Vendor Selection and AI Partnerships

Most organizations rely on external vendors and partners for AI capabilities. Choosing the right partners and managing relationships effectively is critical for AI success.

Vendor Evaluation Framework

Key Selection Criteria

Capability and fit:

Does the solution address our specific needs?
How well does it integrate with existing systems?
What customization is possible?
Does it scale with our requirements?

Technical foundation:

What AI/ML approaches does the solution use?
How is the model trained and updated?
What data does it require?
How explainable are the outputs?

Security and compliance:

Where is data stored and processed?
What security certifications exist?
How does it meet our regulatory requirements?
What data privacy protections are in place?

Vendor viability:

How established is the company?
What is their financial stability?
Who are their existing customers?
What is their product roadmap?

Evaluation Process

Phase	Activities
Discovery	Define requirements, identify candidates
Assessment	Review capabilities, request demos
Due diligence	Check references, security review
Proof of concept	Test with your data and use case
Decision	Compare options, negotiate terms

Partnership Models

Types of AI Partnerships

Technology vendors: Provide AI platforms, tools, or APIs

Clear product scope
Subscription or usage-based pricing
Limited customization
Self-service implementation

System integrators: Implement and integrate AI solutions

Project-based engagement
Custom implementation
Knowledge transfer possible
Higher cost, more support

AI consultancies: Advise on strategy and approach

Strategic guidance
Vendor-neutral (ideally)
Can accelerate learning
May not do implementation

Research partnerships: Access to cutting-edge AI capabilities

Access to latest research
Co-development opportunities
Longer-term relationships
Higher uncertainty

Choosing the Right Model

Need	Best Partner Type
Off-the-shelf capability	Technology vendor
Complex integration	System integrator
Strategic direction	AI consultancy
Competitive differentiation	Research partnership

Managing Vendor Relationships

Contract Considerations

Data rights:

Who owns the data used to train/improve the AI?
Can the vendor use your data for other customers?
What happens to your data if you leave?

Performance guarantees:

What accuracy or performance levels are guaranteed?
What happens if performance doesn't meet expectations?
How are disputes resolved?

Exit provisions:

What is the process for ending the relationship?
Can you export your data and models?
What transition support is provided?

Ongoing Management

Regular reviews:

Track performance against SLAs
Monitor usage and costs
Review roadmap alignment
Address issues promptly

Relationship health:

Maintain multiple contacts
Escalate strategically
Provide constructive feedback
Plan for contract renewals

Common Pitfalls

Vendor lock-in:

Proprietary data formats
Difficult data export
Integration dependencies

Mitigation: Negotiate data portability, avoid single-vendor dependency for critical capabilities.

Scope creep:

Expanding requirements mid-project
Unclear deliverables
Cost overruns

Mitigation: Define scope clearly, use change control processes, break large projects into phases.

Expectation gaps:

Misunderstanding AI capabilities
Unrealistic timelines
Overestimated accuracy

Mitigation: Validate claims with proofs of concept, set realistic expectations, define success criteria upfront.

Successful AI vendor relationships require careful selection, clear agreements, and ongoing management. Evaluate vendors on capability, technical foundation, security, and viability. Choose partnership models that match your needs. Protect yourself contractually around data rights, performance, and exit provisions. And maintain relationships proactively to ensure long-term success.

Next: Learn how to execute your AI vision in the first 90 days and beyond. :::