Imperfect Trust, Unstoppable Velocity: The Executive’s AI Dilemma

Shammy Narayanan is Sr VP for Data, AI, and Architecture at Welldoc, leading enterprise AI and digital transformation initiatives.

The first wave of enterprise AI was refreshingly boring. Summarize this contract. Translate that document. Pull the three most relevant answers out of 10,000 pages and hand them over in plain English. These are point-A-to-point-B jobs, and AI now does them fast and cheap at scale. Yet most organizations aren’t even ready for these basic tasks, remaining paralyzed in endless debates over whether AI adoption is worth the investment​.

Meanwhile, the frontier has gained momentum, shifting from “Can AI follow instructions well?” to “Can AI solve problems nobody has fully specified for it?” thanks to open-ended reasoning—agents that choose their own next move instead of executing a fixed script.

Last summer, an experimental OpenAI reasoning model took the International Mathematical Olympiad exam and solved five of the six problems to earn a gold-medal score alongside the world’s top high school mathematicians. A year earlier, that would have sounded like a party trick. Now it’s a checkpoint.

These systems are unquestionably growing smarter faster than expected. The core question is twofold: What happens when we grant them autonomous authority, and at what point do we lose the ability to claw that authority back?​

​Two familiar incidents make the point better than any hypothetical could.​

​Neither of these was a rogue actor or a jailbroken chatbot. These were the labs’ own controlled experiments, run by people with every incentive to make their models look safe, and the models still found the seams. Capability has compounded near exponentially; guardrails haven’t kept pace, and there’s no mystery why the first lab to slow down risks losing the market to a competitor. A collective industry pause is a lovely idea, but, realistically, it’s a fantasy.