Choosing an AI vendor for industrial applications is harder than choosing enterprise software. The technology is newer, the claims are harder to verify, and the differences between vendors aren’t always obvious.

I’ve helped dozens of manufacturers evaluate AI vendors. Here’s the approach that works.

First: Understand what you’re buying

AI vendors fall into several categories:

Automation platform vendors (Siemens, Rockwell, ABB, Schneider): AI embedded within broader automation platforms. Strength is integration with their own equipment. Limitation is less flexibility and potential lock-in.

AI platform companies (AWS, Azure, Google Cloud, PTC): General AI/ML platforms that can be applied to manufacturing. Strength is flexibility and power. Limitation is that they require significant customisation.

Specialised industrial AI vendors: Companies focused specifically on manufacturing AI applications (predictive maintenance, quality, scheduling). Strength is focused expertise. Limitation is narrower scope.

System integrators with AI capability: Companies that implement AI solutions, potentially using various underlying platforms. Strength is implementation expertise. Limitation varies by integrator quality.

Consultancies: Firms that advise and may implement, often bringing together multiple technology components.

Know which category you’re evaluating. The questions differ.

Evaluation framework

Here’s a structured checklist I use:

Technical capability

Does the solution actually work?

• Demand proof of concept with your data, not just demos
• Request reference customers with similar applications
• Understand what accuracy and performance to expect
• Ask about edge cases and failure modes

Does it fit your technical environment?

• What systems must it integrate with?
• What data does it need, and do you have it?
• Does it run where you need it (edge, cloud, on-premise)?
• What infrastructure is required?

Is the technology mature?

• How long has this solution been in production use?
• How many customers are running it?
• What’s the development roadmap?
• Is the underlying technology stable or rapidly changing?

Implementation reality

What does implementation actually involve?

• How long from contract to live production?
• What resources (your side and vendor side) are required?
• What are the typical risks and delays?
• What’s the implementation methodology?

Who does the work?

• Vendor direct, partners, or you?
• Where are implementation resources located?
• What skills do you need internally?
• What training is provided?

What are realistic expectations?

• How long to see value?
• What does “success” look like for similar customers?
• What percentage of implementations fully succeed?
• What causes implementations to fail?

Support and maintenance

What support is available?

• Hours and response times
• Location of support resources
• Escalation paths
• Self-service vs managed support

How is the solution maintained?

• Update frequency and process
• Model retraining requirements and responsibilities
• Bug fix responsiveness
• Documentation quality

What happens if things go wrong?

• Troubleshooting resources
• Rollback capabilities
• Service level commitments
• Penalties or guarantees

Commercial considerations

Total cost of ownership

• Licensing model (perpetual, subscription, consumption)
• Implementation costs
• Ongoing costs (support, maintenance, updates)
• Infrastructure costs
• Internal effort costs

Contract terms

• Length and flexibility
• Termination provisions
• Data rights (who owns data, what happens at contract end)
• Pricing increases

Vendor stability

• Financial health
• Customer base diversity
• Ownership and investment
• Competitive position

Australian considerations

Local presence

• Office and staff in Australia
• Local implementation capability
• Time zone coverage
• Understanding of Australian industry

Reference customers

• Australian customers available to speak
• Similar industry and application
• Similar scale and complexity

Compliance

• Data residency options
• Local privacy law compliance
• Security certification

Red flags

Watch for these warning signs:

Impressive demos, vague implementation details: Technology that looks great in presentations but can’t explain how it works in practice.

No reference customers for your application type: “Trust us, it will work” without evidence.

Aggressive sales pressure: Vendors pushing for signatures before you’ve done due diligence.

Pricing opacity: Unable or unwilling to explain how pricing works and what you’ll actually pay.

Proprietary lock-in: Data formats, models, or approaches that trap you with one vendor.

Massive scope: Proposals trying to solve everything rather than focused on your specific need.

Unrealistic timelines: “Live in 6 weeks” for complex industrial AI is almost certainly wrong.

Blaming customers for failures: Reference customers who struggled, with vendor blaming the customer.

The evaluation process

A sensible evaluation process:

Define requirements clearly

Before talking to vendors, document:

• What problem you’re solving
• What success looks like (measurable)
• Technical constraints and requirements
• Budget range
• Timeline expectations

Create a short list

Identify 3-5 vendors worth serious evaluation. Don’t try to evaluate 15 options in depth.

Structured information gathering

Send each vendor the same set of questions. Compare responses systematically.

Demonstrations with your context

Ask vendors to demonstrate with scenarios relevant to your situation, not generic demos.

Proof of concept (for major investments)

For significant purchases, request a paid proof of concept with your data and environment. This separates vendors who are confident from those who are hoping.

Reference conversations

Talk to reference customers. Ask about implementation experience, ongoing relationship, and whether they’d choose the same vendor again.

Internal alignment

Make sure stakeholders (operations, IT, finance, procurement) are aligned before commitment. Surprises after vendor selection cause problems.

Making the decision

Ultimately, the decision involves:

Technical fit: Can they solve your problem? Implementation confidence: Will they actually deliver? Commercial viability: Can you afford it, and does the value justify the cost? Risk profile: What’s the downside if things go wrong? Relationship quality: Can you work with these people for years?

There’s rarely a perfect choice. You’re choosing the best option with the risks you’re willing to accept.

After selection

Choosing the vendor is step one. Successful implementation requires:

• Clear project governance
• Executive sponsorship
• Defined roles and responsibilities
• Regular progress reviews
• Early escalation of problems
• Flexibility to adjust as you learn

The best vendor choice won’t save a poorly managed implementation.

Choosing an AI vendor is a significant decision. Take the time to evaluate properly. The months spent on thorough evaluation prevent years of regret from wrong choices.