What to Ask a Software Vendor About Their AI Capabilities Before You Buy

AI has become a standard feature in software marketing. From CRMs and reporting platforms to industry-specific applications, it seems like every vendor has an AI story to tell. 

AI can have a huge impact on business, allowing teams to work faster and make more informed decisions. Still, when every product claims to be “AI-powered,” it becomes difficult to determine which capabilities will actually create value and which are simply new labels for existing features. 

The good news is that evaluating a vendor’s AI capabilities doesn’t require deep technical expertise. You don’t need to understand model architectures, training methods, or the latest terminology. What you do need is a framework for asking the right questions. 

The goal isn’t to find the vendor with the most AI. It’s to find the vendor whose AI capabilities solve meaningful problems, fit your organization’s workflows, and deliver measurable results. 

Key Takeaways

• Start by understanding the business problem the AI is designed to solve. 
• Focus on outcomes and usefulness rather than technical complexity. 
• Understand what data the AI relies on and how your organization’s data is handled. 
• Ask how the vendor measures accuracy, performance, and success. 
• Discuss limitations and understand where human oversight may still be required. 
• Consider how the AI fits into existing workflows and whether teams are likely to adopt it. 
• Look beyond demos and seek evidence of real-world results. 
• Evaluate the vendor’s long-term vision, support model, and flexibility before making a decision. 

Start With the Problem, Not the AI 

The first question to consider when discussing AI with software vendors should be: What specific business problem does this AI capability solve? 

A vendor might showcase an intelligent assistant, automated recommendations, predictive analytics, or AI-generated content. Those capabilities can definitely be valuable, but only if they help move your organization toward its goals. Faster processing times, fewer manual tasks, improved reporting, better customer experiences, or more informed decisions are all examples of outcomes worth evaluating. 

For example, consider a company whose employees spend several hours each week manually compiling information from different systems to create management reports. A vendor might offer an AI assistant that can answer questions about business performance, but the more important question is whether that capability can actually reduce the time employees spend gathering and validating information. 

If the AI can produce reliable reports using existing business data, it may deliver meaningful value. If employees still need to collect, organize, and verify everything manually, the feature may not solve the underlying problem. 

This distinction matters because organizations invest in technology to solve problems, reduce friction, and help people work more effectively. The AI is simply one possible way of achieving those goals. 

Understand What the AI Is Actually Doing 

Once you’ve established that an AI capability addresses a meaningful business need, it’s time to understand how the technology is helping achieve that outcome. 

You don’t need a deep technical explanation, but you should have a basic understanding of the role AI plays within the product. That context can help you evaluate whether a solution aligns with your goals, workflows, and expectations. 

Understanding the role AI plays within a product can help you distinguish between capabilities that genuinely support your needs and features that sound impressive but offer limited practical value. 

Understand the Data Behind the AI 

An AI capability is only as useful as the information it can access. 

Whether the system is generating content, surfacing recommendations, identifying trends, or answering questions, its output depends on the quality, relevance, and completeness of the data available to it. 

What Data Does the AI Rely On? 

Some AI capabilities primarily use information already stored within the platform. Others may pull from connected systems, third-party sources, knowledge bases, documents, or historical records. Understanding those sources can help you evaluate how reliable and relevant the results are likely to be. 

For example, if an AI assistant is expected to provide insights about your operations, what information is it using to generate those insights? Is it drawing from current business data? Historical data? A limited subset of records? The answer can have a significant impact on the quality of the output. 

It’s also worth asking whether the AI has access to the information required to solve the problem you’re trying to address in the first place. An AI capability designed to improve decision-making can only be effective if it has visibility into the factors that influence those decisions. 

Understand How Your Data Is Being Handled 

As AI capabilities become more powerful, questions about data privacy, security, and ownership become increasingly important. 

Will Our Data Be Used to Train AI Models? 

Many AI-powered products process user inputs to generate recommendations, summaries, insights, or other outputs. Depending on the vendor and product, that data may also be used to improve the system itself. 

This doesn’t automatically make a solution a poor fit, but it does make understanding the vendor’s policies essential. 

Organizations should understand: 

• Whether their data is used to train or improve AI models 
• How long data is retained 
• Who has access to that information 
• Whether data is shared with third parties 
• What happens to data if the relationship with the vendor ends

The answers can have significant implications for privacy, compliance, intellectual property, and risk management.

Don’t Be Afraid To Ask for Specifics 

Many vendors have invested heavily in security, compliance, and governance practices. At the same time, AI technology is evolving quickly, and policies can vary considerably from one provider to another. 

Rather than making assumptions, ask direct questions and request clear answers. A reputable vendor should be able to explain how customer data is collected, stored, protected, and used. 

If the explanation feels vague or difficult to follow, that’s often a signal to dig deeper. 

Evaluate Accuracy, Not Just Features 

When evaluating AI capabilities, it’s important to understand not just what the technology can do, but how reliably it performs. 

Performance metrics will vary depending on the application. An AI assistant that summarizes documents may be evaluated differently than a system that predicts inventory needs or highlights operational risks. Regardless of the use case, vendors should be able to explain how they test, monitor, and improve the quality of their AI outputs. 

That doesn’t necessarily mean demanding technical metrics or performance benchmarks. Instead, focus on understanding how the vendor defines success and how they validate that the system is delivering the intended outcome. 

It’s also important to recognize that AI isn’t infallible. Like any technology, it has limitations. Understanding those limitations is often just as valuable as understanding its strengths. 

Ask About Limitations and Human Oversight 

Every technology has scenarios where it performs better than others. AI is no different. 

When evaluating a vendor’s AI capabilities, don’t just ask what the system does well. Ask where it may struggle and what role human oversight should play in the process. 

A trustworthy vendor should be comfortable discussing situations where users may need to review, verify, or supplement AI-generated outputs. In fact, clear expectations around human involvement often lead to better outcomes than complete automation. 

This is particularly important when decisions have meaningful business, financial, regulatory, or customer-facing implications. 

Rather than viewing human oversight as a weakness, organizations should view it as part of a responsible implementation strategy. The goal isn’t to eliminate people from the process. It’s to help people work more efficiently and make more informed decisions. 

Once you’re comfortable with the quality of the results, it’s time to consider how those capabilities will fit into your organization’s day-to-day operations. 

Consider Workflow and Adoption

A common mistake in software evaluations is focusing exclusively on features while overlooking how people will actually use them. 

Even the most capable AI solution won’t create value if it disrupts existing workflows, creates unnecessary complexity, or requires significant effort to adopt. 

Just as importantly, adoption shouldn’t depend on users becoming AI experts. The value should be accessible and understandable to the people who rely on it every day. 

Look Beyond the Demo 

Software demonstrations are designed to showcase a product at its best. 

While demos are an important part of the evaluation process, they rarely tell the whole story. 

A feature that works well with carefully prepared sample data may perform differently when introduced into a real-world environment with unique processes, legacy systems, incomplete information, and competing priorities. 

Whenever possible, ask for examples of how the capability is being used by existing customers. Case studies, references, pilot programs, or proof-of-concept engagements can provide a more realistic picture of how the technology performs outside of a controlled demonstration. 

The goal isn’t to prove that the AI works in theory. It’s to understand whether it works in practice. 

Think Beyond Today

AI capabilities continue to evolve rapidly, which makes long-term considerations just as important as current functionality. 

As part of your evaluation, consider questions such as: 

• How is the vendor investing in AI moving forward? 
• How frequently are capabilities updated? 
• What level of customer support is available? 
• How easily can data be exported if your needs change? 
• Are there risks of becoming overly dependent on a single platform or vendor? 

These questions don’t necessarily need immediate answers, but they can help organizations make more informed decisions about long-term fit. 

The software you choose today may become an important part of your operations tomorrow. Understanding how a vendor approaches support, innovation, and flexibility can help ensure the relationship continues to deliver value over time. 

Better Questions Lead to Better Software Decisions 

The most effective software solutions start with a clear understanding of your organization’s goals, operational challenges, users, and workflows. AI can absolutely play a valuable role, but only when it’s aligned with those broader business needs. 

That’s why the questions outlined here focus on more than just the technology itself. Understanding what problem the AI is solving, how it works, what data it relies on, and how it fits into your existing processes can help you separate meaningful capabilities from marketing claims. 

At AVIBE, we help organizations navigate complex software decisions every day. Whether we’re modernizing an existing application, integrating systems, developing custom software, or helping clients evaluate technology options, our approach starts in the same place: understanding the business problem first. 

If you’re evaluating software vendors, exploring AI-enabled solutions, or trying to determine whether a custom approach may be a better fit, we’re always happy to help you think through the options. 

Frequently Asked Questions

What should I ask a software vendor about their AI capabilities? 

Start by asking what business problem the AI is intended to solve. From there, explore how it works, what data it relies on, how your data is handled, how accuracy is measured, and how it fits into your organization’s workflows. 

Do I need technical expertise to evaluate AI software? 

No. Most buyers can effectively evaluate AI by focusing on business outcomes, data practices, usability, governance, and operational fit rather than technical architecture. 

How can I tell if an AI capability will create value? 

Look for a clear connection between the feature and a measurable outcome, such as reduced manual effort, faster reporting, improved decision-making, or better customer experiences. 

Should I be concerned about how AI vendors use my data? 

Organizations should understand whether their data is stored, shared, retained, or used to improve AI models, as well as what controls are in place to protect that information. 

What are common red flags when evaluating AI capabilities? 

Vague explanations, an inability to discuss limitations, unclear data policies, an overreliance on demos, and a lack of real-world examples can all be signs that further evaluation is needed. 

Is AI always the right solution? 

Not necessarily. AI is a tool, not an objective. The most important consideration is whether it helps solve a meaningful business problem and delivers measurable value for your organization.