AI field note · Mission Delivery Notes
What I heard at the ACT-IAC AI Forum.
AI is already in the work. The harder part is making sure it solves a real problem and that a named person remains responsible for the result.
I recently attended the ACT-IAC AI Forum, where the practical question was no longer whether agencies would use AI. Speakers focused on where it can save time and on the people who still have to check the work and remain responsible for the decision.
AI adoption is now a management question, not a future possibility.
AI can give Government staff more time for judgment and service.
The governing question is where it helps, what evidence is retained, and which accountable person makes the decision.
| Step | Focus | Purpose |
|---|---|---|
| 1 | Old question | Should we use AI? |
| 2 | Problem first | Define the problem before choosing tools. |
| 3 | AI handles repetition | Use AI for search, comparison, summary, and drafts. |
| 4 | People review | Keep judgment, context, and accountability with people. |
| 5 | Better service | Produce faster, clearer answers and free up people for higher-value work. |
| 6 | Improve the system | Use testing, monitoring, training, and feedback to keep improving. |
Use AI for the Repeatable Work
Nearly every speaker stressed that AI should take repetitive, time-consuming tasks off people's plates so they can spend more time solving problems, making decisions, and serving customers.
AI may change how much analysis, drafting, testing, or comparison a team can complete in a given period. The organization must still name who exercises judgment, checks the evidence, and answers for the outcome.
Think about everything we spend hours doing every week:
Reading Long Documents
Policies, contracts, reports, guidance, and reference material that take real time to get through.
Searching for Answers
Finding the right paragraph, requirement, policy clause, or repair note buried in hundreds of pages.
Writing First Drafts
Getting a report, email, summary, or comparison started so a person can refine it.
Comparing Requirements
Checking documents against standards, contract terms, acceptance criteria, or internal rules.
Those are exactly the kinds of tasks AI can help with.
Agents Can Carry Out Multi-Step Work
Most of us have used ChatGPT or another chatbot to ask a question and receive an answer.
Several speakers focused on agents: tools that can carry a task through several steps, then hand the work to a person for review.
Example Request
"Review these five contracts, compare them to our requirements, identify anything that is missing, summarize the risks, and draft a report."
An AI agent could work through those steps while you review, question, and improve the output.
Check the Work Before You Rely on It
One warning came up again and again: an AI answer is not evidence by itself.
Teams need to test the system, retain what it did, and have a person review consequential work.
Clear expectations, training, and quality review make the workflow more dependable.
For anyone working in or around government, this is also where the public guidance matters. The NIST AI Risk Management Framework is a useful reference for thinking about trustworthy AI, and OMB's M-25-21 memo on federal AI use makes clear that agencies are expected to innovate while still protecting privacy, civil rights, and civil liberties.
The Demos Handled Ordinary, Expensive Work
Several useful demonstrations focused on everyday business problems.
Contract Review
Reviewing thousands of contracts in days instead of months.
Customer Service
Helping call centers understand why customers are calling and where service can improve.
Technical Support
Assisting technicians in finding repair information faster.
Document Search
Helping employees search through thousands of documents in seconds.
These use cases removed repetitive work and left people more time for judgment and service.
Choose the Mission Problem Before the Tool
One presentation compared buying AI to buying a car. Sometimes we buy the exciting vehicle with all the bells and whistles because it looks impressive. Then we realize it does not actually fit our needs.
The same thing can happen with AI. Organizations should not start by asking, "Which AI tool should we buy?" They should start by asking, "What problem are we trying to solve?"
That idea also shows up in federal acquisition guidance. OMB's AI acquisition memo encourages performance-based acquisition and mission-focused metrics, alongside testing, disclosure, competition, portability, and protection against costly dependencies. Those provisions address practical acquisition risk.
For acquisition teams, that means specifying the mission result, AI boundary, retained evidence, and approving official before prescribing a tool or labor mix. Leave room for the delivery method to change. How to Buycovers contract structure; Governed AI Integrationcovers implementation.
Training Decides Whether the Tool Helps
One of my favorite quotes from the day came from a Department of Transportation speaker:
"If people aren't being trained, we're leaving them behind."
That really stuck with me. Technology changes quickly. Helping people understand how to use it responsibly is just as important as the technology itself.
OPM's 2026 AI Training Series for Government Employees is a good example of that shift. DOT has also published its AI activities and strategy materials, including how it is thinking about internal operations, research, citizen-facing services, and workforce development.
AI Adoption Is Management Work
Give the tool a defined job and teach staff how to challenge its output.
Named people remain responsible for consequential decisions.
That combination can give employees more time for judgment and service without handing accountability to the tool.