A few years back, our legal team asked us to deploy an internal chatbot for HR queries. It was a simple retrieval-augmented generation setup, pulling from policy docs and a curated knowledge base. We launched it in two weeks. No governance framework, no testing for edge cases, no real oversight beyond a sign-off from the newly appointed chief AI officer who mostly just nodded along. We figured the technology would sort itself out.
The problems started almost immediately. An employee asked about paid family leave and the bot responded with something snide about how some people "always seem to find a reason to be out of the office." I still remember the heat in my face when I saw that screenshot in my inbox. The language model had ingested a set of internal forum posts a coworker added to the training data without telling anyone—posts where people vented about staffing shortages. We never had a pipeline to sanitize or review sources, because nobody thought to build one.
Our CEO got involved. HR spent a week apologizing. We pulled the bot offline and I spent a month in damage control, trying to figure out where else we’d cut corners. What struck me most was how predictable the failure was in hindsight. We had an AI ethics policy, sure—three pages of lofty language we’d borrowed from a consulting white paper. It talked about fairness and transparency in the abstract but gave no one any actual authority to enforce anything. There was no testing protocol for bias or toxicity, no documentation of data lineage, no review board that met regularly. The chief AI officer was a governance figurehead with a title and no budget. That’s not governance. That’s theater.
I had to admit to myself that I’d been complicit. I was the lead on that project, and I let velocity override vigilance. I was so focused on proving we could ship AI quickly that I didn’t slow down to ask the boring questions: where is every byte of training data coming from, who checks it, and what happens when someone’s query hits a sensitive topic we haven’t simulated. Those questions feel slow. But they’re the only thing between a useful tool and a PR nightmare.
After the dust settled, we built a real governance structure. Not an enormous committee that meets quarterly and rubber-stamps things, but a small working group with reps from legal, HR, IT, and a technical reviewer who actually pokes at the model’s outputs. We defined clear lines of accountability for data artifacts. We built a lightweight approval pipeline that includes adversarial testing for toxicity, hallucination, and bias before any model touches production. It doesn’t eliminate risk—nothing does—but it catches the obvious stuff before it ends up in someone’s chat window.
And we stopped pretending a policy document counts as governance. Now when a business unit comes to me wanting to "just spin up a quick AI solution," I tell them about the snarky chatbot. I walk them through what we missed, and why I now consider the governance framework more important than the model weights. That usually sets the right tone. If it doesn’t, I send them the old screenshot.