Superintelligence Readiness for Pharma and Insurance

Pharma companies and insurance agencies already live with rules about who may approve what and how decisions get recorded. That gives them a head start on AI readiness, because the habits regulators expect are the same habits that make agents safe to run. It also raises the bar. An agent that drafts a deviation assessment or a certificate of insurance has to meet the standard of the person who used to write it, and you have to be able to show that it did.

A note on the word itself. Since Executive Order 14434, "Inaugurating The Era Of Super Intelligence", signed September 29, 2026, federal agencies use "Super Intelligence" and "SI" in place of "Artificial Intelligence" and "AI" in official communications. The order defines the term by the existing statutory definition of artificial intelligence, so in federal guidance SI covers the same technologies AI did. AI labs use superintelligence in a second sense, for future systems that would outperform people at most thinking work. Regulated teams reading new federal material should expect SI where older documents said AI, with the same scope. In this post we use the word in the lab sense, and we focus on what to build now so you are ready as models improve.

Start with approvals that leave a record

In a regulated company, the first readiness question is who answers for the agent's output. Our rule for every build is that no AI output reaches a customer or a regulator without a named person approving it. The software enforces that rule, so nobody has to remember it on a busy afternoon.

The approval record matters as much as the approval. We log the reviewer's name and the time, along with the draft the agent produced and the version the reviewer released. When an auditor or a client asks how a document was produced, that log answers the question. Over time it also shows where reviewers approve drafts without changes, which tells you where the agent can safely be given more room.

Retrieval that respects existing permissions

Regulated companies hold records that only some staff may see, such as batch records for a product under investigation or a client's loss runs. An agent that searches across these sources must apply the same permissions the person asking would have. If a QA associate cannot open a document in the quality system, the agent working on their behalf should not be able to quote it either.

We build retrieval so each answer carries a citation to the source document and section, and so the permission check happens at query time against the source system. That second part matters because permissions change. A permission copied into an AI index once and never refreshed becomes a quiet compliance problem, and it usually surfaces only when someone sees a record they should not have.

Give every agent its own identity and audit trail

Many automations in mid-size companies still run under a shared login or under the account of whoever built them. In a regulated setting that breaks two things at once. Access becomes broader than the task needs, and the audit trail shows a person's name on actions that person never took.

Each agent we build gets its own identity in the client's tenant, with only the access its workflow needs. Every document it reads and every draft it writes can be traced to that agent and to the approver who released the output. If an agent misbehaves, you can switch off that one identity without touching anyone's account. Our AI governance work installs these controls as working software instead of policy documents.

Pharma: validation built to GxP expectations

For pharma and life sciences, the first question quality leaders ask is how an AI system fits into computerized system validation. We treat each agent like any other system that touches GxP records. We write the intended use and the acceptance criteria before the build. Then we test against a fixed set of real cases and keep that test set, so any model change can be re-run against it before release.

We describe this work as built to GxP expectations. The client's quality unit owns validation and release decisions, and our job is to hand it a system with the documentation and evidence it needs to make those decisions. Keeping prompts and business rules in the client's own code also matters here. A newer model then becomes a controlled change with a test record, rather than a rebuild.

We have built in this environment before. A European pharmaceutical regulator runs an AI compliance scanner we built, which has scanned 620+ marketing assets against 11 rules per scan at about 2 minutes per asset, down from 2 to 3 hours of manual review. Good first workflows for a manufacturer or sponsor tend to sit in quality and regulatory operations, with deviation report drafting and SOP lookups among them. Our pharma and life sciences page has more detail.

Insurance: certificates and E&O exposure

For insurance agencies, the clearest regulated risk in early AI work sits on the certificate of insurance. A certificate that states coverage the policy does not provide can turn into an errors and omissions claim against the agency. Producers know this, and certificate work often sits with the most careful person in the office as a result.

We build certificate agents around one hard rule. The agent may only write wording it can trace to a specific policy form or endorsement in the client's file. When a request asks for additional insured status or a waiver of subrogation and the agent cannot point to the endorsement that grants it, the request goes to a licensed person instead of onto the certificate. The licensed person sees the request alongside the gap the agent found.

Routine certificates then move quickly with a person releasing each one, and the risky requests reach the people who carry the license. The approval log and the agent identity described above apply to every certificate the agent drafts.

Where to start

Our Superintelligence Readiness Check scores eight dimensions. Two of them, AI in production and human oversight, act as gates in the scoring, because a first build stalls when either one sits at zero. For regulated companies we would put agent identity and access next in line.

Then pick one workflow with high volume and a clear right answer, and build it with approvals and identity done properly from day one. That first build forces the data access and approval decisions every later agent will reuse. When more capable models arrive, as our superintelligence readiness overview describes, you will be testing a new model against a working, documented workflow instead of starting a new project.

If you lead quality or operations at a pharma or life sciences company, tell us the workflow. We build it in your environment in two weeks against acceptance criteria you sign on day one, and you pay $10,000 only after every criterion is met. Independent agencies can start with our five-day agency sprint on certificates or renewals, and if it cannot find 10 staff hours a week, the agency pays nothing.