Risk × Frequency × Reversibility
- High risk + low reversibility → manual or assisted
- Medium risk + reversible actions → assistance + authorization + automation hooks
- Low risk + high frequency + reversible → protected drug → drug
This avoids two costly failure modes.
- Delivery “agency” that always needs a babysitter
- Staying manual as competitors speed up
The autonomy ladder: How should decision-making products evolve?
The path that consistently works is progressive autonomy.
- Observe (Instrumented Manual+). Understand decisions, outcomes, and context. Build a decision dataset.
- Recommended (auxiliary). Provide options and tradeoffs. Improve consistency. Corrections can be made easily.
- Constraint (protected agent). Run within the policies defined by the SME. Anything out of scope will be escalated.
- Representative (Agent). Automate routine decisions. Humans manage exceptions and evolve policies.
The point is that autonomy is earned, not declared.
And the target is not “trust” as an atmosphere. This is proper trust. Users follow the system when it is right and disable it when it is wrong. This distinction is central to Lee & See’s classic work on automation design, which aligns reliability and capability.
A pattern in reality that I have seen over and over again.
In every high-stakes field I’ve ever worked in – security, risk, supply chain, product development – Inflection Point couldn’t have been a better model. It was transforming small business decisions into actionable policy layers.
One common scenario is that a team ships an “assistance AI” that generates recommendations quickly, but results remain inconsistent because the actual rules reside in the heads of three senior people. Amendments are rarely “stronger demanded.” the:
- Capture constraints, escalation triggers, and exception playbooks
- Equipment reversal (when a human overrides the system)
- Treat such reversals as learning signals
- Then automate small actions that can be undone within a narrow range.
At that point, the system stops acting as a suggestion engine and starts acting more like a decision-making product. This means fewer meetings, faster decision-making, and measurably fewer exceptions requiring senior review.
If you want to operationalize DPOM, start with three artifacts and three metrics.
artifact
- Mode selector worksheet: Risk × Frequency × Reversibility per decision
- SME policy layer template: Constraints, thresholds, escalations, exceptions, acceptance tests, rollbacks
- Autonomy ladder criteria: Explicit gate for movement Observe → Recommend → Constrain → Delegate
Metrics (Executive Scoreboard)
- Time to decision (median + P90)
- Exception rate (% escalated to SME)
- Reversal rate (% overwritten by human after recommendation/action)
Reversal rates are especially powerful because they transform “human disagreement” from politics into measurable signals that products can learn from.
Questions that reveal where to start
If you’re leading an AI product strategy in a non-tech industry, the most diagnostic questions I know to ask are:
What are the most frequent decisions in your business that still rely on a few experts, and what does it take to make them secure, consistent, and scalable?
That’s where decision products create multiple benefits.
And that’s how you move from “I added AI” to “I changed the operating system of my business.”
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