AI Impact

About AI Impact

CIOs are being handed massive AI budgets and board-level pressure to prove value. This show features conversations with technology leaders about what's actually driving impact, what's not, and how they're making the case to skeptical executives.

Publisher

Larridin

Category

technology

Language

en

Explicit

No

First episode

12 Mar 2026

Latest episode

15 Oct 2026

Latest episodes

17 episodes in the feed.

1. 

   15 Oct 2026

   The Walk the Dog Problem: What Happens When AI Frees Up Time Nobody Planned For

   AI agents handle the work. Employees walk the dog. Sounds like progress, but without a workforce ready to absorb AI, every enterprise dollar invested is lost. Volker Schrank (http://www.linkedin.com/in/volkerschrank-96066648), Founder of Employee Xperience Labs (https://employeexperiencelabs.com/), walks Russ through his “HR for AI” framework, why leading from the top is the adoption strategy that makes the real difference, and where companies go wrong by automating processes that only create value through human involvement. Topics discussed: Distinguishing “AI for HR” from “HR for AI” Integration and usage as AI’s true limiting factors The “walk the dog problem” of AI productivity gains Leading from the top to drive enterprise AI adoption Moderna’s experiment merging the CHRO and CIO roles Automating HR shared services with AI agent workforces AI-powered manager coaching at scale Knowing when to stop automating human processes Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

2. 

   1 Oct 2026

   From 14% to 98% AI accuracy: the 9-month context engineering playbook | Meenal Iyer, SurveyMonkey

   SurveyMonkey (https://www.surveymonkey.com/) put AI on top of its curated data and got 14% accuracy. Nine months of context engineering later, that number hit 98%. Meenal Iyer (https://www.linkedin.com/in/meenal-iyer), SVP, Data & Enterprise AI, walks Ameya through the semantic layer strategy that closed the gap. Meenal talks through her GET framework, Growth, Effectiveness, Trust, and why she runs enterprise AI as a top-down mandate. SurveyMonkey gives each team its own AI budget with clear return expectations and uses a 30/60/90 day value framework to decide what stays and what gets cut. Next on her roadmap: capturing tribal knowledge so agents can work with real institutional context. Topics discussed: Building a repeatable data playbook across industries The GET framework for growth, effectiveness, and trust Context engineering a semantic layer from 14% to 98% Capturing tribal knowledge for agent workflows Supervised flight hours as a model for agent autonomy Running enterprise AI as a top-down mandate Amending job frameworks to require AI proficiency The 30/60/90 day value framework for AI investments Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

3. 

   17 Sept 2026

   AI didn't cut the work. It revealed more | Andrew Weaver, VF Corporation

   AI is not clearing work off the calendar at VF Corporation (https://www.vfc.com/). It reveals more of it. Capacity gained gets redirected straight into the backlog leaders never had time to touch. Workloads did not get lighter. Ambition got bigger. Andrew Weaver (https://www.linkedin.com/in/andrew-weaver-1397554/), Senior Director of Workforce Intelligence and AI Transformation, walked Russ Fradin through intake meetings, enterprise guardrails, and why an answer that is right 80 percent of the time sometimes belongs in a plain automated workflow. Topics discussed: Why AI reveals more work instead of removing it Building HR products rather than AI solutions Intake meetings that separate AI from plain automation The 80 percent output problem in enterprise workflows Enterprise guardrails and the digital technology partnership Learning from shadow usage instead of policing it Smart experimentation without duplicating work across functions Community of practice for scaling super user wins Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

4. 

   3 Sept 2026

   Adoption without accountability is just expensive experimentation | Smriti Kirubanandan, Innovaccer

   Adoption without accountability is just expensive experimentation. Smriti Kirubanandan (https://www.linkedin.com/in/smritikirubanandan/), Managing Director of Enterprise AI Transformation at Innovaccer (https://innovaccer.com/), reframes the question every enterprise leader spending on AI should be asking. Smriti tells Russ how her team cut a 32% claims denial rate to 11% in 14 months, recovering $23 million in one year. She breaks down why data quality must come before model performance, why every role must function as an AI steward, and where physical AI is headed next. Topics discussed: Why AI adoption and accountability must be simultaneous Cutting claims denial rates from 32% to 11% with AI Measuring data quality before measuring model performance Building circular AI strategy across every organizational level Using small language models to control cost and accuracy Treating continuous data monitoring as a lifelong practice Why physical AI is the next major wave of change Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

5. 

   20 Aug 2026

   How to pitch AI to a CEO on business value and ROI | Matthew Smart, Cardinal Health

   Getting tens of thousands of employees to adopt AI comes down to trust, skills built for each role, and change that moves top down and bottom up at the same time. Matthew Smart (https://www.linkedin.com/in/matthewsmart/), Director, AI Strategy & Business Transformation at Cardinal Health (https://www.cardinalhealth.com/en.html), joins Russ to break down agent hype, why token pricing broke ROI math, pitching value over cost savings, and treating data readiness as the real work under every AI use case. Topics discussed: Driving AI adoption without scare tactics or job threats The four levels of agents most leaders conflate Sequencing quick wins before moonshot AI bets Why token based pricing broke traditional ROI calculations Pitching AI to CEOs on value over cost savings Building trust through role specific prompt libraries Treating data readiness as the real AI foundation Governance across legal, privacy, compliance, and architecture Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

6. 

   6 Aug 2026

   Coding was never the hard part: inside Fanatics' 3-person AI triads

   Fanatics runs 1,000 websites on a single platform, scaling to peak orders per minute when a team wins a championship. Sashanka Vishnuvajhala (https://www.linkedin.com/in/sashanka/), SVP of Engineering, tells Ameya how AI reshaped the way his teams organize, ship, and review code at Fanatics (https://www.fanaticsinc.com/). Three-person triads now tackle complex customer-facing projects in rapid iterations. Intent specs replaced traditional JIRA tickets, and the increased throughput exposed bottlenecks in CI/CD and code reviews that had been hidden for years. Topics discussed: Running 1,000 websites on a single elastic platform Three-person triads for complex customer-facing projects Intent specs replacing traditional JIRA tickets Code reviews as the new throughput bottleneck Building a model routing judgment layer Hiring for aptitude over specific technology experience AI-native young talent approaching problems differently Why increased speed exposed every leaky pipe Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

7. 

   24 Jul 2026

   How Quest measures AI value when no single metric works | Michael Laudon, Quest Software

   Pinching pennies on AI tokens is how AI transformation fails. Michael Laudon, Chief Product and Technology Officer at Quest Software, argues the real question is whether that spend produces measurable output, and no single metric answers it. Michael joins Russ to unpack how Quest runs its AI transformation, from a dedicated AI center of excellence to a governance model tied to business objectives and a deliberate break with the past that immerses eager teams in AI-native work end to end. Topics discussed: Measuring AI value through combined metrics beyond token counts Running an AI center of excellence for enablement Tying AI transformation to business objectives and governance Breaking with the past through AI-native project teams Spreading AI proficiency with a land-and-expand pollination model Preparing data and semantic layers so models perform Splitting model strategy between bleeding-edge and commodity tasks Blurring roles as developers, PMs, and QA converge Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

8. 

   20 Jul 2026

   How Coinbase cut AI inference costs by half while tokens went up | Kyle Cesmat, Coinbase

   Coinbase (https://www.coinbase.com/) drove its token usage up while cutting inference costs by more than half. Kyle Cesmat (https://www.linkedin.com/in/kylecesmat/), who runs Coinbase's Agent Experience team, explains the architecture behind it, from an internal LLM gateway that routes their inference to cache hit rates held above 85 percent. He walks Ameya through his goal of moving 60 percent of engineering work to cloud agents, treating them as auditable service accounts inside a regulated company, and an approval engine that merges some changes without a human. His advice for smaller teams is to narrow their tooling and own inference observability. Topics discussed: - Routing controlled inference through an internal LLM gateway - Holding cache hit rates above 85 percent - Why changing models mid-session busts your prompt cache - Moving 60 percent of engineering work to cloud agents - Treating agents as auditable service accounts with strong identity - Running an approval engine that merges without human review - Setting token budgets for both developers and agents - Narrowing your tooling aperture to control AI costs Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

9. 

   9 Jul 2026

   The 10/20/70 rule for AI budgets that actually pay off | Priyanka Vergadia, Microsoft

   Most AI deployments stall because leaders treat the tool as the whole investment. Priyanka Vergadia, argues technology is only 20 percent of the equation, and the returns show up once you fund people, training, and change management. Priyanka, Senior Director of AI Transformation at Microsoft, breaks down her 10/20/70 budget split, why documenting processes beats chasing tools, and how hackathons surface the use cases that actually become agents. Topics discussed: Why technology is only 20 percent of AI ROI The 10/20/70 budget split for AI investment Documenting every process before building any agent Running hackathons to surface real agent use cases Combining senior engineers and interns to build better agents How the developer role shifts toward directing coding agents Why taste becomes the differentiator for designers and PMs Forward deployed engineering as the evolved solutions role

10. 

    25 Jun 2026

    The three ways CFOs should budget for AI spend

    Iain Hassall (https://www.linkedin.com/in/iainhassall/) sorts AI spend into three buckets, and the trickiest is AI embedded in your own product, where cost basis and margins get murky. His warning to finance leaders is to lock in pricing and packaging before shipping AI features, or risk buying a dollar of inference and selling it for 80 cents. Iain tells Russ how Netlify (https://www.netlify.com/)went from 14,000 to 40,000 daily signups, why multi-year deals have all but disappeared, and the skills the next generation of CFOs will need. Topics discussed: Sorting AI spend into three distinct budgeting categories Locking pricing and packaging before shipping AI features Building a credits model around outcomes rather than inputs Why multi-year vendor deals have all but vanished Running enterprise sales entirely off a PLG motion Going from 14,000 to 40,000 daily signups Staying LLM-agnostic while most users pick the default Why the future CFO must work across functions

11. 

    4 Jun 2026

    Stop Counting Licenses. Start Counting Outcomes.

    Tope Iluyomade has been working in AI since 2004, starting with robotics and reinforcement learning, and has spent two decades advising and leading technology inside large enterprises as both CIO and CISO before moving into executive advisory work. In this conversation, he breaks down why most AI governance fails at the cultural level rather than the technical one, how the CIO-CFO conversation has fundamentally shifted from deployment updates to P&L accountability, and where human judgment still has to override what an AI model would otherwise optimize for. Topics Discussed: Why AI belongs in the decision loop, not the tool stack Two-layer AI portfolio model: foundational access for all, just-in-time specialization with proof of outcomes required before renewal How CIOs are shifting the CFO conversation from deployment updates to P&L accountability The "AI flight school" model for CIO and CHRO co-designing enablement curriculum The greedy algorithm failure mode: when AI optimization overrides human judgment Data masking and tokenization as the practical middle ground on AI data risk Legal and facilities: the two functions still largely untouched by AI, and why Why operators are replacing executive committees in AI experiment sign-off SaaS split: ecosystem winners versus commoditized casualties, and how MCP architecture is shifting the build-versus-buy math

12. 

    21 May 2026

    Inside Vertiv: How the Company Powering AI Data Centers Uses AI

    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 theater Why the CIO–CHRO partnership has changed completely The trailblazer problem and why scaling matters more than experimenting Building a digital workforce without eliminating headcount Governance as education, not lockdown Why this moment is nothing like the dot-com era What he tells CIOs who say they’re behind on AI AI 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  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

13. 

    12 May 2026

    Who Owns the Bot? Bask Iyer on AI Accountability, Pretotyping, and the Vendor Trap

    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 anyway The 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 together Why task-specific agents are breaking the case for monolithic ERP, and what that means for how CIOs should think about build vs. buy today What 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 concept Why "the tool saves me time on weekends" fails the CFO test, and what actual productivity impact has to show up on the bottom line The case against splitting the CIO and Chief AI Officer roles, and what Bask tells boards when they ask Why 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

14. 

    23 Apr 2026

    ClickUp's CFO on the AI Investment Framework That Actually Scales

    Dan Zhang (https://www.linkedin.com/in/danzhangdz/)has been CFO of ClickUp (https://clickup.com/) 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 sandboxed Three-bucket finance ROI framework: 1-to-10 automation, new capability creation, and quality boost Why top-down alignment combined with bottom-up organic adoption is the only way to roll out AI successfully across a company The live offsite methodology that converted finance team skeptics into believers through real-time edge case testing Why the board doesn't want quick wins: tailoring AI impact messaging across frontline employees, business leaders, and investors Two new hiring profiles replacing traditional functional expertise: the context optimizer and the insights interrogator How AI is already rewriting corporate math on enterprise AE quotas, sales engineer ratios, and back-office headcount models Why curiosity and humility, not domain expertise, are now the hiring criteria that determine who succeeds in an AI-first environment Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs) YouTube (https://www.youtube.com/watch?v=6YltXh12W-g&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

15. 

    9 Apr 2026

    Superpowers: How Jesse Built the #1 AI Coding Workflow — and Stopped Writing Code

    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 thing Why the brainstorming phase is designed to trick you, not the agent—and the consulting technique behind it Why 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 misses The 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 ships Why most implementing agents could run on Haiku instead of Opus—and what that means for cost and speed The “fresh eyes” prompt that causes agents to step back and actually think critically—and why managing agents is eerily similar to managing humans Why saying “I love you” to Claude measurably improves output—and how persuasion principles from Cialdini’s Influence transfer directly to frontier AI agents His advice to developers: the spec is your leverage now—learn to write clearly, decompose problems well, and let the agents grind

16. 

    26 Mar 2026

    Stop overcorrecting on AI security: what CIOs actually need to worry about

    Karl Mosgofian (https://www.linkedin.com/in/karl-mosgofian-4594a2/) 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 sale How CFOs became the natural home for IT to report into Where CIO and HR functions are headed as agent workforces grow Why agents need their own directory identity separate from the humans who authorize them What separates AI projects that scale from the ones that stall The small-group peer learning method that actually moves AI adoption Why most orgs are focused on the wrong AI security risk Which job functions will see the clearest AI impact based on what LLMs are actually built for. Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/ai-impact/id1884669036) Spotify  (https://open.spotify.com/show/2xcTWqdFhSQLnxY5mTjUgs?si=ee83a436811a4dc9) YouTube (https://www.youtube.com/watch?v=-DByyiW3NG8&list=PLDydVrYnt9keyv9XBsqtpsDd4IKvxBJBp)

17. 

    12 Mar 2026

    Rubrik's CIO stopped worrying about AI risk. Now he's racing to meet internal demand | Ajay Sabhlok

    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 requests Legal publishing conditional approval lists tied to data sensitivity requirements Why capacity, not skepticism, became the real internal obstacle The data quality foundation Ajay says must exist before any AI use case scales Why pay-as-you-go contracts are replacing multi-year agreements in AI procurement Why Ajay is more bullish on AI now than he was two years ago Sales and marketing as the next major AI productivity frontier after coding and call centers

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