AI Impact

Larridin

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.

  1. 13h ago

    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, Director, AI Strategy & Business Transformation at Cardinal Health, 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  Spotify  YouTube

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

  2. Aug 6

    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, SVP of Engineering, tells Ameya how AI reshaped the way his teams organize, ship, and review code at Fanatics. 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  Spotify  YouTube

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

  3. Jul 24

    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  Spotify  YouTube

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

  4. Jul 20

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

    Coinbase drove its token usage up while cutting inference costs by more than half. Kyle Cesmat, 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  Spotify  YouTube

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

  5. Jul 9

    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

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

  6. Jun 25

    The three ways CFOs should budget for AI spend

    Iain Hassall 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 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

    The three ways CFOs should budget for AI spend

  7. Jun 4

    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

    Stop Counting Licenses. Start Counting Outcomes.

  8. May 21

    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 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

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

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.

Information

  • Creator
    Larridin
  • Years Active
    2026
  • Episodes
    13
  • Rating
    Clean
  • Copyright
    © Larridin
  • Show Website
    AI Impact

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