How the Right KPIs Keep AI Projects on Track

Most AI projects in manufacturing fail not because the models are bad, but because the KPIs guiding the project set the wrong expectations.

Plants often choose KPIs that are:

• Too high-level

• Too long-term

• Too disconnected from daily work

• Too dependent on perfect data

• Too influenced by executive wish lists

The result?

Teams don’t see progress.

Supervisors get frustrated.

Operators don’t trust the tool.

Leadership thinks AI “isn’t delivering.”

Choosing the right KPIs makes an AI project measurable, realistic, and grounded in the actual rhythm of the plant.

The Core Principle: Early KPIs Should Measure Clarity and Stability, Not Transformation

AI’s first job is to improve visibility, reduce variation, and catch problems earlier.

It is not to overhaul the entire plant overnight.

Early KPIs should track:

• How much earlier problems are detected

• How consistently behaviors align

• How fast teams act on new insights

• How visible patterns become

• How much manual work is reduced

These are the foundations that later enable:

• Scrap reduction

• Throughput gains

• Lower downtime

• Reduced changeover variation

Start by measuring the leading indicators, not the lagging outcomes.

The Four Categories of KPIs That Matter in Early AI Projects

1. Visibility KPIs

2. Behavioral KPIs

3. Stability KPIs

4. Actionability KPIs

If your first AI project measures one KPI from each category, the project will have clarity, direction, and undeniable results.

Category 1 - Visibility KPIs (How Much More You Can See)

Early AI use cases improve visibility first.

The KPIs measure how well the system surfaces patterns the plant couldn’t see before.

Key KPIs

• Time between issue emergence and AI detection

• Number of drift events detected vs. previously unnoticed

• Number of scrap-risk sequences surfaced

• Accuracy of cross-shift summaries

• Number of actionable alerts per shift

• Reduction in time spent gathering context

These KPIs show the value of transparency, not performance yet.

Category 2 - Behavioral KPIs (How Teams Respond to New Information)

AI does not improve the plant by itself.

The workforce must integrate insights into their routines.

Behavioral KPIs measure alignment, adoption, and consistency.

Key KPIs

• Operator confirmation rates on alerts

• Supervisor review frequency

• Consistency of drift responses across shifts

• Time from alert to action

• Changeover adherence rates

• Percentage of alerts acknowledged with context

These KPIs show whether humans are using and trusting the AI.

Category 3 - Stability KPIs (Early Indicators of Operational Health)

Before scrap and downtime drop, stability must improve.

Stability KPIs measure

• How stable the process becomes

• How predictable outcomes are

• How early interventions prevent escalation

Key KPIs

• Reduction in drift duration

• Reduction in drift intensity

• Reduction in parameter oscillation

• Startup stability variance

• Changeover stabilization time

• Number of repeated anomalies per shift

These KPIs show the plant becoming more controlled and less chaotic.

Category 4, Actionability KPIs (How Often AI Leads to Real Decisions)

If AI surfaces insights but teams don’t act, the project fails.

Actionability KPIs track:

• Whether AI leads to real decisions

• Whether interventions reduce variation

• Whether teams trust the insights

Key KPIs

• Percentage of insights leading to operator action

• Percentage of insights leading to supervisor intervention

• Time saved per week from automated summaries

• Number of avoided scrap events identified by AI

• Reduction in manual report-building time

These KPIs are the bridge to ROI.

How to Avoid the Most Common KPI Mistakes

Mistake 1 - Choosing KPIs That Depend on Long-Term Data

Scrap reduction, downtime reduction, throughput increases, these matter, but not first.

They take months to show measurable change.

Mistake 2 - Selecting KPIs That Are Not Visible to Operators

Operators must feel the progress.

If KPIs live in a corporate spreadsheet, adoption collapses.

Mistake 3 - Tracking Too Many KPIs

Early AI needs four to seven KPIs maximum.

More than that dilutes focus.

Mistake 4 - Choosing KPIs That Can Be Influenced by External Noise

Material changes, new SKUs, seasonal behavior, or machine wear can distort results.

Pick KPIs that measure the AI’s contribution, not external chaos.

Mistake 5 - Using KPIs That Don’t Map to Daily Workflows

If a KPI cannot be influenced by operators or supervisors, it’s useless for rollout.

The Three KPI Selection Questions Every Plant Should Ask

1. Does this KPI show that AI is making problems more visible?

If not, it’s too advanced.

2. Does this KPI show whether teams trust and act on AI insights?

If not, it won’t help evaluate adoption.

3. Will this KPI reveal stability improvements before performance improvements?

If not, the plant will lose patience.

If a KPI fails any of these criteria, remove it.

Sample KPI Set for a First AI Project

A realistic, effective KPI portfolio might include:

• Time from drift onset to detection

• Drift duration reduction

• Percentage of alerts acknowledged

• Number of issues caught before scrap

• Changeover stabilization time

• Operator-context submission rate

• Weekly supervisor review compliance

• Time saved on reporting

• Variation reduction across shifts

This set is:

• Clear

• Measurable

• Aligned with behavior

• Aligned with operations

• Aligned with stability goals

And it leads smoothly into performance KPIs later.

When to Add Traditional Performance KPIs

Once visibility, behavior, and stability KPIs show improvement, you can introduce:

Performance KPIs

• Scrap reduction

• Downtime reduction

• Throughput increase

• Yield improvement

• Changeover time reduction

These metrics only become meaningful once the plant is operating more predictably.

AI amplifies stability first, performance second.

How Harmony Helps Plants Select the Right KPIs

Harmony works on-site to help plants choose KPIs that match:

• Current maturity

• Data quality

• Operational priorities

• Workforce readiness

• Cross-shift variation

• Machine behavior

• Changeover complexity

• Material sensitivity

Harmony ensures KPIs are:

• Practical

• Realistic

• Actionable

• Inspiring

• Adoptable

And that every metric reinforces real operational behavior, not hypothetical value.

Key Takeaways

• AI projects fail when KPI selection is unrealistic or disconnected from daily work.

• Early KPIs must measure visibility, stability, actionability, and behavior.

• Scrap and downtime KPIs come later, after the system stabilizes.

• KPI selection should reinforce operator and supervisor workflows.

• The right KPIs accelerate adoption and reveal value early.

Want help choosing KPIs that guarantee your AI rollout delivers real, visible impact?

Harmony helps manufacturers design KPI portfolios that drive adoption, stability, and measurable improvement.

Visit TryHarmony.ai