Artificial Intelligence is becoming increasingly embedded in software systems across companies, regardless of size. This means that sales teams are now continuously facing tough technical questions from buyers. 

And of course, your customers want more than a flashy demo. They want to know how your AI works, if it’s secure, and how legally protected they are. Of course, they also want assurances about what will happen if their contract ends. 

To close your AI deals faster and establish trust with your customers, your sales team has to be ready to answer the most common AI questions. In this guide, we’ll walk you through those questions, the context, and the perfect responses. So you’ll be ready with strong answers that beat out the competition.

Need help? Download this template for the most common AI questionnaire responses.

Most Common AI Categories on Questionnaires

To best learn the questions and their responses, it is helpful to understand the categories into which these questions fall. Buyers evaluating AI tools often structure their questions so they cover legal, technical, and operational risks. Here are the five main categories you can expect to see: 

Ownership, Use & Legal Terms

One thing is very clear with 32% of questions around ownership use and legal terms: businesses are simply not comfortable with this tech. They’re worried about lawsuits, rights, and who’s left holding the bag when something goes wrong.

Most businesses are deeply concerned with who owns AI-generated outputs. They also want to see what rights they will have to reuse or commercialize those outputs. You’ll likely need to answer questions about how you handle customer data. All of these questions may also probe into licensing terms, indemnity clauses, and post-contract implications. 

AI Functionality & Deployment

This category deals with the elemental mechanics of AI systems. It will center questions about what the AI system does, how it works, and where it runs. Your buyers will wonder whether your AI is available as a SaaS product, an API, or a deployment on-premises. You may also see inquiries into what base models you have in use and whether the functionality of the system is mature and scalable, or if it’s still in beta. 

Data & Model Transparency

By now, you’ve likely heard, and perhaps said, that transparency is the key to trust. This is nowhere more true than in AI. Your buyers will want to see clear documentation on what data has been used to train your model or to fine-tune it. They’ll also be looking at how representative your data is and whether you took steps to reduce any potential bias. Questions here may also explore how your input and output are filtered to meet critical safety, legal, and ethical. 

Governance, Risk & Compliance 

This category deals with the formal review processes around the risks of AI. These questions can include those concerning bias, privacy, data residency, and explainability. Many buyers already have internal AI governance teams of their own. These teams require documented assessments before giving approval. You may also get questions that ask whether you have a human who stays in the loop for your automated decisions. 

Monitoring, Security, and Controls 

Your clients want to get assurance from you that your AI isn’t a black box. You’ll see questions about usage monitoring, logging, and output traceability. This category will also include issues of hallucination, malicious code generation, prompt injection, and data security. This is especially true when inputs contain sensitive information, either business or personal.

1. What is the intended use or scope of your AI functionality?

Why they’re asking: Your customers want to make sure your AI is appropriate for their specific use case, so they can understand the boundaries of its capabilities. They’re figuring out if the AI will deliver the value they need for their context. They also want to know if you designed it with their industry, team, and workflow in mind. 

How to Respond: It’s important you get clear about what specific problems your AI solves. This includes what tasks it automates or augments and what limitations it has. 

Example of a great response: “Our AI helps sales teams prioritize leads by analyzing historical CRM activity and engagement signals. It also identifies and analyzes buying patterns. You can expect scoring rather than direct recommendations as this system supports sales reps rather than aiming to replace them.” 

2. Who is the provider or vendor of the AI technology or model?

Why they’re asking: Here, your buyer wants to assess the reliability, support structure, and track record of the underlying technology of the AI. They’re looking for confidence in your AI’s stability and support options. And they definitely wonder what kind of long-term viability your system has. This is especially true when you rely on third-party models or APIs. 

How to Respond: You can instill confidence by offering the name of the vendor or model and explaining whether it’s in-house, from a commercial provider, or open source. Also, be sure to highlight the credibility of the AI system. 

Example of a great response: “We use a proprietary model trained by our in-house data science team. The model is built on top of Hugging Face’s Transformers library. It is hosted on AWS. For natural language processing tasks, we integrate OpenAI’s GPT-4 API.”

3. How is the AI functionality deployed (SaaS, API, open source, on-premises)?

Why they’re asking: The deployment options you choose will impact your integration, compliance, and operational control. Your method of deployment will determine whether a buyer can adopt the tool easily and meet regulatory requirements. They also need to know if they can maintain data sovereignty. 

How to Respond: You can clarify for your buyer how the product will be delivered and what options they have at their disposal. 

Example of a great response: “Our AI is delivered via a secure multi-tenant SaaS platform. We offer an API to enterprise clients. We also provide a containerized deployment on premises for any industry under regulation.” 

4. What is the underlying model or base technology (model name, vendor/source)?

Why they’re asking: Your potential clients want to understand the capabilities and limitations of your AI model. They need to assess vendor dependencies. With this question, the buyer is evaluating how your model was built and who’s responsible for updates. They’ll also be looking at how strong the performance history is. 

How to Respond: This one is simple. Just provide the name of the base model and its origin ,and then note any fine-tuning or modifications you performed. 

Example of a great response: “Our chatbot functionality is powered by OpenAI’s GPT-4. We fine-tuned our model using anonymized customer support transcripts. The model also uses custom classifiers that have been trained on the taxonomy of customer product.” 

5. What is the current product maturity/stage (e.g., experimentation, pilot, in production)?

Why they’re asking: Your buyer here is wondering if the AI is battle-tested or if it’s still in the experimental phase. They’re assessing how much risk they may be taking on by adopting your solution over another. They also want to know whether any other customers can vouch for the product. 

How to Respond: You can share with your customer whether your product is in beta or production. Let them know whether it’s scaling, and offer any customer testimonials you may have. 

Example of a great response: “The AI functionality has been in production since Q2 2022. We currently have over 150 enterprise customers using the product, and we can verify more than 10 million interactions are processed each month.” 

6. How is the AI model trained or fine-tuned? What types of data and sources are used?

Why they’re asking: Your buyer here is looking to assess the relevance, quality, and compliance of your training data. Of course, buyers need to know whether your training process aligns with industry standards. They also want to make sure you’re avoiding risky or unvetted sources. 

How to Respond: Reassure your buyer by explaining the types of data, data sources, and training methods you use. 

Example of a great response: “We train our model using anonymized CRM data, support tickets, and call transcripts. We combine this with publicly available sales data. Then, we make sure to clean all data and label it in accordance with GDPR.”

7. Is there transparency regarding the datasets and methods used for model training or finetuning?

Why they’re asking: We’ve said it before, and we’ll say it again: transparency is everything. It will help your buyer evaluate risk levels, biases, and compliance with legal requirements. Because buyers are usually held accountable for their AI sourcing, they’ll need documentation to justify their AI decisions to their internal stakeholders. 

How to Respond: You can provide an outline of how you document your training sources, filtering methods, and version history. 

Example of a great response: “We maintain complete documentation on our entire training pipeline. This includes dataset provenance, data filtering logic, and training configurations. All of our customers under NDA have access to this information.” 

8. Has the legal basis of the source or training data been assessed and documented?

Why they’re asking: Your potential customers need to avoid any potential exposure to copyright infringement or privacy violations. They ask this question to make sure your AI training process is compliant with laws, regulations, and ethical use standards. 

How to Respond: You can confirm for your buyer that you perform legal reviews of data sources. Make sure to provide documentation to back up your claims. 

Example of a great response: “Yes. All of our training data sources undergo legal review. We maintain documentation that proves consent, licensing, and compliance with GDPR, CCPA, and copyright laws.”

9. Is any open-source material included in model training? If so, is it specified and documented?

Why they’re asking: You’re likely aware that open-source materials can pose licensing risks. This is especially true if the usage isn’t being properly tracked. Your buyer wants clarity to make sure you’re in compliance with open-source obligations. 

How to Respond: Let your buyer know whether you use open-source data and how your company manages it if so. 

Example of a great response: “We use a limited amount of open-source content from permissively licensed repositories. We document all of our sources, and we avoid GPL content during training.” 

10. Does the AI tool generate original work, and are there methods to verify results are distinct from training inputs?

Why they’re asking: Obviously, your buyers want to avoid copyright violations or regulatory scrutiny around any content they generate. As a result, they want to get reassurance from you that your AI outputs don’t plagiarize training material.

How to Respond: Describe to your client how you prevent output duplication and what you do to promote originality. 

Example of a great response: “We configure our models to suppress training memorization. We use embedding similarity checks and human spot audits to verify all output is unique.” 

11. How are data representativeness, lack of bias, and fitness for purpose ensured in training/finetuning?

Why they’re asking: AI adoption must be both fair and accurate. Your buyers need to make sure they’re avoiding any tools that present historical bias or that underperform on key user segments. 

How to Respond: Create an outline that reflects your validation process, audit methods, and strategies to mitigate any potential bias. 

Example of a great response: “We use stratified sampling to ensure diverse data coverage, and we test all of our models against synthetic edge cases. This helps us identify and reduce bias long before deployment is even begun.” 

12. Can the AI screen/filter both input and output? What mechanisms are used, and what data is filtered (e.g., personal data)?

Why they’re asking: Your potential clients have to manage legal and ethical obligations around harmful content and misinformation. Making sure to filter helps ensure both safety and compliance. 

How to Respond: You can describe the filters you use in detail, including both pre-processing and post-processing. 

Example of a great response: “We use regex filters, classifiers, and ML-based detectors to block PII and hate speech. We also filter out any unsafe output. Inputs are scanned to prevent all prompt injection.” 

13. How is customer data (input/fine-tuning data) kept confidential and not reused for other clients or model training?

Why they’re asking: Your buyers want to ensure their proprietary data remains just that: private. They also want to be sure their data isn’t being used to improve a competitor’s model.

How to Respond: Here, you can lay out your data isolation policies, your encryption process, and your reuse restrictions. 

Example of a great response: “All of our customer data is encrypted in transit and at rest. We store it in tenant-specific silos, and we never reuse it for model training unless you’ve explicitly authorized your data to be used in this way.” 

14. Who is the owner of the model and outputs generated? What rights do clients have regarding the output?

Why they’re asking: With this question, your buyer wants to know if they can use any AI-generated content at a commercial level. This question also asks about who has the right to retain derivatives. 

How to Respond: This one’s a no-brainer. Just clarify for the client all IP ownership and licensing terms. 

Example of a great response: “Our clients retain full rights to use, modify, and redistribute all of their outputs. Our terms grant our clients full usage rights without any obligations for royalties.” 

15. What happens to custom/fine-tuned versions if/when the contract is terminated?

Why they’re asking: Here, your buyer needs clarity on data portability and intellectual property continuity in the event they decide to end their relationship with you. 

How to Respond: Explain to your client in detail what will happen to their trained models, their stored data, and all client access when their contract ends. 

Example of a great response: “Upon termination, customers can export fine-tuned weights and training logs. We will delete any and all customer-specific data within 30 days, as per our retention policy.”

16. Under what contract/license is the AI functionality provided? Are there any deviations from standard terms?

Why they’re asking: The legal teams in your client’s organization need to assess any potential licensing risk and the need for negotiation. They understand that any non-standard terms may impact their deployment or resale.

How to Respond: You can clearly state your licensing framework and make sure to disclose any and all exceptions to this framework.

Example of a great response: “We operate under a standard enterprise SaaS agreement with an AI-specific annex. We do not have any unusual clauses regarding IP or liability.” 

17. Can the AI provider or distributor terminate access to the model or outputs at any time?

Why they’re asking: If your client suffers a sudden loss of access to their data, their operations could be crippled. Of course, they want assurance that services will continue and that they will be protected from any arbitrary shutdowns. 

How to Respond: You can provide details about your service-level commitments and your policies around termination.

Example of a great response: “Our SLAs guarantee 99.9% uptime. Any termination will require a 90-day notice period. Our emergency termination policy is limited to cases where a client breaches the terms of the contract or willingly and wittingly falls out of legal compliance.” 

18. Are there warranties, IP indemnities, and limitations of liability under the AI service contract or license?

Why they’re asking: Your buyers have to manage risk around IP violations, hallucinations, and service disruptions. As a result, they want to make sure they get a fair allocation of liability. 

How to Respond: Summarize for your client all key contractual protections and any limits you impose therein.

Example of a great response: “We provide IP indemnity and performance warranties, which we tie to our uptime and security benchmarks. We cap liability at annual contract value, which is consistent with the standards of the industry.” 

19. Are there processes for legal, compliance, and risk assessment before using the AI functionality?

Why they’re asking: Your buyer’s governance team wants assurance that your product won’t introduce risks into their environment that are unmanageable. 

How to Respond: You can provide documentation that highlights your compliance review workflows. 

Example of a great response: Yes. We offer compliance documentation, including DPIAs and bias assessments. We also provide audit trails on demand. We will assist your company with any internal procurement evaluations.” 

20. How do you ensure privacy, confidentiality, and security of inputs, outputs, and processing during use?

Why they’re asking: Trust is the most important priority when it comes to relationships that involve AI adoption. Your customer wants to protect all of their sensitive data throughout the duration of the AI lifecycle. 

How to Respond: Provide a detailed outline of your security architecture, your access controls, and your privacy safeguards. 

Example of a great response: “Our platform uses role-based access controls, TLS encryption, and regional data storage. We also offer audit logging and deployments on-premises as an option in use cases that are especially sensitive.” 

Know Your AI

It’s clear at this point. AI questions are no longer just technical jargon. They’re trust signals. When your buyers ask about data provenance, IP ownership, and output filtering, they’re not trying to trick you into saying the wrong thing. They’re managing as much risk as possible in a major investment. 

This means your sales team has to be fluent in the language of AI governance, deployment, and compliance. They can’t just pass these questions off to another team or justify their ignorance. 

Instead, they can prepare thoughtful, transparent responses to these questions. Ideally, they’re utilizing a centralized knowledge base and an automated questionnaire responder. Together with a strong education in the categories included here, your sales team can beat out the competition every time because your buyers will feel confident in turning to you as their solution. 

FAQs