AI

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.

Companies that embrace data-driven approaches stand to perform much better than those that don’t, yet they’re still in the minority. What’s standing in the way?

To become data-driven, companies need a democratization strategy that’s equal parts disciplined and diverse. Data collection, selecting a platform and employee training are just the beginning.

Companies embarking on their automation journeys need to figure out the right process to automate, deal with organizational change, and watch out for security and compliance issues.