and what remains the same. An exception should lead to a clear next step, with an owner, rather than an informal workaround that nobody can explain later.
Agree the handoff
Record the final human-approved goal alongside its baseline, measure, owner and review date. Capture which assumptions still need checking. Explain how the employee can propose a revision if the context changes. Keep only the information needed to run the goal-setting process, following the organisation’s rules for any saved prompts and draft outputs.
Finish by confirming the owner, the next action and the date when the result will be reviewed. Give the person receiving the work enough context to continue without repeating the whole discussion.
Frequently asked questions
Can AI choose performance targets for employees?
It can suggest options, but the employee and accountable manager should agree the target using the actual context. A model may produce plausible numbers without evidence that they are achievable or useful. Check the starting point, workload, dependencies and available support. Do not treat an automatically generated target as a fair expectation simply because it is specific or formatted as a SMART goal.
How do you use AI when there is no baseline?
State that the baseline is unknown and make establishing it an early task. Ask the tool for possible measures and the information each measure would require. Review those options with the people who understand the work. A short measurement period may be more useful than an immediate improvement target. Clearly distinguish a provisional planning assumption from an approved performance commitment.
Should employees know AI helped draft their goals?
Explain the role of the tool and give employees a real opportunity to revise the result. The important conversation concerns the outcome, the evidence and the support needed, rather than the origin of every sentence. Transparency helps prevent people from assuming that a generated recommendation is mandatory. Make clear that the manager remains responsible for approving expectations and resolving disagreements.
Review the first cycle
After the first cycle, examine whether AI-assisted drafts made goal discussions clearer or simply increased the number of goals. Check a sample for invented baselines, measures outside the employee’s control and unclear dependencies. Ask employees how much ownership they felt over the final version. Improve the prompt and review checklist using those findings.
Keep a brief record of what was tried, what participants found useful and what needs to change. Compare the result with the original problem rather than judging success only by completion. If the process created extra work without improving clarity, quality or support, simplify it and test again. Share the agreed change with the people who will use it, and name the person responsible for keeping the guidance current.
About New Dynamics
New Dynamics connects goals, feedback, recognition and reviews around the way organisations work. This guide is published by the New Dynamics Editorial Team as part of our practical library for HR leaders, managers and People teams.
Use the examples and worksheets to structure your own discussions and adapt them to your organisation. Illustrative scenarios are not customer case studies. Policy and employment guidance needs appropriate local review before adoption.
For questions about this guide, corrections or a conversation about your performance management process, email contact@new-dynamics.com. Explore the complete guide library for related resources.
