What is AI Vendor Management?

What is AI Vendor Management?

AI Vendor Management is the structured approach to handling your organisation's relationships with the external companies that supply AI capabilities. This includes cloud AI service providers, specialised AI software vendors, AI consulting firms, data providers, and any other third party that contributes to your AI operations.

For most businesses, especially SMBs, building every AI capability in-house is neither practical nor cost-effective. External vendors provide access to advanced technology, specialised expertise, and proven solutions that would take years and significant investment to develop internally. However, relying on vendors without proper management creates risks: vendor lock-in, hidden costs, data security concerns, compliance gaps, and dependency on systems you do not fully understand.

Why AI Vendor Management is Different

Managing AI vendors requires a different approach than managing traditional technology suppliers for several reasons:

Opacity of AI Systems

Many AI vendor products are effectively black boxes. You send data in and get predictions out, but you may have limited visibility into how the model works, what data it was trained on, or why it makes specific decisions. This opacity creates challenges for compliance, accountability, and troubleshooting.

Data Sensitivity

AI vendors often need access to your business data to deliver their services. This creates data security and privacy risks that must be carefully managed, especially when data crosses borders in a region like ASEAN with varying data protection laws.

Rapid Technology Evolution

The AI vendor landscape changes faster than most technology markets. New providers emerge frequently, existing ones pivot their offerings, and pricing models shift. Effective vendor management must account for this dynamism.

Performance Variability

AI vendor performance can vary based on factors that are difficult to predict, including changes to the vendor's underlying models, shifts in data patterns, or updates that the vendor makes without your direct involvement.

Key Components of AI Vendor Management

1. Vendor Selection and Due Diligence

Before engaging an AI vendor, conduct thorough evaluation:

• Technical capabilities: Does the vendor's solution actually solve your specific business problem? Test with your own data, not just the vendor's demo datasets.
• Transparency and explainability: Can the vendor explain how their AI makes decisions? This is critical for compliance and trust.
• Data handling practices: How does the vendor store, process, and protect your data? Where is data physically located? Who has access?
• Financial stability: Is the vendor financially viable for the long term? Relying on a vendor that may not exist in two years is a significant risk.
• Reference customers: Speak with existing customers, ideally in your industry and region, about their experience with the vendor.
• Integration complexity: How easily does the vendor's solution integrate with your existing systems and workflows?

2. Contract Structuring

AI vendor contracts require specific provisions beyond standard technology agreements:

• Service level agreements: Define specific, measurable performance standards the AI system must meet
• Data ownership and usage: Clarify who owns the data you provide and whether the vendor can use it to improve their models for other customers
• Model transparency: Require the vendor to disclose model updates that could affect your outputs
• Exit provisions: Ensure you can migrate away from the vendor without losing data, functionality, or facing prohibitive costs
• Compliance obligations: Specify which regulatory requirements the vendor must meet, including data residency rules relevant to your ASEAN markets
• Liability for AI errors: Define responsibility when AI outputs cause business harm

3. Ongoing Performance Monitoring

Once engaged, continuously monitor vendor performance:

• Track AI system accuracy, speed, and reliability against contracted service levels
• Monitor costs against budget, watching for unexpected usage-based charges
• Assess vendor responsiveness to support requests and issues
• Review vendor security practices and compliance certifications periodically
• Benchmark vendor performance against alternatives to ensure you are still getting competitive value

4. Relationship Governance

Establish a structured governance framework for the vendor relationship:

• Regular review meetings: Monthly or quarterly reviews of performance, issues, and roadmap alignment
• Escalation procedures: Clear paths for escalating problems when normal support channels are insufficient
• Change management: Processes for managing vendor updates and changes that affect your operations
• Strategic alignment: Periodic assessment of whether the vendor's direction continues to match your needs

AI Vendor Management in Southeast Asia

ASEAN-based organisations face specific vendor management considerations:

• Data residency requirements: Several ASEAN countries have data localisation requirements. Ensure vendors can store and process data within required jurisdictions. Singapore, Indonesia, Vietnam, and Thailand all have relevant regulations.
• Regional support availability: Evaluate whether the vendor provides support in your time zone and in languages your team speaks. A vendor with excellent technology but no regional presence can create operational challenges.
• Local alternatives: The ASEAN AI vendor ecosystem is growing. Local and regional providers may offer better cultural understanding, regulatory compliance, and support compared to global vendors, even if their technology is less mature.
• Multi-vendor strategy: Given the rapid evolution of AI, avoid putting all your AI capabilities with a single vendor. A multi-vendor approach reduces risk and provides negotiating leverage.

Common Vendor Management Mistakes

• Choosing based on demos alone: Vendors optimise demos to impress. Always test with your own data and real-world scenarios before committing.
• Ignoring exit costs: Getting locked into a vendor with no practical exit path gives them enormous leverage over pricing and service quality.
• Insufficient data governance: Sending sensitive data to a vendor without clear contractual protections around data usage, storage, and deletion is a significant risk.
• Set-and-forget relationships: Vendor performance can degrade over time, especially as their technology evolves. Regular monitoring and review are essential.
• Over-reliance on a single vendor: Concentrating all AI capabilities with one provider creates a single point of failure and limits your negotiating position.