Executive Order 14434, signed September 29, 2026, tells federal agencies to call artificial intelligence Super Intelligence, while keeping the existing statutory definition of AI. AI labs use the same word for a future capability level. Either way, models keep improving every few months, and what decides who benefits is readiness more than model choice. This playbook sets out eight readiness dimensions, a first build for each, a realistic 90-day sequence for a company of 50 to 2,000 people, guidance for pharma, insurance, financial services and manufacturing, and a plain summary of the federal policy that applies to private companies today.
Super Intelligence now carries two meanings
On September 29, 2026 the President signed Executive Order 14434, "Inaugurating The Era Of Super Intelligence." It directs executive departments and agencies to use "Super Intelligence" and "SI" in place of "Artificial Intelligence" and "AI" in official correspondence, websites, reports and other non-statutory documents, to the maximum extent permitted by law. Within days the change showed up in public. The ai.gov address now redirects to si.gov, and the NIST center that works with AI developers now carries the name Center for Advancing Innovation and Standards for Super Intelligence.
Section 3 of the order settles what the term means in federal usage. Super Intelligence covers the technologies in the existing statutory definition of artificial intelligence, 15 U.S.C. 9401(3). No new capability threshold was set. The order also gives the President's science adviser 60 days to propose legislative language for a federal definition, so a different legal definition may be proposed later. Until Congress acts or a later Presidential action supersedes it, the federal word SI and the familiar word AI point at the same technology.
AI labs have used the word for years in a different sense. In their usage, superintelligence means systems that would outperform people at most thinking work. Safe Superintelligence Inc. was founded around building such a system safely, and Meta publishes a vision it calls personal superintelligence. That meaning describes a capability level that has not been demonstrated, and nobody can tell you the date it arrives. We will not pretend to.
When someone in your company says Super Intelligence, ask which meaning they intend. In a federal document dated after September 29, 2026, it means AI as the law already defines it. In a lab announcement, it means a future level of capability.
Both meanings point to the same practical question for an operator. Models are getting more capable every few months, and the federal government now names the whole field after where it expects that trend to go. This paper is about what a company with 50 to 2,000 people should put in place so that each step up in capability turns into working results inside its own operations, without waiting for a new project every time.
Readiness decides who benefits as capability rises, more than model choice
Most AI planning we see in mid-size companies starts with the model question. Which vendor, and which license tier. That question has a short shelf life. A model that leads in spring is often matched by a cheaper one by autumn, and the vendor list in a board deck ages faster than the deck itself. Companies that spend a quarter choosing a model often find the choice has been overtaken before the first workflow ships.
What lasts is the environment the model works in. An agent drafting a deviation report in pharma QA needs to reach the batch record and the prior deviations, with the same permissions the QA specialist has. It needs a named person to approve the draft, and a record of that approval an inspector can read. It needs its own login so its actions can be traced. None of that depends on which model runs underneath, and all of it decides whether a better model produces better work or more risk.
In our builds the companies that move fastest when a new model arrives share a few traits. Their data is reachable through permission-aware retrieval. Approvals are named and recorded. Model calls sit behind one layer in their own code, with a test set per workflow. When a new release lands, they run the test set in an afternoon and switch if the results are better. Companies without those pieces start a new project each time, and the gap between the two groups widens with every release.
For that reason we treat readiness as a set of working systems, built one workflow at a time. Each production agent pays for itself in hours returned. Each one also leaves something permanent behind, such as a mapped data source, an approval log, a scoped agent identity or a test set, that the next workflow and the next model can reuse.
The eight readiness dimensions, and what good looks like in each
Our Superintelligence Readiness Check scores eight dimensions. Two of them, AI in production and human oversight, act as gates. A zero on either one blocks a first build no matter how strong the rest looks. For each dimension below we describe what good looks like and the failure it prevents. Each description ends with a first step you can take this month.
AI in production. Good looks like a portfolio of AI workflows that are planned and retired like any other system, each with a named owner and real volume. The failure it prevents is the permanent pilot, where demos impress a steering group and nothing carries real work. Teams that run one agent in production learn which documents are messy and which approvals stall. Pilots teach almost none of that. A first step is to pick one high-volume, document-heavy workflow and get it into production with a person approving every output. Invoice matching, contract review, certificates of insurance and SOP lookups are common starting points.
Data access. Good looks like the main sources reachable through APIs, with retrieval that follows each user's role and freshness checks on the sources agents use. The failure it prevents is the capable model that cannot see the records that matter, or worse, sees records the requesting user was never allowed to open. Access is the slowest dimension to change, so in our readiness check it carries the largest weight. A first step is to list the ten document types your teams touch most and where each one lives. That list usually decides the first workflow more than any model comparison does.
Human oversight. Good looks like risk-tiered approval. Low-risk output flows with spot checks, and high-risk output always stops for a named person, whose sign-off is recorded with the time and any edits. The failure it prevents is the unowned decision. As systems get more capable, the question shifts from what the AI can do to who answers for what it did. Without named approvers, a customer complaint or a regulator's question has no one to land on and no evidence to show. A first step is one written rule. No AI output reaches a customer, a regulator, a carrier or the general ledger without a named person approving it, and the system enforces the rule instead of trusting memory.
AI inventory. Good looks like a complete list of the AI tools in use, each with an owner and a risk tier, kept current by automatic detection. The failure it prevents is ungoverned use. Staff adopt new AI tools faster than any policy update, and you cannot assess a tool against state law or a customer contract if nobody knows it is running. A first step is to pull a month of SaaS and network logs and list every AI service that appears. Then give each one an owner and block any tool that receives sensitive data with no agreement in place.
Agent identity and access. Good looks like one identity per agent, least-privilege access scoped to the actions its workflow needs, full activity logs, and permission reviews on the same cycle as staff access reviews. The failure it prevents is the automation running under a personal account or a shared password. When an agent acts across several systems in one task, a broad login lets one mistake travel everywhere, and nobody can reconstruct which actions the agent took. A first step is to stop creating automations under personal accounts. Create a dedicated identity for the next agent before you build it, and alert on any agent permission unused for 30 days.
Ownership and skills. Good looks like an owner in each major team who finds and ranks that team's AI workflows, with one senior leader accountable for AI outcomes overall. The failure it prevents is drift. AI comes up in every leadership meeting, but with no owner and no budget the inventory goes stale and the approval rule stays on paper. A first step is to name one person who owns AI outcomes and set a 90-day goal measured in hours returned, with a small budget to spend. Pair that person with the team leads whose work is most repetitive, because they know where the hours go.
Measuring results. Good looks like a measured baseline for every workflow before the build, followed by a monthly report of volume, accuracy, time saved and exceptions per workflow. The failure it prevents is the opinion contest. Without a baseline, every AI project gets judged on how it feels, and good projects get cut along with bad ones when budgets tighten. A first step is to time the current process for one week before the next build. Turn that number into pass-or-fail acceptance criteria the system must meet before anyone calls it done.
Model portability. Good looks like model calls behind one layer in your own code, prompts and business rules under version control, a test set per workflow built from your own documents, and a named person who decides when to switch. The failure it prevents is the rebuild. Models improve every few months, often at lower cost. If prompts and rules live inside one vendor's interface, each better model means starting over, and the gains go to whoever can switch. A first step is to write a small test set for your first workflow, real inputs with known correct outputs, and re-run it each quarter against the models available that quarter.
| Dimension | Gate in our check | A first build that moves it | What the build leaves in place |
|---|---|---|---|
| AI in production | Yes | One document-heavy workflow in production, such as invoice matching | A working agent with real volume and a named owner |
| Data access | No | Permission-aware search over the SOPs and policies staff ask about most | Mapped sources with retrieval that respects existing permissions |
| Human oversight | Yes | An approval step in front of every customer-facing draft | A recorded approval trail showing who signed and what changed |
| AI inventory | No | A log scan that lists AI services in use, with owners and risk tiers | A living inventory and a block list for unapproved data flows |
| Agent identity and access | No | A dedicated, scoped identity for the first agent, with activity logging | An identity pattern every later agent reuses |
| Ownership and skills | No | A workflow ranking session run by the AI owner with two team leads | A ranked backlog of workflows by hours at stake |
| Measuring results | No | A one-week baseline timing study before the build | Acceptance criteria tied to measured numbers |
| Model portability | No | A model layer and test set built into the first agent | The ability to compare a new model in an afternoon |
A 90-day sequence a mid-size company can run
A company with a few hundred people does not need a transformation program to make real progress in a quarter. It needs an owner and one well-chosen workflow. The sequence below is the order we use with clients. It assumes no dedicated AI team and a sponsor who can give a few hours a week.
A timeline across twelve weeks. Weeks 1 and 2 cover ownership, the approval rule, the inventory scan and the baseline. Weeks 3 to 6 cover the first production build, with data access and agent identity set up as part of it. Weeks 7 to 12 cover measurement against the baseline, a portability test, inventory cleanup and the choice of the second workflow. A review at the end of week 12 rescores all eight dimensions.
Weeks 1 and 2 set the foundation. Name the AI owner and agree the 90-day goal in hours returned. Write the approval rule and publish it. Start the inventory scan from a month of SaaS and network logs. Choose the first workflow with the team that does the work, and time the current process for a full week so the build has a baseline.
Weeks 3 to 6 put the first agent into production. Write acceptance criteria as pass-or-fail statements tied to the baseline, and have the sponsor sign them before the build starts. Map the data sources the workflow needs and connect them through retrieval that follows existing permissions. Create a dedicated identity for the agent with access limited to that workflow, and log every action. Ship it with a named approver in front of every output that leaves the team.
Weeks 7 to 12 make the next workflow cheaper. Measure the live agent against the baseline and the acceptance criteria every week. Build a small test set from real cases and run one alternative model against it, so the portability path is proven before you need it. Finish the inventory with owners and risk tiers, and close any data flows that lack an agreement. Rank the next candidate workflows by hours at stake and pick the second one.
At the end of week 12, rescore all eight dimensions and compare the result with the score from week 1. In our experience the second workflow reuses most of the data access and approval pattern from the first, which is where the time savings on later builds come from. If a gate still sits at zero, fix it before the second build starts.
A realistic first quarter ends with one agent in production, one recorded approval trail, one scoped agent identity and a measured result. That is enough to make every later model release useful within weeks of its arrival.
Regulated industries need approvals with records
For companies in pharma, insurance, financial services and regulated manufacturing, readiness has a second test. Every step an agent takes may later be read by an inspector, an auditor, a carrier or a court. The eight dimensions still apply, with human oversight and agent identity carrying extra weight, because the record of who approved what is the evidence.
Pharma and life sciences. In QA, an agent can draft a deviation report from the batch record and prior deviations, or check promotional material against the rules a company has to follow. The draft goes to a qualified person who approves or edits it, and the system records the approval with the time and the change. We build these systems to GxP expectations, which means validation evidence for the workflow, change control when prompts or models change, access reviews for the agent identity, and a documented test run before any model swap. A European pharmaceutical regulator runs an AI compliance scanner we built. It 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.
Insurance. For independent agencies, the common first workflow is the certificate of insurance. An agent can draft the certificate from the policy and the client's request, but every coverage statement on it has to trace back to the policy wording and the endorsements that support it. An additional insured status that the endorsements do not grant creates an errors and omissions exposure for the agency. Our agency builds attach the supporting endorsement to each statement and route the draft to a licensed account manager before it goes out. Renewal comparisons and policy changes follow the same pattern.
Financial services. Firms answer to examiners who expect to reconstruct a decision after the fact. An agent that matches invoices or summarizes a contract needs an audit trail covering the inputs it read, the model and prompt version it used, the output it produced and the person who approved it. Agent identity matters here more than anywhere, because a shared login breaks the trail at the first step.
Manufacturing. Plants with strict quality systems often start with SOP lookups on the floor. The agent answers from the current controlled version of the procedure and cites it, and the retrieval layer respects the same document control that governs the paper copies. When the procedure changes, the answer changes with it, and the citation shows which revision was used.
What federal policy asks of a private company today
This section summarizes published federal documents as of October 16, 2026. It is not legal advice, and you should confirm how any of it applies to your company with your counsel. We limit it to facts we could verify in the documents themselves.
The renaming order applies to the executive branch. Executive Order 14434 governs how agencies write and speak. It does not require agencies to alter previously issued regulations, Presidential actions, contracts or grants, and it states that it creates no enforceable rights. A private company has no obligation to adopt the term.
State AI law still applies. Executive Order 14365, "Ensuring a National Policy Framework for Artificial Intelligence," signed December 11, 2025, sets a policy of a minimally burdensome national framework and names Colorado's algorithmic discrimination law as an example of concern. It directs the Justice Department to form an AI Litigation Task Force to challenge state AI laws, and it asks the Commerce Department to publish an evaluation identifying state laws it considers onerous, with some federal broadband funding tied to the result. An executive order cannot itself preempt state law. Until a court rules or Congress enacts a statute, companies remain subject to the state AI laws that cover them, so the inventory and oversight work in this paper is also your state-law preparation.
The NIST AI Risk Management Framework is voluntary. NIST describes it as intended for voluntary use, and a Generative AI Profile followed in July 2024. NIST also says version 1.0 is being revised as part of the White House AI Action Plan. Many companies use it as the reference for their AI governance, and the eight dimensions in this paper map onto it without strain. Expect the text to change.
OMB memos bind agencies. M-25-21 and M-25-22, both issued April 3, 2025, are directed to the heads of executive departments and agencies and govern how agencies use and buy AI. They matter to you if you sell AI to federal agencies. M-25-22 is also a useful signal for everyone else, because it stresses avoiding vendor lock-in and keeping data portable in agency AI contracts, which is the model portability dimension written into federal buying guidance.
Frontier model rules are voluntary and aimed at developers. Executive Order 14409, signed June 2, 2026, sets up a voluntary framework for frontier developers to give the government early access to covered models and states that it authorizes no mandatory licensing or preclearance requirement. On October 16, 2026, NIST and two federal security agencies published voluntary key practices for incident readiness and response in Super Intelligence training and evaluation, aimed at frontier developers. Neither one places duties on a company that uses AI models in its own operations.
Training support is a recommendation for now. America's AI Action Plan, published in July 2025, recommends that Treasury issue guidance saying many AI skills programs may qualify for tax-free employer reimbursement under Internal Revenue Code Section 132. We have not confirmed that Treasury has issued that guidance. We found no federal statute that sets binding general AI obligations on private companies.
This summary explains published federal documents as of October 16, 2026. It is not legal advice.
How QueryNow works
We are an AI implementation firm in Plano, Texas and Munich, and we have shipped more than 200 production deployments since 2014. We do not build superintelligence and will not claim to. We build the readiness described in this paper as production AI agents, one workflow at a time.
We scope one workflow with you and sign acceptance criteria on day one. Then we build it in your environment in two weeks, and you pay $10,000 only after every criterion is met, with nothing upfront. Larger programs run as a series of two-week builds, each with its own signed criteria. To start, take the readiness check at querynow.com/superintelligence/readiness or tell us the workflow at querynow.com/build.
What to do with this on Monday morning
- Name one owner for AI outcomes this month and give them a 90-day goal measured in hours returned, with a small budget they control.
- Publish one approval rule. No AI output reaches a customer, a regulator, a carrier or the general ledger without a named person approving it, and the system records each approval.
- Pull a month of SaaS and network logs to list every AI service in use, then assign each one an owner and a risk tier that reflects the state laws where you operate.
- Pick one document-heavy workflow and time it for a week. Write pass-or-fail acceptance criteria tied to that baseline before any build starts.
- Give every agent its own identity with access scoped to its workflow and a full activity log. Retire automations that run under personal or shared accounts.
- Keep prompts and business rules in your own code behind one model layer, with a test set per workflow, and re-run it each quarter against the models available that quarter.
- The White House, Inaugurating The Era Of Super Intelligence, Executive Order 14434 (September 29, 2026)
- SI.gov, America's Super Intelligence Initiatives (accessed October 16, 2026)
- NIST, Center for Advancing Innovation and Standards for Super Intelligence (CAISSI) (accessed October 16, 2026)
- The White House, Ensuring a National Policy Framework for Artificial Intelligence, Executive Order 14365 (December 11, 2025)
- The White House, Fact Sheet: President Donald J. Trump Ensures a National Policy Framework for Artificial Intelligence (December 11, 2025)
- NIST, AI Risk Management Framework (accessed October 16, 2026)
- OMB, M-25-21, Accelerating Federal Use of AI through Innovation, Governance, and Public Trust (April 3, 2025)
- OMB, M-25-22, Driving Efficient Acquisition of Artificial Intelligence in Government (April 3, 2025)
- The White House, America's AI Action Plan (July 2025)
- The White House, Promoting Advanced Artificial Intelligence Innovation and Security, Executive Order 14409 (June 2, 2026)
- NIST, Key Practices for Incident Readiness and Response in Super Intelligence Training and Evaluation (October 16, 2026)
- Safe Superintelligence Inc. (accessed October 16, 2026)
- Meta, Personal Superintelligence (accessed October 16, 2026)