AI to ROI

Ray Rike
  • 4.8 (41)
  • Management
  • Updated Weekly

AI to ROI is a podcast that shares how enterprises translate AI investments into measurable business value. Hosted by Ray Rike, Founder and CEO of Benchmarkit, the show features senior enterprise leaders and AI software executives who share how AI initiatives move from pilots to production, and how ROI is actually measured and achieved. In addition, each week, we publish a bonus episode with AI to ROI Newsletter co-author, Peter Buchanan to discuss the Big Story of the Week. The AI to ROI podcast is the evolution of the original "Metrics to Measure Up" podcast.

  1. AI Governance, Ethics, and the Stewardship Framework: A Conversation with Evan Schwartz, Chief Innovation Officer at AMCS Group

    18h ago

    AI Governance, Ethics, and the Stewardship Framework: A Conversation with Evan Schwartz, Chief Innovation Officer at AMCS Group

    Most companies jumped straight to headcount reduction when AI arrived. AMCS Group went the other direction, starting with governance, ethics, and a question most companies never ask: not what can AI do, but what should it do? In this episode, Ray talks with Evan Schwartz, Chief Innovation Officer at AMCS Group, a platform serving resource-intensive industries across 80 countries, about how that single question reframed their entire AI strategy and produced results measured in multiples rather than percentage points. Topics we discussed include" Why "person plus AI" beats "AI replaces person" from day one Companies that moved quickly on headcount reduction before AI left the lab found themselves rehiring at a higher cost when the technology did not perform in the wild as it did in controlled conditions. AMCS took a different path. Rather than treating headcount reduction as the goal (a finite game with a ceiling of zero), they pursued asymmetric growth: amplifying the capabilities of experienced employees so the business could scale revenue without incurring proportional costs. The result was output multiples, not efficiency percentage points. The governance and ethics framework that drove better AI decisions Operating across 80 countries with GDPR, SOC 1, SOC 2, and a range of regional regulatory requirements, AMCS could not afford to move fast and fix things later. They codified existing governance frameworks (including the EU AI Act and NIST standards) into a use-case design framework that forced a structured question before any deployment: what should this AI do? That question filtered out low-value applications, surfaced the high-impact ones, and created the foundation for what Evan calls the stewardship model. What an AI steward actually does, and why the role is human As AMCS built out orchestrator agents and sub-agents, they needed a clear accountability structure. The steward is always a human. Effective AI stewards share three skills: they communicate tasks clearly to orchestrators, they understand what data context the agent needs to do the job well, and they know what good output looks like even without knowing how the system produced it. That last skill, the ability to look at a result and say "that number is wrong," is what keeps agentic systems on the rails and prevents AI sprawl from becoming unmanageable. Two external agentic AI use cases with hard ROI numbers The dispatch management agent now monitors 700,000+ trucks globally, dynamically reroutes based on real-time events (blocked containers, missed pickups), and automatically notifies customers through their preferred channel, including rescheduling VIP accounts before they can call in a complaint. The result: 17 gallons of diesel saved per truck per month in fuel optimization, plus a $650,000 pull-forward of aged receivables (from 90-day to 30-day collection cycles) in just the first month at one customer. The customer service agent enables CSRs to double or triple their customer-touch volume by having AI handle all post-call documentation, action items, scheduling, and follow-up. That increased coverage cut AMCS's own churn rate from 6% to 3%. How AMCS justifies AI investments internally, and why it starts with board-level metrics AMCS is targeting ISO 42001 compliance (the AI management system standard) by year-end, which requires registering every AI tool, documenting bias risks and mitigations, and tying each use case to measurable outcomes. Evan's framework for approval is straightforward: identify your current baseline, set a target, and trace the expected return all the way to a board-level financial metric, EBITDA, free cash flow, or SG&A. Stopping at "we saved three hours" is what he calls lazy intellectualism. The real question is what those three hours produce when redirected to high-value work. Career advice for the AI era: stop valuing yourself by the output. Evan's message to early-career professionals is direct. If AI can produce the output, the output itself has an approaching-zero value. What has value is the ability to get AI to produce it, to steward the system, to know what good looks like, and to course-correct when it does not. The leaders of the next decade will be those who can direct a digital workforce of agents toward outcomes that matter, not those who were best at producing the deliverables themselves. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    34 min
  2. The AI Coding Wars, Inflection Point and the Cursor-SpaceX Deal | AI to ROI: The Big Story

    18h ago

    The AI Coding Wars, Inflection Point and the Cursor-SpaceX Deal | AI to ROI: The Big Story

    The April 2026 announcement that SpaceX may acquire Cursor for $60 billion, or alternatively pay $10 billion for a compute partnership, stopped the enterprise tech world in its tracks. A four-year-old company founded by four MIT students with $2.7 billion in annualized revenue but nearly $900 million in losses on $700 million in actual revenue. This deal is not primarily a valuation story. It is a signal and a cautionary tale about the economics of the AI coding tool market. In this Big Story edition, Ray Rike and Peter Buchanan break down what is really happening in the AI coding wars, why Cursor ended up at SpaceX's door, and where this market goes from here. Key topics covered in this episode: From copilot to autonomous agent: how the AI coding market structurally shifted. Four years ago, AI coding tools suggested your next line of code. Today, they read entire codebases, plan multi-step tasks, edit files across a project, run tests, and submit pull requests with minimal human direction. Claude Code reached $1 billion in annualized revenue six months after launch, the fastest of any enterprise software product in history, and crossed $2.5 billion by February 2026. Meanwhile, 90% of enterprise developers now use at least one AI coding tool, and nearly half of all GitHub code is AI-generated or AI-assisted. The productivity gains are real but uneven, and the risks are underappreciated. JPMorgan deployed AI coding agents to 40,000 engineers and reported 10 to 20% productivity gains in code creation and conversion, along with a 70% increase in code deployments. But CodeRabbit's research found 1.7 times as many defects in AI-authored pull requests as in human-authored code. Meta's brief "token maxing" leaderboard experiment, designed to spotlight power users, had to be taken down within two weeks after producing high token consumption and limited usable code. Senior developers are shifting toward architecture and review roles while junior developer pipelines are shrinking, even as total software developer job postings are up 5 to 10% year over year. A tour of the seven major players and where the structural tension lives. Ray and Peter profile Anthropic Claude Code, GitHub Copilot, Cursor, OpenAI Codex, Google Gemini Code Assist, Replit Agent, Lovable, and Cognition's Devin across revenue, differentiation, and risk. The common thread: most point-solution coding agents run on Anthropic or OpenAI models, and those same model companies have now launched their own competing coding products. The Oracle database-to-applications parallel is not subtle. Why the Cursor-SpaceX deal happened and what it actually reveals. Cursor had $2.7 billion in annualized revenue, negative 23% gross margins, and was losing money faster than it was growing. Even with a $2 billion funding round in process from Andreessen Horowitz, Thrive Capital, NVIDIA, and Battery Ventures, Cursor's leadership concluded they would need to raise billions more by year-end to fund compute costs. SpaceX's acquisition offer, or the $10 billion partnership payment that Ray reads as a very generous breakup fee, solved that problem while giving XAI a revenue base three times its current size ahead of a $1.75 trillion IPO valuation push. The Chinese open-source threat and three scenarios for where this market goes. Kimi, DeepSeek, and Qwen models are improving rapidly and are significantly cheaper. They are, as Peter puts it, lurkers haunting every company on the list. Ray and Peter then lay out three scenarios: model makers consolidate, and IDE players get marginalized; a durable multi-tool ecosystem persists because different tools serve different workflow stages and buyer profiles; or a compute-native player builds a fully autonomous coding agent that eliminates the need for an IDE entirely. Claude Code already resolves 64.3% of real-world GitHub issues, and full autonomy for defined-scope tasks may be 18 to 36 months away. The AI coding market is projected to reach $49-$50 billion by 2030, at a 38% CAGR. The speed gains are real. So are the defect rates, the governance gaps, the model-dependency risks, and the token-budget surprises landing on CFO desks. If you are a CIO, CFO, engineering leader, or investor trying to make sense of who wins and what it costs, this is the episode to start with. Read the full story at ai2roi.substack.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    34 min
  3. Big Book of AI Metrics

    5d ago

    Big Book of AI Metrics

    The AI to ROI team, Ray Rike and Peter Buchanan, mark the official launch of The Big Book of AI Metrics, an 180-page, 81-metric operator's reference guide built to close the gap between AI adoption and AI ROI. Twenty-seven percent of executives say AI has met their ROI expectations, enterprise AI token spend is up 13x since last year, and most companies still can't explain what they got for the investment. Ray and Peter break down why that gap exists and what to do about it. Topics covered: Why adoption, utilization, and outcomes are three different things, and why most companies stop measuring at adoptionThe five layer causal chain framework: input signals, leading indicators, operational KPIs, financial outcomes, and strategic valueWhy establishing a baseline before deployment is the single most skipped step, and why skipping it turns results into opinion instead of evidenceFour real world case studies: Petrobras ($120M in tax savings), Stocks Insurance (83% reduction in claims processing time), Uber's cautionary token budget blowout, and Klarna's revenue per employee gainsThree actions operators should take this week: define the outcome metric, establish a baseline, and build a measurement cadence before and after deployment Key quote: "Adoption still is not ROI. Outcomes are ROI. And outcomes that translate into better financial performance, that's true ROI that a CFO, investor, and a board of directors can get behind." - Ray Rike The Big Book of AI Metrics is organized into 13 functional roles, covering both operating executives investing in AI to improve their functions and B2B software executives whose product economics now depend on token consumption, inference costs, and gross margin impact. Get the Big Book of AI Metrics: https://www.benchmarkit.ai/ai-big-book-of-metrics See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    34 min
  4. Measuring the costs, utilization, proficiency and impact of AI - with Russ Fradin, Founder and CEO, Larridin

    Jun 9

    Measuring the costs, utilization, proficiency and impact of AI - with Russ Fradin, Founder and CEO, Larridin

    Most enterprises have deployed AI broadly. Far fewer know what they are actually getting from it. Russ Fradin, Co-Founder and CEO of Larridin, has spent his career building measurement infrastructure at inflection points in technology adoption, from early days at ComScore measuring internet advertising to founding Larridin with backing from Andreessen Horowitz and Google's Gradient fund. In this episode, Russ makes the case that AI spend is on a trajectory to become the number-one or number-two driver of enterprise OpEx, and that most organizations still lack the basic visibility needed to manage it. Topics covered: The AI visibility gap: Why AI adoption moved faster than measurement infrastructure, and why enterprises are only now scrambling to answer fundamental questions about what they are spending, where, and by whom Utilization vs. proficiency vs. business impact :Why these three dimensions require separate measurement, and why the 1,800 heavy users at a 30,000-person company are not a success story on their own Token spend as a new category of OpEx risk: How consumption-based pricing turns every employee into a cost endpoint, with real examples of runaway agent spend and blown budgets that no one turned off CFO ownership of AI investment: Why AI spend is the first technology cost category large enough to pull the CFO into governance conversations that historically belonged to the CIO and department heads Change management as the bottleneck: Why the hard work is not experimentation but operationalizing what works, scaling proven behaviors from the top 5% of users to the full organizationCareer advice for AI-era professionals: Work harder than the room, achieve deep tool mastery, and invest in relationships, the same fundamentals that applied before AI, now with higher stakes for the people who act on them Russ closes with a memorable framing: "Companies have committed to a fitness journey but have not yet bought a scale; Larridin is building that scale." See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    33 min
  5. Leveraging AI to Reduce Churn and Increase NRR - with Dan Harmeson, Co-Founder and Co-CEO at QuadSci

    Jun 2

    Leveraging AI to Reduce Churn and Increase NRR - with Dan Harmeson, Co-Founder and Co-CEO at QuadSci

    Most B2B software companies are sitting on one of the most powerful and underutilized data assets in their business: product telemetry. Every click, API call, and feature interaction is a signal. The question is whether your go-to-market organization knows how to read it. In this episode, Ray Rike is joined by Dan Harmeson, co-founder and co-CEO of QuadSci, to explore how machine learning applied to telemetry data is changing how software companies predict churn, protect the base, and accelerate expansion revenue. Key topics covered in this episode: Why telemetry data is the largest untapped GTM asset in B2B software. Dan defines telemetry data, from front-end product analytics events to back-end observability metrics, and explains why these trillions of usage signals are the single biggest data set B2B software companies generate but rarely use to make go-to-market smarter. QuadSci deploys AI locally inside the customer environment so sensitive data never moves to a third party.How QuadSci builds trust before the sale. Rather than asking customers to take predictions on faith, QuadSci runs a retrospective exercise: predicting churn and growth events that already happened, including data the model never trained on. Customers consistently see 90%+ accuracy, which becomes the foundation for acting on forward-looking risk signals.Gross revenue retention is under pressure and the data is clear. Per Benchmarkit's not-yet-published 2026 benchmarking data, GRR has declined four percentage points to 84% as an industry benchmark. For companies above $100M in ARR, roughly 95% of revenue comes from renewals and expansion, which means a two-point GRR drop cannot be offset by new logo acquisition within a 12-month window.Expansion revenue is a precision play, not just a CS motion. Dan walks through how QuadSci identifies Goldilocks-zone consumption patterns, surfaces cross-sell opportunities aligned to actual usage behavior, and helps account teams build nine-to-twelve month consumption forecasts that customers can actually plan around. The result is expansion conversations grounded in data, not intuition.Token consumption is the next frontier. As agentic AI deployments scale, CIOs and CFOs are facing unpredictable inference costs. Dan explains why the same telemetry-based approach that protects software GRR today is directly applicable to governing AI token spend inside Fortune 5,000 enterprises, a market QuadSci is beginning to address.Rapid fire: ROI measurement, ownership, and career advice. Dan ties AI ROI to trust and verifiability rather than vanity metrics, identifies StratOps as the emerging owner of go-to-market performance measurement, and offers practical guidance for early-career professionals on why deep business process expertise paired with AI fluency is the highest-value combination in the market right now.If your company is facing pressure on retention, trying to build a more systematic expansion motion, or wrestling with unpredictable AI infrastructure costs, this episode delivers both the framework and the evidence behind it. Subscribe to AI to ROI on your favorite podcast app, leave a five-star rating, and connect with Ray at Ray Rike on LinkedIn to suggest a future guest. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    31 min
  6. The AI Agent Outcome-Based Pricing Journey - with Kunal Agarwal, CFO Gorgias

    May 27

    The AI Agent Outcome-Based Pricing Journey - with Kunal Agarwal, CFO Gorgias

    What does it actually look like when a CFO drives the strategic, pricing, and financial decisions behind an AI-first product transformation? Kunal Agarwal, CFO at Gorgias, the leading e-commerce customer experience platform for Shopify merchants, joins our host, Ray Rike to share the unfiltered story of how Gorgias built, priced, and operationalized its AI agent product from the ground up. This episode goes well beyond theory, covering the real decisions, real numbers, and real lessons learned from a company that has roughly half its customer base already using its AI agent product. Episode Highlights: The build decision: re-architect, don't bolt on. In early 2024, Gorgias made the deliberate choice to re-architect its platform around an agentic future rather than layering AI on top of an existing help desk product. The first AI agent focused exclusively on email support, shipped in July/August 2024, and expanded from there into chat and shopping assistance. Kunal explains why starting with a single, high-confidence use case was critical to earning early adoption and trust from merchants. The North Star metric: full resolution rate, not deflection. Gorgias intentionally moved away from deflection rate as its primary success metric, which can mask frustrated customers who simply abandon a conversation, and anchored instead on end-to-end AI resolution rate. That metric started with a target of 20 to 25% and has scaled to 60 to 80% for their largest enterprise customers. Why outcome-based pricing was the only intellectually honest answer. Seat-based pricing misaligns incentives, and per-ticket pricing creates the wrong incentive to grow ticket volume rather than resolve issues. Gorgias charges per resolution, meaning it only gets paid when the AI agent delivers a measurable outcome. Kunal explains how that pricing model forces the company to stand behind product quality and why keeping it simple, at the cost of short-term revenue maximization, was the right call to accelerate adoption. Gross margin reality: AI-native economics are structurally different from SaaS. Kunal is candid that AI agent gross margins are lower than traditional SaaS and that denying that fact is living in an alternate reality. With LLM inference costs running approximately 55 to 60% of fully loaded cost per interaction, and infrastructure as the fastest-growing expense line, Gorgias built real-time cost instrumentation by feature, a rolling 28-day average LLM cost per interaction, and a CFO-led governance model with weekly to bi-weekly engineering check-ins to stay ahead of cost drift. The shopping agent and the attribution problem. Gorgias expanded its AI platform from post-sale support into pre-sale shopping assistance, helping Shopify merchants drive incremental AOV and repeat purchases. The challenge is attribution: when a customer engages with a product recommendation but converts two to three days later, did the AI agent drive that sale? Kunal describes the approach of co-creating attribution logic with customers, which is the only way to make the ROI story believable and defensible. The CFO as owner of AI ROI, internally and externally. On measuring the return on internal AI investments, Kunal's view is clear: the Office of the CFO owns AI ROI measurement across every function, including product, marketing, and sales. Product and engineering teams are important stakeholders but have inherent incentives to measure outcomes favorably. Independent, finance-led measurement is what gives the numbers credibility with the board. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    33 min
  7. AI to ROI: OpenAI - The Most Important AI Company in the World, and the Most Fragile

    May 19

    AI to ROI: OpenAI - The Most Important AI Company in the World, and the Most Fragile

    OpenAI built $25 billion in annualized revenue and 910 million weekly active users in three and a half years. It also has 33% gross margins, a projected $14 billion loss, a CFO who was reportedly demoted for saying the company is not ready to go public, and an investor presentation that told its software partners it plans to replace them. In this episode, Ray and Peter work through six documented challenges facing OpenAI, six specific actions that could right the ship, and what enterprise leaders should actually do with their AI strategy given all of it. What we covered in this episode: The model is not the moat, and ChatGPT's market share is eroding Analyst Benedict Evans has noted that the six leading large language model companies are now roughly equivalent in capability, with no proprietary data advantage or network effect allowing any one to pull decisively ahead. ChatGPT's share of enterprise and developer usage has fallen from roughly 80% two and a half years ago to around 60% today, growing at just 4% while Claude grew 14% and Gemini 12%. OpenAI is a consumer-first product trying to pivot to enterprise at a moment when Anthropic is already the preferred first purchase for 73% of enterprise buyers according to Ramp data. Leadership integrity and financial credibility are both under pressure A 16,000-word New Yorker profile drawing from over 100 interviews raised serious questions about Sam Altman's management behavior and integrity. The Wall Street Journal followed with reporting on his personal investment conflicts. The CFO, Sarah Friar, was reportedly demoted after privately advising colleagues the company is not ready for an IPO. At a $852 billion valuation (roughly 28x projected 2026 revenue) with 33% gross margins and a $14 billion projected loss, institutional investors interviewed by The Information said they would not buy the stock and some indicated they would short it. The partner ecosystem problem could be existential In a February investor presentation, OpenAI stated it intends to build products that replace Salesforce, Workday, Adobe, Slack, and Atlassian, companies with whom it has active revenue-generating partnerships. Every systems integrator and enterprise software company building on top of OpenAI's models is now evaluating whether that is a safe long-term bet. Bill Gates defined a platform as something that creates more value for partners than for itself. OpenAI's current stated strategy is the opposite. Six actions that could change the trajectory Ray and Peter walk through a specific set of recommendations: launch a structured enterprise customer evidence program with named deployments and quantifiable outcomes; stop the public sniping at competitors and replace it with product and customer communication; fund an independent AI governance and safety board with real veto authority; impose IPO-grade communications discipline and treat major leaks as firing offenses; commit credibly to a partner ecosystem with defined product boundaries that give integrators a durable business case; and operate as a mature growth company, not a startup, because $30 billion in revenue demands the leadership behaviors that go with it. What enterprise leaders should watch and do right now Three signals will tell the real story over the next 12 months: whether Sarah Friar stays or exits, whether the IPO timeline slips to 2027, and whether enterprise case studies with quantifiable outcomes start appearing in volume. In the meantime, the strategic prescription is straightforward. Do not build single-model dependency into your AI architecture. Require the same evidence from OpenAI you would from any other vendor: verified outcomes, clear product roadmap, and accountability. And build API portability into your application design so you can move if you need to. The closing question: if you had to pick one LLM company to invest a million dollars in, where does it go? Peter picks Google, citing distribution advantages, DeepMind's research depth, and full control over its own financial destiny. Ray picks Anthropic, citing a lower revenue base with larger upside, near-universal goodwill across hyperscalers and enterprise buyers, and a safety-first positioning that is proving to be a genuine competitive differentiator. They agree on the conclusion: OpenAI is the defining company of the AI generation, but Netscape, Lotus, and BlackBerry were all category leaders too. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    39 min
  8. NVIDIA – The Full-Stack Maestro

    May 13

    NVIDIA – The Full-Stack Maestro

    Five months ago, Ray and Peter called NVIDIA the maestro of the AI economy. Since then, NVIDIA has not just conducted the orchestra. It has rewritten the music and may be building the entire concert hall. In this episode, Ray and Peter revisit their October thesis, walk through everything NVIDIA unveiled at GTC, and break down what it all means for enterprise AI buyers navigating infrastructure, inference costs, and procurement strategy. What we covered in this episode: From GPU maker to full-stack AI platform: the transformation is complete NVIDIA's strategic intent is no longer just selling chips. It is embedding its technology across the entire AI stack and becoming the foundational layer on which the rest of the AI economy rests. Ray draws the only historical parallel he can find: what IBM was to enterprise technology from the 1960s through the 1980s. The difference is NVIDIA is moving faster, with more cash, and with a software flywheel IBM never had. GTC was not a product launch, it was a platform declaration NVIDIA unveiled the Vera Rubin platform, a fully integrated AI supercomputer with liquid cooling and a two-hour installation window. They licensed Groq's LPU architecture in a $20 billion deal that combines GPU and LPU chips to deliver 35x token throughput over current Blackwell systems. They launched NemoClaw (an enterprise-grade agent framework already partnered with Adobe, Salesforce, and SAP), Dynamo (an open-source inference operating system), and the Nemotron family of open-source frontier models. Jensen committed $26 billion over five years in free cash flow to build best-in-class frontier models with no outside funding required. The financial performance is in a category by itself Fiscal year 2026 revenue came in at $215.9 billion, up 65% year over year and 8x since 2022. Data center revenue exceeded $190 billion. Free cash flow hit $97 billion, translating to a 47% free cash flow margin. Combined with 65% growth, that is a Rule of 40 score of 109. Ray notes he has never seen anything like it at scale, and NVIDIA is a hardware company running 80% gross margins. CFO Colette Kress described their inference position as: "right now, we are the king of inference." The moat is not hardware. It is ecosystem lock-in Since 2022, NVIDIA has committed over $50 billion across 170 venture deals, with corporate deal volume growing from 12 deals in 2022 to 67 deals in 2025. Portfolio companies include OpenAI, Anthropic, xAI, CoreWeave, and Lambda. Sovereign AI contracts signed since October total $30 billion across France, the Netherlands, Canada, Singapore, and the Middle East. Hyperscalers still represent roughly 50% of revenue, but the faster-growing segments are sovereign entities, enterprise verticals, and NeoCloud providers, which is exactly the diversification NVIDIA needs as hyperscaler CapEx normalizes. The risks are real but manageable from where NVIDIA sits today Custom ASICs from Google, Amazon, Meta, and Microsoft represent the most credible competitive threat, though those chips are optimized for internal platforms and do not solve multi-cloud or on-premise deployment needs. Export control escalation remains a live risk, with NVIDIA restarting NH200 production for China. TSMC concentration is a structural vulnerability, especially given geopolitical risk around Taiwan. And three hyperscalers account for over half of NVIDIA's receivables, some of whom are actively building competing chips. What enterprise AI buyers should do right now. Ray and Peter close with four concrete takeaways for enterprise buyers: evaluate the full infrastructure stack, not just GPU cost; model inference economics carefully before deciding which models to run and where; pursue a strategic partnership with NVIDIA rather than transactional procurement, because partnership creates supply access standard customers do not get; and do not assume custom silicon from hyperscalers solves your problem, because data residency and on-premise requirements often mean NVIDIA needs to be part of the solution regardless. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

    34 min
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4.8
out of 5
41 Ratings

About

AI to ROI is a podcast that shares how enterprises translate AI investments into measurable business value. Hosted by Ray Rike, Founder and CEO of Benchmarkit, the show features senior enterprise leaders and AI software executives who share how AI initiatives move from pilots to production, and how ROI is actually measured and achieved. In addition, each week, we publish a bonus episode with AI to ROI Newsletter co-author, Peter Buchanan to discuss the Big Story of the Week. The AI to ROI podcast is the evolution of the original "Metrics to Measure Up" podcast.

Information

  • Creator
    Ray Rike
  • Years Active
    2020 - 2026
  • Episodes
    247
  • Rating
    Clean
  • Show Website
    AI to ROI

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