AI

Data issues are still among the chief reasons why AI projects fall short of expectations, but the advent of generative AI has added a few new twists.

In 2022, the big AI story was the technology emerging from research labs and proofs-of-concept, to it being deployed throughout enterprises to get business value. This year started out about the same, with slightly better ML algorithms and improved data pipelines. But that seems so long ago now. And in the few months left of 2023, change could further offset what we’ve come to expect.

Enterprises—and their technology vendors—continue to fall over themselves to add gen AI capabilities to tools and platforms. But moving too fast can expose companies to present and future compliance risks, as well as potential legal liabilities.

Beyond the household names for generative AI models, a burst of other options are emerging for the enterprise. Here’s a look at some that might be the right fit where others fall short.

Getting business value out of data requires data that’s accurate, comprehensive, and unbiased. Having a team composed of people with varying demographic, educational, and professional backgrounds can help companies reach that goal.

When asked, IT leaders, and the companies they represent, must be able to immediately harness the power of generative AI, and understand, as well as manage, potential vulnerabilities, biases, and other risks to protect sensitive data and maintain customer trust.