What is AI Procurement?

What Is AI Procurement?

AI Procurement is the end-to-end process of acquiring AI capabilities from external providers. This includes everything from identifying potential vendors and evaluating their solutions to negotiating contracts and managing ongoing vendor relationships. Unlike procurement of traditional software, AI procurement involves unique considerations around data ownership, model performance guarantees, and the ongoing nature of AI system improvement.

For most organizations, especially in the mid-market, buying or licensing AI solutions from specialized vendors is faster and less risky than building from scratch. However, AI procurement requires a different evaluation framework than purchasing conventional software because the value of an AI solution depends heavily on data quality, model accuracy, and integration complexity — factors that are often difficult to assess before deployment.

Why AI Procurement Is Different

Traditional software procurement focuses on features, scalability, pricing, and vendor stability. AI procurement adds several additional dimensions:

• Performance uncertainty — Unlike conventional software where features either work or they don't, AI model performance varies depending on your specific data and use case
• Data requirements — AI vendors may need access to your proprietary data for model training or customization, raising security and ownership questions
• Continuous improvement — AI systems should improve over time, so procurement must account for ongoing model updates and retraining
• Integration complexity — AI solutions often need deep integration with existing data systems and business processes
• Explainability requirements — You may need to understand how the AI makes decisions, especially in regulated industries

The AI Procurement Process

Step 1: Define Requirements

Before approaching vendors, clearly articulate:

• Business problem — What specific problem or opportunity are you addressing?
• Success metrics — How will you measure whether the solution is working?
• Data availability — What data can you provide for model training and operation?
• Integration needs — What existing systems must the AI solution connect with?
• Performance expectations — What level of accuracy, speed, or reliability do you require?
• Compliance requirements — What regulatory, security, and privacy standards must the solution meet?

Step 2: Market Scan

Survey the available options across different categories:

• Horizontal AI platforms — General-purpose platforms that can be configured for many use cases
• Vertical AI solutions — Purpose-built solutions for specific industries or functions
• AI-as-a-Service — Cloud-based API services for specific AI capabilities like natural language processing or computer vision
• Custom development partners — Consulting firms or agencies that build bespoke AI solutions

Step 3: Vendor Evaluation

Assess potential vendors across multiple criteria:

• Technical capability — Does the solution actually work for your use case? Request demos with your data if possible
• Data handling practices — How does the vendor handle your data? Who owns the trained models? Where is data stored?
• Track record — Has the vendor delivered similar solutions for comparable organizations?
• Pricing model — Is pricing based on usage, seats, data volume, or flat fees? How does it scale?
• Support and maintenance — What level of ongoing support is included? How are model updates handled?
• Security and compliance — Does the vendor meet your security standards and regulatory requirements?
• Vendor viability — Is the vendor financially stable? What happens if they go out of business?

Step 4: Proof of Concept

Before committing to a large contract, run a structured proof of concept (PoC) with one or two shortlisted vendors:

• Define clear success criteria before the PoC begins
• Use your actual data, not synthetic or demo data
• Test integration with your existing systems
• Evaluate usability with your actual end users
• Set a fixed timeline, typically four to eight weeks

Step 5: Negotiation and Contracting

AI contracts should address several specific provisions:

• Data ownership and portability — Ensure you retain ownership of your data and any models trained on it
• Performance guarantees — Define minimum performance levels and remedies if they are not met
• Model transparency — Specify your right to understand how the AI makes decisions
• Exit provisions — Ensure you can migrate away from the vendor without losing access to your data or trained models
• Pricing protections — Include caps or predictability mechanisms for usage-based pricing models
• Liability allocation — Clarify who is responsible when AI-generated decisions cause harm

Step 6: Ongoing Management

AI procurement does not end at contract signing. Effective vendor management includes:

• Regular performance reviews against agreed metrics
• Quarterly business reviews with the vendor
• Monitoring for model drift and degradation
• Evaluating whether the vendor continues to innovate and improve their solution

AI Procurement in Southeast Asia

Several regional considerations affect AI procurement in Southeast Asia:

• Data sovereignty — Some countries require data to be stored within national borders, affecting which vendors and cloud configurations are eligible
• Local language support — Ensure AI solutions support the languages relevant to your market, including Bahasa Indonesia, Thai, Vietnamese, and Tagalog
• Regional vendor landscape — Evaluate local AI vendors alongside global providers for potentially better cultural fit and regional expertise
• Currency and payment — Consider the impact of currency fluctuations on contracts denominated in foreign currencies

Key Takeaways for Decision-Makers

• AI procurement requires a different evaluation framework than traditional software purchasing due to performance uncertainty, data dependencies, and ongoing improvement requirements
• Always run a structured proof of concept with your actual data before committing to a large contract
• Pay special attention to data ownership, model transparency, and exit provisions in AI contracts
• Treat vendor management as an ongoing activity, not a one-time procurement event