How to Score AI Vendors Using Operational Criteria

AI sales conversations often promise the world: automation, prediction, digital transformation, end-to-end visibility, “Industry 4.0,” and more.

But most plant managers aren’t looking for buzzwords; they’re looking for clear, operational value.

They want to know:

• Will this help me stabilize production?

• Will operators actually use it?

• Will supervisors trust it?

• Will it reduce scrap or downtime?

• Will it simplify shift-to-shift communication?

• Will this make my weeks more predictable?

Most AI vendors can’t answer these questions clearly. That’s why plant managers need a simple, structured evaluation method that cuts through hype and focuses on what matters: real decisions, real workflows, and real outcomes.

This guide lays out a practical way to evaluate any AI solution, without getting lost in marketing language.

Step 1 - Start With Your Operational Problems, Not Their Features

The fastest way to cut through hype is to anchor the evaluation to your own plant’s constraints.

Ask vendors:

• Which of our top 3 operational problems does your system help us solve?

• Which decisions in our plant does your AI improve?

• How does your tool reduce scrap, downtime, or instability?

• How do operators and supervisors use it day-to-day?

If the vendor can’t speak to your problems, they don’t understand your environment.

Good AI starts with your bottlenecks, not their platform.

Step 2 - Ask the Vendor to Describe a Day in the Life With Their Tool

This is the most revealing question you can ask.

Ask them:

“Walk me through how an operator, a supervisor, and a maintenance tech would actually use your system during a real shift.”

You want to hear:

• When operators see alerts

• How they give feedback

• How the system fits into startup

• How supervisors use it during standup

• How it helps maintenance triage issues

• How it supports shift handoffs

If they talk only about dashboards or analytics, and not workflows, it’s a red flag.

Workflow integration is the difference between AI that gets used and AI that gathers dust.

Step 3 - Evaluate How the System Handles “Messy Plant Reality”

Every plant has:

• Inconsistent data

• Missing records

• Tribal knowledge

• Handwritten notes

• Different operator habits

• Shift-to-shift variation

• Aging machines

• Noisy PLC signals

Ask the vendor:

• How does your system handle inconsistent data?

• Can it operate without 12–24 months of historical data?

• Do you help create structure (categories, forms, metadata)?

• How much IT lift is required?

• Do you support data contracts or cleanup?

Hype-driven vendors assume the plant will fix the data first.

Practical vendors build systems that work with the data you have today.

Step 4 - Assess the Vendor’s Human-in-the-Loop Design

No AI system should operate without human judgment.

Ask:

• How do operators correct AI outputs?

• How do supervisors approve or validate insights?

• How does human feedback improve the model?

• Does the AI provide explanations?

If the vendor cannot describe a human-in-the-loop workflow, their system will degrade quickly, and adoption will collapse.

AI must amplify frontline expertise, not replace it.

Step 5 - Determine Whether the AI Improves Predictability

Plants don’t buy AI for dashboards, they buy it for predictability.

Ask:

• Does your system help me see problems earlier?

• Does it reduce firefighting?

• Does it stabilize startup?

• Does it make scheduling more reliable?

• Does it reduce variation across shifts?

If the answer isn’t a confident “yes,” the platform is likely hype.

Predictability is the real ROI.

Step 6 - Look for Evidence of Floor-First Deployment

This is where many vendors fail.

Ask:

• Do you deploy on-site?

• Do you walk the floor with operators?

• Do you observe setups, drift, and changeovers firsthand?

• Do you adapt the model to our workflows?

• Do you provide ongoing coaching?

AI cannot be implemented entirely from Zoom calls.

Vendors must understand the real environment, not the theoretical one.

Step 7 - Ask for Results From Plants That Look Like Yours

Not generic case studies, relevant ones.

Request examples from plants with:

• Similar equipment

• Similar processes

• Similar headcount

• Similar maturity

• Similar production challenges

And request specifics:

• What decision improved?

• What leading indicators moved?

• What KPI shifted as a result?

If they can’t give specifics, they may have more marketing than experience.

Step 8 - Score the Vendor Using a Three-Category Framework

A practical evaluation method uses three categories:

1. Operational Fit

• Does it solve real bottlenecks?

• Is it designed for operators and supervisors?

• Does it integrate into daily routines?

2. Technical Feasibility

• Can it work with existing data and equipment?

• Does it require heavy IT lift?

• Does it support structured inputs and metadata?

3. Adoption Readiness

• Do frontline teams find it intuitive?

• Does it support HITL workflows?

• Does the vendor provide coaching?

Score each category 1–3.

The best vendors score high across all three, not just in demos.

Biggest Red Flags to Watch For

These red flags separate hype from practical tools:

• “We need 12 months of clean historical data before we can start.”

• “The system is fully automated.”

• “We don’t need operator input.”

• “Everything is in the dashboard.”

• “We can handle deployment remotely.”

• “The plant just needs to adapt to the new system.”

• “There’s no need for supervisor approval.”

If you hear these, you’re dealing with hype.

How Harmony Helps Plant Managers Cut Through the Noise

Harmony avoids hype and focuses on what works.

Harmony provides:

• On-site engineering support

• Operator-first workflows

• Supervisor coaching tools

• Human-in-the-loop design

• Drift, startup, and scrap-risk prediction

• Cross-shift consistency tools

• Real-time summaries

• KPI-first deployment

• Minimal IT lift

• Support for data contracts and structured inputs

Harmony focuses on practical AI models that improve performance, reduce losses, and increase predictability, without disrupting operations.

Key Takeaways

• Plant managers don’t need AI hype; they need practical solutions that improve daily decisions.

• Good AI systems start with bottlenecks, not dashboards.

• Workflow integration, HITL, and predictability are stronger indicators than features.

• Vendors must demonstrate floor understanding, not theoretical capability.

• Strong evaluation frameworks prevent wasted time, failed pilots, and low adoption.

Want AI that improves real operations, not just dashboards and demos?

Harmony delivers on-site, operator-first AI that strengthens decision-making and stabilizes production.

Visit TryHarmony.ai