AI Strategy Foundation: Breaking it Down to the Basics

One question I used to hear a lot as a kid was this: “If your friend jumped off a bridge, would you do it, too?” Yes, there were lots of bad trends in the 80s, and I may have fallen prey to more than a few of them. But that idea of blindly following a trend doesn’t just apply to questionable hair and clothing choices. It also applies to every technology that pops up in digital transformation when building an AI strategy foundation. And one of the technologies getting adopted most blindly in today’s marketplace: artificial intelligence (AI).

I can hear it now: “But AI is the future! If we don’t adopt it now, our company will fall behind!” My answer: Yes, AI is amazing. It can transform your business in powerful ways. Adopting it sooner than later: also a good idea. My issue is not with the technology itself—it’s when companies adopt it without a clear AI strategy foundation in place.

Why Do I Need an AI Strategy Foundation?

Let’s make sure we’re tracking: I have said many times before that no technology is a magic bullet in and of itself. Despite its tremendous promise, the same is true for AI. Still, AI has become such a hot-button technology that many companies are rushing to adopt it far before they have a solid AI strategy foundation. Without a clear strategy, those companies are at risk for mining errant or irrelevant data. And if you’re mining dirty data—you may as well have skipped investing AI in the first place.

When it comes to AI strategy foundation, it doesn’t take a lot of effort to make the most of your resources. Make a plan. Create clear goals. Ensure data integrity. Define ownership. Have an open mind. Create clear and open paths to action when the data supports it. There is a difference between being quick and being hasty in creating AI strategy foundation. The following are a few solid rocks to use in building that steady foundation.

Data Mastery

Haven’t we all been in a situation where a boss or colleague asks us to run a report, and we sheepishly acknowledge that we can’t guarantee the report will be accurate? Garbage in, garbage out. That’s one simple way to sum up many companies’ experience with AI today. Without a strategy in place, they mine loads of data—much of it irrelevant to their business goals—and then fail to find effective insights from the information they collect. What’s more, they don’t make the ongoing “cleansing” of that data a priority. Within a few days or weeks, that giant lake of data they’ve spent millions to dig has turned into a questionable landslide of information—much of which has no insights to provide.

That’s why your company’s AI strategy foundation needs to focus on the all-encompassing mastery of your data. You need to have clear and solid methods for storing it, collecting it, cleaning it, and detecting anomalies within it. Otherwise, you may as well skip collecting it altogether.

Analytics

This should be a no-brainer. After all, most companies are adopting machine learning simply to help process all the data AI is churning for their companies. Still, the responsibility of choosing which analytics to track—which variables to include—which segments of the population, geographical location, or marketing channel to analyze—that’s all on you. And without a clear strategy, you’ll be running lots of numbers that tell you a lot of nothing.

Machine Learning

Remember the olden days when everyone would gather in a dusty board room and share their data and financials from the previous quarter? That no longer cuts it in digital transformation. While AI is helpful in speedily describing past performance, its main value comes in predicting the future—showing companies where to go, what their customers want, and how they want to experience it. If your AI strategy foundation focuses on data from the past, you’ll never grow in digital transformation.

Computer Vision

Once you get a solid grounding in AI in your company, you can move on to using AI in even more astounding ways, especially the collection and analytics of atypical data. Computer vision, for instance, uses cameras, sensors, and deep learning to enhance doctor’s abilities to diagnose diseases. It can find anomalies faster and more accurately than any human—all while sorting data and finding trends within it. If you’re in the field of healthcare especially, this is one part of AI you will want to factor in to your AI strategy foundation.

Natural Language Processing

Another piece of AI technology I believe will become a solid backbone to any AI strategy foundation in the future is natural language processing. As the technology grows, it will be able to better understand inflection, emotion, dialect, and mood—enhancing your ability to capture customer feedback, for instance through recorded customer service calls. What’s more, it can find trends and insights faster than any human ever could.

The purpose of AI isn’t just to get leaner—it’s to grow your business in new and exciting ways. Without a clear AI strategy foundation, your company will never do either because it will be hastily plotting away to keep up with competitors, rather than understand or conquer them. If you’re interested in jumping the AI bandwagon, take it from me: go fast, but be mindful. AI without a strategy foundation is just another line item on your budget.

This article was originally published on Futurum.