The AI Vendor Vetting Checklist: What Asian businesses should check before buying AI in 2026

In 2026, most organisations are not building AI from scratch. They are buying it.That shift makes sense. Building large-scale AI systems internally is expensive, slow, and rarely a core competency. But buying AI comes with a quieter risk that many teams underestimate.

When you procure AI, you do not just acquire a tool. You inherit the vendor's legal assumptions, technical constraints, governance posture, and long-term incentives. If those are misaligned with your own, the consequences surface later, often when it is hardest to unwind.

This checklist is designed to help Asian businesses ask better questions before they sign.

Not to slow innovation.

To protect it.

1. Data ownership

Who really owns what you put in?

This should always be the first conversation.

Many AI vendors still rely on loosely worded clauses that allow them to reuse customer inputs to "improve their models".

That can include prompts, documents, workflows, behavioural signals, and decision logic that are commercially sensitive.

You need clear answers on a few things. Does all input data remain your property? Can they train on it, fine-tune with it, or reuse it without your explicit consent? Is there strong separation between you and their other customers? What are the retention and deletion timelines, and can you actually enforce them?

If a vendor struggles to explain this without legal gymnastics, treat that as a signal.

2. Regulatory readiness

Compliance is now part of the product

The EU AI Act has reset expectations globally, including across Asia. Regulators in Singapore, Japan, South Korea, Australia, and beyond are converging around similar principles even where enforcement frameworks differ.

Procurement teams should stop accepting vague reassurances and start asking for artefacts.

Does the vendor have a Model Card or equivalent technical disclosure? Can they articulate their risk classification where applicable? What do they actually know about their training data sources? And for higher-risk use cases, where are the human oversight and escalation paths?

Groups like Project Asia Data have been highlighting the gap between what regulators expect and what vendors are actually prepared to deliver. That work matters because regulatory posture is no longer abstract. It directly affects enterprise adoption, government use cases, and cross-border deployments. This is especially relevant as AI faces growing opposition over pollution, jobs.

3. Exit strategy

What happens when the relationship changes?

Every vendor looks stable until the moment they are not.

Prices rise. Terms change. APIs are deprecated. Acquisitions happen. Startups fail. You need to understand your exit before you ever need it.

Can all data be exported in a usable, structured format? Can workflows, configurations, and logic be migrated? Are there proprietary dependencies that prevent switching? And is there a contractual right to exit without punitive cost?

If leaving is deliberately painful, that is not accidental. It is a business model. This concern is also a factor when AI chatbot giants restrict free access.

4. Uptime and fallbacks

AI systems do go down

AI relies on infrastructure, compute, third-party models, and APIs. Outages are inevitable. What matters is how well they are handled.

You should know what happens if the system is unavailable for 24 hours. Is there a manual or degraded fallback mode? How are rate limits and throttling managed at scale? And what are the actual SLA commitments in writing, not just in the sales pitch?

A vendor claiming perfect uptime is not being honest. A vendor with a clear contingency plan is.

5. Decision accountability

Who owns the outcome when AI is wrong

This is one of the most overlooked questions in AI procurement.

If an AI system influences hiring, credit, pricing, moderation, or customer decisions, accountability must be unambiguous.

Look for robust audit and decision logs. Explainability that is appropriate to the decision context. Clear human override and escalation mechanisms. And defined responsibility boundaries between vendor and client.

AI should support judgement, not dilute responsibility. The challenge of AI's inner workings baffle experts at major summit further complicates this.

Final thought

Buying AI is no longer a technical experiment. It is a governance decision, a legal commitment, and an operational dependency that compounds over time.

The strongest vendors expect scrutiny. They document clearly. They welcome these conversations.

The ones that resist are revealing something important.

As AI adoption accelerates across Asia, the work that organisations like Project Asia Data are doing becomes essential. They are helping keep innovation grounded in accountability, not assumption.

Before you sign, ask the uncomfortable questions. For businesses in Singapore, this is particularly relevant as Singapore MSMEs Are Getting An AI Power-Up!.

Putting this into practice

If you are evaluating AI vendors right now, use this checklist as a live conversation tool, not a compliance exercise. Try it with your next vendor meeting. Notice which questions are answered directly and which ones are deflected.

We would love to hear from you. What checks have helped you avoid a poor AI decision? What questions would you add to this list?

Share your experience in the comments and help shape how Asia buys AI responsibly.