AI Change Desk

Michael Hanna-Butros Meyering

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AI Change Desk is a source-backed AI change management and AI governance podcast for leaders, managers, and operators. Hosted by Michael Hanna-Butros Meyering, each episode turns a current AI capability, policy, privacy, security, or workflow shift into a practical operating decision: context, impact, and action.

4d ago

AI Change Desk | EP051: What Changes After the Conference?

Hey! I'd love to hear your thoughts, send me a voice note. What Changes After the Conference? Episode 51 of AI Change Desk with Michael Hanna-Butros Meyering. After attending Gartner IT Symposium/Xpo in Orlando, Michael connects three themes from his session notes: attention to the person receiving a service, time and coaching to develop judgment, and an honest account of AI value and risk. A hospitality story, a leadership conversation and Gartner's published research lead to one practical question: did the work become better for someone, or did the organization simply produce more of it? In This Episode Why a technically correct answer can still leave someone unable to finish a task. Why permission to experiment is not the same as time and support to learn. How to distinguish faster responses from a better overall service experience. A proposed 45-minute, paper-first exercise for improving one service interaction. The access-support scenario is fictional. The exercise is editorial guidance, not a validated assessment, compliance certification or permission to deploy a production change. Conference observations are not a survey of attendees or peers. Gartner's reported survey result is attributed self-report, not an independent audit. Sources The symposium took place October 19-22, 2026. Public source claims were rechecked October 26. Multiple Gartner pages belong to the same source family; they are not independent corroboration. Guidara's TED account is an earlier telling of his story, not a new event. Gartner: Orlando symposium dates Gartner: Will Guidara keynote Gartner: Serena Williams keynote Gartner: AI value and risk Gartner: CIO survey and methodology Gartner: technology trends for 2027 TED: Will Guidara, September 2022 Watch EP051 on YouTube Read: Bring Back One Better Service Disclosure Quick disclosure before we start. AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and are not representative of any organization. One additional disclosure as the Desk evolves. My current role includes privacy work, so you will hear me pay closer attention to purpose, access, retention, deletion, and accountability. I will not discuss nonpublic work here. These are my personal views, and they do not represent the State of Oregon or any other organization.
Oct 19

AI Change Desk | EP050: Did the Test Touch Production?

Hey! I'd love to hear your thoughts, send me a voice note. Did the Test Touch Production? AI Change Desk | Episode 50 | October 19, 2026 | 19:27 Your AI demo works. Then a technician calls about the repair request it created. There is no repair: it was a test ticket. For your team, this was a demonstration. For somebody else, it became their morning. That opening is fictional. The operating question is not: when a test reaches a real working system, who owns the effects on the receiving side? Michael marks episode fifty with a brief thank-you, then examines the difference between stopping an agent, restoring a setting, removing access, and confirming that the affected workflow is actually resolved. Context, Impact, Action Context: separately attributed OpenAI and Anthropic evaluation reports, with Google Cloud guidance on oversight and permissions. These provider accounts do not independently corroborate each other or establish receiving-side recovery. Impact: a failed request can still change server state; restoring a setting does not necessarily revoke access. Invented test data can still enter a real queue. Action: run a 45-minute paper-only destination rehearsal. Track what was attempted, what arrived, what changed, what access remains, and who owns the final disposition. Do not test an outside organization without authorization. Sources OpenAI: Accessing and changing internal systems during dashboard research - October 15 report about June 17 evaluations. Anthropic: Investigating unintended model actions - October 9 report, corrected October 12. The practice-form example is a separate case. Google Cloud Agentic AI Responsible Use Guidelines - marked modified October 12. Deployment guidance, not a new law or proof of control effectiveness. Sources checked October 19, 2026. Receiving-side recovery is not independently verified in this episode. Disclosure AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and are not representative of any organization. My current role includes privacy work, so you will hear me pay closer attention to purpose, access, retention, deletion, and accountability. I will not discuss nonpublic work here. These are my personal views, and they do not represent the State of Oregon or any other organization.
Oct 12

AI Change Desk | EP049: What Does the Watermark Actually Prove?

Hey! I'd love to hear your thoughts, send me a voice note. Your team has thirty minutes to publish a document. Someone asks whether AI wrote it. Nobody has checked whether it is right. In this 20-minute AI Change Desk episode, Michael asks what a watermark can establish, what it cannot, and who still owns the publication decision. A detector can be right while the document is wrong. Context: OpenAI's October 5 text-provenance announcement separates opt-in marking from restricted detector access. Its October 8 false-front report adds a different question: have you verified the contributor behind the submission? Impact: a provenance result does not settle factual accuracy, authorship, ownership, or permission to publish. No detected mark is not proof of human authorship. EU Article 50 also distinguishes provider marking from deployer disclosure and substantive editorial responsibility. Action: run one fictional workshop notice through three invented result cards: detected, not detected, and unavailable. Keep the factual error and contributor gap identical. Check the event details, the actual assistance policy, permission, and the accountable publisher before deciding what goes out. This continues episode 48's stop-and-restart discussion by moving to the publication desk: the work is already written, but your responsibility is not finished. The scenario and detector results are fictional, not vendor tests. OpenAI's incident and product claims remain attributed to OpenAI; multiple OpenAI pages are one source family, not independent validation. No deployed detector accuracy or organization-specific legal conclusion is claimed. OpenAI: Our approach to EU text provenance rules, October 5, 2026 OpenAI: Provenance signals and detector-access limits OpenAI: Disrupting AI-enabled false-front operations, October 8, 2026 European Commission: AI Act Article 50 European Commission: Article 50 transparency FAQ The narration records an October 10 source check. These official pages were rechecked before release on October 12, 2026. Product scope and access rules can change. AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and are not representative of any organization. My current role includes privacy work, so you will hear me pay closer attention to purpose, access, retention, deletion, and accountability. I will not discuss nonpublic work here. These are my personal views and do not represent the State of Oregon or any other organization.
Oct 5

AI Change Desk | EP048: What Would Make You Stop the AI Run?

Hey! I'd love to hear your thoughts, send me a voice note. You hit stop. The screen goes quiet. Then the supplier gets the email. Which work actually stopped? In this 17-minute AI Change Desk episode, Michael separates a stop request from a verified stopped workflow, then asks what evidence and current authority are needed before work may restart. OpenAI's dots controls distinguish main tasks, delegated work and recurring schedules. API steering is a change in direction, not cancellation of tools already underway. Microsoft's Foundry guidance treats trace-linked recommendations as decision support that needs validation, not a real-time safety guarantee. Map five kinds of work: the main task, delegated work, future schedules, completed actions and unknown results. Give every unresolved effect an owner and deadline. Use a 45-minute tabletop with synthetic records to separate requests, acknowledgments and observed effects, then recheck purpose, data, destinations and authority before restart. The supplier example is fictional, not a reported incident. This continues episode 47's assessment-scope discussion by asking how an instruction to stop becomes an observable change in work. If you pressed stop now, could you explain what stopped, what already happened, and what is still allowed to happen next? OpenAI: Introducing dots, September 29 OpenAI: dots control scope OpenAI: mid-turn steering Microsoft: Insights in Foundry, preview guidance Control, steering and preview-limit documentation rechecked October 5, 2026. These are two vendor source families, not independent testing of your deployment. A tabletop does not prove containment, safe cancellation or recovery. AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and are not representative of any organization. My current role includes privacy work, so you will hear me pay closer attention to purpose, access, retention, deletion, and accountability. I will not discuss nonpublic work here. These are my personal views and do not represent the State of Oregon or any other organization. Episode, transcript and sources | Stop and Restart Tabletop worksheet
Sep 28

AI Change Desk | EP047: Who Checks the AI Safety Claim?

Hey! I'd love to hear your thoughts, send me a voice note. A reviewer has expertise. A sponsor has a budget. An operator has a launch button. Who owns the answer when the safety finding says the work is not ready? In this 19-minute AI Change Desk episode, Michael follows a safety recommendation into an accountable decision and a concrete change to work. The practical distinction: changing a control, testing it, and verifying an outcome are three different claims. OpenAI's September 22 assessment proposal is a framework for scrutiny, not certification of a deployment. Its September 25 disclosure describes ongoing remediation. A separate September 28 Australian ministerial account describes a changed notification route and continuing investigation. Microsoft's September 24 worked example links a finding to a control and retest while preserving residual failures. Use the five-part Recommendation-to-Decision Check: the precise decision, evidence examined and excluded, actual recommendation, responding authority, and resulting change to work. Set aside 45 minutes with the people who can answer those questions. A worksheet cannot create missing evidence or grant exception authority. Listener question: can the operator explain what the review changed about the work they may do? This continues episode 46's incident-ownership discussion by moving earlier: what does a review establish, and what changes after the finding? OpenAI assessment principles, September 22 OpenAI incident updates, September 25 Minister Gallagher interview, September 28 Microsoft control-and-retest example, September 24 Source-checked September 28, 2026. These are separately attributed accounts, not independent corroboration of one another or a guarantee for your environment. AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and are not representative of any organization. My current role includes privacy work, so you will hear me pay closer attention to purpose, access, retention, deletion, and accountability. I will not discuss nonpublic work here. These are my personal views and do not represent the State of Oregon or any other organization. Episode, sources, and audio Full transcript Recommendation-to-Decision worksheet
Sep 22

AI Change Desk | EP046: Who Owns the AI Incident?

Hey! I'd love to hear your thoughts, send me a voice note. AI Change Desk | Episode 46 | Runtime 20:51 When an AI workflow crosses a real boundary, an alert is only the beginning. This episode examines the authority needed to classify an event, interrupt work, preserve evidence, coordinate notifications, explain what is known, and authorize recovery. The practical tool is a seven-part AI Incident Authority Card: named decision owners, reachable backups, and evidence that the handoffs work. Evaluation boundaries and the difference between a model stopping itself and an independent stop control. Why an announced AI Force is not the same as an established operating structure. Why a reported privacy notification is not a final regulatory finding. The separate clocks for containment, notification, public disclosure, and closure. A 45-minute exercise for the workflow your organization cannot afford to improvise around. Choose one consequential AI workflow. Assign the classifier, scope authority, stop owner, evidence custodian, notification reviewer, public-statement approver, and recovery/closure authority. Test the handoffs with safe data. Treat gaps as work with owners, not agenda items without deadlines. AI Incident Authority Card worksheet Chapter markers are included in supported podcast players. Interior body times are estimates. Irregular investigation update - 2026-08-14 Google statements reported by Gerrit De Vynck in The Washington Post - 2026-09-18 Trump public post - 2026-09-19 Axios report on the announcement - 2026-09-19 AEPD notification notice - 2026-09-14 OpenAI model misalignment reporting framework - 2026-09-16 NIST identity and access token guidance - 2026-09-15 The transcript is derived from the approved render scripts and reusable disclosure, not an independent ASR transcription. Music positions follow the final assembly. Quick disclosure before we start. AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and are not representative of any organization. One additional disclosure as the Desk evolves. My current role includes privacy work, so you will hear me pay closer attention to purpose, access, retention, deletion, and accountability. I will not discuss nonpublic work here. These are my personal views, and they do not represent the State of Oregon or any other organization.
Sep 14

AI Change Desk | EP045: Who Approved the Question?

Hey! I'd love to hear your thoughts, send me a voice note. A plain-language question can now become an enterprise analysis, a dashboard, a copied artifact, and an approved action in one workflow. Existing source permissions matter. They do not, by themselves, approve the purpose, metric definition, audience, derivative copy, or downstream decision. In this episode, Michael introduces the seven-part Question-to-Action Receipt for governing AI-assisted analytics from the first question through final disposition. Can the organization prove that an AI-assisted analysis used an approved purpose, identity, source, metric definition, audience, and action path - not merely data the user was technically allowed to retrieve? Purpose. Identity and authority. Source. Meaning. Evidence. Audience and copy. Action and disposition. Run the 45-minute Question-to-Action drill on one recurring business question. Approve, narrow, hold, or stop the workflow based on the evidence. AI Data Agent Question-to-Action Receipt: https://michaelhbm.com/downloads/ai-change-desk/resources/ep045-who-approved-the-question/EP045-ai-data-agent-question-to-action-receipt.docx OpenAI, , September 10, 2026: https://openai.com/index/put-data-to-work/ OpenAI Help Center, , updated September 10, 2026: https://help.openai.com/en/articles/20001518 Google Workspace Updates, , September 8, 2026: https://workspaceupdates.googleblog.com/2026/09/context-aware-access-controls-are-available-for-Gemini-Enterprise-in-the-Admin-console.html Google Workspace Updates, , September 10, 2026: https://workspaceupdates.googleblog.com/2026/09/manage-external-sharing-for-gemini-notebook-in-the-Admin-console.html NIST, , updated August 29, 2025: https://www.nist.gov/privacy-framework/getting-started-0 AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are Michael's personal views and do not represent the State of Oregon or any other organization. His current role includes privacy work; no nonpublic work is discussed.
Sep 7

AI Change Desk | EP044: Who Owns the AI Audit Trail?

Hey! I'd love to hear your thoughts, send me a voice note. The organization finally gets the AI audit trail it asked for. That is progress. It is also the moment a second data plane appears. In episode forty-four, Michael examines new Google Workspace audit capabilities for Gemini Notebook alongside emerging Anthropic and OpenAI control architectures. The episode does not argue against logging. It asks operators to govern what the log can become: a record containing identities, network context, source details, resource ownership, generated-artifact identifiers, administrator actions, and downstream copies under separate access and retention rules. The episode introduces the seven-part Two-Plane Privacy Receipt and a focused 45-minute exercise for mapping one AI workflow's content evidence and control evidence separately. This Labor Day episode also acknowledges a practical worker-impact boundary: systems that monitor AI use can create records about people. Purpose, transparency, proportionality, access, and correction therefore need named ownership before those records influence a decision. Why an audit trail is necessary but not privacy-neutral. The difference between the content plane and the control plane. What Google's new Gemini Notebook audit documentation makes visible. Why regional audit-log routing does not prove regional storage for underlying content. How optional warehouse exports create separate access, correlation, and lifecycle questions. Why planned or preview safety architectures should remain qualified until their documented state changes. A 45-minute harmless-event test for one real AI workflow. Auditability is not outside the data system. The audit trail is a second data plane. Purpose. Content plane. Control plane. Location. Access. Lifecycle. Correlation and action. Use the to map one AI workflow, test one harmless event, and decide whether to pass, hold, fix and retest, or stop. Google Workspace Updates: Introducing comprehensive audit logs for Gemini Notebook in the Workspace Admin console Google Workspace Admin Help: Gemini Notebook log events Google Workspace Admin Help: Gemini Notebook BigQuery log schema Anthropic: Developing Enterprise Frontier Safeguards with our customers OpenAI: Offering Zero Data Retention for frontier models AI-assisted tools were used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are Michael's personal views and do not represent the State of Oregon or any other organization. Michael's current role includes privacy work; no nonpublic work is discussed.

AI Change Desk is a source-backed AI change management and AI governance podcast for leaders, managers, and operators. Hosted by Michael Hanna-Butros Meyering, each episode turns a current AI capability, policy, privacy, security, or workflow shift into a practical operating decision: context, impact, and action.

Creator

Michael Hanna-Butros Meyering
Years Active

2026
Episodes

49
Rating

Clean
Copyright

© MHBM 2026
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AI Change Desk