By Joe McKendrick – Artificial intelligence technologies have reached impressive levels of adoption, and are seen as a competitive differentiator. But there comes a point when technology becomes so ubiquitous that it is no longer a competitive differentiator — think of the cloud. Going forward, those organizations succeeding with AI, then, will be those that apply human innovation and business sense to their AI foundations. 

Such is the challenge identified in a study released by RELX, which finds the use of AI technologies, at least in the United States, has reached 81% of enterprises, up 33 percentage points from 48% since a previous RELX survey in 2018. They’re also bullish on AI delivering the goods — 93% report that AI makes their business more competitive. This ubiquity may be the reason 95% are also reporting that finding the skills to build out their AI systems is a challenge. Plus, these systems could be potentially flawed: 75% worry that AI systems may potentially introduce the risk of bias in the workplace, and 65% admit their systems are biased.  

So there’s still much work to be done.  It comes down to the people that can make AI happen, and make it as fair and accurate as possible. 

“While many AI and machine learning deployments fail, in most cases, it’s less of a problem with the actual technology and more about the environment around it,” says Harish Doddi, CEO of Datatron. Moving to AI “requires the right skills, resources, and systems.” 

It takes a well-developed understanding of AI and ML to deliver visible benefits to the business. While AI and ML have been around for many years, “we are still barely scratching the surface of uncovering their true capabilities,” says Usman Shuja, general manager of connected buildings for Honeywell. “That said, there are many valuable lessons to be gleaned from others’ missteps. While it’s arguably true that AI can add significant value to practically any department across any business, one of the biggest mistakes a business can make is to implement AI for the sake of implementing AI, without a clear understanding of the business value they hope to achieve.” Read On: