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How can I use AI to improve employee performance and engagement?
TL;DR
- AI can meaningfully improve employee performance, but only if it’s used to enable managers to do better work, not to replace the human judgment that makes feedback land.
- The biggest gap in most companies isn’t data or tooling; it’s that first-time managers lack the scaffolding to give useful, consistent feedback without significant support.
- Continuous feedback isn’t about increasing review frequency. It’s an architectural shift that brings performance conversations into the tools where work actually happens.
- Legacy tools like Lattice, Leapsome, and 15Five address the same problems the same way. AI-native approaches are architecturally different, not just faster versions of the same thing.
- If you’re setting up or rebuilding performance management, start with manager enablement as your design principle, not compliance or data collection.
What does “using AI to improve performance” actually mean?
Using AI to improve employee performance means giving managers better input, prompts, and visibility so they can make faster, more consistent decisions about their people. It does not mean automating the human relationship between a manager and their direct report.
According to Mercer, 32% of companies are considering AI-enabled performance feedback processes in 2025.
Most articles on this topic lead with sentiment analysis dashboards and predictive attrition scores. Those things exist, and some of them are useful. But they miss the actual problem most People Leaders face: managers who do not know how to give feedback, do not know what good looks like for their team, and are not using the PM system already in place.
The more useful framing is this: where in the performance cycle does human judgment break down, and can AI provide just enough scaffolding to keep things moving?
Why are first-time managers the real bottleneck?
First-time managers are often the single biggest constraint on team performance, and they are also the group least supported by traditional PM tools. Most of them were promoted for being excellent individual contributors. They have never been trained to give structured feedback, set expectations clearly, or run a 1:1 with any real purpose beyond status updates.
The problem compounds at scale. When a company grows from 30 to 150 people, the number of managers increases, and most are new. People Leaders we talk to consistently describe the same situation: a cohort of junior team leads who want to do right by their people but do not know how, and a PM system that assumes they already do.
This is where AI becomes genuinely useful. Not as a replacement for manager judgment, but as a scaffold. AI can suggest feedback structures, surface patterns in 1:1 notes, prompt managers to address a topic they have been avoiding, and help them translate vague impressions into specific, actionable observations.
One People Lead at a 150-person tech company described the shift this way: “We stopped trying to train managers in workshops and started embedding the prompts directly into the workflow. They’re not reading a guide; they’re just responding to a Slack message that’s already half-structured for them.”
What does AI coaching for managers actually look like in practice?
AI coaching for managers works best when it is invisible. The goal is not to give managers a new tool to learn; it is to surface the right prompt at the right moment within a tool they already use.
In practice, this looks like a few distinct interventions. First, expectation scaffolding: helping a manager define what “good” looks like for each role on their team. Many first-time managers skip this step because building competency frameworks feels like a six-month project. AI can compress that down to a structured conversation that outputs a working framework in an hour.
Second, feedback drafting: when a manager needs to give feedback after a specific event, AI can help them move from “I’m not sure how to say this” to a draft that is specific, non-personal, and tied to a clear outcome. The manager still edits, still delivers it, still owns the relationship.
Third, 1:1 structure: many managers run 1:1s with no agenda, which end up as status updates. AI can suggest topics based on recent feedback threads, flag unresolved items, and prompt the manager to close any open loops.
How is this different from just running more frequent reviews?
Frequent reviews and continuous feedback are not the same thing, and conflating them is one of the most common mistakes in PM design. Running quarterly reviews instead of annual ones is a cadence change. Continuous feedback is an architectural change.
The difference is where feedback lives. In a traditional PM system, feedback is handled through a portal. Someone logs in, fills out a form, and submits it. The feedback is structurally disconnected from the work that generated it.
Continuous feedback means feedback lives where work conversations happen. For most teams, that is Slack. A quick reaction to a piece of work, a note after a presentation, a prompt when a project closes. These are not mini-reviews; they are ambient signals that accumulate over time and make the formal review far more accurate when it does happen.
One People Leader building this kind of system described the design goal as making feedback feel “like a quick Slack task, not a tax return.”
What performance data should People Leaders actually be tracking?
People Leaders should be tracking two categories of data: manager behavior data (are managers actually doing the people work?) and outcome data (is the work producing the right results?).
Manager behavior data is more useful and more actionable. It tells you whether feedback is being given consistently, whether 1:1s are happening, whether expectations have been set for each role, and whether managers are closing the loops they open.
A useful starting set includes: feedback frequency per manager, 1:1 completion rates, expectation-setting coverage across roles, and whether feedback is being acted on.
Why do most PM tools feel the same, and what would actually be different?
Most PM tools feel the same because they were built on the same mental model: a structured review process, a portal to complete it in, and a dashboard to report it on. Lattice, Leapsome, 15Five, and their competitors all execute that model with different levels of polish.
An AI-native approach starts from a different assumption: managers will perform good performance management if it is easy enough to fit within their existing workflow. The design goal is not to build a better portal; it is to remove the portal entirely and let feedback accumulate through ambient signals in the tools managers already use.
Instead of a system that managers have to use, you are building one they barely notice. The feedback still gets captured. The expectations still get set. The review still happens. But from the manager’s perspective, they mostly just answered a few Slack messages.
How should People Leaders think about enabling versus policing managers?
Enabling managers means giving them the tools, prompts, and visibility to do good people work without requiring them to become PM system experts. Policing managers means using the PM system to monitor compliance and escalate gaps.
The enabling philosophy shows up in how the system is designed. Prompts come to managers rather than managers having to seek out a form. Feedback is suggested, not mandated. Dashboards are for the manager’s own visibility first, and for People Leaders second.
A useful test for any PM design decision: does this feature help the manager, or does it help the People Leader watch the manager? Features in the first category drive adoption. Features in the second category drive resentment.
How do different approaches to AI-assisted performance management compare?
| Approach | What it looks like | Manager experience | Data quality | Time to value |
|---|---|---|---|---|
| Annual reviews + AI summarization | Same review cycle, AI writes the summary | One less writing task per year | Low: infrequent, retrospective | Slow |
| Pulse surveys + sentiment analysis | Frequent short surveys, AI flags trends | Another thing to fill out | Medium: frequent but shallow | Medium: 4-8 weeks |
| Legacy PM tool + AI add-on | Lattice/Leapsome adds an AI feature | Same portal, one new button | Medium: structured but siloed | Medium |
| Slack-native ambient feedback | Feedback lives in Slack, AI structures it | Feels like a quick message | High: frequent, contextual, natural | Fast: 2-4 weeks |
| AI coaching for managers | AI prompts and scaffolds manager behavior | Active support, not a form | High: behavioral + outcome data | Fast: visible in first 1:1 cycle |
What are the prerequisites before AI can actually help?
The first prerequisite is clarity about what good looks like for each role. AI can help draft competency frameworks, but someone has to decide what competencies matter.
The second prerequisite is an adequate feedback signal. Continuous feedback architectures work well when feedback is genuinely frequent. If the average manager is giving feedback four times a year, AI does not have much to surface.
The third prerequisite is manager buy-in at the basic participation level. AI can lower the activation energy for giving feedback, but it cannot create motivation from nothing.
The good news is that none of these prerequisites requires a six-month project. Competency frameworks can be drafted in a day with AI assistance. Feedback culture can shift quickly when the friction is removed.
FAQ
What is the most practical first step for using AI to improve employee performance? Start with manager enablement, not data collection. Identify your highest-leverage managers, map where their feedback process breaks down, and introduce an AI scaffolding layer at that specific point.
How is AI-native performance management different from adding AI features to Lattice or 15Five? The difference is architectural, not cosmetic. Legacy tools were designed around a portal-based review process. AI-native tools start from the assumption that feedback should live where work happens, usually Slack, and AI structures that ambient input.
Can AI actually help first-time managers give better feedback? Yes, within specific constraints. AI can address the blank-page problem by suggesting feedback structures and helping managers connect specific behaviors to role expectations. What AI cannot do is substitute for the manager’s own observation or replace the trust that makes feedback land.
What manager performance metrics should People Leaders report to leadership? The most useful leading indicators are behavioral: feedback frequency per manager, 1:1 completion rate, expectation-setting coverage across roles, and whether development areas from previous cycles are being addressed.
How long does it take to see results from an AI-assisted performance management approach? For changes in manager behavior, a meaningful signal is typically visible within four to six weeks when the system is embedded in existing workflows. Outcome-level changes usually take two to three quarters.