Artificial intelligence is ready To become a great presence and present everywhere in our lives. It has enormous potential value, but we cannot meaningfully contribute to technology we do not understand.
When a user intends to buy a new piece of technology, he is not particularly concerned with what it might do somewhere in the future. The potential user needs to understand what the solution will do for them today, how it will interact with their existing technology stack, and how the current iteration of that solution will provide ongoing value to their business.
But since this is an emerging field that changes by the day, it can be difficult for these potential users to know what questions to ask, or how to evaluate products so early in their life cycles.
With that in mind, I’ve provided a high-level guide to evaluating an AI-based solution as a potential customer – an enterprise buyer’s scorecard, if you will. When evaluating artificial intelligence, consider the following questions.
Does the solution fix a business problem, and do the builders really understand the problem?
For example, Chatbots perform a very specific function that helps boost individual productivity. But can the solution get to the point where it is effectively used by 100 or 1,000 people?
The fundamentals of enterprise software deployment still apply – customer success, change management, and the ability to innovate within the tool are key requirements for delivering ongoing business value. Don’t think of AI as an incremental solution; Think of it as a little piece of magic that completely removes a pain point from your experience.
But it only seems like magic if you can literally hide something by making it independent, which takes us back to a real understanding of the business problem.
What does the security stack look like?
The data security implications around AI are next level and far beyond the requirements we are used to. You need built-in security measures that meet or exceed your regulatory standards.
Here’s a high-level guide to evaluating an AI-based solution as a potential customer – an enterprise buyer’s scorecard, if you will.
Today, data, compliance, and security are top priorities for any program, and they are even more important for AI solutions. The reason for this is two-fold: First of all, machine learning models work against massive data sets, and it can be an unforgiving experience if this data is not handled with strategic care.
With any AI-based solution, regardless of what it is meant to achieve, the goal is to make a significant impact. Therefore, the audience facing the solution will also be large. How you leverage the data generated by these expanding groups of users is very important, as is the type of data you use, when it comes to keeping that data secure.
Second, you need to make sure that any solution you have allows you to maintain control of that data to consistently train machine learning models over time. It’s not just about creating a better experience; It’s also about ensuring your data doesn’t leave your environment.
How do you protect and manage data, who can access it, and how do you secure it? The ethical use of AI is already a hot topic and will continue to be with impending regulations on the way. Any AI solution you deploy must be built on an inherent understanding of this dynamic
Is the product really something that can improve over time?
As machine learning models age, they start to drift and start coming to wrong conclusions. For example, ChatGPT3 only received data until November 2021, which means it cannot understand any events that occurred after that date.
Enterprise AI solutions must be optimized to change over time to keep pace with new and valuable data. In the world of finance, the model may have been trained to detect a specific system that changes with new legislation.
A security vendor may train its model to detect a particular threat, but then a new attack vector comes along. How are these changes reflected to maintain accurate results over time? When purchasing an AI solution, ask the vendor how they update their models, and how they consider model skew in general.