5 Biggest Blunders Organizations Make with AI

According to Gartner, implementation of artificial intelligence has skyrocketed by 270% over the last four years, with spending on AI software and hardware anticipated to soar from the present amount of $37.5 billion to a whopping $97.9 billion by the year 2023. There’s no argument. AI is here to stay, and it’s going to become a truly integral part of our everyday lives. As such, business leaders in every industry are wondering what AI means for their organizations and, more importantly, how they can capitalize on the tremendous opportunities this innovative technology presents.

In order to be successful with AI, however, it’s imperative that leaders not move forward too quickly without adequate awareness of the many obstacles that could delay, limit or even completely destroy their efforts. Specifically, here are five common traps many organizations have already fallen into that you can hopefully avoid with your own AI initiative.

Being misled.

Sorry to be the bearer of bad news, but not every AI product on the market is what it’s presented to be. In fact, many less-than-forthcoming and often downright dishonest service providers are selling what amounts to glorified automation tools. Automation is great, but the true value of AI lies in its ability to become smarter and more autonomous over time. Don’t be led astray. Do your homework and, if possible, take potential platforms for a test-drive before you invest. Trust us – you’ll know the difference.

Putting the cart before the horse.

Once you’ve got a true AI platform at your disposal, it’s easy to get so excited about the potential artificial intelligence presents for your company that you end up getting ahead of yourself. Keep in mind that AI isn’t an end, but a means to that end. Achieving big-picture goals often requires accomplishing smaller milestones along the way. Focus first on identifying specific business issues, pain points and areas of the business where AI can provide fast, measurable wins and then scale from there. Slow and steady wins the race.

Forgetting data.

The fact is, the outcome of an artificial intelligence engine is only as good as the quantity and quality of the data it is able to ingest. You can’t expect an AI platform to start churning out valuable insights or begin learning and improving contextually if you don’t adequate data to begin with. It’ll work to a degree, but it’ll produce substandard results, so what’s the point? Start by determining whether or not you have the right architecture in place, and if not, focus your efforts there first.

Leaving silos.

If you’re looking to store grain, silos are great. If you’re trying to implement an organization-wide AI initiative, not so much. Why? Because an effective, end-to-end AI strategy inherently depends on integration of data and collaboration of teams. You may have mapped out areas within your organization where AI might help, but if you don’t break down the roadblocks that exist within your infrastructure, don’t expect good results. Data gathered from one team that isn’t shared with the data scientists managing AI models is useless without seamless collaboration.

Failing to invest in skills.

Sure, the beauty of artificial intelligence is, well, that it’s artificial. Implemented properly, AI can run circles around human employees, performing work faster and eliminating error. That said, humans are still very much needed. In fact, some would argue even more-so now than ever before. Without the right team to ensure everything is running smoothly, you’ll quickly lose ground. Thankfully, fighting the war for external talent isn’t necessary. Oftentimes, reskilling in-house employees is not only sufficient, but a much better approach. In either case, an intentional investment in skills is equally as important as choosing the right AI platform.

Artificial intelligence presents tremendous opportunity for organizations of every size and industry. But in order to be successful with AI, leaders must apply their own human intelligence to the process. By knowing which common blunders to steer clear of, like the five listed above, successful implementation and the business value that follows will be well within your reach.