AI procurement differs from traditional software purchasing. The technology is less mature, vendor claims are harder to verify, and the stakes of getting it wrong are higher. Here’s how to approach AI procurement effectively.

Before You Start: Requirements Definition

Poor requirements lead to poor procurement outcomes. Before issuing RFPs:

Define the Problem

What business problem are you solving? Be specific. “We want to use AI” isn’t a requirement. “We need to reduce document processing time by 50% while maintaining 95% accuracy” is.

Clarify Success Criteria

How will you know if the AI solution works? Define measurable success criteria before evaluating vendors.

Assess Readiness

Do you have:

• Data in appropriate format and quality?
• Technical infrastructure to integrate?
• Skills to operate and maintain?
• Governance framework to manage?

If not, factor readiness work into your procurement timeline.

Set Realistic Expectations

AI isn’t magic. Set expectations with stakeholders about what’s achievable, what timelines look like, and what internal effort is required.

Evaluation Framework

Functional Fit (30%)

Does the solution solve your actual problem?

Assessment approach:

• Demo with your use cases, not vendor’s standard demo
• Proof of concept with your data
• Reference checks with similar use cases

Red flags:

• Vendor can’t demo your use case specifically
• No references for similar applications
• Generic claims without specifics

Technical Architecture (20%)

Does the solution fit your technical environment?

Assessment approach:

• Architecture review with your technical team
• Integration assessment
• Security and compliance review

Red flags:

• Proprietary everything with no standards
• Complex integration requirements
• Security practices below your standards

Vendor Viability (15%)

Will the vendor be around to support you?

Assessment approach:

• Financial stability assessment
• Client retention rates
• Product roadmap credibility

Red flags:

• Won’t share financials
• High customer churn
• Key person dependency

Implementation Capability (15%)

Can they actually deliver?

Assessment approach:

• Implementation methodology review
• Team assessment (who specifically will work on your project)
• Reference checks focused on implementation experience

Red flags:

• Vague implementation approach
• Can’t commit specific resources
• Poor implementation track record

Commercial Terms (20%)

Are the economics and terms acceptable?

Assessment approach:

• Total cost of ownership analysis
• Contract term review
• Pricing structure assessment

Red flags:

• Hidden costs
• Inflexible contracts
• Aggressive pricing that seems too good

The Proof of Concept

Require a paid proof of concept before major commitments:

Scope

Define specific, bounded scope that tests critical capabilities. Not the full solution – enough to validate the approach works.

Data

Use your actual data, not vendor sample data. This is non-negotiable. AI performance varies dramatically with data quality and characteristics.

Success Criteria

Define measurable criteria before starting. Accuracy thresholds, processing speeds, integration validation points.

Timeline

Two to four weeks is typical. Longer POCs risk scope creep; shorter POCs risk insufficient testing.

Independence

The POC should give you information to make a decision. It shouldn’t lock you into further commitment.

Contract Essentials

AI contracts need specific provisions beyond standard software agreements:

Data Rights

• Your data belongs to you
• No use of your data for training without explicit consent
• Clear data handling and deletion requirements
• Right to audit data handling

Performance Commitments

• Specific, measurable performance standards
• Remedies if performance doesn’t meet standards
• Protection against model degradation over time

Service Levels

• Availability commitments with meaningful remedies
• Response times for different issue severities
• Clear escalation paths

Pricing Protection

• Caps on annual price increases
• Clear definition of usage-based charges
• Protection against surprise true-up costs

Exit Rights

• Data portability requirements
• Reasonable termination notice periods
• Assistance with transition to alternatives

Change Management

• Notification of material product changes
• Protection against forced upgrades
• Clear version support timelines

Procurement Process

Step 1: Market Scan (2-4 weeks)

Understand what’s available. Talk to analysts, peer organisations, and industry contacts. Don’t issue RFP blind.

Step 2: Shortlist (1-2 weeks)

Based on market scan and requirements, identify 3-5 vendors for detailed evaluation. More wastes time; fewer limits choice.

Step 3: RFP/Evaluation (4-6 weeks)

Issue detailed RFP. Conduct demos, reference checks, and technical assessments.

Step 4: Proof of Concept (2-4 weeks)

Run POC with preferred vendor(s). Validate capability with your data and use case.

Step 5: Negotiation (2-4 weeks)

Negotiate final terms. Don’t accept first offer. Use competition as leverage.

Step 6: Contracting (2-4 weeks)

Final contract negotiation and execution. Legal review essential.

Total timeline: 3-5 months for meaningful AI procurement. Trying to compress this excessively creates risk.

Common Mistakes

Rushing

Pressure to move fast leads to inadequate evaluation. The cost of wrong vendor selection far exceeds the cost of thorough evaluation.

Accepting Demo Performance as Representative

Vendor demos are optimised. Your data and use cases will produce different results. Always test with real data.

Ignoring Total Cost

License fees are often the minority of total cost. Include implementation, integration, training, operations, and evolution in your analysis.

Underweighting Vendor Viability

AI market has many startups. Some will fail or be acquired. Factor vendor stability into selection, especially for critical applications.

Single-Source Dependency

Locking into single vendor without alternatives creates risk. Consider how you’d switch if needed.

Working with Procurement

Technology and procurement teams often struggle to collaborate on AI purchases. Bridge the gap:

Involve procurement early. Don’t complete evaluation then throw to procurement for contracting.

Explain AI specifics. Help procurement understand why AI purchases differ from standard software.

Focus on outcomes. Frame requirements and evaluation in business terms procurement can engage with.

Be patient. Good procurement takes time. The protection is worth it.

Final Thought

AI procurement is high-stakes purchasing. The vendors are sophisticated, the technology is complex, and the risks of poor selection are significant.

Apply rigorous process. Require proof with your data. Negotiate contracts that protect your interests. Don’t let hype or pressure compromise evaluation quality.

Good AI procurement isn’t about finding the best technology – it’s about finding the right solution for your specific needs that you can successfully implement and sustain.