Superintelligence and Jobs in Companies: Which Roles Change First

Superintelligence and Jobs in Companies: Which Roles Change First

Superintelligence, meaning an AI far superior to people in almost every cognitive task, is still a hypothesis, and no serious person can say whether or when it arrives. What a manager can do now about superintelligence and jobs in companies is look at tasks, not job titles. As AI becomes more capable, drafting, triage and lookup tasks change first, and roles reorganize around decisions, accountability and oversight.

Key takeaways

• Superintelligence is a debate about the future. What affects your team today is AI systems that are increasingly capable at specific tasks.
• Jobs rarely disappear all at once. What changes is the mix of tasks inside each job.
• Repetitive, text-based tasks with clear rules change first. Tasks involving accountability, relationships and exceptions tend to stay with people.
• Managers reorganize roles by mapping tasks, setting limits on AI autonomy and naming who answers for each decision.
• You can plan without predicting dates: small, reversible decisions measured in pilots beat bets on scenarios.

Is superintelligence what is changing jobs today?

Not exactly, and it helps to separate three things that often get mixed together. The first is what official vendor documentation and reference bodies describe: language models and agents that perform defined tasks, with known limitations. The second is implementation experience: what happens when these systems enter real processes, with messy data, exceptions and skeptical people. The third is the hypothesis: general systems, better than human experts at almost everything, on an uncertain timeline.

What companies run today belongs to the first two categories. If you want to go deeper on this distinction, we wrote about the difference between AGI, ASI and AI agents and what your company can actually run. The practical conclusion is simple. The useful question for a manager is not when superintelligence arrives. It is which tasks in my operation can already be done by a supervised system, and what I do with the time of the people who did them.

A good test is this: if your reorganization decision only makes sense when one specific prediction comes true, it is fragile. If it makes sense with today's AI and still makes sense if AI improves, it is solid.

Why look at tasks and not job titles?

A job is a bundle of tasks with a name. The receptionist at a clinic, for example, answers questions, confirms appointments, chases documents, calms an irritated patient and resolves a scheduling conflict. AI can handle some of those tasks well and others very poorly. Debating whether the receptionist will be replaced hides the question that matters: which parts of her day are mechanical, and which require judgment and presence.

To break a job down, classify each task by four characteristics:

• Format: are the input and output text, a spreadsheet, a form or a message?
• Rule: is there a clear standard for right and wrong, or does it depend on context and sensitivity?
• Risk: does a mistake cause cheap rework, or harm to a customer, health, money or reputation?
• Reversibility: can the action be undone, or does it leave the company and not come back?

Tasks with a text format, a clear rule, low risk and a reversible action are the first candidates. Tasks with the opposite profile stay human for a long time, no matter how much the model improves, because the problem stops being capability and becomes accountability.

Which tasks change first, and in which functions?

Without predicting dates, you can still describe the direction. The first tasks to change are those that consist of producing a first draft, classifying, summarizing, querying a database and filling in fields. By function, it looks like this:

• Customer service and support: triage, answers to frequent questions, status lookups and case logging. Human work concentrates on complex complaints, exceptions and retention. We detail this split in AI customer support: what to automate and what to keep human.
• Sales and pre-sales: initial qualification, proposal drafts, CRM updates. The salesperson gains time to negotiate and understand the customer.
• Administration and finance: document reconciliation, data checks, report drafts. The person starts reviewing exceptions and approving.
• Marketing and content: drafts, variations, source research. Curation, positioning and fact-checking become more important, not less.
• Technology: code generation and review, tests, failure investigation. The weight shifts to specifying well, reviewing and taking responsibility for what goes to production.
• Legal and compliance: research and first reading of documents. The decision and the signature stay with the qualified professional.

Notice that in almost every line the function does not vanish. The center of gravity moves from executing to specifying, reviewing and deciding. This creates a real management risk: keeping the person in the old role while the work has already changed, or cutting the review step to gain speed.

What tends to stay with people?

Not out of sentimentality, but according to criteria a manager can apply. Tasks tend to stay human when:

• someone has to answer legally or contractually for the decision;
• there is a relationship of trust, a negotiation or an emotional conflict;
• the context is new and not written down anywhere;
• the decision directly affects a person and requires a right to review, as we discuss in LGPD automated decision-making and the right to review (Art. 20);
• the cost of being wrong is high and the error is hard to detect later.

This is a working hypothesis, not a guarantee. If a future system is far more capable, the boundary may move. That is why the recommended design is modular: each autonomy limit is recorded and can be revisited when the evidence changes.

Whoever answers for the decision defines the human role, not whoever is slower at the task.

How does a manager reorganize roles, step by step?

The plan below works with today's AI and stays useful if AI improves. It does not depend on predictions.

• Step 1, inventory: list the recurring tasks of one area, who does them, how much time they take in qualitative terms (little, medium, a lot) and which systems they touch. Talk to the people who do the work, not only to their managers.
• Step 2, classification: apply the four criteria (format, rule, risk, reversibility) and sort tasks into three groups: automate with review, support with AI and keep human.
• Step 3, limits: for each task in the first group, write down what the AI can do alone, what requires approval and what it never does. Define the moment of handoff to a person.
• Step 4, owners: name one person responsible for each flow, including the quality of the answers and the record of decisions.
• Step 5, pilot: pick one real, low-risk flow and run it with logs and a success metric agreed before you start.
• Step 6, role redesign: with the pilot result, redefine the job description. The person who did the task becomes a reviewer, a curator of instructions, the owner of exceptions, or moves to other, more valuable work.
• Step 7, decision: expand, adjust or stop. Stopping is a legitimate outcome of a well-run pilot.

Two points of conduct. First, communicate early and clearly what is being tested and what is not at stake. Mishandled uncertainty creates resistance and quiet sabotage. Second, do not use AI as a pretext for individual surveillance. Measuring AI productivity without turning it into surveillance means measuring the flow, not spying on the person.

What does this look like in practice? An illustrative scenario

Illustrative scenario: a mid-sized electrical supplies distributor has three people handling customer service on WhatsApp. Their day is dominated by availability questions, invoice copies, delivery status and simple quote requests. Exceptions (a customer with an overdue payment, a wrong product, a delivery deadline negotiation) take less time but cause more strain.

The manager takes the inventory and classifies. Delivery status and invoice copies go into automate with review, with the AI querying the system in read-only mode. Simple quotes go into support: the AI builds the draft and a person approves it before sending. Deadline negotiation and complaints with a risk of losing the customer stay human. It is also defined that, when in doubt or facing a complaint, the AI hands the conversation over with a summary.

The three people's roles change gradually. One becomes responsible for the flow: reviews sampled conversations, adjusts instructions and follows the handoffs. Another concentrates on quotes and relationships with larger customers. The third takes over after-sales and exceptions. Nobody was removed from the process. Each job description was rewritten, and the manager defined in advance what the pilot would measure, such as first response time and correct handoff rate, without promising a result.

If the channel is WhatsApp, learn the rules on the service window and outbound messages before designing the flow. We cover that in how to integrate AI into your company's WhatsApp.

How mapping, limiting, piloting and handing over apply here

The method we use in AI projects follows the same logic as above. Mapping is taking the task inventory and the baseline of the current process before any tool. Limiting is restricting data and actions: what the system can read, write and send, and when it needs human approval. Piloting is running a real flow with logs, a record of decisions and an agreed success metric. Handing over is delivering the operation with documentation and defined owners, and only then deciding whether to expand, adjust or end it.

For governance, two references help structure the internal conversation. The NIST AI Risk Management Framework organizes AI risk management around governing, mapping, measuring and managing, and the OECD AI Principles address human oversight, transparency and accountability. Anthropic's guidance on building effective agents recommends starting with the simplest solutions and increasing autonomy only when the need is demonstrated, which fits a design made of small, reversible steps. To follow what Brazilian legislation is addressing, see Brazil AI regulation: what companies should watch and do now.

Frequently asked questions

Will superintelligence eliminate jobs in my company? Nobody can claim that on the basis of evidence today, because superintelligence is a hypothesis, not a product you can buy. What is already observable is that more capable AI systems take over specific tasks and shift human work toward review, exceptions and relationships. Plan on that basis and revise the plan when new evidence appears.

Which functions should I reorganize first? Start with areas that have a lot of repetitive text work, such as customer service, administration and pre-sales, where the risk per error is lower and actions are reversible. Avoid starting with flows that make decisions about people or involve sensitive data without a clear human review. Choose a single flow and run a pilot before expanding.

Do I need to lay off or hire because of AI? That decision should not come from a prediction. It should come from the result of a pilot measured with criteria defined beforehand. Often the first effect is reallocation: the person stops executing the task and starts reviewing it, handling exceptions or serving more complex customers. If there is a change in headcount, it should be handled transparently and within the applicable labor rules.

How do I prepare the team without creating fear? Explain which flow will be tested, what limits the AI will have and what is not up for discussion, and involve the people who do the work in the mapping and the review. Give them a concrete role in the new design, such as owners of quality and exceptions. Avoid using AI to measure individuals. Measure the flow and share the pilot results with the team.

Sources

• NIST: AI Risk Management Framework
• OECD AI Principles
• Anthropic: Building effective agents