A screenshot buried in the comment section of a Marc Benioff LinkedIn post around a year ago gave Salesforce’s Bernard Slowey a jolt. It showed the then-new Agentforce help portal directing the customer to a Salesforce competitor. “I was literally like, ‘Oh my god, what has happened here? This is not good. My job is going to be gone tomorrow,’” Slowey, who is—spoiler alert—still Salesforce’s SVP of digital customer success, told us. You may have heard the now-infamous stat that 95% of enterprise generative AI pilots fail before they reach production. Or maybe you have some thoughts on why that MIT report’s methodology was flawed. In any case, abundant data show that a sizable chunk of AI prototypes don’t go as planned—and ROI is questionable. We wanted to talk with companies about how they’ve dealt with unexpected AI complications or projects that just didn’t work out—what they learned and how they subsequently recalibrated. Some of them told us that three-plus years of experimentation with unpredictable and fast-changing generative AI have reshaped how they build things and make decisions. Keep reading.—PK |