The Essential Questions to Ask Your AI Vendor Before Deploying Artificial Intelligence at Your Organization

Key Questions to Ask:

• What is the specific business problem you are trying to solve and how does your AI solution address it? This helps confirm that you have a clearly defined use case in mind, and you can evaluate how the specific AI solution solves that problem. AI is not always the right choice. Also, this helps show if the vendor has a proven track record in your area.
• Can your AI system be trained on our proprietary data? AI that learns from your specific data may provide greater contextual insights and effectiveness – and minimizes the risk of biased or compromised external data from being introduced into your organization.
• How scalable is your solution? If your needs grow, a scalable AI system will ensure continued support without a costly overhaul.

These high-level questions encourage the vendor to provide a concise overview of their solution’s capabilities, offering you an early look into how well it will serve your team.

Key Questions to Ask:

• Can you show me how user-friendly your AI solution is for non-technical workers? You’ll want to know if everyday employees can comfortably work with the AI – so rather than just ask a leading question, ask the vendor to point out specific examples that demonstrate the ease of use.
• Based on prior experience, what is the expected ramp-up time for users? This helps anticipate the time needed for your team to adopt the tool.
• Do you offer a trial period, a pilot, or demo? A trial run lets your team get hands-on experience with the product, so you can evaluate its fit with your operations.

These questions are especially helpful for assessing whether the vendor is committed to a smooth user experience – a critical aspect for the successful adoption of AI.

Key Questions to Ask:

• What hardware or software is required to deploy your solution? This will clarify if any additional resources are needed.
• How compatible is your AI with our current tech stack? Ensuring compatibility can prevent disruptions and make integration seamless.
• How does the vendor monitor the performance of the AI application post-deployment? AI tools, especially GenAI tools, change and grow over time – leading to problems like algorithm and model drift. This can greatly impact the performance of the AI application. Vendors must have a plan to monitor the performance of their applications post-deployment and alert you to any changes.
• What is the projected timeline for integration? Knowing the timeframe upfront helps align the project with other business goals and resources.

These deployment-focused questions offer insight into how quickly and smoothly the AI can become operational in your environment, minimizing downtime and unexpected costs.

Key Questions to Ask:

• What internal skills are required to implement and maintain this solution? Confirm whether your existing team has the necessary expertise, or if training or hiring will be required.
• What type of training and onboarding resources do you offer for our team? Some vendors provide substantial training and onboarding support to help bridge skill gaps.
• Can you recommend third-party providers or partners who can assist with implementation and ongoing support? Many companies rely on third-party specialists to ensure smooth AI operations over time.

Asking about personnel requirements helps determine if your organization is fully equipped for AI adoption or if additional resources are needed to make the implementation a success.

Key Questions to Ask:

• Will your AI solution connect directly to our data? This will clarify if the vendor can use your data where it is today or if data preparation is required.
• How is data stored and handled within your AI solution? Does our data leave the organization at any time (such as to a public large language model like OpenAI or Claude or Gemini)? Understanding data retention and storage policies will help you track where your data is stored and where it may be vulnerable.
• What data was used to train the AI system—both in the past and on an ongoing basis?  Did the vendor have the right to use the data for training? Were the datasets used for training appropriate with the intended use of the AI system? It’s critical to make sure that the AI vendor had the right to use the training data and selected the right datasets for training its system.
• Who owns the AI model, user input, and output? Consider whether the vendor should have ownership or be licensed to use your user’s input, and the output from the AI, for other purposes. (The input and output could contain confidential information.) Some vendors will want to use this information to improve the substance of their product beyond normal troubleshooting, so ensure you have clarity on this point.

Confirming your data is AI-ready will help you avoid unexpected delays for time-consuming data cleanup.

Key Questions to Ask:

• What measures are in place for data security, particularly around sensitive information? Look for encryption, anonymization, and other best practices in data governance. This includes protecting any input to, and output from, the model.
• Does the vendor have an incident response plan? Make sure the vendor has a specific plan for dealing with any security incidents and will give you immediate notice of any security incidents.
• What options are available for security settings and access permissions? Ensuring the vendor will utilize existing user permissions can prevent unauthorized and unintended access to confidential information.
• Does your AI system adhere to relevant regulations, like CCPA or GDPR? What are you doing to monitor new regulations and other legal developments, and how quickly can you adapt to changes? A commitment to regulatory compliance safeguards against legal risks and data breaches.

These high-level questions provide a snapshot of the vendor’s data security approach, allowing you to probe further if necessary.

Key Questions to Ask:

• How do you prevent bias in your AI system? Knowing a vendor’s approach to bias mitigation can help protect against unintended discriminatory outcomes. Ask if the vendor conducts any bias audits (and ask them to share the results).
• What steps do you take to make your AI model explainable? Explainability is key for building trust and understanding AI’s decision-making process.
• What ethical standards guide your AI’s development? A vendor’s commitment to ethics indicates their dedication to fair and responsible technology.

These questions encourage transparency and highlight the vendor’s commitment to building AI that aligns with ethical guidelines—a necessity for today’s responsible organizations.

Key Questions to Ask:

• What type of ongoing support and access to AI experts do you offer? Knowing if a team of experts is available will reassure you that help is there when needed.
• What documentation and resources do you provide? Comprehensive, accessible resources empower your team to troubleshoot and resolve issues independently.
• How do you collect and incorporate customer feedback? A vendor that prioritizes a feedback loop is likely to address issues and improve the AI’s functionality over time.

Asking about support options helps set expectations for what kind of assistance your team can expect, reducing the likelihood of future frustration.

Key Questions to Ask:

• What is your pricing model? Make sure there are no hidden fees, over-usage caps or triggers, and confirm if the price structure aligns with your budget.
• Can you provide an ROI estimate for our specific use case? Getting a tailored ROI projection can help justify the investment.
• Do you have case studies or testimonials that demonstrate success? Proven results from similar companies can validate the vendor’s claims.

Clear answers here will help you set realistic financial expectations and justify the investment to other stakeholders.

Key Questions to Ask:

• What LLM (Large Language Model) or LLMs are powering the AI system? Make sure the LLM or LLMs being used are from stable and reputable companies.
• What backup plans does the vendor have if that LLM (or LLM company) goes down? Many of the major LLMs have recently seen significant outages. Make sure the vendor can seamlessly pivot to another LLM without any interruptions in service.
• How quickly will you take advantage of updates in the LLM models? LLM models are constantly updating. Make sure you know how quickly those updates will be incorporated into the AI system.
• Does the vendor have a decommissioning plan? AI systems do not last forever, and you will stop using it at some point. Have a plan for what happens to your data and tech stack when you stop using the AI system.

Confirming the stability of the AI solution up-front will help avoid frustration and disappointment in the future.