Superintelligence Scenario Planning: Three Plans for Your Company

Superintelligence scenario planning is the practice of choosing a small set of plausible futures for capable AI, then selecting the actions that still pay off if your forecast is wrong. For most companies, three scenarios are enough: steady improvement inside current workflows, a fast step change in what software can do on its own, and an uneven path where capability outruns reliability and control. Plans that hold up across all three share a common base of well-documented knowledge, scoped permissions, measurable outcomes and operations you can move between vendors.

Key takeaways

• Plan against three plausible futures instead of one forecast, and name the early signals that would move you from one to another.
• Prioritize no-regret actions, meaning moves that help in every scenario, such as documented know-how, scoped permissions and measurable tasks.
• Separate the decisions you must make now from the ones you can defer until evidence arrives.
• Review the scenarios on a fixed calendar with a named owner, not only when news forces the question.
• Treat every agent as an operating asset you control, so a better model can be adopted without rebuilding your memory and rules.

Why one forecast is the wrong tool

Leadership teams are regularly asked when advanced AI will change their market, and the honest answer is a range. A single forecast invites a single bet. That bet either spends budget on the wrong thing or leaves the company unready when the timing shifts.

Scenario planning replaces the forecast with a set of stated assumptions, each linked to decisions the company would make differently. The method predates AI and is built for uncertainty that cannot be removed. The NIST AI Risk Management Framework takes a similar stance toward AI risk, organizing work around governing, mapping, measuring and managing risk rather than predicting one outcome. For a business, the useful question becomes: what do we do now that still holds up under several things that might happen?

Scenario one: steady improvement inside current workflows

In the first scenario, models keep getting better at the tasks they already handle, but the change arrives gradually. Drafting, summarizing, classifying and retrieving documents become more accurate, and agents take on longer chains of steps under supervision. Competitors look much like you, only faster at routine work.

What this scenario demands is steady discipline. The bottleneck is rarely the model. It is whether your team has written down how work is done, which sources count as the record, and who approves any action that touches money or customers. Companies in this scenario gain the most by choosing one workflow, measuring it well and improving it on a schedule.

Scenario two: a fast step change in autonomy

In the second scenario, agents become able to plan and execute multi-step work with little supervision within a short period. Tasks that once needed a person to route information between systems can be delegated end to end. Adoption speed becomes the constraint, and the companies that benefit are those able to absorb new capability without losing control.

The no-regret moves here are mostly about boundaries. When an agent can act faster than people can review it, approval rules, audit trails and a clear way to pause the agent carry the weight. Decide in advance which actions an agent may take alone, which need a named human sign-off, and which are never delegated. Write those rules before you need them, because in a fast scenario there is no time to write them.

Scenario three: capability outruns reliability and control

In the third scenario, models become impressive in demonstrations but remain uneven in production. They perform well on common cases and fail quietly on rare ones. Security weaknesses grow in importance as agents touch more systems, including prompt injection, where content an agent reads tries to override its instructions. Rules on AI use also tighten unevenly across markets, so a design acceptable in one place may need changes in another.

This is the scenario that rewards humility most. The OWASP Top 10 for LLM Applications describes the classes of risk that recur in these systems, from injected instructions to excessive permissions, and a company in this scenario should treat that list as a checklist for its own agents. Build evaluations that test the rare cases, log what agents did and why, and never give an agent more access than its job requires. If one provider pulls ahead in this scenario, keep prompts, memory and tool definitions in formats you own, following the guidance on how to avoid AI vendor lock-in.

The no-regret actions that hold in every scenario

A no-regret action creates value or reduces risk under all three futures. Most of the useful ones are dull, which is why they are often skipped. Work through this list before buying additional capability:

• Inventory the knowledge your company depends on, including procedures, pricing rules, customer history and the people who hold tacit know-how, and mark what is written down.
• Name a decision owner for each important process, with authority to approve or stop automation in that area.
• Write one observable success criterion for each candidate workflow, so every future model is judged against the same yardstick.
• List every system an agent could touch, the permission it needs, and the permissions it does not need.
• Sort proposed agent actions into three groups: allowed alone, allowed with human approval, and never delegated.
• Keep logs of inputs, tool calls and outputs so any decision can be reconstructed later.
• Store memory, prompts and tool definitions in formats you can export and move.
• Schedule a recurring permission and scenario review with a date already on the calendar.

The first two steps cost little and mostly take attention. The sorting step and the review schedule are where many teams stall, because they require rules agreed before a dispute arises. The AI stack architecture map helps locate each layer on one page, and the quarterly permission review describes how to keep access current once it is scoped.

How to run a planning session in one afternoon

A scenario session needs a small group: the executive who owns operations, a finance lead, someone responsible for data and security, and one manager close to daily work. Plan for half a day and insist on a written output that fits on one or two pages.

• Write the three scenarios, each with two sentences describing the world and one describing what your market looks like in it.
• For each scenario, list the decisions you would make differently and the decisions that stay the same.
• Agree on two or three early signals per scenario, such as a candidate workflow failing under realistic tests or a regulatory change your counsel flags as material.
• Assign each no-regret action an owner and a review date.
• Set the review cadence and decide who can move the company from one scenario plan to another.

Illustrative scenario: a mid-sized distributor of building materials runs this session and finds that its order-status questions look the same in every scenario. It starts there, with a documented answer library and an agent that can read orders but not change them. When the team later considers letting the agent issue credit notes, the scenario notes show that the approval boundary must be written first, since that is the decision that matters most if change accelerates.

How Be Human handles this in practice

Be Human designs, implements and operates personalized AI infrastructure for companies, with agents that keep their identity, memory, channels, tools, permissions and operating context under human control. A first engagement defines one useful role, the memory and tools it needs, its approval boundaries and an observable success criterion, which is the same discipline the no-regret list asks for. Managed operations then cover monitoring, recurring evaluations, integration maintenance, permission review, incident response and controlled improvements, so the scenario plan gets revisited with evidence rather than left in a slide deck. The service is remote and available in English and Portuguese.

Take the free Be Human AI diagnostic to find the first task worth handing to an agent.

Frequently asked questions

Is superintelligence scenario planning only for large enterprises? The method works the same way for a company of any size, though a smaller company can run it in a single meeting with fewer stakeholders. The essential output is a short written set of scenarios, the decisions tied to each, and the no-regret actions you will take this quarter. Smaller teams often finish the inventory and permission steps faster because there are fewer systems to document.

How often should we revisit the scenarios? Review them on a fixed calendar, most often once a quarter, and also whenever a signal you named in advance appears. Waiting for news to force a review tends to produce rushed decisions. Keep each review short and tied to the same success criteria so you can see what has changed.

Who should own the scenario plan? A named executive should own it, usually the person accountable for operations, with finance and security as required reviewers. The owner decides which scenario the company is leaning toward and approves changes to approval boundaries. Ownership matters more than the document format, because a plan without an owner becomes a file nobody opens.

What if we cannot tell which scenario we are in? That uncertainty is normal, and it is the reason the plan covers three scenarios. Start with the no-regret actions, since they are useful in every case, and delay scenario-specific moves until your early signals point clearly one way. Waiting for certainty before acting on the shared base usually costs more than acting on it now.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.