Decision rights
Who may decide what, on which evidence, and who can stop or reverse it. Without explicit decision rights, faster AI output turns into a queue of approvals nobody owns.
Decision rights define who owns a decision, what evidence that owner needs, and who can override or stop the outcome. They are the part of an organization that AI tends to expose first, because automation makes slow or unclear approvals visible as a measurable queue.
In an AI setting, decision rights should be written for the specific decisions the system influences: which ones it may take alone, which need a named person to approve, and who can halt the system when its output is wrong. A capability that is technically available but has no owner for its decisions is not yet usable in the business.
Decision rights are also what separates a second team that can reproduce a result from one that depends on the first team's rescue. See AI governance and task-scoped authority.
Related terms
Authority latency
The elapsed time between a workflow becoming ready for a consequential decision and an authorized person or system actually taking that decision or action.
AI governance
The rules, owners and checkpoints that decide what an AI system may access, decide and change, and who answers when it goes wrong. Governance that treats every agent the same is a weak control.
Task-scoped authority
A temporary authorization envelope for one AI-agent task, limited by allowed action, resource, consequence, duration and recovery conditions rather than broad standing role permissions.
AI absorption
An organization's capacity to take in AI capability: to accept, operationalize, own and retire old work. Model capability can rise faster than absorption, which then becomes the binding limit.
Used in these essays
A pilot with no kill criteria is not an experiment
A pilot is not an experiment if every result can be used to justify continuing. The missing design element is the authority to stop.
Stop asking what AI can do. Ask what your company can absorb.
The gap that matters is between what AI can produce and the changes an organization can reliably absorb into ordinary work.
AI is removing the coordination tax. The next bottleneck is authority
As AI reduces the cost of moving work between teams and systems, many organizations will discover that the remaining delay is not coordination. It is permission to decide.
Stop Adopting AI
AI adoption is rising faster than enterprise value because companies keep installing new intelligence inside old operating models.
OpenAI Dots is a test of whether AI can carry a goal, not just complete a task
Dots matters less as another capable assistant than as a test of persistent delegation: can AI keep carrying a goal without giving the user a new system to manage?
The second team is the real innovation test
A successful pilot proves that one team could make something work. Organizational capability begins when a different team can reproduce the useful result without inheriting the original team's exceptional conditions.
The agent is not the operating model
Giving AI more autonomy does not remove organizational complexity. It gives that complexity permission to act.
Why emerging technology should not belong to one department
Emerging technology needs a home, but giving one department permanent ownership can turn a shared organizational question into a local agenda.