7 key questions to buy an AI solution efficiently: discover the best way now!

The importance of understanding AI to take full advantage of it

Artificial intelligence (AI) is poised to become a significant and ubiquitous presence in our lives. It offers enormous potential, but we can't make a significant contribution to a technology we don't understand.

When a user is considering buying a new technology, he's not particularly interested in what it might be able to do in the future. A potential user needs to understand what a solution will do for them today, how it will interact with their existing technology stack, and how the current iteration of that solution will deliver ongoing value to their business.

But given that this is an emerging field that is constantly evolving, it can be difficult for these potential users to know what questions they should be asking, or how to evaluate products so early in their lifecycle.

With this in mind, I've provided a high-level guide to evaluating an AI-based solution as a potential customer - a rubric for business buyers, if you will. When evaluating AI, consider the following questions.

Does the solution solve a business problem, and do manufacturers really understand this problem?

Chatbots, for example, perform a very specific function that promotes individual productivity. But can the solution be scaled to be used effectively by 100 or 1,000 people?

The fundamentals of enterprise software deployment remain valid - customer success, change management and the ability to innovate within the tool are fundamental requirements for delivering ongoing business value. Don't think of AI as an incremental solution; think of it as a little magic trick that completely removes a pain point from your experience.

But it only seems magical if you can literally make something disappear by making it autonomous, which comes down to understanding the business problem.

What does the security stack look like?

The data security implications of AI are of a higher order, and far exceed the requirements we're used to. You need integrated security measures that meet or exceed your own organizational standards right from the start.

Today, data security and compliance are prerequisites for any software, and they're even more important for AI solutions. The reason for this is twofold: firstly, machine learning models run on huge amounts of data, and this can be an unforgiving experience if not managed with strategic care.

With any AI-based solution, whatever its purpose, the aim is to make a big impact. Consequently, the audience using the solution will also be large. How you exploit the data generated by these extended user groups is very important, as is the type of data you use to guarantee the security of this data.

Secondly, you need to ensure that the solution you have in place allows you to maintain control of this data to continue training machine learning models over time. It's not just about creating a better experience; it's also about ensuring that your data doesn't leave your environment.

How to protect and manage data, who has access to it and how to secure it? The ethical use of AI is already a hot topic, and will remain so with the imminent arrival of regulations. Any AI solution you deploy must have been built with an inherent understanding of this dynamic.

Is the product really something that can improve over time?

As machine learning models age, they begin to drift and draw erroneous conclusions. For example, ChatGPT3 only considered data up to November 2021, meaning it could not understand events occurring after this date.

Enterprise AI solutions need to be optimized to evolve over time to keep up with valuable new data. In the world of finance, a model may have been trained to spot a specific regulation that evolves with new legislation.

A security vendor may train its model to spot a specific threat, and then a new attack vector appears. How are these changes reflected to maintain accurate results over time? When purchasing an AI solution, ask the vendor how it keeps its models up to date, and how it looks at model drift in general.

What does the technical team behind the product look like?