{"resourceId":"oregon-eis-official-ai-pilot-accountable-disposition-govtech-20261015","versions":[{"version":"external-01aba9bc67307aef39eae00ab2ce0bd3ae0f0819e5d80046e0aab0fa2c9e842f","resource":{"id":"oregon-eis-official-ai-pilot-accountable-disposition-govtech-20261015","title":"Oregon EIS privacy chief (GovTech Voices, Oct 15): every public-sector AI pilot should end with a documented decision to scale, narrow, revise, pause or retire, owned by a named person","organization":"Michael Hanna-Butros Meyering, chief privacy and communications officer, Oregon Enterprise Information Services (writing in a personal capacity); published by Government Technology","sector":"State AI pilot governance and evaluation practice","geography":"Oregon (author's agency); guidance framed for state and local government","publishedAt":"2026-10-15","publicationDate":"2026-10-15","eventDate":null,"sourceName":"Government Technology (Voices)","sourceLabel":"GovTech Voices, 'The Question to Ask After an AI Pilot: Revise, Scale or Retire?' by Michael Hanna-Butros Meyering, Oct 15, 2026","sourceUrl":"https://www.govtech.com/voices/the-question-to-ask-after-an-ai-pilot-revise-scale-or-retire","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","operating-model","knowledge-work"],"finding":"A state privacy official argues that AI pilots should end with an 'accountable disposition': a documented decision to scale, narrow, revise, pause or retire, naming who owns it, what evidence supports it and what would trigger reconsideration. Before testing, name the person authorized to decide whether the workflow continues, and agree on success criteria, unacceptable outcomes and limits. Measure a faster first draft only alongside final accuracy, verification effort and whether residents understand next steps, since a tool that shifts work from drafting to correcting may be worth less than its demo. Test ambiguous requests, conflicting sources, hand-offs to humans, languages and accessibility; record what remains untested so a narrow trial does not become permission for broad rollout. Count review, training, support and content-upkeep time, not only licenses. Approval should carry a review date, named suspension authority and a fallback service. The author uses a hypothetical county permit assistant and cites the NIST AI RMF 1.0.","sledRelevance":"Directly addresses the question states raised in this week's archived StateTech feature (Oct 15) on moving GenAI from pilot to production: what to do when a pilot ends. It gives state agencies a concrete decision record and test plan, from a practitioner inside a state IT agency, at a time when Utah, Virginia and others are writing new oversight requirements.","evidence":"Opened the GovTech article over HTTPS and read it in full; page metadata and byline show Oct 15, 2026. The author bio states his Oregon EIS role, prior Nevada Governor's Technology Office role, and that he writes in a personal capacity and does not represent his employer.","architectureImplications":"Limited. Implies pilots should retain the source material and system configuration used, logs of failures needing human intervention, and comparison data with the existing process, while following privacy and records rules rather than copying sensitive prompts wholesale.","governanceImplications":"Proposes a named decision owner before a pilot starts, five explicit disposition outcomes with conditions (a revision needs a change and deadline; a pause needs an owner and reconsideration condition; retirement needs service transition and data handling), and a planned review date with triggers such as material system change or serious error.","securityPrivacyImplications":"Calls for privacy, accessibility and security specialists in the pilot review and for preserving evidence without indiscriminately retaining sensitive prompts or resident information.","caveats":"Opinion and guidance written in a personal capacity; not Oregon policy and not an evaluation. The worked example is hypothetical. No measured results are reported. Its main relevance is as a practitioner checklist.","streamIds":["state-government"],"roles":{"sales":"Interpretation: the useful offer here is honest pilot closeout, not expansion. Agencies finishing GenAI pilots often lack a decision record, success criteria or cost view beyond licenses. Propose a bounded pilot-disposition review that produces a scale, narrow, revise, pause or retire recommendation with evidence. Be explicit that 'retire' or 'narrow' are acceptable outcomes. Do not promise that pilots will scale or that time savings will hold once verification effort is counted; the source reports no measurements.","engineering":"Interpretation: fit is any pilot of drafting or answer assistants, internal or resident-facing. Engineering supports the disposition by capturing configuration, source material versions, failure cases needing human correction and verification time. Proof of value should include ambiguous and conflicting-source tests, hand-off-to-human checks, language and accessibility coverage, and a comparison with the current process. Evidence retention must follow privacy and records rules, so log design should avoid storing sensitive prompts wholesale.","delivery":"Interpretation: delivery owns the closeout process: name the decision owner up front, agree success and failure criteria, run realistic tests, record what remains untested and produce a decision with review date and suspension authority. Acceptance criteria should be proposed and measurable, such as verification time per response and escalation accuracy. Risks include cooperative-user testing that overstates value, unbudgeted review and content upkeep, and narrow trials becoming de facto permission for broad rollout."},"roleDetails":{"sales":{"customerProblem":"Agencies end AI pilots without a documented decision, defined success criteria or a full cost picture, so pilots expand or stall by default.","stakeholders":["Agency program owner (decision owner)","Frontline staff using the tool","Privacy officer","Accessibility lead","Security officer","Budget office"],"discoveryQuestions":["Who is authorized to decide whether this pilot continues?","What success criteria and unacceptable outcomes were agreed before testing?","How much staff time goes to verifying AI drafts?","What has not been tested, such as languages or edge cases?"],"valueHypothesis":"A documented disposition may prevent scaling tools whose verification cost outweighs drafting gains and may focus spend on workflows with evidence; this is unmeasured.","potentialEngagement":"Three-to-four-week pilot closeout review for one or two pilots: test plan for edge cases, verification-time sampling, total-cost estimate and a written disposition recommendation for the named owner.","unsupportedClaims":["That following this process improves outcomes","Any time-savings figure","That the guidance reflects Oregon EIS policy","That pilots reviewed this way will scale"]},"engineering":{"fit":"GenAI drafting and answer assistants in permit, benefits, licensing or similar information-heavy workflows.","architectureIntegration":"Capture model and configuration versions, source-document snapshots, interaction logs with human corrections, escalation events and timing data, under records and privacy controls.","prerequisites":["Baseline data from the existing process","Versioned source content","Logging that records human edits and escalations","Test sets with ambiguous and conflicting cases"],"constraints":["Records-retention and privacy limits on storing prompts","Internal-only trials cannot validate public-facing readiness","Language and accessibility scope must be defined"],"security":"Retain only the evidence needed for the decision, protect logs containing resident data, and include the security officer in the closeout review.","proofOfValue":["Measure final-answer accuracy and verification time, not only draft speed","Test hand-off to a human for out-of-scope questions","Run tests in in-scope languages and with assistive technology","Compare against the current process on the same cases"]},"delivery":{"work":"Design and run a pilot closeout: pre-registered criteria, realistic testing, cost accounting and a written disposition with owner, review date and suspension authority.","dependencies":["Named decision owner","Access to frontline staff and specialists","Baseline metrics","Budget view beyond licenses"],"ownership":"The program owner named before the pilot as the decision owner, supported by IT, privacy, accessibility and security leads.","skillsAdoption":"Train staff to locate sources behind answers, recognize when a draft exceeds its authority and correct without restarting; create a safe channel to challenge output.","governanceCheckpoints":["Decision owner and criteria agreed before testing","Closeout disposition documented","Review date and reconsideration triggers set","Suspension authority and fallback service named"],"acceptanceCriteria":["Proposed: average verification time per response below an agreed threshold","Proposed: escalation to human on 100% of out-of-scope test cases","Proposed: documented coverage of in-scope languages and accessibility tests","Proposed: written disposition signed by the decision owner"],"risks":["Tidy test questions overstating value","Unbudgeted review and content upkeep","Narrow trial treated as permission to scale","Oversight lapses when pilot team disbands"]}},"retrievedAt":"2026-10-17T22:22:51-05:00","enrichedAt":"2026-10-17T22:27:28-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Recommends testing the languages and accessibility needs in scope, observing employees under realistic conditions, and asking whether staff can challenge AI output without feeling they undermine the pilot. Flags verification time as a real workload cost.","procurementImplications":"Interpretation: license fees understate cost; contracts and budgets for scaling should include review, training, support and content maintenance, and pilot terms should allow narrowing or retirement without penalty.","operatingModelImplications":"Treats the pilot closeout as a governance gate with a decision record, owner, review date and suspension authority, and asks who will carry oversight work after the pilot team disbands.","sourceVerification":{"openedUrl":"https://www.govtech.com/voices/the-question-to-ask-after-an-ai-pilot-revise-scale-or-retire","referenceExcerpt":"I would like to see every public-sector AI pilot end with an accountable disposition, or a full audit","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}