6 common mistakes organizations make when investing in AI

Here are six common mistakes organizations make when investing in AI and machine learning.

1. Putting the cart before the horse

Embarking on an analytics program without knowing what question you are trying to answer is a recipe for disappointment. It is easy to take your eye off the ball when there are so many distractions. Self-driving cars, facial recognition, autonomous drones, and the like are  modern-day wonders, and it’s natural to want those kinds of toys to play with. Don’t lose sight of the core business value that AI and machine learning bring to the table: making better decisions.

2. Neglecting organizational change

The difficulty in implementing change management is a large contributor to the overall failure of AI projects. There’s no shortage of research showing that the majority of transformations fail, and the technology, models, and data are only part of the story. Equally important is an employee mindset that is data-first. In fact, the change of employee mindset may be even more important than the AI itself. An organization with a data-driven mindset could be just as effective using spreadsheets.

3. Throwing a Hail Mary pass early in the game

Just as you can’t build a data culture overnight, you shouldn’t expect immediate transformational wins from analytics projects. A successful AI or machine learning initiative requires experience in people, process, and technology, and good supporting infrastructure. Gaining that experience does not happen quickly.

4. Inadequate organizational structure for analytics

AI is not a plug-and-play technology that delivers immediate returns on investment. It requires an organization-wide change of mindset, and a change in internal institutions to match. Typically there is an excessive focus on talent, tools, and infrastructure and too little attention paid to how the organizational structure should change.

5. Not embedding intelligence in business processes

One of the most common stumbling blocks in deriving value from AI initiatives is incorporating data insights into existing business processes. This “last mile” challenge is also one of the easiest to solve using a business rules management system (BRMS). The BRMS is mature technology, having been installed in large numbers since the early 2000s, and it has gained a new lease on life as a vehicle for deploying predictive models. The BRMS makes an ideal decision point in an automated business process that is manageable and reliable. If your business is not using a BPM (business process management) system to automate (and streamline and rationalize) core business processes, then stop right here. You don’t need AI, you need the basics first—i.e., BPM and BRMS.

6. Failure to experiment

The key take-away here is that failure will occur. Inevitably. The difference between Google and most other companies is that Google’s data-driven culture allows them to learn from their mistakes. Notice as well the key word in Schmidt’s testimony: experiments. Experimentation is how Google—and Apple, Netflix, Amazon, and other leading technology companies—have managed to benefit from AI at scale.

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