Organizations rarely suffer from a shortage of information. They face a conversion problem.
Research, technical releases, policy changes, customer behavior, competitive moves, hiring patterns, and weak signals from adjacent markets arrive continuously. Most are read once, summarized once, and then lost. Even when information is saved, the connection between what changed, what it might mean, which option it informed, and what happened next is rarely preserved.
Agentic AI creates an opportunity to improve that conversion—not by automating strategy, but by helping people build a more continuous and disciplined intelligence practice.
Most organizations do not suffer from a shortage of AI ambition. They struggle with the system between a promising idea and a capability that people can trust, adopt, operate, and improve.
The National Institute of Standards and Technology (NIST) has expanded the scope of its Artificial Intelligence Consortium and invited new members. The consortium is organizing work around testing, evaluation, verification, and validation; documentation; adoption; and specialized security questions.
The structure reflects an important reality: no organization can build the measurement science for artificial intelligence alone.
OpenAI's account of the next phase of enterprise artificial intelligence describes rapid growth in organizational use and increasing demand for agents that can operate across real workflows. The direction is clear: companies are moving beyond isolated conversations toward systems that research, update records, create artifacts, and complete multi-step work.
That movement makes the operating model—not access to the model—the limiting factor.
Google's December 17 release of Gemini 3 Flash presents frontier-level artificial intelligence (AI) at lower latency and cost for high-frequency work. It is a fitting close to 2025: another substantial model advance whose real significance will be determined by the systems able to use it.
Throughout this year, the center of gravity has moved from answers toward action.
Any plan of that scale contains choices people can debate. The implementation lesson is less partisan and more practical: strategy succeeds or fails in the handoff from an announced action to an accountable operating system.
Revised and substantially expanded July 17, 2026, with the subsequent change in federal AI policy reflected explicitly.
Executive Order 14110 was remarkable in scope. Issued in October 2023, it assigned artificial-intelligence actions across a wide portion of the federal government: safety and security, privacy, civil rights, consumer protection, workforce, innovation, competition, international leadership, federal use, and technical standards. Many assignments carried deadlines measured in days or months.
By early 2024, progress was naturally reported as a sequence of completed actions. Agencies had issued requests for information, convened experts, begun standards work, created hiring pathways, launched pilots, and prepared guidance. The original version of this post praised that momentum but did little to explain what “progress” should mean.
The later policy record creates a useful natural experiment. In January 2025, a new administration revoked Executive Order 14110 and established a different federal AI policy. OMB subsequently replaced core agency-use and acquisition guidance. Yet many technical problems, statutory obligations, agency missions, and organizational constraints remained.
This reveals the correct unit of progress. It is not the number of executive-order tasks marked complete. It is the amount of durable state capacity created: people, evidence, standards, architectures, acquisition mechanisms, data, evaluation systems, and decision processes that remain useful when the policy language changes.
A label on a political advertisement can tell us that artificial intelligence helped make it. It cannot tell us whether the message is true, who authorized the representation, how materially the content was altered, or whether millions of people saw it before the label appeared.
That is the problem the Artificial Intelligence (AI) Transparency in Elections Act tried to address in 2024. Senate Bill 3875 would have directed the Federal Election Commission (FEC) to require disclosures when covered political communications contained content “substantially generated” by AI. The bipartisan proposal recognized a real gap: voters could encounter a synthetic voice, image, or video without knowing that part of the apparent evidence had never occurred.
The bill advanced out of committee and reached the Senate calendar, but it did not become law before the 118th Congress ended. The later record makes the underlying design question more useful, not less. What would an effective disclosure regime need to accomplish—and what should no one expect a label to solve?