Which Do You Fear? Missing Out or Messing Up?

Your sobering headline of the week, from No Jitter: AI agent rollbacks more common than AI agent deployments. In the article, Matt Vartabedian reports on a Sinch survey that found more than 60% of enterprises had deployed agents for customer communications, but, “Nearly three-quarters of organizations that successfully deployed AI communications agents shut them down or rolled them back.” The survey cited three main reasons for the pullbacks:

• Personally identifiable information (PII) or data leakage
• Hallucinations or brand risk
• Lack of auditability

Explaining the drivers behind these rollbacks, Matt quotes Rebecca Wettemann, CEO and principal analyst with Valoir: “There's this real fear now that if I put a customer-facing agent into production it's either going to run off and do something it shouldn't do or it's going to burn through a huge token credit overnight. We’ve moved from the fear of missing out on AI to the fear of messing up.”

The journey from FOMO to FOMU should probably be seen as a healthy development. Enterprise IT decision-makers tend to be a cautious lot, cognizant at all times of everything that’s riding, both literally and figuratively, on the systems they run. You’d be hard-pressed to find anyone in the enterprise today whose biggest concern with AI is that they’ll miss out on the big killer transformation because they didn’t move fast enough. That kind of panic is so 2023.

On the other hand, there are lots of ways to overspend on AI and to deploy AI that creates security or governance risks. So it makes sense to slow your AI roll, and always be prepared to pull back on a project.

Which doesn’t necessarily mean that all those withdrawn agents in the Sinch survey represent a failure. There are so many unknowns with AI right now that it’s probably only through experience that you can fully understand the scope of the risk for your enterprise in a given use case.

This article from CIO Dive gives a good sense of what this balance looks like in practice. The piece surveys recent commentary from leaders at financial institutions during recent quarterly earnings calls, about the progress their companies are making on AI. Bank of America, Citigroup, Wells Fargo, and BNY leaders all cited progress on AI implementation and claim they’re seeing productivity gains. However, Bank of America’s example is illustrative: The firm has over 300 approved AI use cases, but only 34 are “fully implemented,” according to CIO Dive.

The most telling example cited by CIO Dive, however, may be that of JP Morgan Chase, which has over 1,000 approved use cases, according to CEO Jamie Dimon, speaking on the firm’s most recent earnings call. But, CIO Dive notes, Dimon “said AI is expensive and he doesn’t expect to see it increase company margins anytime soon as usage scales.” Dimon instead believes the beneficiaries will be JP Morgan Chase's customers.

Looks like when it comes to AI, swinging for the fences may wind up being a good way to strike out big time.

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