How to Prepare Your Company for Superintelligent AI Today

To prepare a company for superintelligent AI, do the work that makes any capable AI safe to use. Keep your knowledge clean and current, own the memory your agents build, give each agent only the permissions its role needs, measure its work with evaluations you control, and keep people approving consequential actions. Nobody can say when, or whether, systems far beyond human ability will arrive, but these five moves pay off with the models you use now and carry over to stronger ones.

Key takeaways

• Nobody can date or define superintelligent AI, so prepare for a rising capability curve, not a single event.
• The model is the part you will replace. Your knowledge, memory, permissions, tests and approvals are the part you keep.
• Clean, owned and current documents and memory make today's agents better and give stronger models a trustworthy base.
• Narrow permissions and human approval limit the damage when any model, of any strength, gets something wrong.
• Evaluations you own let you judge a stronger model on your own work instead of on a vendor's claims.

What does preparing for superintelligent AI actually mean?

Superintelligent AI usually means systems that outperform people across most intellectual work. Whether and when that happens is disputed by serious people, and a company cannot plan around a date nobody can give. What a company can plan around is a pattern it can already observe: each generation of models does more with the context, tools and access it is given.

That makes a more capable model a stronger amplifier. Give it accurate documents, narrow access and a clear way to be checked, and it amplifies good work. Give it contradictory files, broad credentials and no tests, and it amplifies those problems faster and with more confidence. So preparation is mostly not about the model. It is about the environment around the model, which you control and which does not have to change when the model does.

The NIST AI Risk Management Framework points the same way: risk is managed through organizational practice, not through the properties of one product. The five moves below are a practical, small-company version of that idea.

Why does clean knowledge come first?

An agent is only as reliable as what it reads. Anthropic's writing on context engineering makes the point plainly: what you put in front of a model shapes what it does, and more context is not the same as better context. A stronger model reading two conflicting policy documents will not know which one is current. It will pick one, convincingly.

Cleaning knowledge is unglamorous and it is the highest-return move on this list. Start with the sources one real role needs, not the whole company.

Use this checklist:

• Name one owner for each source. A person, not a team alias.
• Mark what is current, what is superseded and what is archived, and remove superseded files from anywhere the agent can read.
• Resolve contradictions between documents, especially approval rules, discount rules and customer commitments, before the agent sees them.
• Write down the unwritten rules: exceptions, tone, who decides what. Short plain pages beat long polished ones.
• Add a review date to each source and a habit of revisiting it.
• Separate what the agent may read from what it may not, such as HR files or contracts under confidentiality.

Here is a simple test. Ask a new employee to answer five real questions using only these sources. If they find conflicting answers, an agent will too.

Who should own the memory your agents build?

Memory is what an agent accumulates over time: customer preferences, decisions and their reasons, corrections from your team, the shape of recurring tasks. It is often the most valuable thing an AI deployment produces, and the easiest to lose control of, because it can end up inside a vendor's product in a format only that product reads.

Own it by design:

• Keep memory in storage under your company's accounts, in a readable and exportable format.
• Separate memory from the model, so changing models does not mean starting from zero.
• Record where each memory came from and who approved it, so wrong entries can be found and removed.
• Decide what the agent may remember and what it must not keep, personal data in particular.
• Back up memory and restore it once in a test, before you need it.

This is the practical core of avoiding vendor lock-in with business agents. When a stronger model arrives, owned memory means it starts with your company's accumulated context on the first day instead of a blank page.

How narrow should permissions be?

As narrow as the role allows, and narrower than is convenient at first. The rule that applies to people and software applies to agents: grant the least access needed, deny by default, and separate reading from writing and drafting from executing. OWASP's authorization guidance describes these principles, and they apply directly to an agent acting through tools.

Capability raises the stakes. A weak agent with broad access mostly fails by being unhelpful. A strong agent with broad access can do real damage quickly while appearing to do exactly what was asked, including when a hidden instruction in an email or document steers it. Narrow scope limits the damage from mistakes, manipulated instructions and your own misconfigurations.

Illustrative scenario: a finance operations agent reads incoming supplier invoices, matches them to purchase orders and drafts payment proposals. It can read the invoice mailbox and the order list, and it can write only to a proposals folder. It cannot release payments, change bank details or open payroll. When a stronger model replaces the old one, none of that changes. The new model gets better at difficult matches, and the limits stay where the company put them.

Give each agent its own identity instead of a shared login, put an expiry on temporary access, and review the list on a schedule. A step-by-step permission review turns that last point into a routine instead of a good intention.

How do evaluations prepare you for models you have not seen yet?

An evaluation is a set of real tasks from your own work, each with a description of a good result, that you run against an agent repeatedly. It is the difference between believing a new model is better and knowing whether it is better for you. Public benchmarks measure general ability. They do not tell you whether a model handles your refund exceptions or your naming conventions.

Anthropic's guidance on evals for agents recommends starting small with real cases and checking outcomes, not only the wording of answers. That is the right scale for a company.

Build the first set in this order:

• Collect real tasks from the role, including awkward ones where your own people disagree.
• For each task, write the expected outcome and the things that must never happen.
• Include cases where the right move is to stop and ask a person.
• Run the set on every change: new model, new instruction, new tool, new document set.
• Keep the results history so you can see drift, not only a single score.
• Add a case every time the agent makes a real mistake.

Tie the set to the observable success criterion you defined for the role. If you have not set one, how to define success criteria for an AI agent pilot is a good place to start. The payoff when models change quickly: adoption becomes a measured decision your team can make on its own evidence.

Where should humans stay in the loop?

At the points where an action is hard to reverse, reaches someone outside the company, spends money or commits the company to something. Stronger models make more actions possible, which makes it more important to decide in advance which ones need a person.

A simple decision guide:

• Act without approval: reversible, internal, low-consequence work such as drafting, summarizing, tagging and filing. Log it.
• Act after approval: anything external or hard to undo, such as sending a customer message, changing an account record, issuing a refund or releasing a payment.
• Never delegate: decisions about people's employment, legal positions, and changes to the agent's own permissions.

Two details decide whether approval works. First, show the reviewer what they need: the proposed action, the sources used and what would change. An approval step that hides its evidence trains people to click yes. Second, treat changes to the agent itself, such as new tools, wider access or a different model, as approvals too, with an owner and a record. A clear process for requesting and approving changes keeps this from turning into informal edits.

Over time, evaluation results and logs can justify moving an action from approval to autonomy. That should be a decision a named person makes with the record in front of them, not a drift.

What is a practical sequence to start with?

You do not need a program. You need one role, done properly. Follow these steps in order:

• Step 1: Pick one role where an agent would help, with a person who owns the outcome.
• Step 2: List the sources that role needs and clean them with the checklist above.
• Step 3: Decide where the memory lives, who can read it, and how it is exported and restored.
• Step 4: Write the permission list: what the agent reads, what it drafts, what it may execute, and nothing else.
• Step 5: Build the first evaluation set from real tasks and set one observable success criterion.
• Step 6: Define the approval boundaries and who approves each category.
• Step 7: Run the agent, review logs and evaluation results on a schedule, and change one thing at a time.
• Step 8: When a new model is released, run your evaluation set before deciding anything.

Be Human works this way. A first engagement defines one useful role, the memory and tools it needs, the approval boundaries and an observable success criterion. Managed operations can then include monitoring, recurring evaluations, integration maintenance, permission review, incident response and controlled improvements, while identity, memory and permissions stay under your control. Service is remote, in English and Portuguese.

Frequently asked questions

Is superintelligent AI close enough that we should plan for it? Nobody can answer that reliably, and you do not need to. The moves in this article are justified by the AI you already use, so they pay off whether progress is fast, slow or uneven. Treat any confident timeline, from either side, with caution.

Should we wait for more capable models before investing? Waiting rarely helps, because this preparation does not depend on the model. Clean knowledge, owned memory, scoped permissions, evaluations and approval rules will be the same work next year, and a company that already has them can adopt a better model faster than one starting from scratch.

Do smaller companies need all five moves? Yes, in a lighter form. A company with one agent can keep a single page of sources, a short permission list, a handful of evaluation cases and a plain rule for what needs approval. Scale the effort to the role, not to the size of the company.

Does training or fine-tuning our own model count as preparation? Usually not as a first step. Most business needs are met by giving a capable model good context and the right tools, which also keeps you free to switch models. Fine-tuning can have a place later, but it adds maintenance and ties your work to one model.

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