Deploy and Monitor the AI System

Even when you build an AI model that works, there is still more work to do. You need to find ways to deploy and monitor it.

This requires change management, which is always complex and difficult. AI is different than a typical IT implementation since it involves using predictions and insights for decision-making. This means people will need to rethink how they interact with the technology.

Also consider that the chances are that the end-users will be non-technical people, whether employees or consumers. This is why there needs to be much work on making the AI model as easy as possible. For example, if you have built a system for online marketing, you might want to limit the options for the user—say to just four or five of them.

Why? If there are too many, then users may get frustrated and not even know where to start. This is all part of the so-called “analysis paralysis” problem. When this happens, there will inevitably be little adoption of the AI model, which will severely result in impeded progress.

Another good strategy is to use visualizations that are interactive. In other words, you can easily see how the trends change by adjusting some variables. You can also allow for clicking a certain part of the chart to drill down into more details.

It’s also essential to create documentation. But this should be more than just written materials. For example, an effective approach is to develop video tutorials. Such an effort will go a long way in creating strong adoption.

As a best practice, the initial deployment should be limited. Perhaps this could be to a small group of beta users and a small section of the customer base. There should also be warnings that the AI model is in the early stages and may have bugs.

Therefore, this phase is about learning. What works? What should be removed? Where can things be improved?

This is definitely an iterative process that must not be rushed.

Then once the AI model is ready for full deployment, there should be enough support in place and someone to lead the management of the project. There also must be recognition for the team for the win. Hopefully, the praise will come from the highest levels of the company, which will help encourage more and more innovation.

There are a variety of automated platforms to help streamline the workflow process, such as Alteryx. The company’s vision is to democratize data science and analytics, regardless if someone has a technical background or not. The Alteryx system handles the key areas of the process: data discovery, data preparation, analytics, and deployment. And all of this is done with code-free drag-and-drop tools. Furthermore, many of the company’s customers are non-technology operators like Hyatt, Unilever, and Kroger.

Again, AI development is really a journey—and your strategy will inevitably change. This is inevitable. According to Kurt Muehmel, who is the VP of Sales Engineering at Dataiku²⁰:

• What businesses sometimes fail to realize is that the path to AI is a long-term evolution of not only technology but in the way the company collaborates and works together. So, in addition to education, one of the key components to an AI strategy should be overall change management. It is important to create both short- and long-term roadmaps of what will be accomplished with first maybe predictive analytics, then perhaps machine learning, and ultimately—as a longer-term goal—AI, and how each roadmap impacts various pieces of the business as well as people who are a part of those business lines and their day-to-day work.