Overhyping a technology like generative AI can be dangerous, but sometimes the debunking urge can be almost as unwelcome. Take for example last month’s MIT report claiming that 95% of generative AI projects fail. The most dogmatically anti-AI folks took that statistic as confirmation that AI is as pointless as it is, in their minds, evil. But if you look a little closer, there’s a lot more nuance.
As I noted in this space a couple of weeks ago, it’s down to execution. The MIT report’s lead author blamed the high failure rates on enterprises’ choice of tools and use cases, as well as poor integration; Fortune reported that, “MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows.”
“The data also reveals a misalignment in resource allocation,” the article continues. “More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.”
These are the kinds of use cases where contact center managers are hoping for big AI payoffs. And on this point, there’s reason for optimism.