Last week, one of our product managers (PMs) built and shipped a feature. Not spec'd it. Not filed a ticket for it. Built it, tested it, and shipped it to production. In a day.

Many people have tried AI tools and walked away unimpressed. I get it — many demos promise magic, but in practice, the results can feel underwhelming.

Not long ago, the idea of being a “generalist” in the workplace had a mixed reputation. The stereotype was the “jack of all trades” who could dabble in many disciplines but was a “master of none.” And for years, that was more or less true. 

Look, we've spent the last 18 months building production AI systems, and we'll tell you what keeps us up at night — and it's not whether the model can answer questions. That's table stakes now. What haunts us is the mental image of an agent autonomously approving a six-figure vendor contract at 2 a.m. because someone typo'd a config file.

For more than two decades, digital businesses have relied on a simple assumption: When someone interacts with a website, that activity reflects a human making a conscious choice. Clicks are treated as signals of interest. Time on page is assumed to indicate engagement. Movement through a funnel is interpreted as intent. Entire growth strategies, marketing budgets, and product decisions have been built on this premise.

Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical.

It’s always the same story: A new technology appears and everyone starts talking about how it’ll change everything. Then capital rushes in, companies form overnight, and valuations climb faster than anyone can justify. Then, many many months later, the warnings arrive, and people suddenly remember the dot-com crash or crypto.

With agentic AI, businesses are conducting business more dynamically. Instead of traditional pre-programmed bots and static rules, agents can now “think” and invent alternate paths when unseen conditions arise. For instance, using a business domain ontology like FIBO (financial industry business ontology) can help keep agents within guardrails and avoid unwanted behavior.