How to Choose an AI Technology Partner: 10 Questions Every Leader Should Ask

The Vendor Selection Problem

The market for AI services has grown faster than the average buyer's ability to evaluate it. Marketing language in AI is particularly aggressive: every firm claims to be at the cutting edge, every engagement promises transformation, and every proposal includes the right keywords.

The gap between what AI firms promise and what they deliver is wide. Selecting the wrong partner is expensive in direct costs and even more expensive in the opportunity cost of a failed or delayed AI initiative.

These 10 questions cut through the marketing and reveal the substance.

Question 1: What AI systems are you currently running in production?

This is the first and most important question. You want the names of specific systems, deployed with specific clients, handling real traffic, and producing real value. If the answer is primarily pilots, proofs of concept, demos, or internal tools, the firm has not yet crossed the threshold from AI experimentation to AI delivery.

Follow up: can we speak with a client whose AI system you built?

Question 2: Who will actually build my system?

The people who appear in sales presentations are rarely the people who build the system. Ask specifically: which engineers will be assigned to this project? What is their background? How long have they been working on AI systems?

Request CVs of the proposed delivery team. Talk to the technical lead who will own your project. Evaluate their depth in the specific type of AI system you need (LLM systems, ML pipelines, AI agents, etc.).

Question 3: What data will you need, and what happens if it is not available or not clean?

Strong AI firms start every engagement with an honest assessment of data requirements and data reality. Weak firms promise AI outcomes without adequately engaging with the data constraints that determine whether those outcomes are achievable.

A good answer includes: specific data requirements for the proposed approach, a methodology for assessing your data against those requirements, and a clear plan for what happens when data is insufficient (data engineering investment, alternative approaches, or an honest recommendation that the use case is not ready for AI yet).

Question 4: How do you handle a situation where the AI approach you proposed is not working?

Every AI project encounters unexpected technical challenges. The difference between good and bad AI partners is what they do when the initial approach is not producing the expected results.

A good answer describes a pivot process: evaluating alternative approaches, communicating transparently with the client, and adjusting the plan. A bad answer is defensive or dismissive of the scenario.

Question 5: What does your delivery process look like, and how will I see progress?

AI projects fail most often because of misaligned expectations between the client and the vendor. Ask for a specific delivery framework: milestones, deliverables at each milestone, review process, and decision points.

The best AI engineering firms deliver working software at each milestone, not just documentation. Ask specifically: what will I be able to test at the end of each phase?

Question 6: Who owns the IP and the models?

This should be in the contract, not the conversation, but asking upfront reveals the firm's commercial approach. Full IP transfer to the client should be the default. Be wary of firms that retain rights to models, training data, or code, even if it is framed as necessary for the firm to improve their services.

Question 7: How do you handle data security and privacy?

For AI systems processing sensitive data, security and privacy architecture is not optional. Ask specifically: where does data go during training and inference? What are the data retention policies? What certifications or audits has the firm undergone? How does the system handle personally identifiable information?

Question 8: What is your approach to model evaluation and quality assurance?

Building a model that works in the lab is different from building a model that works reliably in production. Ask how the firm evaluates model quality: what metrics do they use, how do they test on edge cases, what is the evaluation process before deployment.

Strong AI engineering firms have systematic evaluation approaches and are comfortable discussing evaluation methodology. Firms without deep evaluation practices tend to have vague answers.

Question 9: What does ongoing support look like after deployment?

AI systems require ongoing maintenance: models drift, data distributions change, edge cases surface, and the business requirements evolve. Ask specifically what the firm offers after the initial deployment: monitoring, retraining, prompt updates, bug fixes, and feature additions.

The best AI engineering firms offer structured ongoing support rather than treating deployment as the end of the engagement.

Question 10: What should we not use AI for in our situation?

This is a test of intellectual honesty. Firms that will tell you where AI is not the right solution are firms that understand both AI capabilities and their clients' actual needs. Firms that find an AI application for every problem are selling AI rather than solving problems.

An honest AI partner will identify use cases where the data is not ready, where a simpler solution would work better, or where the ROI does not justify the investment. This honesty is more valuable than optimistic proposals that do not survive contact with reality.

Using These Questions

These questions are designed to distinguish firms with genuine AI engineering capability and client-aligned values from firms that are primarily selling AI marketing. Use them as a structured evaluation framework across every firm you consider.

The right AI technology partner will welcome these questions. They demonstrate a sophisticated client who will be a productive partner. Firms that are uncomfortable with rigorous evaluation questions are firms you should not be working with.

TunerLabs is a specialist AI engineering company based in Bengaluru, India. We welcome all 10 of these questions. Contact us to start the conversation about your AI initiative.