Your Data Guys are Your AI Guys
There is much being discussed about AI, but one might wonder who is actually doing the AI work? This field, after all, especially generative AI, is barely a few years old. Nobody has fifteen years of gen-AI experience, at least not that many can rightfully claim that. There is no seasoned AI workforce to hire from, because there has not been time to season one. And yet enterprises everywhere are staffing AI teams, filling AI roles, running AI programs. But the people in those seats have come from somewhere.
Look at the job descriptions and the answer is probably hiding in plain sight. The vocabulary is a little new and there are also new AI jargons. Prompt pipelines, retrieval, grounding, evaluation, MCP, RAG, orchestration, hallucinations and the list goes on. The change in the foundation is not that different. Expertise has defaulted to the nearest adjacent discipline as this is how expertise works: you can only build on what you already have and know. There were data teams, data platforms, data judgement, and AI landed on top of them. The people who know where the numbers came from in the data are the same people who might guess when a model is likely to be behaving abnormally. The people who watched the figures or screened for software bugs also spot patterns in confidently wrong AI outputs. These instincts have come from years of being accountable for answers.
The field of data is two distinct areas wearing one name. There is a technical side that builds and moves, and a business side that governs and directs - and both of these are doing AI too now. The technical side became AI engineering: pipelines, grounding, evaluation, the platform itself. The business side became AI governance, AI solutions, AI strategy, AI transformation: deciding what the models are for, what they are allowed to do, where they are applied and what changes because of them. Neither side crossed into brand new territory; each quietly carried itself forward towards the future.
Prompting makes the point at the level of the individual where a prompt is perhaps thinking made more visible. What you ask, what you know to ask next, what you catch in the answer; it reveals the expertise behind it. Which is why prompting does not turn anyone into something new - it amplifies what is already there. The analyst prompts like an analyst and discovers more things. The engineer is now a prompt engineer. The mathematician prompts in proofs, step by step. The writer prompts for the right ideas and how to put them across. The architect prompts for the whole structure and whether it will hold before any of the parts. The strategist prompts three moves ahead to win. The finance head prompts about cost before anything else and makes sure all this still makes money. The inventor has something great again too.
AI has made people become more of who they are.
The titles also moved with the new times. Data solutions became AI solutions. Data engineering became AI engineering. Data strategy became AI strategy. The rename happened quickly, as renames do. How much the people changed underneath the titles remains to be seen. Some carried real depth across: the same judgement, the same accountability, now pointed at models instead of reports. The rename was the easy part, but whether the work underneath has deepened or just re-labelled itself is the question every organization will answer in its own time, and mostly through delivery and performance.
So who now is the AI guy? There are many candidates to choose from. Perhaps it is the one with the new AI certificate from a reputed institution. Or the one whose role was AI amplified. The best thinkers of this time whose roles transcend AI agents, regardless of where they sit. The people who know where the real problems exist. The ones in leadership, with the ability to roll out even more impactful change. Each claim has its logic. But my perspective is that the closest match is quieter and perhaps the most obvious one: deep tech and your data guys. Deep tech because these are where products are invented and built. This is bigger than just the well-known brands as they are also the serious players inventing underneath, whether or not anyone knows their name. And your data guys, freshly promoted, still continue to hold the foundation everything runs on and apply it for your business context. Between them, these are the ones who will make AI products and implement them for your organization. Everyone else will use it well — the way everyone used data before, working alongside the same guys made that possible too.
Everything has changed, and yet everything is the same.
