1 rich Libsyn https://www.libsyn.com 90 600 Most leaders have no clear, single picture of how AI is actually being used inside their organization. In this solo episode, host Susan Diaz walks through a practical, human-first AI audit you can run in weeks (not years) to map tools, workflows, adoption patterns, and risks - so your AI strategy isn’t built on vibes and vendor decks. Episode summary This episode tackles a simple but uncomfortable question: “Do you actually know what’s happening with AI inside your organisation right now?” Not what vendors say. Not what a slide in a strategy deck says. What people are really doing - with official tools, embedded features, and personal accounts. Susan breaks down a four-part AI literacy audit that gives leaders a coherent baseline: Tools - Which AI-powered tools are already in play, where AI is embedded in existing platforms, and where spend and capabilities overlap. Workflows - Where AI is already changing how work is done, which tasks are automated or accelerated, and which manual processes are obvious candidates for support. Adoption patterns - Who’s confident, who’s dabbling, who’s avoiding AI entirely, and how evenly (or unevenly) AI usage is distributed across teams and levels. Risks and blind spots - Shadow AI, unsanctioned tools, data exposure, governance gaps, and the places where “nothing’s gone wrong… yet” is not a strategy. She then walks through a step-by-step approach to running an audit without turning it into a year-long consulting project, and shows how to turn your findings into training, workflow redesign, and a credible AI ROI story. Key takeaways If you skip the audit, you’re flying blind. Without a baseline, every AI decision - platforms, pilots, hiring, training - is a shot in the dark based on guesswork and anecdotes. A good AI audit is four-dimensional, not just a tools list. You need to understand tools, workflows, adoption patterns, and risk/gaps together if you want a true picture of AI activity. The hidden costs of “no audit”: Duplicate spend on overlapping tools in different departments. Shadow AI and data risk from personal accounts and unsanctioned apps. Wasted efficiency gains because great use cases stay trapped in individual heads and folders. No convincing story of AI ROI for your CFO, board, or leadership. Think of the audit like an MRI, not a court case. The goal is visibility, not blame. If people feel they’ll be punished for experimenting, they’ll simply stop telling you the truth. You can run a meaningful audit in five practical steps: Listen - Short surveys + focused interviews with department heads, AI champions, and sceptics. Map tools and spend - Inventory official tools, quiet add-ons, free/low-cost apps, and personal subscriptions used for work. Document workflows - Pick priority functions (often marketing, HR, sales, ops) and map how work gets done today, then mark where AI shows up or could. Assess risk and governance - Where does confidential data touch AI? What’s policy on paper vs in practice? Where are the biggest gaps? Build an opportunity backlog - Quick wins, experiments, and longer-term projects that emerge from the audit. Your audit output should be short and usable, not a 90-slide graveyard: An executive summary with top risks, opportunities, and 3/6/12-month priorities. A tool + workflow map that shows overlaps, gaps, and shadow usage. A risk and governance section with clear start / stop / continue recommendations. An opportunity backlog that can plug into project management and resourcing. Don’t make it an IT-only exercise. AI touches how people think and work across functions. The audit should be leadership-backed and cross-functional, not dropped on a single department. The audit is the bridge, not the endpoint. Once you can see what’s happening, you can design training, governance, workflow changes, and ROI tracking that match reality instead of hopes. Episode highlights [00:02] “Do you know exactly what’s happening with AI inside your organisation right now?” [01:10] Why an AI audit should come before platforms, hires, or big training programmes. [03:18] Reframing audits: from “innovation killers” to foundations for better decisions. [04:00] The four dimensions of an AI literacy audit: tools, workflows, adoption, risk/blind spots. [05:24] Questions to ask about tools: what’s in play, where AI is embedded, where teams overlap. [04:52–05:24] Questions to ask about workflows: where AI is changing work, what’s automated, what’s still painfully manual. [05:24–06:56] Mapping adoption patterns: power users, dabblers, avoiders, and distribution across departments and levels. [06:56] Shadow AI, unsanctioned tools, and governance gaps as audit essentials. [07:23] Why a single coherent picture of AI activity becomes your baseline for everything that follows. [07:54–10:28] Four costs of skipping the audit: duplicate spend, risk, wasted gains, and weak ROI stories. [10:51–13:03] Step 1 + 2: listening through surveys and interviews, then mapping tools and spend without turning it into a witch hunt. [13:03–14:22] Step 3: documenting workflows in priority functions and spotting patterns. [14:22–15:40] Step 4 + 5: assessing risk/governance and surfacing quick wins + deeper opportunities. [16:15–17:47] What a practical audit output looks like (and why it shouldn’t die in a folder). [18:16–18:57] Common traps: making it IT-only, punitive, or overcomplicated. [19:56–21:10] Turning audit insights into training, governance, workflow redesign, and credible ROI tracking. If your current AI strategy rests on vendor promises, scattered pilots, and vibes, this episode is your sign to step back. Share it with: The exec who keeps getting asked for AI ROI. The IT or ops lead worried about shadow AI but unsure where to start. The internal AI champion who’s been documenting everything in a lonely Notion doc. Then ask as a leadership team: “What would it take for us to have a clear, one-page picture of AI activity across this organisation in the next 60 days?” Connect with Susan Diaz on LinkedIn to get a conversation started. Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here. 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