Do org charts and task checklists describe the same workplace problems, whether the team is people, AI, or both?

This explores whether sorting work by task type and assigning work through roles are two descriptions of the same organizational problems. The corpus suggests they overlap a lot but split at one point. Task typologies say what the work needs. Role models say who is allowed to do it, who reports to whom, and who answers for the result. Each one keeps missing something the other catches.

The clearest task-first view comes from Puranam, who treats a human working with an AI as a small organization. In that view, the design question is how to divide decisions depending on the type of task and how feedback flows back to each party How should humans and AI systems divide decision-making tasks?. The role-first view is MOISE+, which builds organizations out of nested links: you can't have authority over a role without a communication channel to it, and you can't have that channel unless the agents can represent each other How do authority, communication, and acquaintance relate in organizations?. These look like different vocabularies, but they meet. When Puranam says a task needs a certain decision architecture, MOISE+ asks which authority and communication links have to exist for that architecture to work at all.

The surprising finding is that roles on paper don't do much by themselves. In multi-agent experiments, tagging one agent as 'accountable' using the RACI framework changed outcomes only when the workflow actually routed the deliverable through that agent Does accountability placement actually shape multi-agent outcomes?. A role label without a matching flow of work is decoration. That points back toward the task view: roles matter through the way the task's work moves between agents. A related line of research found that LLMs critiquing and rewriting team setups came up with chain and dual-leader structures that beat human designs on communication cost Can LLMs automatically discover better team structures than humans?. Those structures are role arrangements, but they were found by optimizing how the task gets done.

Where the two views come apart is in real organizations. Executives surveyed by MIT Sloan and BCG largely see agentic AI as a coworker rather than a tool, yet it both replaces and supports human labor. So it needs to be managed through HR-style roles and asset-style task allocation at once, and neither framework covers it alone Does agentic AI fit the tool-or-coworker framework?. Evans adds a gap that sits before either framework: most workers don't see their own tasks as automatable. A task typology does nothing until someone recognizes the task, and adoption decisions cut across departments, which is a role-and-authority problem Does easier tool-building actually solve enterprise adoption problems?. Research on single agents makes a similar point from the engineering side: once tasks need mixed expertise and independent checking, you need distributed roles, because adding capability to one agent doesn't help Do single agents always hit organizational limits?.

The short answer is that they describe the same problems from opposite ends. Task typologies tell you which structure the work needs, and role models tell you whether that structure can actually be built and enforced. The useful lesson for anyone designing AI teams is that you need both. A role is only real when the task's workflow passes through it, and a task typology is only useful when someone holds the authority to act on it. The corpus does not yet have a study that maps a task typology directly onto a role model.