How to Choose an AI Vendor in Indonesia: 10 Questions to Ask Before You Sign

AI demos almost always look convincing. The model answers smoothly, the dashboard looks clean, and the presentation is full of success stories. The problem is that a demo runs on data prepared in advance. What decides the outcome in your company rarely shows up in a demo: where the data is stored, how the AI connects to the systems you already run, who is responsible when something goes wrong, and what happens if the partnership ends.

This article sets out 10 questions to ask any prospective AI vendor before you sign a contract. The questions are neutral and work for any kind of vendor, whether a platform provider, an integrator, or a consultancy. A clear, written answer is a good sign. A vague one is a signal to dig deeper.

In short:

• Data first: make sure data location, access rights, and ownership of data and outputs are written into the contract.
• Real integration: ask the vendor to explain how it connects to the systems you already use.
• Measurable pilot: agree on the pilot scope and success measures before the project starts.
• Exit plan: make sure your data can be exported and the service can be ended without losing assets.

Why Choosing an AI Vendor Is Different from Buying Ordinary Software

Ordinary software generally follows fixed rules: the same input produces the same output. AI systems work with probabilities. Answers can vary, quality depends on the data, and behavior can change when the model is updated.

That is why evaluating an AI vendor means looking beyond features and price. You are also judging how the vendor manages data, measures quality, handles errors, and keeps the system fit for use after launch. A good contract settles all of this up front, not after a problem appears.

10 Questions to Ask Before You Sign

1. Where is our data stored and processed, and who can access it?

Ask about server locations or cloud regions, whether data is sent to third-party model providers, and who on the vendor's side can see your data. Ask too whether your data is used to train models, whether the vendor's own or a third party's. A good answer names the locations, the parties involved, and the access arrangements specifically.

2. How does the solution connect to the systems we already use?

AI that is not connected to your CRM, ERP, ticketing system, or internal databases usually ends up as a separate tool that few people use. Ask the vendor to explain the integration method (API, connector, or data export), who does the work, and what it needs from your IT team. Be wary of answers that require you to replace core systems just so the AI can run.

3. What is the pilot scope, and how will success be measured?

A good pilot has clear limits: one process, one unit, or one group of users, for a set period. Agree on success measures before the pilot starts, such as resolution time, error rate, or the share of cases completed without help. Ask too what happens if the targets are not met, and whether there is a decision point where you can stop without further obligation.

4. Who owns the data, the configuration, and the outputs?

Make sure the contract states that your data remains yours. Clarify the status of AI outputs, prompts, knowledge bases, and configurations built during the project as well. If the vendor develops something specifically for you, ask whether you can keep using it after switching vendors.

5. How are security and the audit trail handled?

Ask about role-based access control, encryption of data at rest and in transit, and activity logging. An audit trail matters because it lets you trace what the AI did, when, based on which data, and who approved it. If the AI can take actions in your systems, ask how risky actions are limited and approved.

6. How does the solution support compliance with the PDP Law?

If the system processes personal data of customers or employees, your company must comply with Law No. 27 of 2022 on Personal Data Protection. Ask how the vendor helps you meet obligations such as the legal basis for processing, access restrictions, data retention and deletion, and incident handling. Clarify each party's role in processing the data and put it in the agreement.

7. What do support and maintenance cover after launch?

AI systems need care: knowledge bases get updated, answer quality gets monitored, and model changes get tested. Ask about support hours, incident response times, who monitors quality, and how changes are communicated. Ask for a sample of the reports or monitoring dashboard you will receive.

8. What happens if we want to stop or switch vendors?

An exit plan is easiest to discuss before the contract is signed. Ask about data export formats, how long the process takes, whether there are extra fees, and when the vendor deletes your data after the contract ends. A good answer names commonly used formats and a documented process.

9. What is the pricing model, and which costs can change?

AI pricing can be based on licenses, number of users, usage volume, or a mix of all three. Ask for a breakdown of implementation, subscription, AI usage, integration, and support costs. Ask which costs rise as volume grows, and whether there are caps or alerts before a bill spikes.

10. Can we speak with customers using a similar solution?

References show what a presentation does not. Ask for contacts at customers with a process, scale, or industry similar to yours. Ask them how the implementation went, what problems came up, and how the vendor handled them. If the vendor cannot provide references yet, ask for a smaller pilot instead.

The Questions in One Table

How to Run the Evaluation

1. Define the business problem. Write down the process to improve, its owner, and how you will measure impact before contacting vendors.
2. Involve the right people. Bring in representatives from the business, IT, information security, legal, and finance from the start.
3. Send the questions in writing. Use the 10 questions above so every vendor's answers can be compared fairly.
4. Ask for a demo with your data. Where possible, use anonymized samples of your own data rather than the vendor's demo data.
5. Run a limited pilot. Test in a real environment against the success measures you agreed on.
6. Decide based on results. Continue, adjust, or stop based on the pilot numbers, not on impressions from a presentation.

For a broader guide to the first steps, see also how to start AI implementation in your company.

Frequently Asked Questions

Does an AI vendor have to store data in Indonesia?

It depends on the type of data, your sector, and the regulations that apply to your company. Discuss it with your legal and compliance team, then make sure the vendor's answer on data location fits those requirements.

How long should an AI pilot run?

Long enough to gather data you can compare with the current process, but short enough that the decision is not delayed. The right length depends on transaction volume and how complex the process is.

Is the biggest vendor always the safest choice?

Not necessarily. A vendor's size does not guarantee a fit with your systems, processes, and compliance needs. The answers to the 10 questions above matter more than a big name.

What if a vendor cannot answer some of the questions?

Give the vendor time to answer in writing. If questions about data, security, or the exit plan remain unanswered, treat that as a risk to weigh before you go further.

Conclusion

Choosing an AI vendor is a decision about data, processes, and accountability, not only about technology. The 10 questions above help you see what sits behind the demo: how your data is protected, how the AI fits into the systems you already run, and what happens when things do not go to plan.

Start with one clear business problem, ask for written answers, run a limited pilot, and decide based on measurable results.

Preparing an AI vendor evaluation? IOI's AI consultation can help you map the process, the pilot scope, and its success measures.