The numbers come first.
A new vision for the workplace from MIT professor Paul Cheek would replace the old reporting tree with a live map of access, budgets and decision rights. In that model, an AI-native org chart would show which employees and AI agents can use which systems, who can spend from which budgets, and who gets consulted on which projects.
Cheek is pitching that idea to CHROs as companies fold AI deeper into their operations. He says HR leaders have an opening to shape how humans and AI agents are organized across the enterprise, and to make AI literacy a core part of that shift.
The study behind the pitch comes from Cheek’s independent research organization, the AI-Driven Enterprise Institute. It found that just over 30% of executives at S&P 500 companies are AI-literate. That group is achieving 78% stronger AI execution than their peers, according to the study.
Cheek says the first move for CHROs is training. He wants companies to invest in AI training and upskilling for executives and employees, then rethink which work should be done by people and which should be done by AI.
'The CHRO title itself needs to change from managing headcount to auditing whether a given role needs a human’s judgment or an AI agent’s uptime,' Cheek says.
In Cheek’s model, the org chart would become a dynamic document that changes as AI agents are added, retired, or even build their own agents. It would track access to tools and systems ranging from social media accounts to Salesforce, along with spending authority and decision-making responsibilities.
Cheek says he has not yet found a company that has implemented this kind of technologically advanced org chart. But he says AI could help generate one, and that he has built a prototype using AI.
His message to CHROs is simple: learn how AI works, teach those skills across the company, and decide which tasks belong to people and which belong to machines. He argues that the executives who take that job seriously will become the most influential strategic leaders in the organization.
The market has already voted on the direction of work: firms are no longer asking whether AI belongs in the enterprise, but how much authority it should be given. That matters because once spending power and system access are mapped, control shifts from old job descriptions to measurable productivity.
Capital rewards clear rules. If AI is going to do more of the work, companies will need tighter controls, sharper accountability and a cleaner line between human judgment and machine uptime. The firms that get that right may gain an edge; the ones that do not will be left with more technology, but less clarity.
