AI is not clearing work off the calendar at VF Corporation. 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, 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

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Adoption without accountability is just expensive experimentation. Smriti Kirubanandan, Managing Director of Enterprise AI Transformation at Innovaccer, 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

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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

  
  

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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

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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

  
  

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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

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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

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

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

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.

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