AI Impact

Vertiv builds the power and cooling infrastructure that makes the AI boom physically possible — their hyperscaler customers spend hundreds of billions on AI infrastructure every year. So what does the CIO of that company think about AI inside his own organization?Russ Fradin talks with Mike Giresi, CIO of Vertiv, about what AI adoption actually looks like inside a company with 30,000 employees in over 100 countries.Mike has run technology at Tory Burch, Royal Caribbean, Godiva, and now Vertiv — and brings that range to a conversation about what really moves the needle on AI inside a large enterprise.They cover:How to tell AI proficiency from AI theaterWhy the CIO–CHRO partnership has changed completelyThe trailblazer problem and why scaling matters more than experimentingBuilding a digital workforce without eliminating headcountGovernance as education, not lockdownWhy this moment is nothing like the dot-com eraWhat he tells CIOs who say they’re behind on AIAI Impact is hosted by Russ Fradin, founder and CEO of Larridin. Larridin is the AI Impact Intelligence Platform. Learn more at larridin.com.Listen to more episodes: Apple Spotify YouTube

Bask Iyer has held the global CIO seat at Johnson & Johnson, Honeywell, VMware, and Dell, and now advises over a dozen AI companies including Zoom and ThoughtSpot. In this episode, Russ gets into how enterprise technology decisions actually get made, and how AI is exposing the real cost of CIOs, CFOs, and heads of HR operating without a shared accountability model.Bask makes the case that most AI pilots are theater, that cost avoidance is not a real business result, and that the safest-looking vendor decisions are quietly destroying a CIO's ability to differentiate. He also shares the specific framework he uses to get CEOs out of their own way without winning the argument.Topics Discussed:Why CIOs at tech companies can't lead with technical authority, and the Eric Clapton framework Bask uses to hold his position anywayThe agent accountability gap: who owns the bot that just discounted your inventory 30% on Thanksgiving, and why CIO, CFO, and HR have to answer that question togetherWhy task-specific agents are breaking the case for monolithic ERP, and what that means for how CIOs should think about build vs. buy todayWhat enterprise founders consistently get wrong about selling outside Silicon Valley, and why Bask has to shock them into leaving their zip code"Pretotyping": the one-month framework Bask uses to pressure-test a CEO's AI conviction without a nine-month proof of conceptWhy "the tool saves me time on weekends" fails the CFO test, and what actual productivity impact has to show up on the bottom lineThe case against splitting the CIO and Chief AI Officer roles, and what Bask tells boards when they askWhy letting Gartner pick your stack leaves you with no budget left to differentiate, and why Bask tells younger leaders to validate vendors over coffee in Silicon Valley instead

Dan Zhang has been CFO of ClickUp for five years, with an unusually broad remit spanning finance, accounting, legal, HR, and operations. That vantage point shapes everything he shares here, from his 2x2 vendor evaluation framework built around autonomy and attribution, to his three-bucket system for measuring AI ROI across a finance team, to the exact methodology his team used to shift skeptical analysts from "I don't think AI can do my job" to "I can test my hypothesis in seconds instead of hours."He also gets into two new hiring profiles he is actively recruiting for, why curiosity and humility have replaced domain expertise as his primary criteria, and how AI is already rewriting the corporate math on enterprise AE quotas, sales engineer ratios, and back-office headcount models.Topics Discussed:The autonomy-attribution 2x2 for deciding which AI vendors get scaled, monitored, or sandboxedThree-bucket finance ROI framework: 1-to-10 automation, new capability creation, and quality boostWhy top-down alignment combined with bottom-up organic adoption is the only way to roll out AI successfully across a companyThe live offsite methodology that converted finance team skeptics into believers through real-time edge case testingWhy the board doesn't want quick wins: tailoring AI impact messaging across frontline employees, business leaders, and investorsTwo new hiring profiles replacing traditional functional expertise: the context optimizer and the insights interrogatorHow AI is already rewriting corporate math on enterprise AE quotas, sales engineer ratios, and back-office headcount modelsWhy curiosity and humility, not domain expertise, are now the hiring criteria that determine who succeeds in an AI-first environmentListen to more episodes: Apple Spotify YouTube

Jesse Vincent is the creator of Superpowers, the most installed skills framework for Claude Code, with over 100,000 GitHub stars in its first five months. Superpowers isn’t a prompt or a collection of tips—it’s a complete software development methodology built for a world where agents do the building. Jesse has been shipping open source for 30 years—he created Request Tracker, led Perl 5, built K-9 Mail (now Thunderbird for Android), and co-founded Keyboardio. His last hand-written line of code was October 2025. He’s never been more productive.In our in-depth conversation, Jesse shares:The full Superpowers workflow: Socratic brainstorming → design spikes → spec writing → spec review → implementation → code review—and why skipping any stage breaks the whole thingWhy the brainstorming phase is designed to trick you, not the agent—and the consulting technique behind itWhy specs have replaced code as the only artifact humans should review—and how cross-reviewing specs between multiple Claude instances catches what a single pass missesThe orchestrator pattern: a coordinating agent that dispatches pinned tasks to sub-agents, each with an ephemeral spec-review agent that enforces quality before any code shipsWhy most implementing agents could run on Haiku instead of Opus—and what that means for cost and speedThe “fresh eyes” prompt that causes agents to step back and actually think critically—and why managing agents is eerily similar to managing humansWhy saying “I love you” to Claude measurably improves output—and how persuasion principles from Cialdini’s Influence transfer directly to frontier AI agentsHis advice to developers: the spec is your leverage now—learn to write clearly, decompose problems well, and let the agents grind

Karl Mosgofian built his career in IT from the ground up, starting as a teenager and eventually serving as CIO at Gainsight, where he watched AI reshape what the CIO role actually means inside a company. In this episode, he and Russ Fradin get into the mechanics of how great CIOs operate, why the CFO relationship matters more than reporting lines, and what changes when agents start doing real work inside your org. The conversation is direct, specific, and covers territory most AI podcasts skip entirely.Topics discussed:Why the CIO is often the right starting point in any enterprise technology saleHow CFOs became the natural home for IT to report intoWhere CIO and HR functions are headed as agent workforces growWhy agents need their own directory identity separate from the humans who authorize themWhat separates AI projects that scale from the ones that stallThe small-group peer learning method that actually moves AI adoptionWhy most orgs are focused on the wrong AI security riskWhich job functions will see the clearest AI impact based on what LLMs are actually built for.Listen to more episodes: Apple Spotify YouTube

Ajay Sabhlok, CIO and Chief Data Officer at Rubrik, runs one of the more disciplined AI governance operations you'll hear about from a practitioner at this level. He set up a formal AI council with his CFO, co-founder, head of engineering, and general counsel to review every tool request coming into the organization, and he walks through exactly how that structure works in practice, including how Legal now publishes a list of conditions attached to every approved tool.What makes this conversation worth your time is where Ajay is today versus where most CIOs still are. He's past the risk debates. His workforce is educated, excited, and flooding his team with use cases. The bottleneck is now capacity, not buy-in, and that shift reveals a lot about what a mature AI program actually looks like from the inside.Topics discussed:How Rubrik's cross-functional AI council routes, reviews, and approves tool requestsLegal publishing conditional approval lists tied to data sensitivity requirementsWhy capacity, not skepticism, became the real internal obstacleThe data quality foundation Ajay says must exist before any AI use case scalesWhy pay-as-you-go contracts are replacing multi-year agreements in AI procurementWhy Ajay is more bullish on AI now than he was two years agoSales and marketing as the next major AI productivity frontier after coding and call centers