Four AI marketing maturity levels
A widely shared thread laid out four levels of AI marketing maturity—from simple automation to bespoke model-based tools—and recommended forming a cross‑functional ‘marketing council’ that stitches multiple models to company data. The framework gives a practical roadmap for organizations and classroom exercises on governance, tooling and measurement across maturity stages. (x.com)
A viral framework from AI marketer Shann Holmberg breaks marketing teams into four stages, and the jump between stage one and stage four is the difference between using artificial intelligence like a calculator and using it like an operating system. An expanded writeup of the thread says most marketers are still stuck in the first two stages. (orlandosorio.com) Stage one is the person who opens ChatGPT, types “write me a launch email,” and starts over from zero every time. The model has no memory of the brand, no campaign history, and no connection to what worked last quarter. (orlandosorio.com) Stage two looks more sophisticated because the marketer brings a long prompt template, a messaging framework, and a repeatable structure. The same writeup says these prompt “skills” can run more than 1,200 lines, but the human is still doing the driving on every single task. (orlandosorio.com) The next jump is where teams stop treating artificial intelligence as a chat box and start wiring it into workflow. That means brand rules, audience research, past campaign results, and approval steps move out of one person’s head and into a shared system that can be reused. (martech.org) At the top end, the model is not just drafting copy. It is connected to company data, wrapped in governance, and used across roles like strategy, operations, legal review, and measurement instead of living inside one marketer’s browser tab. (jasper.ai) That is why Holmberg’s thread pushed the idea of a “marketing council” instead of a lone artificial intelligence champion. Cross-functional ownership matters because the biggest scaling problems in 2026 are brand review, legal and compliance review, output quality, and data risk. (jasper.ai) The numbers behind that shift are already moving fast. Jasper’s 2026 survey of 1,400 marketers says 91 percent now actively use artificial intelligence at work, up from 63 percent a year earlier, but only 41 percent say they can prove return on investment. (jasper.ai) That gap is the whole point of a maturity model. Buying a few tools can speed up drafts, but operational maturity means deciding who owns policy, which data can be used, how outputs are reviewed, and what business metric each workflow is supposed to improve. (marketingaiinstitute.com, jasper.ai) The structure of teams is changing to match that reality. Jasper says 65 percent of marketing teams now have designated artificial intelligence roles, and one-third of marketers have added strategy, policy, or governance work to their existing jobs. (jasper.ai) So the framework is less a scorecard than a map. Stage one saves minutes, stage two saves better minutes, and the later stages turn scattered prompts into a system that can remember, measure, and survive contact with finance, legal, and the rest of the company. (orlandosorio.com, jasper.ai)
