How do I track whether users trust and act on AI recommendations?

You track trust and action through observable decision behavior, outcome quality, and auditability of the decision path. In practice, that means logging when an AI recommendation was shown, who saw it, what context was available, what action was taken, whether a human overrode it, and what happened next.

Do not rely on a single metric like acceptance rate. A high acceptance rate can mean the model is useful, but it can also mean users are rubber-stamping. A low acceptance rate can indicate poor trust, or it can indicate the workforce is correctly rejecting weak recommendations. You need a small set of measures interpreted together.

What to measure

• Exposure rate: how often recommendations are actually surfaced to the intended role in the normal workflow.

• Action rate: how often users accept, apply, schedule, or otherwise act on the recommendation.

• Override or rejection rate: how often users decline the recommendation, including the stated reason where possible.

• Time to decision: whether recommendations speed up, slow down, or defer decisions.

• Outcome quality: whether acting on the recommendation improved the operational result, such as yield, scrap, cycle time, downtime, or review effort.

• Escalation rate: how often users seek supervisor, engineering, or quality review before acting.

• Repeat usage: whether the same users keep returning to the feature when it is optional.

• Contextual consistency: whether trust and action vary by shift, product family, site, asset, role, or data quality condition.

What trustworthy instrumentation looks like

At minimum, each recommendation event should carry a unique ID and be linked to the user, role, timestamp, model version, input data version where feasible, confidence or ranking output if exposed, and downstream action. If the recommendation affects a regulated record, change control and traceability matter more than analytics convenience.

You should also capture structured reasons for non-acceptance. Free text alone is hard to analyze and usually degrades quickly. Common reason codes include missing context, poor timing, conflicted with procedure, upstream data error, local equipment condition not reflected in the model, and user lacked authority to act.

How to interpret trust carefully

Trust is not the same as compliance with the recommendation. In industrial settings, appropriate skepticism is often desirable. A better working definition is whether users consider the recommendation credible enough to review, whether they understand why it was presented, and whether they can use it without creating traceability or process risk.

That means you should separate at least three things:

• Viewed and considered

• Accepted and acted on

• Produced a better or worse result

If you collapse those into one dashboard number, you can easily misread behavior.

Use a decision funnel, not one KPI

A practical framework is a recommendation funnel:

1. Recommendation generated

2. Recommendation delivered in workflow

3. User opened or reviewed it

4. User accepted, modified, deferred, or rejected it

5. Execution occurred in the source system or process

6. Outcome observed after execution

This helps identify where trust or adoption is breaking down. For example, if many recommendations are generated but few are viewed, the problem may be workflow placement, alert fatigue, or role mismatch rather than model quality. If many are accepted but outcomes do not improve, the issue may be poor model fit, bad source data, or incorrect assumptions about local process constraints.

Brownfield reality

In most plants, AI recommendations do not live in a clean, single platform. They coexist with MES, ERP, QMS, PLM, historian, CMMS, spreadsheets, email, and local operator practices. Tracking trust and action therefore depends on integration quality.

If the AI presents a recommendation in one system but execution happens in another, your measurement can be wrong unless those systems are linked reliably. That is common in brownfield environments. Full replacement is usually not the answer. In regulated, long-lifecycle operations, replacement programs often fail because of qualification burden, validation cost, downtime risk, integration complexity, and the need to preserve traceability across legacy processes. Instrumenting coexistence is usually more realistic than rebuilding the stack.

Recommended implementation approach

• Start with one decision type that already has measurable outcomes.

• Define the exact user actions you will treat as accept, reject, defer, or modify.

• Log recommendation IDs and downstream execution IDs across systems.

• Capture reason codes for overrides and rejections.

• Review results by role, site, product, shift, and asset class.

• Put event definitions and metric logic under change control.

• Re-baseline metrics whenever model logic, workflow placement, or upstream data pipelines change.

Common failure modes

• Proxy metrics only: clicks are measured, but not actual execution or outcomes.

• No denominator control: acceptance rate is quoted without showing when recommendations were irrelevant or not visible.

• Silent workarounds: users act outside the tracked workflow, so the data understates usage.

• Overtrust: users accept recommendations without adequate review because the interface implies certainty.

• Data drift: trust falls because source data quality changes, but the decline is blamed on user resistance.

• Version ambiguity: you cannot tell which model or rule set produced the recommendation.

Bottom line

Yes, you can track whether users trust and act on AI recommendations, but only indirectly and only with disciplined instrumentation. The useful signal comes from traceable recommendation events, user response patterns, downstream execution, and outcome comparison. If your workflows span multiple legacy systems, expect measurement gaps until those handoffs are explicitly connected and governed.