Before signing with an AI vendor, ask questions in five areas: whether the product solves your specific problem and can prove it, how your data will be used, stored and protected, how the vendor measures accuracy and handles model changes, what the full cost and contract terms look like, including how you can leave, and whether the vendor can support you over the long term. Clear answers to these questions reduce the risk of expensive surprises and help you compare vendors on substance rather than sales presentations.
AI products can look similar in demos, but they often differ significantly in data practices, reliability, pricing and support. The questions you ask before signing often matter more than any feature list.
Questions About the Problem and Proof
Start by testing fit. Ask the vendor to explain exactly how their product addresses your use case, whether that’s customer support, document processing, forecasting or content generation. What results have similar customers achieved, and how were those results measured? Request case studies from companies in your industry or with similar scale and complexity.
Ask for references you can speak to directly. Conversations with existing customers often reveal practical details, such as implementation challenges, support quality and whether promised benefits materialized. Ask references what they’d do differently if they started again.
Whenever possible, request a pilot or proof of concept using your own data. Generic demos rarely show how a system performs with your documents, terminology and workflows. Agree on clear success criteria in advance, such as accuracy targets, time saved or cost reduction, so you can judge the results objectively.
Questions About Data, Privacy and Security
Data practices deserve close scrutiny. Will your data be used to train the vendor’s models, either their own or those of third-party providers? Can you opt out? How long is data stored, where is it stored and who can access it? For organizations handling sensitive information, answers to these questions can decide whether a vendor is viable.
Ask about security certifications and practices. Independent audits and certifications, such as SOC 2 Type II or ISO 27001, show that a vendor’s controls have been reviewed externally. Ask how data is encrypted in transit and at rest, how access is managed and how security incidents are handled and reported.
Check compliance with privacy regulations that apply to you, such as GDPR in Europe. Ask whether the vendor provides a data processing agreement, which subprocessors they use and whether data can be kept within specific regions if required. Understanding the full chain of providers involved is essential, since many AI tools rely on third-party models and cloud services.
Questions About Performance, Accuracy and Model Changes
AI systems can make mistakes, so ask how the vendor measures and manages accuracy. What testing do they perform, which metrics do they track and how do they handle errors or incorrect outputs? For generative AI, ask how they reduce the risk of fabricated or misleading information and what tools they provide for human review.
Model updates can change how a system behaves. Ask whether the vendor uses its own models or relies on external providers, how often models are updated and whether you’ll receive notice before changes. A model update that improves general performance might still affect your specific workflows, so ask whether you can test updates before they’re applied.
Service reliability matters too. What uptime guarantees does the vendor offer, and what happens if they aren’t met? How does the system perform under heavy use? Because these questions can be technical and vendor claims are hard to verify, some organizations bring in vendor selection support from specialists who can compare offerings across the market, check claims against industry benchmarks and help design fair evaluation criteria.
Questions About Cost, Contracts and Exit
AI pricing can be complex. Some vendors charge per user, others by usage, such as the number of queries, documents processed or tokens consumed. Ask for a clear estimate of total costs based on your expected usage, including implementation, integration, training and support fees. Find out how overages are charged and whether prices can change during the contract.
Review ownership and liability terms carefully. Who owns the outputs generated by the system? Does the vendor offer protection if its outputs infringe on third-party intellectual property? What are the limits of the vendor’s liability if something goes wrong? These questions are particularly important for companies using AI to create customer-facing content or support important decisions.
Plan your exit before you enter. Ask how you can export your data, configurations and any custom models if you decide to leave, and in what format. Understand notice periods and termination terms. Avoiding lock-in gives you flexibility to switch vendors if needs change or better options emerge.
Questions About Support, Roadmap and Viability
Implementation often determines success. Ask what support the vendor provides during setup, including integration help, training for staff and dedicated account management. How quickly do they respond to support requests, and what channels are available?
Understand the vendor’s roadmap. Which features are coming, and how do they align with your future needs? Ask about planned integrations with systems you already use and how the product will adapt to evolving regulations, such as new AI rules in the European Union.
Consider the vendor’s stability. The AI market is changing quickly, and some startups may be acquired, change direction or struggle financially. Ask about funding, customer base and long-term plans. A vendor’s financial health matters when you’re relying on them for important business processes.
Before your next vendor meeting, turn these questions into a simple scorecard and use it for every provider you’re considering. Scoring each vendor on fit, data practices, accuracy, cost and support will make comparisons clearer and help you choose the partner most likely to deliver value long after the contract is signed.
