The White House’s September 2026 fact sheet adopts Super Intelligence and SI as the administration’s executive-branch terminology for AI. Vietnamese SMEs can understand that policy news while making implementation decisions around their own operations. This 90-day framework is Knock Knock’s practical recommendation, not an instruction from the US order or a promised timetable for technical superintelligence.
Connect the SI headline with a specific business need
Write “Super Intelligence” as two words when introducing the policy announcement, then explain the current tool or workflow the business is evaluating. Useful questions include which information the process needs, who checks the result and what outcome would justify continuing. This keeps a changing public vocabulary connected with decisions a team can actually make.
The plan below is designed to produce evidence, not to require every company to adopt a fashionable label. Its stages organize knowledge, test a limited workflow and review the result. A team should be free to revise or stop an unsuccessful pilot. Being prepared for more capable technology includes knowing how to assess it without assuming that the name alone establishes value.
Why new capabilities make data choices important
Google DeepMind’s 23 September 2026 update describes an architecture intended to support persistent, server-side AI memory with privacy protections. This is a technical development, not a universal assurance about every AI service. It highlights a practical issue: assistants that retain context require careful choices about which information is stored and how it is controlled. The original announcement is linked below.
For an SME, our recommendation is to evaluate the service actually being considered. Ask what information it retains, who can access it, how permissions work and how records can be removed or exported. Match those answers to the intended workflow. Do not assume that an impressive capability announcement settles the privacy requirements of an unrelated deployment.
Days 1–30: organize knowledge and choose a workflow
Identify the documents employees use to answer common questions. Remove outdated versions, name an owner for each authoritative source and make the approved material easy to find. Review access to customer, staff and commercial information with the people responsible for it. Better knowledge organization helps the company even if the eventual AI pilot is postponed.
Choose one bounded activity with a measurable baseline. A useful example could be drafting an internal project summary from approved notes, with a person checking the result. Record current effort, frequent errors and the required quality. Write a short task definition that explains the input, expected output and activities that remain outside scope. This becomes the foundation for a fair evaluation.
Days 31–60: test with review and limited access
Build a pilot using the minimum information and permissions required. Include realistic examples, difficult cases and situations with missing information. Ask the employees who perform the task to assess whether outputs are useful. Keep checking and correction time in the measurement so that an apparently fast draft does not hide extra work downstream.
Define a simple exception route. The system should hand a case back when it lacks reliable information or receives a request outside its scope. Keep a manual fallback and a named person who can pause the workflow. Document the recurring problems and improve the source material or process before blaming every failure on the model. The pilot is a learning exercise as well as a performance test.
Days 61–90: decide whether to scale, revise or stop
Compare the pilot with the baseline using total effort, output quality and the effect on the next person in the process. Gather staff feedback about usability and confidence. Review whether the workflow stayed inside its agreed data and permission boundaries. These findings should support a concrete decision, including the possibility that the task is not suitable for the current tool.
If the evidence is positive, expand gradually and assign ownership for maintenance, onboarding and review. If the pilot is mixed, reduce the scope or improve the information before retesting. If it is unsuccessful, retain the process lessons and stop the implementation. A responsible readiness program does not need every experiment to succeed; it needs a clear method for deciding what deserves further investment.
Keep the plan useful under several AI futures
Track capability changes that affect the selected workflow rather than following every announcement. More reliable retrieval, better handling of exceptions or clearer permission controls may be more relevant to an SME than a broad claim about intelligence. Revisit the decision when the evidence changes. Maintain portable knowledge and avoid making essential operations depend on a tool the team cannot supervise.
Connect AI readiness with the customer experience, staff training and the company’s digital foundation. Keep the September Super Intelligence announcement in its policy context and evaluate technical capability separately. A business that understands its information, responsibilities and service standards can adapt as evidence changes. The goal of the 90-day plan is better judgment and stronger execution, not a prediction about when ASI might arrive.
Sources & further reading
Reviewed on 1 October 2026. Research findings are attributed above; business recommendations and future scenarios are Knock Knock’s editorial analysis.
- White House fact sheet: The Era of Super Intelligence — 29 September 2026
- White House: Inaugurating the Era of Super Intelligence — Executive Order, 29 September 2026
- VOV: Trump signs order changing AI terminology to SI — 30 September 2026 (Vietnamese)
- Google DeepMind: Private AI Compute and server-side memory — 23 September 2026
Editorial imagery is AI-generated and illustrates the topic; it does not document the named research projects.
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