The Intersection of AI and End-User Adoption


Earlier this year, I wrote about artificial intelligence (AI) and how it’s transforming both traditional and corporate education programs. With the support of AI, programs can be designed to support the learning styles of students, enhancing their comprehension and retention. As exciting as these advances may be, law firms aren’t always in a rush to adopt new technology. Even with the prospect of better efficiency and improved business performance, it can be tough to get employees engaged and sell the incredible value of AI. So how can your firm overcome resistance and encourage end-user adoption?

From Data to Knowledge to Insights

Let’s face it: the promise of a constant stream of new, fresh data isn’t what’s going to be what encourages user adoption. Employees are busy and they aren’t going to want anything new on their plate unless it’s going to make their life easier in the long run. So how can you make the sell? It’s vital to understand how data translates to knowledge, which leads to the insight that will help your firm excel. Insight gained must fulfill a need that your employees have. That means that end-user adoption must be the ultimate goal and any data collected should support this objective.

It can be difficult to express the journey from data to insights. One way to start is by evaluating the needs of those who will eventually be the end users. What challenges are they facing and what insights could help them better perform their jobs? The answer to this question should frame the foundation on which any data collection and analysis programs should be built. The path to efficiency starts at raw data, which is transformed by AI into the knowledge that is used to develop actionable insights.

The Path to Insights

Getting to insight sometimes requires turning the process on its head and then back over again. Large amounts of data, without context can lead to overwhelm, which is exactly what you are trying to prevent by adopting AI. This means that before the design even begins, you’ll need to consider what the ideal user practice will look like and how this will be developed. Then the cycle of design, development, delivery, and implementation can begin.

As I’ve mentioned previously, it’s vital to resist that nagging feeling that you have to add AI to your programs in order to increase employee productivity. The tools that you choose should always support rather than drive the design. If the understanding and embracing of your new programs is the ultimate goal of the design from the very beginning, user adoption won’t be an insurmountable obstacle.

I hope you’ll join me at 11:00 am on June 27th at the 2018 Accelerate Business Development Summit, hosted by LexisNexis® InterAction® for a panel session on end-user adoption. Together with Maggie Hepburn, Anna Hedgepeth, and Myles Ahearn, I’ll be discussing some of the challenges of end-user adoption and solutions for positive change.