You'd talk to an intelligent person to get an answer. Not read their dashboard. Not wait for their quarterly report. You'd ask them a question, in plain language, and they'd draw on everything they've learned to answer it — and they built that ability the same way every intelligent person does: by being asked, again and again, and getting better at answering.
Enterprises need the same thing built. Right now, almost nobody is building it — because almost nobody owns building it. That's the gap this piece is about.
What We Were Already Reaching For
Roughly ten years ago, I built an anomaly-detection and incident-avoidance system that, at its core, tried to do exactly this. It layered real-time metric streams from monitoring tools and, when something went wrong, surfaced a plain-English readout of where the problem was — not a wall of graphs, not a raw alert, a sentence a person could read and act on. We didn't have generative AI to build that readout. We hand-constructed the text on a front end, pulling from different sources — there was no way to make the answer conversational, or let someone ask a follow-up and go deeper. It took real correlation logic, hand-built against the specific systems we were watching, to get even that far.
The intent was right. The capability just wasn't there yet.
What's Different Now
The capability is here now. Take the same problem — an operations question like why is this happening right now — and the raw material to answer it hasn't changed: historical incident data, real-time monitoring streams, instrumentation logs. What's changed is that a RAG harness can pull all of it together, chunk it, connect it, vectorize it, and answer the question directly, without hand-built correlation logic for every new scenario.
The way to build that harness is the same discipline from Monday's piece: one question at a time, one set of integrations at a time, prioritized by what actually impacts the business — not a platform buildout, a stack of answered questions. Ask "why is throughput dropping on this line right now," and the harness that gets built to answer it — which sources it pulls from monitoring and logs, how it's chunked and connected — becomes the template for the next operations question. Answer enough of these and the enterprise has something it didn't have before: a place it can ask a plain-language question about what's happening right now, and get an answer grounded in everything it has already been asked.
That accumulation is the intelligence. Not the vector database. Not the RAG framework. The fact that each question the enterprise asks makes the next answer better.
Nobody Owns This
When I was building the anomaly-detection work, the hardest part wasn't the correlation logic. It was finding a business leader willing to own a set of questions and the actions that followed from answering them. Without that ownership, the work stays a promising pilot. With it, it becomes something teams build under and other teams — the ones building analytical capability, the ones running the underlying platforms — start interacting with, and the enterprise's intelligence compounds instead of restarting from zero on every initiative.
That ownership gap hasn't closed. If anything, it's gotten more visible now that the tooling to build this — LLMs and SLMs, RAG and other augmentation techniques, knowledge graphs and vector databases, natural-language query — is finally available cheaply enough that the constraint is no longer technical.
Look at what enterprises are actually hiring for, and the gap is plain. The Chief AI Officer role that's become a core executive-suite hire this year is largely scoped around AI governance, platform strategy, and change management — real work, but adjacent to the problem. It's about adopting and governing AI. It's not about owning a portfolio of business questions and being accountable for whether they get answered.
The Role That's Missing
What I was looking for a decade ago — and what's still missing — is closer to a Chief Intelligence Officer: someone accountable not for AI adoption, but for enterprise intelligence actually getting built, one consequential question at a time.
The mandate has to be different from a governance role, in three specific ways:
Prioritization authority over business impact, not IT backlog order. This person decides which question gets a harness built next based on what it's worth to the business, not what's next in a project queue.
A direct line to the leaders who own the questions — CFO, CRO, COO — not just to IT and data teams. The anomaly-detection work only moved because a business sponsor owned the questions being asked. Every example I've walked through this week needed the same thing.
Accountability for the same simple test from Monday's piece: did it answer the question, and did it hand off the right action — not uptime, not adoption numbers, not how many agents got deployed. That's a different scorecard than any AI governance role is currently measured against.
Define the role by that mandate, and it's not another AI title competing for board attention. It's the role that makes every other AI investment — the agents, the platforms, the pilots — worth something, because it's the one accountable for making sure the enterprise can actually answer the questions that matter.
If your organization has AI initiatives everywhere and still can't answer a plain-language "why" without convening a meeting, you don't have an adoption problem. You have an ownership problem.