Ai Change Desk

Episode date: 2026-05-06 Format: Wednesday brief Runtime target: 9-12 minutes Agents are moving from answering questions to taking assignments. This episode connects Microsoft Copilot Cowork, Microsoft Agent 365, and Anthropic's financial-services agent templates into one operating question: when AI does the assignment, who owns the review? Delegation is becoming embedded inside everyday work surfaces, not just chat windows. Agent control planes help with inventory and governance, but dashboards do not replace workflow ownership. Vertical agents in finance make review, source lineage, evidence, and fallback ownership more urgent. Useful output is not the same as ready output. By Wednesday, May 13, 2026, run a 30-minute delegation-quality review for one AI-assisted workflow. Capture the task, approved users, sources, output artifact, reviewer, evidence, fallback owner, stop condition, and user message. Microsoft: Copilot Cowork: From conversation to action across skills, integrations, and devices - https://www.microsoft.com/en-us/microsoft-365/blog/2026/05/05/copilot-cowork-from-conversation-to-action-across-skills-integrations-and-devices/ Microsoft: Microsoft Agent 365, now generally available, expands capabilities and integrations - https://www.microsoft.com/en-us/security/blog/2026/05/01/microsoft-agent-365-now-generally-available-expands-capabilities-and-integrations/ Anthropic: Agents for financial services - https://www.anthropic.com/news/finance-agents Microsoft: Microsoft 365 Copilot, human agency, and the opportunity for every organization - https://www.microsoft.com/en-us/microsoft-365/blog/2026/05/05/microsoft-365-copilot-human-agency-and-the-opportunity-for-every-organization/

Advanced Account Security, OpenAI on Amazon Bedrock, FedRAMP availability, partnership changes, and the May 8 macOS remediation deadline all point to one Monday operating question: when AI becomes infrastructure, who owns the trust boundary across identity, cloud channel, compliance scope, endpoint evidence, and agent logging? OpenAI introduced Advanced Account Security for ChatGPT accounts, with Codex coverage through the same login. Amazon Bedrock added OpenAI models, Codex, and Managed Agents powered by OpenAI in limited preview. OpenAI and Microsoft updated their partnership terms, changing the cloud-channel dependency map. OpenAI announced FedRAMP 20x Moderate authorization for ChatGPT Enterprise and API Platform. OpenAI's macOS app remediation deadline remains May 8, 2026. AI approval is no longer just tool approval. Teams need evidence that account access, cloud channel, data scope, endpoint/client trust, and audit ownership all line up with the work people are actually doing. Before scaling an AI workflow, answer five questions: Which account boundary carries the work, and is phishing-resistant authentication required? Which cloud channel carries the work: direct provider, Azure, Amazon Bedrock, FedRAMP environment, pilot, or blocked? Which data class is allowed on that channel? Which endpoint/client requirement must hold before use? Where is the evidence, and who owns the exception path? Run a 45-minute trust-boundary check across the top five AI workflows people are using or requesting this week. For each workflow, map account, channel, data, endpoint, evidence owner, and exception owner. Then send one plain-language memo: what is approved, what is limited preview, what needs evidence, what is blocked, and who approves exceptions. OpenAI, Introducing Advanced Account Security: https://openai.com/index/advanced-account-security/ AWS, Amazon Bedrock now offers OpenAI models, Codex, and Managed Agents: https://aws.amazon.com/about-aws/whats-new/2026/04/bedrock-openai-models-codex-managed-agents/ Amazon, OpenAI Models on Amazon Bedrock: https://www.aboutamazon.com/news/aws/bedrock-openai-models OpenAI, The next phase of the Microsoft OpenAI partnership: https://openai.com/index/next-phase-of-microsoft-partnership/ OpenAI, OpenAI available at FedRAMP Moderate: https://openai.com/index/openai-available-at-fedramp-moderate/ OpenAI, Our response to the Axios developer tool compromise: https://openai.com/index/axios-developer-tool-compromise/ 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.

OpenAI Workspace Agents, FedRAMP Moderate availability, the OpenAI-Microsoft partnership update, Anthropic-Amazon compute expansion, and the Sora shutdown all point to one Wednesday operator question: when AI access expands, shifts, or disappears, who owns the lifecycle before teams build on the wrong surface? Workspace Agents point to reusable agent surfaces inside business workspaces. FedRAMP Moderate availability expands the regulated-access surface for ChatGPT Enterprise and the API Platform. OpenAI and Microsoft updated their partnership structure, creating a dependency-map refresh signal. Anthropic and Amazon expanded their compute collaboration for up to 5 gigawatts of capacity. Sora discontinuation keeps the sunset/export/migration question on the table. AI access is no longer a yes-or-no inventory question. Teams need to know whether each AI surface is approved, piloted, sunsetting, or blocked, and who owns evidence, fallback, communication, and exceptions. Run a 30-minute access lifecycle check: List three AI surfaces people actually use or are requesting this week. Mark each as approved, pilot, sunset, or blocked. Name the admin owner, evidence owner, and sunset/migration owner. Confirm export and fallback paths. Send one plain-language memo about what is allowed, changing, ending, blocked, and who approves exceptions. OpenAI Help Center, ChatGPT Enterprise and Edu release notes: https://help.openai.com/en/articles/10128477-chatgpt-enterprise-edu-release-notes OpenAI, OpenAI available at FedRAMP Moderate: https://openai.com/index/openai-available-at-fedramp-moderate/ OpenAI, The next phase of the Microsoft OpenAI partnership: https://openai.com/index/next-phase-of-microsoft-partnership/ Anthropic, Anthropic and Amazon expand collaboration: https://www.anthropic.com/news/anthropic-amazon-compute OpenAI Help Center, What to know about the Sora discontinuation: https://help.openai.com/en/articles/20001152-what-to-know-about-the-sora-discontinuation 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.

GPT-5.5, Anthropic-Amazon compute expansion, the May 8 OpenAI remediation deadline, and NIST's critical-infrastructure AI RMF concept note all point to the same operator question: when model access changes during the week, do routing rules, fallback paths, patch ownership, and evidence controls still hold? A stronger model is a routing change, not just a capability upgrade. Tier variance and fallback behavior create immediate operator drift risk. Compute concentration and provider dependence belong in workflow continuity planning. Dated remediation deadlines need named owners and daily evidence, not passive awareness. One weekly owner/evidence/due-date control map is more useful than another abstract AI policy memo. Run one model-routing control check: Name the primary model, fallback model, and escalation owner. Re-test the top 10 prompts across primary and fallback paths. Measure correction load, access variance, and failure rate by team. Publish one dated remediation status block. Create one standards-shaped owner/evidence/due-date control map for a critical workflow.

OpenAI's April 21 ChatGPT Images 2.0 update and Anthropic's April 17 Claude Design preview point to the same operating shift: visual work is becoming a governed production surface. When AI-generated images, thumbnails, decks, prototypes, and export bundles can move across public channels, teams need source, brand, accessibility, rights, approval, and rollback controls before the asset ships. Visual quality is not release readiness. A generated image becomes a production artifact once it is public, branded, reused, exported, or handed to another team. ChatGPT Images 2.0 widens access to more capable image creation inside everyday assistant workflows. Claude Design turns visual work into a collaborative, exportable, brand-aware handoff surface. The practical control is a one-page visual asset manifest for public AI-generated images. Publish one visual asset manifest and route every public AI-generated image through it for seven days. Required fields: Asset name Owner Approver Tool and model Prompt Source inputs Date generated Allowed uses Forbidden uses Brand notes Accessibility notes Rights notes Export sizes Where it appears Rollback file OpenAI: Introducing ChatGPT Images 2.0 — https://openai.com/index/introducing-chatgpt-images-2-0/ OpenAI Help Center: ChatGPT release notes — https://help.openai.com/en/articles/6825453-chatgpt-release-notes OpenAI Deployment Safety Hub: ChatGPT Images 2.0 System Card — https://deploymentsafety.openai.com/chatgpt-images-2-0 OpenAI Help Center: Creating images in ChatGPT — https://help.openai.com/en/articles/8932459-creating-images-in-chatgpt Anthropic: Introducing Claude Design by Anthropic Labs — https://www.anthropic.com/news/claude-design-anthropic-labs Anthropic: Introducing Claude Opus 4.7 — https://www.anthropic.com/news/claude-opus-4-7 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 episode is operational guidance, not legal advice.

Stronger AI models are not just feature upgrades. They change operating conditions: prompt behavior, long-running task supervision, cyber-use boundaries, token/task budgets, and compute dependency. This episode turns three current signals into one practical release-gate loop. Claude Opus 4.7 as a model-release governance signal. OpenAI cyber-access and Axios-remediation posts as cyber trust-chain signals. CoreWeave and Jane Street's AI-cloud agreement as a capacity-continuity signal. A six-step release gate loop: detect, baseline, limit, approve, monitor, rollback. Before scaling a stronger model or longer-running agent workflow, run one gate that covers model behavior, cyber trust, and capacity continuity. The goal is not to slow the team down. The goal is to keep the airlock working when the tool gets more powerful. Anthropic: https://www.anthropic.com/news/claude-opus-4-7 Anthropic migration guide: https://platform.claude.com/docs/en/about-claude/models/migration-guide OpenAI Trusted Access for Cyber: https://openai.com/index/scaling-trusted-access-for-cyber-defense/ OpenAI cyber defense ecosystem: https://openai.com/index/accelerating-cyber-defense-ecosystem/ OpenAI Axios developer tool compromise response: https://openai.com/index/axios-developer-tool-compromise/ Microsoft Security on Axios npm compromise: https://www.microsoft.com/en-us/security/blog/2026/04/01/mitigating-the-axios-npm-supply-chain-compromise/ CoreWeave and Jane Street: https://www.coreweave.com/news/jane-street-signs-6-billion-ai-cloud-agreement-with-coreweave CoreWeave investor release mirror: https://investors.coreweave.com/news/news-details/2026/Jane-Street-Signs-6-Billion-AI-Cloud-Agreement-With-CoreWeave/default.aspx CoreWeave/Meta capacity context: https://www.coreweave.com/news/coreweave-and-meta-announce-21-billion-expanded-ai-infrastructure-agreement CoreWeave/Anthropic capacity context: https://www.coreweave.com/news/coreweave-announces-multi-year-agreement-with-anthropic This episode is for operational education and commentary. It is not legal, financial, cybersecurity, or investment advice. Cybersecurity examples are framed for authorized defensive work only.

Two operator signals this week belong in the same review loop: upstream model-governance drift and direct-pay audience momentum. ChatGPT release-note and workspace-control changes are a reminder that defaults, fallback behavior, and access assumptions can move without a local deploy. Patreon says podcasters earned more than $629 million on the platform in 2025, which is a strong revenue signal for tighter packaging experiments. The practical move is one Wednesday operating loop that checks model drift, quality, spend, and membership tests together. Snapshot the current model default, fallback path, and workspace access assumptions. Run three workflow checks: normal path, edge case, and escalation path. Choose one membership-facing test for the next episode cycle. Publish one short operating note that links quality, spend, and the offer test. 00:00 Hook: model story and money story are the same story 00:40 Contract, disclosure, and framing 01:40 Story 1: release notes, fallback behavior, and workspace controls 05:10 Story 2: Patreon revenue signal and packaging discipline 08:10 One Wednesday loop linking governance to monetization 09:30 Next-week action artifact 10:30 Close ChatGPT release notes: https://help.openai.com/en/articles/6825453-chatgpt-release-notes Workspace access controls: https://help.openai.com/en/articles/8555535-managing-gpt-access-in-enterprise-and-edu-workspaces Patreon announcement: https://news.patreon.com/articles/podcast-creators-earn-more-than-629-million-on-patreon-in-2025 Podnews coverage: https://podnews.net/update/patreon-629-million Research shortlist: /Users/michael/Podcast Engine/ops/research/shortlist/shortlist-2026-04-14.md Daily log: /Users/michael/Podcast Engine/ops/research/daily-log.md Publish after one 45-minute review with Operations, Editorial, and Growth. AI-assisted tools were used in parts of research and production support. Final editorial judgment and release approval remained human-led. This is operational guidance, not legal advice.

AI shopping is getting more complicated in a way that looks neat in demos and messy in operations. This episode follows EP014 and asks the tighter version of the same question: once discovery starts in ChatGPT, Google AI Mode, or another AI shopping surface, who actually owns the sale, the attribution, the checkout path, and the support policy that comes after it? Why OpenAI’s shift toward product discovery and merchant-controlled checkout matters Why Shopify’s agentic storefront tools make AI shopping feel more like channel ops than hype Why Google’s personalization and protocol work make QA and merchandising harder to reproduce Why “we showed up in the answer” is still not a sufficient success metric OpenAI: https://openai.com/index/powering-product-discovery-in-chatgpt/ OpenAI Help: https://help.openai.com/en/articles/11128490-shopping-with-chatgpt-search Shopify: https://www.shopify.com/news/agentic-commerce-momentum Shopify Help: https://help.shopify.com/en/manual/online-sales-channels/agentic-storefronts/chatgpt Google: https://blog.google/products-and-platforms/products/search/personal-intelligence-expansion/ Google India: https://blog.google/intl/en-in/products/explore-communicate/new-ways-google-is-using-ai-to-make-shopping-easier/ Google UCP updates: https://blog.google/products-and-platforms/products/shopping/ucp-updates/ Search Engine Land: https://searchengineland.com/google-updates-universal-commerce-protocol-to-help-retailers-sell-on-the-open-agentic-web-456891 EP017 Practitioner Worksheet — AI Commerce Control Check EP014: Commerce Surface Check EP015: Retained Artifact Check EP016: National Capacity Check

This week is not really a feature week. It is a capacity week. Anthropic's compute expansion, Project Glasswing, and Microsoft's nation-scale AI commitments in Japan and Singapore all point to the same shift: compute, defense, and skills are starting to move together. Anthropic expanded its Google Cloud and Broadcom partnership to secure multiple gigawatts of additional TPU capacity starting in 2027. Anthropic launched Project Glasswing, a defensive cybersecurity initiative with more than 40 organizations, up to $100 million in credits over five years, and access to non-public defensive model support. Microsoft paired nation-scale AI infrastructure and workforce commitments in Japan and Singapore, including infrastructure investment, cybersecurity collaboration, and large-scale skills programs. If the serious players are organizing compute, security, and skills together, AI is no longer just another software category. That means leaders need to think about provider concentration, defensive coordination, and workforce readiness as one operating board instead of three separate conversations. Name your top two AI-provider dependencies. Identify one workflow where provider concentration is now a real operating risk. Confirm who owns external AI-safety or cyber-intake if a report lands. Check where AI training is actually happening, not just where it is theoretically available. Write down one future-access assumption your team is making without evidence. Anthropic: Google/Broadcom partnership on compute (link) Reuters: Anthropic expands Google and Broadcom partnership on AI compute (link) Anthropic: Project Glasswing (link) The Verge: Anthropic launches Project Glasswing (link) Microsoft: Japan AI infrastructure, cybersecurity, and workforce commitment (link) Microsoft: Singapore AI access and skills commitment (link) Reuters: Microsoft to invest $10 billion in Japan for AI infrastructure and skills (link) The Straits Times: Microsoft to invest more in Singapore and expand AI skilling (link) This is operational analysis, not legal advice. AI-assisted tools may be used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stay human-led.

AI CHANGE DESK | EP015: RETAINED ARTIFACT CHECK AI work no longer disappears when the chat ends. This week's main episode looks at what changes when AI artifacts stick around: files stay in Library until deletion, model cutovers need verification after the deadline passes, open-model options are getting more credible, pricing shifts can show up as same-week variance, and outside-in safety reporting only helps if someone owns intake. EPISODE SUMMARY The point for EP015 is simple: once AI work survives the session that created it, convenience has already turned into ownership. That means retention, reuse, verification, deletion, cost review, and safety intake all need named owners instead of vague good intentions. WHAT CHANGED THIS WEEK • OpenAI's File Library guidance makes it clear that uploaded and created files can persist in ChatGPT until users delete them manually. • The GPT-4o Custom GPT cutoff is now past the April 3 line, which means teams should be doing post-cutoff verification instead of waiting for a migration window. • Google's Gemma 4 launch makes hosted-versus-open optionality more practical, not just theoretical. • OpenAI's built-in tool session pricing is now live, while the Safety Bug Bounty keeps outside-in reporting active. WHAT THIS MEANS FOR OPERATORS Treat retained AI artifacts like reusable work objects, not disposable prompt residue. If a file, prompt pattern, or Custom GPT behavior can carry forward into next week, it needs a retention tier, a deletion expectation, and a named owner who can answer for reuse. WHAT I'D DECIDE BY FRIDAY • Name one owner for retained-file policy in the workflows that matter most. • Run a post-cutoff verification pass on the Custom GPTs people actually depend on. • Check same-week cost variance on any workflow touching built-in tool sessions. • Name a real intake owner and SLA for outside-in safety or abuse reports. • Decide whether one open-model evaluation lane is worth running this quarter. LISTENER QUESTION What is your team currently keeping, reusing, or routing forward through AI systems without a named owner? SOURCES • OpenAI release notes: https://help.openai.com/en/articles/6825453-chatgpt-release-notes • OpenAI File Storage and Library: https://help.openai.com/en/articles/20001052-file-storage-and-library-in-chatgpt • OpenAI Business / Enterprise model limits: https://help.openai.com/en/articles/12003714-chatgpt-business-models-limits • Google Gemma 4 announcement: https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/ • Google for Developers coverage: https://developers.googleblog.com/en/gemma-4/ • OpenAI API pricing: https://openai.com/api/pricing/ • OpenAI developer pricing docs: https://developers.openai.com/api/docs/pricing • OpenAI Safety Bug Bounty: https://openai.com/index/safety-bug-bounty/ • OpenAI company announcements: https://openai.com/news/company-announcements/ DISCLOSURE AI-assisted tools were used in research, packaging, and production support. Final editorial judgment, release approval, and risk decisions remain human-led.

ChatGPT is becoming a product-discovery surface, not just a side assistant. This episode looks at what changes when shoppers start comparing products inside AI before they ever land on a retailer site, and what operators should measure before they confuse visibility with conversion. OpenAI upgraded shopping in ChatGPT on March 24, 2026 with richer product comparisons, image-based finding, and better data freshness and coverage. Shopify said millions of merchants can now sell in AI chats, with ChatGPT referral attribution and merchant-of-record control. Sephora launched an app in ChatGPT tied to beauty guidance, loyalty benefits, and future in-app checkout. A new surface matters when it changes where decisions begin. If discovery starts in AI before it starts on your site, your old reporting stack only sees the second half of the journey. That means product data quality, merchant attribution, and category readiness matter more than another generic “we show up in AI” status update. Pick one category where comparison behavior is already common. Audit titles, attributes, images, price accuracy, reviews, and availability. Define an AI-originated attribution view before the internal story turns into vibes. Decide whether you are optimizing only for discovery or for deeper merchant-side experiences too. Put one owner on the workflow. OpenAI: Powering Product Discovery in ChatGPT (link) OpenAI Help Center: ChatGPT Release Notes (link) Shopify: Millions of merchants can sell in AI chats (link) Sephora Newsroom: Sephora App in ChatGPT Brings a New Personalized Beauty Experience (link) OpenAI Help Center: Shopping with ChatGPT Search (link) This is operational analysis, not legal advice. AI-assisted tools may be used in parts of the research and production workflow. Final editorial judgment, risk posture, and release approval stay human-led.

Summary AI is becoming career infrastructure before most schools, employers, and training systems know how to teach it, measure it, or distribute its benefits evenly. This episode looks at the education capability gap, worker compensation behavior, and the institutional response now forming around AI-shaped work. What changed OpenAI argues that education systems need to close an AI capability gap as college-age adults become the biggest adopter cohort and advanced student users still lag well behind power-user behavior. OpenAI says Americans are sending nearly 3 million messages per day to ChatGPT about wages, compensation, or earnings, making AI a live part of worker pay and career decisions. Microsoft launched Elevate for Educators and free student career subscriptions with Copilot features, showing a two-track response: train the teacher and equip the student. Microsoft and Victoria University launched a Datacentre Academy, signaling that AI-driven infrastructure demand is already reshaping workforce pipelines and training priorities. What this means Access is not the same as readiness. Fluency is not the same as frequent use. Institutions now have to answer career questions with more specificity, speed, and trust than they did before AI became the default guide in the browser. Action block — Career infrastructure sweep (45 minutes) Pick one career-facing workflow: internship prep, internal mobility, salary benchmarking, or educator training. Identify where people are already using AI in that workflow. Find one place where AI is faster than your official guidance. Add one verification step and one named owner. Define what “good use” looks like in plain language. Sources OpenAI: Ensuring AI use in education leads to opportunity https://openai.com/index/ai-education-opportunity/ OpenAI: Equipping workers with insights about compensation https://openai.com/index/equipping-workers-with-insights-about-compensation/ Microsoft: Elevate for Educators and new AI-powered tools https://news.microsoft.com/source/2026/01/15/microsoft-expands-its-commitment-to-education-with-elevate-for-educators-program-and-new-ai-powered-tools/ Microsoft and Victoria University: Datacentre Academy https://news.microsoft.com/source/asia/2026/03/27/datacentre-academy-vu/ Disclosure AI-assisted tools were used in parts of research and production support. Final editorial judgment, risk posture, and release approval stayed human-led. This is operational guidance, not legal advice. These are my opinions and not representative of any organization.

AI is moving from side-chat into the live work surface. That means the next management problem is not just launch. It is visibility. Can you tell where adoption is real, where it is helping, and where the rollout is mostly theater? This episode covers: write actions moving AI deeper into connected Google and Microsoft apps, OpenAI's workspace analytics, analytics viewer role, and impact-survey layer, and one practical Adoption Visibility Sweep you can run before Friday. OpenAI's March 13 enterprise release notes show ChatGPT supporting write actions for connected Google Docs, Google Sheets, and calendar apps, plus Microsoft Outlook email and calendar actions. OpenAI's workspace analytics rollout includes an analytics viewer role, and the March 20 release notes added Admin-created surveys and moved OpenAI-created impact surveys to begin on or after March 31. OpenAI's March 5 Adoption news channel makes the vendor shift clear: adoption visibility is now part of the product story. OpenAI's March 11 Wayfair case study gives a concrete example of workflow-level deployment with measurable, vendor-reported results. If AI is now editing the work where the work already lives, leaders need a cleaner way to tell: whether usage is real, whether outcomes improved, and where friction is still hiding. Run a 45-minute Adoption Visibility Sweep: Pick one workflow. Name the artifact that matters. Track usage, outcome, and friction. Ask one manager where the change is real and where it is still cosmetic. Make one Friday decision: train, simplify, standardize, or pause. OpenAI Help Center release notes: https://help.openai.com/en/articles/10128477-chatgpt-enterprise-edu-release-notes OpenAI workspace analytics: https://help.openai.com/en/articles/10875114-workspace-analytics-for-chatgpt-enterprise-and-edu OpenAI adoption news channel: https://openai.com/index/introducing-the-adoption-news-channel/ OpenAI x Wayfair case study: https://openai.com/index/wayfair/ Microsoft Wave 3: https://www.microsoft.com/en-us/microsoft-365/blog/2026/03/09/powering-frontier-transformation-with-copilot-and-agents/ 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.

AI CHANGE DESK | EP011: CONTROL SURFACE CHECK EPISODE SUMMARY AI is getting harder to manage for one simple reason: it is disappearing into the normal work surface. This week’s episode looks at three connected signals: • Google pushing Gemini deeper into Docs, Sheets, Slides, and Drive • Anthropic research showing people question polished AI output less once it looks finished • OpenAI positioning GPT-5.4 for professional work, which turns model choice into a cost, confidence, and review-burden decision If episode 8 was validate before you scale, episode 9 was harden the controls, and episode 10 was name the owners at the handoff, episode 11 is the next layer: what happens when AI stops feeling like a separate tool and starts feeling like ordinary work. WHAT CHANGED • Google is embedding Gemini more deeply into the files people already live in, making AI feel less like a separate stop and more like part of the default work surface. • Anthropic’s AI Fluency Index found that users iterate a lot, but they become less critical once Claude produces polished artifacts like code, documents, and interactive outputs. • OpenAI is positioning GPT-5.4 for professional work and saying it improves factual performance versus GPT-5.2, which makes model choice less about hype and more about acceptable error and review burden. WHAT THIS MEANS FOR OPERATORS The management problem is no longer just tool approval. It is: • where inside normal work the human still needs to slow down • how teams keep skepticism alive after output starts looking finished • which workflows deserve the fastest model versus the most trusted model WHAT I’D DECIDE BY FRIDAY 1. Pick one default work surface and mark three friction points where a human has to slow down. 2. Teach one collaboration habit people will actually use: what is missing, what should I verify, or where is confidence weak. 3. Separate the fast model from the trusted model instead of pretending one default fits every workflow. LISTENER QUESTION Where is your bigger gap right now: noticing AI inside normal work, challenging polished output, or choosing the right model for the job? LISTEN AND WATCH • Episode page: https://www.michaelhbm.com/AIChangeDesk/episodes/ep011-control-surface-check.html • Archive: https://www.michaelhbm.com/AIChangeDesk • Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 • Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD SOURCES • https://openai.com/index/introducing-gpt-5-4/ • https://openai.com/index/introducing-gpt-5-2/ • https://techcrunch.com/2026/03/05/openai-launches-gpt-5-4-with-pro-and-thinking-versions/ • https://blog.google/products-and-platforms/products/workspace/gemini-workspace-updates-march-2026/ • https://techcrunch.com/2026/03/10/google-rolls-out-new-gemini-capabilities-to-docs-sheets-slides-and-drive/ • https://www.anthropic.com/research/AI-fluency-index • https://www.forbes.com/sites/danfitzpatrick/2026/02/23/anthropics-new-ai-index-shows-what-sets-top-ai-users-apart/

If Episode 9 was about hardening controls, Episode 10 is about making those controls survive real handoffs. AWS published a stakeholder-focused guide for operationalizing agentic AI, reinforcing that ownership must be explicit before scale. YouTube expanded likeness-detection protections for civic leaders and journalists, signaling that identity-integrity response is now an operating requirement. Anthropic announced a Sydney APAC office expansion, reinforcing that control design must travel across regions without losing clarity. "Approved app" is no longer enough; action-level workflow gates are the unit of control. Detection tools are useful, but they fail without named routing, pause authority, and response ownership. Handoffs are the drift zone: reviewer ambiguity, escalation lag, and rollback uncertainty. Pick top two high-impact AI workflows. Assign four named owners per workflow: approver, pause owner, rollback owner, public-response owner. Add one hard gate: no owner, no launch. Run one impersonation tabletop and log decisions. Ship a one-page operator memo (changes, approvals, restrictions, exceptions, next review date). AI-assisted tools were used in parts of research and production support. Final editorial judgment and release approval remained human-led. This is operational guidance, not legal advice. https://aws.amazon.com/blogs/machine-learning/operationalizing-agentic-ai-part-1-a-stakeholders-guide/ https://blog.youtube/news-and-events/expanding-our-likeness-detection-tools-to-protect-civic-leaders-journalists-and-more/ https://techcrunch.com/2026/03/10/youtube-ai-deepfake-detection-politicians-government-officials-journalists/ https://www.anthropic.com/news/sydney-fourth-office-asia-pacific

AI CHANGE DESK | EP009: CONTROL HARDENING WEEK EPISODE SUMMARY This episode translates this week’s control signals into one operator contract: • legal claim confidence, • monitoring evidence, • suite-level governance assumptions, • and fallback ownership. This is the bridge from: • Episode 5 (access control), • Episode 6 (continuity), • Episode 7 (security workflow contract), • Episode 8 (release validation). WHAT CHANGED THIS WEEK 1. OpenAI published a legal notice on unauthorized equity transactions (Mar 12, 2026). 2. Anthropic launched The Anthropic Institute and expanded policy posture signals (Mar 11, 2026). 3. NIST advanced practical guidance for monitoring deployed AI systems (Mar 6 + Mar 9, 2026). 4. Microsoft framed Agent 365 + E7 Frontier as an integrated intelligence/trust operating model (Mar 9, 2026). 5. Podbean switched off dynamic ad insertion in EEA/EU/UK, highlighting continuity dependency risk (Mar 10, 2026). OPERATIONAL TRANSLATION • If a vendor claim cannot be evidenced, treat it as risk until validated. • Monitoring must be release-adjacent and reconstructable, not dashboard-only. • Suite selection is an operating-model decision, not only a capability decision. • Continuity planning must cover policy/platform dependency changes, not just system outages. MONDAY ACTION BLOCK (45 MINUTES) 1. Triage this week’s legal/monitoring/suite/continuity signals. 2. Exposure-map top five workflows. 3. Lock owners for thresholds, exceptions, rollback, and communications. 4. Send one operator memo: changed / approved / restricted / exception path. 5. Set due dates and next review checkpoint. LISTENER QUESTION Where is your highest exposure this week: • claim confidence, • monitoring evidence, • or fallback ownership? WATCH + LISTEN • Episode hub: https://www.michaelhbm.com/AIChangeDesk • YouTube channel: https://www.youtube.com/@AIChangeDesk • RSS feed: https://media.rss.com/aichangedesk/feed.xml • Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 • Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD SOURCES • https://openai.com/policies/unauthorized-openai-equity-transactions/ • https://www.anthropic.com/news/the-anthropic-institute • https://www.axios.com/2026/03/11/anthropic-dc-presence • https://www.nist.gov/news-events/news/2026/03/new-report-challenges-monitoring-deployed-ai-systems • https://www.nist.gov/publications/challenges-monitoring-deployed-ai-systems-center-ai-standards-and-innovation • https://blogs.microsoft.com/blog/2026/03/09/introducing-the-first-frontier-suite-built-on-intelligence-trust/ • https://www.microsoft.com/en-us/security/blog/2026/03/09/secure-agentic-ai-for-your-frontier-transformation/ • https://podnews.net/update/podbean-switches-off-dai • https://www.reddit.com/r/podcasting/comments/1rozrcy/dynamic_ads_policy_change/ DISCLOSURE AI-assisted tools were used in parts of research and production support. Final editorial judgment, risk posture, and release approval remained human-led. This is operational guidance, not legal advice.

Two current operator signals, translated into a plain-language weekly control block.OpenAI announced plans to acquire Promptfoo, pushing testing/eval workflows further into default AI release practice.Anthropic launched The Anthropic Institute while NIST reinforced monitoring guidance context for deployed AI systems.A 35-minute operator block you can run weekly with one owner and clear pause authority.Require a tiny evidence packet for each AI behavior change (3 prompts + pass/fail + approver + rollback owner).Publish a one-page operator memo in plain language (approved, restricted, paused, exception path, next review).Run one mini pause drill each week: "output is wrong; who pauses in 10 minutes?"Block scale-up on any workflow missing named approver or rollback owner.00:00 Cold open + framing00:55 Boundary note complete / theme intro in01:10 Signal 1: OpenAI/Promptfoo and release evidence03:58 Signal 2: Anthropic Institute + NIST monitoring pressure06:05 Next-week 35-minute action block07:25 Close + outrohttps://openai.com/index/openai-to-acquire-promptfoo/https://www.promptfoo.dev/blog/promptfoo-joining-openaihttps://techcrunch.com/2026/03/09/openai-acquires-promptfoo-to-secure-its-ai-agents/https://www.anthropic.com/news/the-anthropic-institutehttps://www.theverge.com/ai-artificial-intelligence/892478/anthropic-institute-think-tank-claude-pentagon-jack-clarkhttps://www.nist.gov/news-events/news/2026/03/new-report-challenges-monitoring-deployed-ai-systemshttps://www.nist.gov/publications/challenges-monitoring-deployed-ai-systems-center-ai-standards-and-innovationEpisode page: https://www.michaelhbm.com/AIChangeDesk/Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGDAI-assisted tools were used in parts of research and production support. Final editorial judgment and release approval remained human-led. This is operational guidance, not legal advice.

AI CHANGE DESK | EP007: SECURITY WORKFLOW CONTROL CONTRACT If your AI can find a vulnerability, draft a patch, and open a PR, your biggest risk is no longer detection quality. Your biggest risk is workflow ownership: • who can analyze, • who can approve, • who can merge, • who can pause, • and who can attest the execution chain under pressure. This episode translates four current signals into one operational playbook for next week. WHAT CHANGED THIS WEEK 1. OpenAI launched Codex Security in research preview (2026-03-06). 2. Anthropic + Mozilla published concrete AI-assisted vulnerability workflow details (2026-03-06), including CVD and exploit-analysis references. 3. NIST published AI 800-4 on monitoring deployed AI systems (2026-03-06). 4. OpenAI launched GPT-5.4 and ChatGPT for Excel beta (2026-03-05), expanding business-user AI execution surfaces. OPERATOR TRANSLATION • Treat AI security pipelines as action-controlled workflows, not assistant features. • Separate discovery throughput from remediation readiness. • Move monitoring from dashboarding to a named ownership control. • Add spreadsheet-AI usage controls where sensitive decisions or data handling occur. MONDAY BLOCK (45 MINUTES, ONE OWNER) • Minute 0-10: action matrix lock (Analyze, Draft fix, Open PR, Merge, Deploy) with allowed/checkpointed/restricted levels. • Minute 10-20: credential and identity check (remove over-scoped inherited credentials). • Minute 20-30: evidence contract (logs, retention, export path, access controls). • Minute 30-40: disclosure + rollback ownership (name owners, define stop authority). • Minute 40-45: operator memo (what changed, what is approved, what is restricted, who approves exceptions, next review date). LINKS • Episode page: https://www.michaelhbm.com/AIChangeDesk/episodes/ep007-security-workflow-control-contract.html • YouTube channel: https://www.youtube.com/@AIChangeDesk • RSS show: https://media.rss.com/aichangedesk/feed.xml • Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 • Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD SOURCES • OpenAI (2026-03-06): https://openai.com/index/codex-security-now-in-research-preview/ • Anthropic + Mozilla collaboration post (2026-03-06): https://www.anthropic.com/news/mozilla-firefox-security • Anthropic coordinated disclosure policy (2026-03-06): https://www.anthropic.com/coordinated-vulnerability-disclosure • Anthropic exploit analysis (2026-03-06): https://red.anthropic.com/2026/exploit/ • Mozilla Firefox blog corroboration (2026-03-06): https://blog.mozilla.org/en/firefox/hardening-firefox-anthropic-red-team/ • NIST AI 800-4 publication page (2026-03-06): https://www.nist.gov/publications/challenges-monitoring-deployed-ai-systems-center-ai-standards-and-innovation • OpenAI GPT-5.4 launch (2026-03-05): https://openai.com/index/introducing-gpt-5-4/ • OpenAI ChatGPT for Excel (2026-03-05): https://openai.com/index/chatgpt-for-excel/ 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.

Two current operator signals, translated into one concrete next-week action block. OpenAI released GPT-5.3 Instant and published system-card details. Vendor continuity pressure stayed elevated through Anthropic policy-dispute and blacklist-risk signals. A 30-minute Monday control loop to keep model release and fallback controls current. Treat model releases as workflow change events, not just product updates. Run a 3-prompt regression pack before broad rollout after model changes. Confirm rollback owner + stop authority for critical AI workflows. Define one tested fallback path for top three AI-enabled workflows. Send a plain-language operator memo each Monday (approved/restricted/escalation). 00:00 Cold open + framing 00:39 Boundary note complete / theme intro in 00:54 Signal 1: GPT-5.3 Instant and release governance 02:25 Signal 2: vendor continuity pressure 03:45 Monday action block (30-minute control loop) 04:31 Close + outro https://openai.com/index/gpt-5-3-instant/ https://openai.com/index/gpt-5-3-instant-system-card/ https://www.anthropic.com/news/statement-comments-secretary-war https://techcrunch.com/2026/03/02/tech-workers-urge-dod-congress-to-withdraw-anthropic-label-as-a-supply-chain-risk/ https://techcrunch.com/2026/02/27/anthropic-vs-the-pentagon-whats-actually-at-stake/ Episode page: https://www.michaelhbm.com/AIChangeDesk/episodes/brief-2026-03-04-ai-brief.html Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD AI-assisted tools were used in parts of research and production support. Final editorial judgment and release approval remained human-led. This is operational guidance, not legal advice.

AI CHANGE DESK | EP005: RUN AGENTS WITHOUT LOSING CONTROL If AI systems can execute actions in your environment, governance has to move from policy language to access control execution. This episode translates current signals into practical controls for operators: action-tier permissions, scoped credentials, human approval thresholds, deployment tier decisions, and a weekly control desk teams can run quickly. WHAT YOU WILL GET • A practical access-control framework for agent-enabled workflows. • Action-tier classification you can apply this week (read, draft, update-internal, external-send, system-admin). • A deployment control checklist for connected/hybrid/disconnected environments. • A standards-aligned procurement starter (identity, interoperability, proportional controls). • A Monday control desk + metrics scorecard + 30-60-90 implementation sequence. TIMESTAMPS • 00:00 Cold open — access control is the operating risk • 00:50 Intro, disclosure, and show contract • 02:15 Why EP005 now (bridge from EP003 + EP004) • 04:10 Story 1 — Anthropic + Vercept and action-tier controls • 08:30 Story 2 — OpenAI elevated-risk controls and malicious-use patterns • 12:10 Story 3 — Sovereign deployment and architecture obligations • 15:35 Story 4 — NIST standards + proportional controls • 18:55 Scenario walkthrough + risk check • 21:40 Monday Access Control Desk • 24:15 Metrics, 30-60-90 plan, FAQ, and control drills • 25:04 Close + outro MONDAY ACTIONS (RUN THIS NEXT WEEK) 1. Classify top five AI workflows by action tier. 2. Scope credentials for the highest-impact workflow. 3. Name stop-authority owner for each critical workflow. 4. Set approval thresholds for external-send and system-admin actions. 5. Publish one-page operator update with approved/restricted actions and escalation path. SOURCES • https://www.anthropic.com/news/anthropic-acquires-vercept • https://techcrunch.com/2026/02/25/anthropic-acquires-vercept-to-expand-computer-use-agents/ • https://openai.com/index/introducing-lockdown-mode-and-elevated-risk-labels-in-chatgpt-safety/ • https://openai.com/index/disrupting-malicious-ai-uses/ • https://www.microsoft.com/en-us/microsoft-cloud/blog/2026/02/24/announcing-sovereign-cloud-ai-updates/ • https://www.microsoft.com/en-us/industry/blog/government/2026/02/24/accelerating-government-mission-with-microsoft-sovereign-cloud/ • https://www.nist.gov/caisi/ai-agent-standards-initiative • https://www.nist.gov/artificial-intelligence/ai-agent-interoperability-and-efficiency-standards-request-information • https://digital-strategy.ec.europa.eu/en/library/eu-ai-office-and-jrc-publish-report-proportionality-ai • https://ai-watch.ec.europa.eu/publications/eu-ai-office-and-jrc-report-proportionality-trustworthy-ai LISTEN • YouTube: https://www.youtube.com/@AIChangeDesk • Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD • Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 LISTENER QUESTION Where is your organization most exposed right now: permission scope, approval thresholds, or action logging? DISCLOSURE AI-assisted tools were used in parts of drafting, synthesis, and production support. Final editorial judgment and release approval remained human-led.

Two operator-relevant signals from this week, translated into concrete controls teams can execute immediately. Distillation attacks moved from model-lab concern to enterprise operations risk. NIST's AI Agent Standards Initiative reinforced near-term interoperability and accountability expectations. A 25-minute weekly governance desk loop you can run every Monday. Treat provider security bulletins as workflow events, not background reading. Classify AI usage into open-assist, controlled-assist, and restricted classes. Add interoperability and control portability checks to AI procurement intake. Require a human accountability map for every agent-like workflow. Ship a one-page operator update: what changed, what to do, what not to do. 00:00 Cold open: policy that cannot survive Monday is policy theater 01:00 Theme intro 01:16 Framing and disclosure 01:57 Signal 1: distillation attacks and model-control hardening 04:30 Signal 2: standards momentum as procurement and controls signal 06:57 Monday checklist: 25-minute governance desk 08:06 Close 08:18 Final reminder: one owner, one decision, one due date 08:27 Brand outro https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks https://www.businessinsider.com/anthropic-deepseek-distillation-minimax-moonshot-ai-2026-2 https://www.nist.gov/caisi/ai-agent-standards-initiative https://www.ansi.org/standards-news/all-news/2-18-26-nist-launches-ai-agent-standards-initiative https://www.nist.gov/news-events/news/2026/02/nist-seeks-public-input-advance-ai-agent-interoperability-and-efficiency Website episode page: https://www.michaelhbm.com/AIChangeDesk/episodes/brief-2026-02-25-ai-brief.html Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD AI-assisted tools were used in research and production support. Final editorial judgment and release approval remained human-led.

EP003: AI GOVERNANCE IMPLEMENTATION FOR OPERATORS AI governance breaks when it lives as a policy document and not as a weekly operating loop. In this main episode, we use current market signals (model updates, AI security tooling, regional deployment strategy, and standards activity) to show how leaders and operators can run governance as execution instead of theory. WHAT YOU WILL GET • A practical model-change governance workflow you can run every week. • Security workflow controls for AI-assisted code review. • Procurement and data-governance actions triggered by regional/partner deployment signals. • A reusable weekly AI Governance Desk format with owner, controls, and communication outputs. • A late-update block on alignment-research funding and regulated-industry deployment signals. TIMESTAMPS • 00:00 Cold open — governance is a workflow, not a PDF • 00:59 Intro music + disclosure • 01:20 Why this episode now (EP001/EP002 bridge) • 03:20 Story 1 — Claude Sonnet 4.6 and model-change governance • 07:50 Story 2 — Claude Code Security and human-in-the-loop controls • 12:20 Story 3 — OpenAI for India + Tata and procurement reality • 16:00 Story 4 — NIST AI agent interoperability signal • 18:10 Late updates — alignment funding + regulated-industry collaboration • 19:00 Weekly AI Governance Desk (25-minute operating loop) • 22:05 Postscript — chat-code controls + workflow-class policy mapping • 23:25 Monday morning actions • 24:25 Outro + listener question MONDAY MORNING ACTIONS 1. Name one owner for weekly AI governance desk operations. 2. Run a model-change regression check on your top workflows. 3. Require human approval for AI-generated security patches/findings. 4. Update procurement clauses (data handling, change notifications, sub-processors). 5. Publish a one-page internal update: what changed, what to do, what not to do. SOURCES • https://www.anthropic.com/news/claude-sonnet-4-6 • https://docs.anthropic.com/en/release-notes/api#feb-17th-2026 • https://www.anthropic.com/news/claude-code-security • https://docs.anthropic.com/en/docs/claude-code/security • https://openai.com/index/openai-for-india/ • https://www.tata.com/newsroom/openai-and-tata-group-announce-strategic-collaboration • https://www.nist.gov/news-events/news/2026/02/nist-seeks-public-input-advance-ai-agent-interoperability-and-efficiency • https://www.federalregister.gov/documents/2026/02/20/2026-02979/ai-agent-interoperability-and-efficiency-standards-request-for-information • https://openai.com/index/advancing-independent-research-ai-alignment/ • https://alignmentproject.aisi.gov.uk/ • https://www.anthropic.com/news/anthropic-infosys • https://www.infosys.com/newsroom/press-releases/2026/advanced-enterprise-ai-solutions-industries.html LISTEN • Spotify: https://open.spotify.com/show/5X1sLLTeULqFCdt7aaisGD • Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-change-desk/id1876677295 DISCLOSURE AI-assisted tools were used in parts of drafting, synthesis, and production support. Final editorial judgment and release approval remained with the host.

EP002: AI policy basics for operators. This episode translates AI policy concepts into practical operating decisions for leaders, managers, and delivery teams. Episode: 002 Title: AI policy basics for operators Runtime: 10m 30s Host: Michael Hanna-Butros Meyering AI policy works only when it is written as operational guidance people can apply in daily workflows. 00:00 Why AI policy fails in real teams 01:20 Story 1: Claude Sonnet 4.6 and model-change governance 04:40 Story 2: AI infrastructure cost signals and procurement controls 07:40 Action block: policy + change management implementation 09:40 Monday-morning actions + outro Anthropic launched Claude Sonnet 4.6 (February 17, 2026), which reinforces the need for model-upgrade controls and evaluation gates in internal policy. Anthropic announced it will cover electricity price increases tied to data-center growth (February 17, 2026), making infrastructure impact a practical procurement and governance issue. Scope: which AI use cases are allowed, restricted, or prohibited. Data: which data classes may be used with which tools. Controls: review, logging, exception handling, and escalation. Accountability: who owns policy updates and incident response. Add a model-change trigger section to your AI policy (when re-evaluation is mandatory). Add three infrastructure-risk questions to AI vendor intake. Run one manager briefing with a clear script for allowed/restricted use. Audit one active AI workflow for drift between policy and real usage. Anthropic, “Announcing Claude Sonnet 4.6”: https://www.anthropic.com/news/claude-sonnet-4-6 TechCrunch coverage, “Anthropic releases Claude Sonnet 4.6”: https://techcrunch.com/2026/02/17/anthropic-releases-claude-sonnet-4-6/ Anthropic, “Covering electricity price increases from AI data centers”: https://www.anthropic.com/news/covering-electricity-price-increases Reuters coverage (via Investing.com): https://www.investing.com/news/stock-market-news/anthropic-to-cover-electricity-price-increases-in-areas-where-it-builds-data-centers-3894580 NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework NIST Generative AI Profile: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence OECD AI Principles: https://oecd.ai/en/ai-principles ISO/IEC 42001 overview: https://www.iso.org/standard/81230.html This episode uses AI-assisted production tools (voice rendering, editing support, and publishing automation). Final editorial and risk decisions are human-led.

Welcome to episode one of AI Change Desk. This launch episode introduces the mission of the show and a practical framework you can use immediately to manage AI rollout decisions in your organization. Episode: EP001 Title: Welcome to AI Change Desk Runtime: 6m 25s (launch edition) Host: Michael Hanna-Butros Meyering 00:00 Cold open: the 3 questions teams keep asking about AI 00:42 Intro (show ID) 00:57 Show mission: AI as an operating shift, not a tool announcement 01:39 Plain-English definitions: AI, LLM, and change management 02:34 Personal context + why this show exists 03:21 Boundaries + AI-use disclosure 04:06 Show contract: practical, credible, actionable 04:39 4D Desk Memo: Decision, Data, Drift, Deployment 05:30 Inner workflow: how this podcast is produced 06:04 Listener question + outro 06:15 Outro (show close) AI rollouts fail more often from adoption and governance gaps than model quality. Treat AI changes as operational decisions with clear ownership and controls. Use the 4D Desk Memo to make fast, defensible decisions: Decision, Data, Drift, and Deployment. This episode used AI-assisted production for: Script drafting support Voice synthesis through an authorized ElevenLabs voice model Packaging and publishing automation Final editorial decisions, risk posture, and publication approval were made by Michael Hanna-Butros Meyering. Daily research scan Source verification and editorial filtering Script lock in Context -> Impact -> Action format Voice rendering through ElevenLabs API Audio QA RSS.com episode publishing Google Cloud Storage + Google Sites web publishing ElevenLabs API quickstart: https://elevenlabs.io/docs/eleven-api/quickstart RSS.com Core API docs: https://api.rss.com/v4/docs Google Cloud Storage static hosting: https://cloud.google.com/storage/docs/hosting-static-website Episode page: https://www.michaelhbm.com/AIChangeDesk/episodes/ep001-welcome-to-ai-change-desk.html Transcript (TXT): https://storage.googleapis.com/site-app-html/AIChangeDesk/transcripts/ep001-welcome-to-ai-change-desk.txt RSS feed: https://media.rss.com/aichangedesk/feed.xml What is one AI-related decision your organization keeps postponing right now?