Jain said he had tried to automate internal workflows at Glean, including an effort to use AI to automatically identify employees' top priorities for the week and document them for leadership. "It has all the context inside the company to make it happen," said Jain, adding that he thought AI would "magically" do the work. The idea seemed simple, but it hasn't worked.
It was a distinctly clever, if somewhat surprising, choice from Altman who has mostly kept his personal life out of the media spotlight. But Altman is a salesman, and a good salesman understands the optics of good television. So he talked about being a dad and being worried that his son-who wasn't crawling at six months-was developing slower than other children (spoiler: he's not). "I cannot imagine having gone through, figuring out how to raise a newborn without ChatGPT," Altman told Fallon. "People did it for a long time, no problem. So clearly it was possible, but I have relied on it so much."
Every C-suite executive I meet asks the same question: Why is our AI investment stuck in pilot purgatory? After surveying over 200 AI practitioners for our latest research, I have a sobering answer: Only 22% of organizations have moved beyond experimentation to strategic AI deployment. The rest are trapped in what I call the "messy middle"-burning resources on scattered pilots that never reach production scale.
Despite continued doubts over AI hype, it's clear that real-world deployments are demonstrating that AI can deliver measurable value across different sectors. Cutting costs, boosting productivity, and enabling smarter decisions are all potential benefits of AI, both generative and otherwise. Yet the most successful implementations share something in common: they start with a clear business challenge, not a fascination with technology for its own sake.
What we found is that employees want about five hours of hands-on training, and coaching, and mentoring. Only about a third are actually getting that.
On paper, it looked a lot like entrepreneurship: validate an idea, conduct research, raise or allocate funds, build capabilities, codify processes, launch SaaS platforms, measure value creation, and implement a communication plan. In practice, it was very different. Big organizations are optimized for productivity and predictability, not the full lifecycle of experimentation that product building requires. That law of nature creates a constant source of friction between innovation and day-to-day business.
While banks announce AI deployments and digital transformations, the real platform shift is happening through something more mundane: solving customer servicing headaches one API call at a time. Banks have a servicing problem masquerading as a platform opportunity. You won't read the evidence in their press releases, but you can see it in how they're actually solving operational friction for business customers who want banking to work well.
Rather than pursuing massive, resource-intensive AI initiatives that take years to deliver, Huss argues for Minimum Viable AI - a pragmatic approach that focuses on getting functional, well-governed AI into production quickly. It's not about building the flashiest model or chasing state-of-the-art benchmarks; it's about delivering something useful, measurable, and adaptable from day one.
Wells Fargo's adoption of Google Agentspace marks a bold step forward in making banking simpler and smarter—for our customers and employees. By leveraging advanced agentic AI capabilities, we can get answers and insights faster, work more efficiently, and free up time to focus on what matters most: helping people reach their financial goals.
