As AI takes over more repetitive work, it does not mean fewer roles are needed. Different roles become more decisive to deliver quality, learning, and control in the system, not just more output. - The right kind of generalist and project lead. Hands‑on, structured, and business‑savvy people who can coordinate across in‑house teams, agencies, agents, and tech vendors. The need for process design grows as the agent landscape expands, ownership, decision chains, QA gates, budget rules, documentation, and traceability. Without that you often get faster delivery but more friction, rework, and mistakes. - The creative analytical prototyper. A growth profile with creative range and technical and analytical ability, ideally with measurement insight and platform understanding. This person can rapidly test hypotheses, build prototypes, set up experiments, and create learning loops while keeping creative quality. Creative edge becomes strategically more important when many can produce okay at scale. The advantage comes from ideas, craft, and a clear bar. - The platform skeptic with deep channel expertise. The role many underestimate, someone who truly understands channels and platforms and actively challenges vendor best practice, understands incentives behind recommendations, and builds an optimization engine that benefits the company, not the platform. As more people run on autopilot or buy AI employees, this competence becomes a differentiator. - The measurement and experiment lead. Someone who can establish an evaluation method you can actually steer by, incrementality, experiment design, MMM thinking, method discipline, and source of truth. This becomes critical as journeys grow more hybrid and agent‑supported and signals from more surfaces must connect to business outcomes. - Enablement, training, manual craft, and debugging. This is the point many miss. When AI helps a lot it is easy to delegate away learning. That is exactly the risk highlighted in [the paper you linked](https://arxiv.org/pdf/2601.20245), using AI as a shortcut can make you faster at output but weaker at building conceptual understanding, reading and critically reviewing, and especially debugging when something goes wrong. Translated to marketing, growth, and ops, if AI does everything you eventually lose the ability to judge whether a suggestion is reasonable, find the root cause of a data or performance anomaly, understand what actually caused an effect, and build robust guardrails and QA checks. That is why training and ways of working are central. You need a culture where people practice manually at times, debug without autopilot, and use AI in a way that keeps humans cognitively engaged, for example asking AI to explain tradeoffs, justify decisions, show alternatives, and force reasoning, not just deliver an answer. Why the interplay between the generalist and the prototyper is decisive. The prototyper tests and shapes quickly, the project lead drives it through, builds endurance, and ensures it gets done with the right process, QA, and measurement. Without one you get ideas without adoption. Without the other you get production without edge and often without learning.