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Super Intelligence preparedness: a decision brief

Start with a decision, not a prediction.

OXHN Partners · Reviewed 16 October 2026

Super Intelligence (SI) is used differently across research and policy discussions. Here, preparedness means considering how increasingly capable AI could change a business decision—without assuming a date for broadly superhuman systems or calling today's tools superintelligent.

OpenAI's historical Superalignment discussion distinguishes superintelligence from AGI to emphasize a much higher capability level; NIST's current SI program spans research, evaluation and applications. Neither source establishes what a particular tool can do for your workflow.

You can test a present-day tool and consider future scenarios separately. This brief helps a leadership team record what it knows, what remains uncertain and what would justify changing direction. It is not a maturity score, a forecast or a certification.

1. Name one decision

We need to decide whether to ______ because ______. The decision owner is ______. We will revisit this on ______ or when ______ happens.

Choose a real decision: whether to pilot a report-drafting tool, renew a platform, redesign a handoff or preserve an alternative supplier. Avoid “become AI-ready” without a specific choice.

2. Separate evidence from assumptions

For each important claim, record the evidence, its date, what remains uncertain and who will verify it.

Evidence to gather—not a completed assessment
Claim	Evidence and date	What remains uncertain	Who will verify it?
What the current tool can do	A representative test, source-linked output or documented limitation	Performance on exceptions, changes in input or review workload	Named internal owner
What the business needs	Current process, acceptable quality and actual operating constraints	Missing baseline or unresolved decision rights	Process owner
What could change	A clearly labelled scenario or provider announcement	Availability, timing and whether results transfer to your task	Review owner

A demonstration is a reason to investigate, not proof of dependable performance on your work. Keep a vendor claim distinct from your own test. Record “unknown” when evidence is missing rather than assigning a score.

3. Compare actions across three scenarios

These are planning scenarios, not predictions:

Progress is incremental: Would a simpler process or existing tool solve enough of the problem?
Relevant capability improves: What small, reversible trial would show whether the improvement matters?
Capability or availability disappoints: Can the team continue without the tool, recover its work and change supplier?

For each option, record the expected benefit as an assumption until tested; implementation and review effort; dependencies; an accountable owner; and the cost of reversing the decision. Do not choose the same answer for every scenario by default.

4. Define the signal that changes the decision

If ______ is observed in ______, we will ______. The owner is ______. Before expanding use, we need ______.

Useful signals include performance on your agreed cases, the effort needed to check outputs, a material change in access or product terms, and an unresolved failure affecting the task. “A new model launched” is a reason to review evidence, not automatically to rebuild the workflow.

Keep success and stop conditions separate. For an existing workflow, retest after material model, prompt, input or integration changes. Preserve the current approved process until the responsible owner decides otherwise.

5. Leave with one accountable next step

Choose act, test, monitor, defer or stop. Write the next action, owner, review date or trigger, remaining unknowns and where the evidence will be kept. “Defer until we have representative examples” is a usable decision; “keep an eye on AI” is not.

Decision and next action: Act / test / monitor / defer / stop: ______________________________
Accountable owner: ________________________________________________________
Review date or observed trigger: ________________________________________________________
Remaining unknowns and evidence location: ________________________________________________________

Keep your completed record in your team's approved environment. This page does not collect or store it. Use non-identifying references instead of confidential client details.

A fictional example

Decision: A professional-services team is considering AI-assisted report drafting.

Evidence now: The team has not yet measured review time or tested representative reports. A supplier demonstration is available, but it is not evidence from this team's workflow.

Next action: Keep the existing report process. With approved material, compare a bounded drafting trial against that process, including source accuracy, omissions, correction work and reviewer effort. The process owner defines acceptance and stop conditions before the trial.

Trigger to reconsider: A later model or product change may justify retesting if it addresses an observed limitation. It does not authorize automatic expansion. If the team cannot validate sources or assigns no review owner, defer the trial.

This example is fictional. It reports no client result, measured saving or guaranteed capability.

Put the brief to work

For a current workflow, use the AI workflow assessment worksheet. Compare a process fix, existing tools, configuration and custom work in the build-or-buy guide. See how source references and human decisions appear in the synthetic requirements example. For a scoped implementation discussion, see the focused workflow pilot.

To discuss a workflow or preparedness decision, use the existing booking route. Do not include confidential information in booking details.

Book a workflow conversation

Opens Google Calendar scheduling. Please don’t include confidential information.

Sources and limits

OpenAI, Introducing Superalignment: historical distinction between AGI and a higher capability level; not a current model assessment or a forecast adopted here.
NIST, Super intelligence: current program vocabulary and evaluation/research scope; not endorsement of OXHN.
NIST, AI Risk Management Framework 1.0 and AI RMF Playbook: voluntary resources supporting context, ownership and evaluation questions. The Playbook is not an exhaustive checklist. This brief is not a certification or complete application of the framework. The source pages note that AI RMF 1.0 is being revised; check for updates before reusing them.

Sources checked 16 October 2026. This decision brief is OXHN's original planning aid; the scenario structure and example are editorial synthesis, not a published research finding or validated scoring instrument.