AI to ROI (fka Metrics that Measure Up)

On June 12th, the US Department of Commerce ordered Anthropic to suspend global access to Fable 5 and Mythos 5, its most powerful frontier models, with no advance warning and criminal penalties attached. In this week's Big Story episode, Ray Rike and Peter Buchanan walk through the first use of export controls to take a deployed frontier model offline, why it backfired for security rather than strengthening it, and what a coherent federal AI regulatory framework would actually need to look like.The shutdown mechanics. Because Anthropic could not verify user nationality in real time, the directive knocked out access for every user globally, including Anthropic's own non US employees and more than 150 companies across 15 countries running critical infrastructure workloads.Three reaction tracks. Industry, the developer community, and allied governments each responded differently. OpenAI pushed back on talent restrictions while its legal team blocked coordination with Anthropic on antitrust grounds, and a coalition of 150 cybersecurity leaders published an open letter asking not for less regulation but for a transparent, science based process.The regulatory vacuum, in five forces. Ray and Peter unpack the drivers behind what they call a hair on fire crisis: capability jumps outpacing any statutory framework, a state level policy tsunami of over 1,500 AI bills across 45 states, data center community opposition, the unresolved Anthropic and DOD dispute, and a bipartisan Congressional letter questioning why comparable models were treated differently.A six part framework. The episode lays out proposed solutions modeled on existing regulatory precedent: global market access agreements similar to military sales processes, mandatory pre release testing run by NIST modeled on FAA certification, clear federal versus state jurisdictional lines modeled on pharmaceutical regulation, expanded child safety authority, FERC fast tracking for data center grid access, and coordinated environmental standards.What enterprise leaders should do now. Ray closes with practical guidance: review AI vendor contracts for shutdown protection since force majeure clauses were never written with export controls in mind, build fallback infrastructure for critical AI dependent workflows, delay rushing into brand new model releases, and automate tracking of the fast growing state regulatory landscape.Full details are in the AI to ROI newsletter at ai2roi.substack.com. Subscribe, and consider reaching out to your representatives on this one.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most companies treat AI as a layer they add on top of how work already gets done. Dan Zhang, Chief Business Officer and CFO at ClickUp, argues that it is exactly backward. In this episode, Ray sits down with Dan to unpack the "100x Org," ClickUp's framework for rebuilding the business around AI rather than sprinkling tools and tokens on top of a human-driven workflow, and why that distinction determines whether an AI initiative shows up as activity or as income statement impact.The conversation covers:Why ClickUp expanded the CFO's charter to own AI transformation end to end, after both a top-down mandate and a bottoms-up experimentation push failed to produce results that made it into productionThe "jobs to be done" framework Dan uses to separate primary work that actually moves the business from secondary work that just generates busy AI activity, and why most companies have a work redesign problem before they have an AI problemDan's psychological test for AI ROI (would you pay for it with your own money) and why ARR per headcount is the right metric, but a lagging one that plays out over years, not weeksHow ClickUp instrumented daily, not monthly, visibility into AI cost and token consumption, and why over half of enterprise companies in Ray's own research have blown their AI budget by 25% or moreWhy the real anxiety CFOs have about AI ROI isn't the return, it's confidence in cost management, and how a governance layer turns that anxiety into a guardrail instead of a monthly surpriseDan's rapid-fire advice on who should own AI ROI measurement, the two-to-three variables every CFO needs in place, and how early and mid-career professionals protect their relevance by owning the question, not just the answerIf your organization has AI activity but can't yet point to AI impact, or your CFO isn't sure whether AI spend is a competitive advantage or a leak, this episode gives you the operating model and the financial discipline to tell the difference.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Token prices have fallen 98 percent since GPT-4, but enterprise AI bills are up 320 percent. In this week's Big Story episode, Ray Rike and Peter Buchanan trace where that money is actually coming from, and the answer is not the software budget. Drawing on Gartner, Oxford Economics, Zylo, Challenger Gray and Christmas, and Goldman Sachs data, the two lay out why labor, not IT, is becoming the primary funding source for AI at scale, and why almost no company has the measurement infrastructure to manage it.The price paradox. Per token costs have collapsed, but usage has grown faster than costs have fallen. Ray walks through the math behind average enterprise AI budgets rising from $1.2 million to $7 million in two years.Three cautionary tales. Uber consumed its entire annual Claude Code budget in under four months, Microsoft revoked thousands of Claude Code licenses over cost, and one unnamed enterprise ran up a $500 million bill in a single month. Ray and Peter break down why each was a governance failure rather than a technology failure.Only two budget pools are big enough. The IT and software budget represents just 3 to 4 percent of revenue, while labor represents 25 to 40 percent depending on industry. Ray makes the case that labor is the only pool large enough to absorb the AI spending trajectory Gartner and Oxford Economics are projecting.The attrition lever. Ray and Peter unpack how not backfilling open roles has quietly become the primary way enterprises are funding AI investment, supported by data showing over 113,000 tech layoffs in 2026 with 48 percent explicitly attributed to AI.Revenue per FTE as the tell. Ray shares benchmark data showing SaaS company revenue per employee up 25 to 35 percent over the last twelve quarters, and explains why this metric will be the clearest signal of whether the AI budget transfer is actually working.Five metrics every CFO needs now. The episode closes with a practical starting list: AI spend as a percent of revenue, AI spend per employee, inference spend as a percent of opex, inference cost as a percent of COGS for AI enabled products, and revenue per FTE tracked against labor cost and agent cost as a percent of OPEX.AI to ROI is always looking for guests with real-world examples of measuring AI budget impact. Reach out Ray on LinkedIn (@rayrike). Subscribe to AI to ROI at ai2roi.substack.com for the full June 9th edition, and leave a review wherever you listenSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Professional services firms have billed by the hour for over 200 years. That model is now under direct attack from a new category of company: the AI Native Services firm. Ray Rike and Peter Buchanan break down exactly what makes these companies structurally different from traditional professional services firms and AI-augmented incumbents, profile four companies proving the model at scale, and lay out the six critical success factors that will separate the winners from the well-funded failures.Episode Highlights:Defining the category. Drawing on the Emergence Capital AI Native Services Playbook, Ray and Peter establish a clear three-part taxonomy: AI Native Services companies (AI does 80 to 90% of the work, a licensed human reviews and is accountable for the outcome, and the client pays for results), AI Augmented Services companies (humans still do most of the work with AI as a productivity layer), and traditional SaaS tools (the customer's team does the work, and the vendor takes no accountability for the outcome). The distinction matters enormously for enterprise buyers evaluating contracts and liability.How the operating model actually works. The AI Native Services delivery model runs in four phases: client intake and data ingestion, AI-driven execution of the primary service work, licensed human review and approval, and outcome delivery back to the client. Critically, that fourth phase is where the model compounds, because every accepted output becomes training data that makes the system smarter and harder to displace over time.Four companies are proving the model. Ray and Peter profile four AI Native Services companies at different stages of scale, each dominating a regulated vertical wedge. Top AI-Native Service companies covered include: 1) Field Guide is automating audit workflows for nearly half of the top 100 US accounting firms, including KPMG and RSM, and recently raised a $75 million Series C at a $700 million valuation; 2) Even Up has built a proprietary PI AI model trained on hundreds of thousands of personal injury cases, processing 10,000 cases per week for over 2,000 law firm clients, following a $385 million funding round; 3) A-Bridge converts physician-patient conversations into structured clinical notes integrated with Epic and other EMR platforms, serving over 150 enterprise health systems including Kaiser Permanente, Mayo Clinic, and Johns Hopkins, with $100 million in ARR and a $5.3 billion valuation; 4) Harper is a licensed commercial insurance broker, not a software tool, that processes applications across 160+ carriers simultaneously and delivers final coverage in 24 to 48 hours versus the industry standard of five to seven days.Six critical success factors. The hosts lay out what separates durable AI Native Services companies from those that will stall: genuine domain expertise on day one, a proprietary data flywheel that compounds with every case resolved, a clear migration path from labor-based to outcome-based pricing, honest gross margin accounting that properly classifies LLM inference and human labor as cost of goods sold, narrow vertical focus on a specific wedge rather than broad horizontal expansion, and distribution through regulated industry incumbents who provide both credibility and enterprise access.Gross margin as the early warning signal. If gross margins are declining as an AI Native Services company scales, that is a signal the human-in-the-loop is becoming the bottleneck rather than the leverage point. The financial goal is to continuously increase gross margins as AI does more of the work, moving from a 35 to 45% gross margin profile toward 50 to 60% over time.What enterprise buyers should ask. Ray closes with a direct call to action for executive buyers: if an AI vendor is pricing by the seat, by the partial FTE, or by the hour, push hard on how much of that is human supervision of AI versus a truly AI-native delivery model. You are no longer contracting a resource to do the work. You are contracting an organization to deliver the outcome you need.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Token spend is exploding across the enterprise, but the value it creates remains largely invisible on corporate dashboards. In this week's Big Story episode, Ray Rike and Peter Buchanan unpack why AI investment and ROI visibility are moving in opposite directions, and what enterprises need to do about it. Drawing on Ramp data, Exponential View, Semianalysis, and Ray's recent conversation with Russ Frayden, CEO of Lariden, the two dig into the measurement infrastructure gap that is turning individual AI productivity gains into an unmeasured expense line.The productivity paradox. Individual output is up across engineering, sales, and research functions, but those gains are not translating into company-level financial impact. Ray connects this to Parkinson's Law and explains why more productive workers do not automatically produce more profitable companies.AI dark output. Peter introduces the concept from Semianalysis: real economic value created by AI that never registers on a P&L, using the example of a legal document that drops from $400 to $5 to produce, where the savings disappear while the token expense shows up in plain sight.The cost to compensation shift. Ray walks through why token spend approaching 50 to 100 percent of engineering compensation changes the entire calculus for measurement, contrasted against IT's historical 3.5 to 6 percent share of revenue.Case studies in good and bad. The episode breaks down three real examples: Uber's Claude Code rollout that ran out of budget without measurable output gains, Lowe's cross-functional agent deployment that built proper context tracking, and Petrobras's narrow tax compliance pilot that identified $120 million in savings and is scaling toward $1 billion.A four-stage framework for real measurement. Ray and Peter lay out the progression from cost visibility to utilization, proficiency, and business impact, and explain why almost every enterprise is stuck at stage one.Six tactical takeaways. The episode closes with concrete actions: measure outcomes not activity, design for the middle 70 percent of users rather than power users, give CFOs real budget ownership, start narrow and expand from proof points, redesign decision rights as AI scales, and treat organizational role changes as a planned program rather than a side effect.Subscribe to the AI to ROI newsletter at ai2roi.substack.com for the full breakdown, and leave a review wherever you listenSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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.

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 oneCompanies 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 decisionsOperating 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 humanAs 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 numbersThe 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 metricsAMCS 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.

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 deploymentKey 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 RikeThe 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-metricsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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 whomUtilization 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 ownToken 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 offCFO 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 headsChange 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 themRuss 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.

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.

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.

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 erodingAnalyst 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 pressureA 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 existentialIn 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 trajectoryRay 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 nowThree 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.

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 completeNVIDIA'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 itselfFiscal 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-inSince 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 todayCustom 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.

Our host, Ray Rike sits down with Jake Saper, General Partner at Emergence Capital, to unpack the firm's AI-Native Services Playbook. Jake brings a unique lens: 12 years at Emergence, early-stage bets on companies like Zoom, and a portfolio of seven AI-native services businesses already in the portfolio. The conversation covers what separates AI-native services from SaaS, why the business model is harder to execute than it looks, and the five metrics and structural choices that determine who wins.WHAT WE COVER IN THIS EPISODEDomain Expertise: Critical - But Not Required from the FoundersAI-native services companies are selling outcomes, not products. That means trust and credibility are the first sales. Domain expertise is non-negotiable, but it does not have to live in the founding team if two conditions are met: the founders go as deep as humanly possible on the service before launch, and they hire senior domain experts early. Emergence portfolio company Hanover Park, an AI-native fund administrator, is the case study. The founder interviewed 150 CFOs before writing a line of code and hired respected fund accounting veterans to sit alongside the AI. That combination unlocked enterprise trust from day one.Hire a Product Leader Before You Think You Need OneThe biggest structural trap in AI-native services is over-relying on human delivery while the product falls behind. Market pull is strong by design — if you promise faster, better, cheaper outcomes in an existing market, customers will buy. But if delivery is primarily human, you have a services company with venture capital financing and no AI leverage. The fix is a dedicated product leader whose sole KPI is productizing the service. The best AI-native services companies run a tight feedback loop between the doers (service delivery) and the builders (engineering), and the PM owns that loop.The Mirage of Product Market FitIn SaaS, fast growth plus strong net dollar retention meant you had product market fit. In AI-native services, those are necessary but not sufficient. Revenue growth powered by human labor is a false signal. True product market fit requires that AI is delivering the majority of the service value. Jake's framework: track both leading indicators (a North Star product metric showing AI leverage improvement, such as human review time per contract or time to migrate a line of code) and lagging indicators (revenue per FTE trending up quarter over quarter, and gross margin). The leading indicators tell you if you're building leverage. The lagging indicators confirm it.Outcome-Based Pricing: The Direction of TravelAI-native services companies that started with labor-based pricing will need to migrate toward outcome-based pricing over time, and the transition requires patience. Emergence portfolio company Prosper AI, an AI-native healthcare services provider handling prior authorization and benefits verification, navigated this by moving a portion of contracts to resolution-based pricing while keeping the remainder on a per-minute basis. That hybrid approach gave both sides the data and comfort to expand the outcomes-based portion at renewal. Jake's view: as AI does more of the work, downward pricing pressure is inevitable, but upward margin pressure offsets it.Revenue Per FTE and Gross Margin: The Two Metrics That Matter MostRevenue per FTE is the primary signal of AI leverage, but it needs to be benchmarked two ways: against the legacy service provider in the same vertical, and against itself quarter over quarter. The latter is more important. If revenue per delivery FTE is not improving each quarter, the AI is not compounding. On gross margin, the industry is still in the Wild West. Two common errors: allocating service delivery headcount to R&D instead of COGS because the team "helped train the model," and excluding inference spend from COGS. Both understate the true cost of delivery. Customer-specific model training belongs in COGS. Base model training belongs in R&D.The Moat QuestionBrand trust and proprietary data are the two sources of durable advantage. Brand matters because enterprises buying AI-delivered outcomes need a trusted guarantor. Data matters because high-volume AI-native operations accumulate transaction data that legacy providers, running at lower volume with more human overhead, simply cannot match. Emergence portfolio company Harper, an AI-native insurance broker, is outperforming brokerages ten times its size on placement speed and carrier-risk matching because its data volume is superior.LINKS Emergence Capital AI-Native Services Playbook: em.cap.comABOUT AI TO ROI Ray Rike is the Founder and CEO of Benchmarkit, the leading B2B SaaS and AI-native software benchmarking company. The AI to ROI podcast brings a metrics-first lens to enterprise AI adoption, ROI measurement, and the business models being built on top of AI. Subscribe on your favorite podcasting app and connect with Ray on LinkedIn.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ray Rike sits down with Jim Piazza, Chief AI Officer at Ensono, a managed services provider scaling AI across both its internal operations and customer environments. Jim brings a rare combination of deep infrastructure experience, nearly a decade at Meta scaling data center operations with machine learning, and a rigorous framework for connecting AI investments to business outcomes that executive operators can actually measure.Key Topics:Defining the Chief AI Officer Role in an MSP: Jim describes the CAIO role as a blend of CDO, CIO, and CTO with an AI lens, but with a critical distinction: the job is not to ask what AI can do. It is to identify where AI improves service delivery, customer outcomes, and financial performance. At Ensono, that meant starting small as VP of Predictive Systems, demonstrating results, and earning the mandate to expand. Prioritization, not ideation, is the core skill.Building AI Tools That Drive Internal Operational ROI: Ensono developed three production AI systems for internal use. Envision Predictive Engine analyzes telemetry data across systems to predict failures before they cause business impact, including one case where a problem was detected 144 minutes before it would have affected a major logistics customer outside Ensono's own scope of responsibility. Diagnose Now puts the right diagnostic data in front of engineers at the right moment and has delivered up to a 66% reduction in mean time to repair in A/B testing. ChangeGuardian assesses risk scores for the 8,000-plus changes Ensono executes monthly, auto-generating methods and procedures from a decade of historical change data to reduce both risk and manual effort.Structuring AI Governance: The Three Musketeers Model: Jim, the CTO, and the CIO operate as a deliberate leadership triad. The CTO owns the platforms. The CIO owns data quality and structure. The CAIO owns the build-versus-buy decision and solution development. Shared accountability, not siloed ownership, drives alignment. Each business unit also contributes one to two subject matter experts through a formal value stream mapping process to identify where AI should focus first.Measuring AI ROI Before Writing a Line of Code Jim's most consistent lesson: define your value metrics before touching the technology. AI use cases must tie back to core business metrics such as mean time to repair, customer satisfaction, SLA risk reduction, and gross margin improvement. Business unit leaders own the outcome measurement. The CAIO owns the budget and the technology. That separation of responsibility keeps AI programs anchored to results rather than activity.The CAIO and CIO Relationship: Where the Lines Get Drawn: For companies bringing in a Chief AI Officer alongside an existing CIO, Jim offers a practical delineation. The CIO owns data infrastructure and quality. The CAIO is a consumer and a builder who depends on that foundation. Without clean, accessible data, AI programs stall regardless of the use case. The CAIO's job is to surface missing or insufficient data and partner with the CIO to close the gap.Lessons Learned and Career Advice for the AI Era: Jim's framework for AI program success: start with one or two high-probability use cases where data is already in good shape, build credibility through results, then expand. Avoid the ten-pilot trap. Kill weak use cases early. For early-career professionals, his advice is equally direct: learn to work with AI, not compete with it. Build problem framing, critical thinking, and business judgment. Technical fluency matters, but business judgment is what separates the people AI replaces from the ones AI makes more valuable.This episode is essential listening for technology and operations executives navigating the practical reality of AI deployment inside complex enterprise environments. If you are a CIO, CTO, COO, or Chief AI Officer trying to figure out how to structure governance, measure impact, and build internal credibility for AI programs, Jim Piazza gives you a real-world operating model, not theory. For managed services leaders and enterprise buyers evaluating MSP capabilities, the Ensono case studies show what it looks like when an MSP moves from reactive service delivery to predictive, AI-driven outcomes. And for executives still debating whether to hire a Chief AI Officer, this conversation makes a direct case for what the role should own, how it should partner, and what success looks like when it is done right.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

SpaceX filed for what could be the largest IPO in history, targeting a $1.75 trillion valuation and $75 billion raise on NASDAQ in June. Ray Rike and Peter Buchanan cut through the narrative and go straight to the numbers, business unit by business unit.Key Topics:The Launch Services Monopoly Falcon 9 launches cost roughly $67 million, compared to $110-160 million for competitors. With over 100 launches per year, $4 billion in NASA contracts, and a freshly awarded Space Force contract, SpaceX has no meaningful competitor at scale. The catch: the next-generation Starship rocket, critical to everything else in the bull case, is already five years behind its original commercial timeline.Starlink: The $10 Billion Business You Never Think About Starlink generates nearly $10 billion in annual revenue from 10 million global subscribers, representing 54% of SpaceX's total revenue. The real margin engine is not residential subscribers but aviation and maritime, where per-customer annual revenue runs $300K and $34K respectively. Amazon's Project Kuiper remains far behind with under 700 satellites versus Starlink's 10,000-plus.XAI and X: The Problem Child SpaceX acquired XAI in February 2026 in an all-stock deal valued at $250 billion. The financial reality is stark. XAI burned $9.5 billion in cash during the first nine months of 2025 on only $210 million in revenue, nearly $28 million per day. A combined 2025 P&L would have shown a $5 billion net loss on $18.5 billion in revenue, reversing SpaceX's standalone $8.5 billion profit in 2024. Grok, its large language model, is described in internal SpaceX memos as clearly behind Anthropic, OpenAI, and Gemini, and Elon Musk himself has said publicly it needs to be rebuilt.The IPO Mechanics: Structure, Retail Allocation, and a Controversial NASDAQ Rule Change Five banks are co-leading the offering with no single lead book-runner, and each was reportedly required to purchase Grok subscriptions as a condition of participation. Retail investors receive a 30% share allocation, three times the typical size. Most controversially, NASDAQ shortened its index inclusion waiting period from 90 days to 15, which could trigger mandatory passive fund buying from vehicles like Invesco's QQQ shortly after listing. Market veterans are calling it structural manipulation.The Bull and Bear Case The bull case requires Starship reaching commercial operations within 18 months, Grok building a real enterprise sales engine beyond Elon's existing relationships, and the vertical integration thesis playing out as planned. Starlink as a global AI distribution layer, Grok trained on real-time X data, and orbital data centers as a structural competitive moat. The bear case is simple: every element depends on Starship staying on schedule, and if it slips again, the entire investment thesis slips with it.Executive Takeaways for Technology Leaders The valuation is not priced on current fundamentals. It is priced on a version of this business that does not exist yet and may not until the early 2030s. For technology executives evaluating SpaceX or XAI as vendors or partners, multi-year contract stability is a real consideration. The NASDAQ rule change also has downstream implications for OpenAI, Anthropic, and other AI companies in the IPO pipeline.This episode is designed for B2B SaaS and enterprise AI executives who need to understand where capital is flowing and why it matters in their own strategic context. If you are making decisions about AI vendor relationships, enterprise infrastructure partnerships, or simply need a clear-eyed read on how AI-era IPO valuations are being constructed, Ray and Peter give you the data behind the headlines, not just the hype. No investment advice. Just the numbers, the business model mechanics, and the questions every executive should be asking before the June listing.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ray Rike and Peter Buchanan dig into McKinsey's 2026 State of Organizations Report, a landmark study drawing on more than 10,000 senior executives across 15 countries and 16 industries. The central finding is both simple and uncomfortable: the vast majority of organizations are actively experimenting with AI, and that same majority reports no meaningful impact on their bottom line. This episode is about closing that gap.Topics CoveredThree Tectonic Forces Reshaping Every Organization. McKinsey identifies AI and agentic systems, economic and geopolitical fragmentation, and workforce transformation as structural shifts rather than temporary headwinds. Ray and Peter unpack why these forces are interdependent and why three in four leaders say their organizations are not ready to face what is coming, including leaders who describe themselves as optimistic.Why AI Initiatives Keep Falling Short. The diagnosis is clear: most organizations are running scattered pilots and point solutions that augment individuals but never transform the enterprise. McKinsey's data shows that organizations redesigning entire domains, marketing, finance, and operations, see dramatically greater financial impact than those pursuing isolated use cases. Ray calls this systems thinking and walks through five specific variables required to move from pilot to production at scale.Humans and AI Agents: A New Collaboration Model. Only one in four executives expect AI to take on truly agentic, autonomous roles in the next 12 to 24 months. Ray and Peter discuss why senior leaders are more conservative than younger high-potential talent, what the Hitachi and Allianz case studies reveal about workforce redesign versus workforce replacement, and why demand for AI fluency has increased 7x faster than any other skill tracked in job postings.Geopolitical Disruption and the Cost of Organizational Rigidity. Three in four leaders report a material impact from geopolitical uncertainty on their organizations. Ray and Peter discuss the Tonies case study, a German toy company that launched a production facility in Vietnam on the same day US tariffs were announced, as a model of what organizational preparedness looks like in practice. Two thirds of surveyed executives also said their organizations are overly complex and inefficient, and McKinsey's diagnosis of why traditional structural fixes are no longer working is worth hearing.People and Performance: The Four-Times Multiplier. McKinsey's data shows that organizations investing equally in people development and operational performance are four times more likely to sustain top-tier financial results, grow revenue twice as fast, and carry half the earnings volatility of peers. Ray and Peter connect this to why 80% of leaders leave non-financial motivation levers completely untouched, and to what GE's model of purpose, autonomy, recognition, and growth still gets right.Business as Change: The New Operating Condition. McKinsey's closing argument is that transformation is no longer a periodic program with a defined start and end. It is a permanent operating condition. Ray frames four implications for leaders, and Peter adds the critical point that the gap between AI activity and AI impact is an organizational problem, not a technology problem. The tools exist. The redesign is the work.Why ListenThis episode is for senior executives who are experiencing growing discomfort between how much their organization is investing in AI and how little of it is showing up in the numbers. Ray and Peter move well beyond summarizing the McKinsey findings. They connect the research to hands-on operating experience, call out where most organizations get stuck, and give listeners a practical framework for thinking about workforce redesign, change management, and leadership accountability. If you are responsible for AI strategy, organizational performance, or the people agenda at a B2B software or enterprise company, this is one of the most data-rich and actionable conversations you will find on the topic.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this week's AI to ROI: Big Story episode, Ray Rike and Peter Buchanan unpack the OpenClaw phenomenon and what it reveals about the future of personal AI agents for both individuals and enterprises.From a solo developer's side project to 1.5 million active agents in two months, OpenClaw has ignited a new category and forced every major AI company to respond. Ray and Peter break down what is working, what is still broken, and which vendors have the best shot at winning the enterprise.Top Insights from This EpisodeOpenClaw Proved the Market, But Not the Product Peter Steinberger built OpenClaw in days and attracted 1.5 million users before OpenAI acquired him and opened the codebase. The product validated massive pent-up demand for always-on personal AI agents, but security researchers at Cisco and Northeastern University quickly surfaced serious vulnerabilities, including data exfiltration risks and prompt injection without user awareness. Even the Chinese government restricted its use in state agencies. The pioneer made the promise real; the product is not yet enterprise-safe.NVIDIA Jumped In Fast with NemoClaw, But Gaps Remain NVIDIA wrapped OpenClaw with a three-layer security architecture (OpenShell runtime, privacy router, and governance layer) and launched NemoClaw at GTC with nearly 20 partners, including Box and Cisco. Box demonstrated human-matching permission controls for enterprise file workflows, and Cisco showed a zero-day vulnerability response with a full audit trail. But governance experts noted NemoClaw still lacks basic IT safety features, particularly around rollback, audit trails, and policy enforcement. Fast to market; not yet enterprise-ready.Perplexity Made a Quiet Pivot to Enterprise AI Agent Infrastructure Six months ago Perplexity was an AI search company. Today they are building a three-product personal agent suite: Perplexity Computer for multi-model orchestration across 18-plus AI models, Personal Computer for local 24-7 file and compute access on Mac, and Comet Enterprise as an AI-native browser tying the stack together. Their Samsung Galaxy S26 integration via Bixby gives them significant distribution, and their CEO framed the shift simply: traditional operating systems take instructions; AI operating systems take objectives. The model-agnostic architecture may be their biggest differentiator.Anthropic Is Playing a Different and Potentially Smarter Game Rather than shipping a standalone personal agent, Anthropic is embedding agentic capability into existing products. Claude Code scaled to an estimated $2.5 billion in ARR in nine months. Claude Cowork gives Claude direct control of Mac-level tasks with a permission layer built in. And the Microsoft partnership puts Claude Cowork as the multi-step reasoning engine inside Microsoft 365 Copilot Wave 3, branded as Copilot Coworks. A recent survey showed 66 percent of enterprise technical buyers said they purchased Claude first, with ChatGPT in the thirties. Anthropic's enterprise trust advantage may matter more than feature parity.Enterprise Adoption Will Be IT-Led and Slow by Design Unlike SaaS, which grew through decentralized, shadow-IT purchasing that bypassed central IT, personal AI agents require direct access to local files, compute, and company systems. That puts CISOs and IT leaders in the approval seat from day one. Ray and Peter agree the enterprise version of personal AI agents is likely 12 to 24 months away from broad deployment, with adoption following a managed, permission-controlled model rather than the freewheeling consumer version that drove OpenClaw's early growth.If you are a company executive, evaluating allowing, enabling or even developing personal AI agents for your company, this episode is a great listen...it might even inspire you to create your own personal AI agent for your personal use!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Eye tracking has moved far beyond the clinic and the sports performance lab. In this episode, Ray Rike sits down with Adam Gross, co-founder and CEO of HarmonEyes, to explore how AI-powered eye-tracking is being deployed in enterprise environments to measure cognitive load, predict performance degradation, and reduce costly employee burnout and attrition before problems occur.What You Will Learn:What eye tracking actually measures and why objective, passive, quantifiable eye movement data is more reliable than self-reported assessments for measuring cognitive and attention statesHow AI transforms raw eye data into actionable intelligence, including real-time model inference, individual adaptation across a population normative database of 15 million+ records, and predictive time-to-transition modelingWhy personalization at scale matters and how Harmonize uses advanced machine learning to adapt its models to individual differences in age, sex, and experience level, making population-level models actually work for every individualEnterprise use cases with measurable ROI, including pilot training in flight simulators (shorter time to proficiency), remote operator and call center environments (fatigue and overload intervention before safety incidents), and employee burnout detection over extended time horizonsThe device-agnostic deployment advantage, covering webcams, phone cameras, smart glasses, and vehicle cabin cameras as signal sources that eliminate the need to purchase dedicated hardwareHow team leaders use real-time cognitive state data to shift from reactive management to proactive intervention, reducing performance risk across shifts and high-stress operating environmentsPrivacy as a design principle, not an afterthought: Harmonize does not collect, store, or record eye tracking data or PII; the prior second of data is destroyed with each new output deliveryWhere to start as an enterprise buyer: the highest-value entry points are high-stress, high-stakes roles where burnout and performance degradation already show up as operational problems with measurable costsCareer advice for early professionals: the best defense against AI-driven job displacement is not avoidance but mastery; become the human in the loop who knows the technology bestSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Pricing is one of the most underleveraged strategic levers in B2B SaaS and AI Software. Most companies are getting it wrong. In this episode, Ray Rike sits down with Dan Balcauski, founder of Product Tranquility and a 20-year software industry veteran, to cut through the noise around consumption, usage, outcome, and hybrid pricing models. Dan brings a practitioner's perspective on when to review pricing, who should own it, and how the CFO fits into the equation.Signs Your Pricing Needs a ReviewBest-in-class companies review pricing at least quarterly -- but review does not always mean changeKey warning signals include declining net revenue retention and unexpected shifts in win/loss conversion ratesAI-native companies are iterating on pricing monthly due to rapid competitive dynamicsSales cycle length is a practical constraint: a 12-month enterprise cycle limits how frequently you can test and observe pricing changesThe Role of Customers in Pricing StrategyNever anchor your pricing strategy entirely to your existing customer base -- they carry inherent biasA practical research mix: roughly one-third existing customers, two-thirds prospectsExisting customers know your real value; prospects only know what you show them -- both perspectives matterWhen introducing a second product, maintain structural similarity in pricing tiers even if the pricing metric differsPricing Ownership and GovernanceBelow $5M ARR, the founder/CEO owns pricing; above $20M it shifts to Product or Marketing -- the gap in between is where ownership gets dangerously vagueProduct Marketing is best positioned to own pricing because it sits at the intersection of positioning and value communicationSales owning pricing is a misalignment of incentives -- "like putting Dracula in charge of the blood bank"Best practice is a pricing council with a designated decision-maker, not design by committeeDiscounting and the CFO's RoleDiscounting policy is often the easiest and fastest win -- and one of the first places Dan looks with any clientEnforcement matters as much as policy: without monitoring, no new pricing strategy will ever reach the market as intendedThe CFO plays a dual role -- operational (contracts, billing, deal desk guardrails) and strategic (modeling cash flow and KPI impact when shifting pricing models)Caution: A finance-led focus on consistent margin profiles across products can misread how different market segments actually behaveOutcome-Based Pricing: Hype vs. RealityOutcome-based pricing is "the future and always will be" -- it is not new, and it is genuinely difficult to executeTrue outcome pricing only works when you are directly in the revenue or savings transaction, as Stripe isA more practical frame is output-based pricing -- Intercom's 99 cents per resolved support ticket is a strong example of measuring a clear, attributable unit of valueIf you are involved in how best to monetize and price your B2B AI or SaaS product - this is a very valuable listen!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

While the AI headlines obsess over foundation model fundraises and hyperscaler spending, a quieter revolution is generating real, measurable returns. In this episode of AI to ROI: The Big Story, Ray Rike and Peter Buchanan break down why vertical AI companies may be building the most durable and valuable businesses in the history of enterprise software, and why most people aren't paying attention yet.What's covered in this episode:Defining Vertical AI: What separates vertical AI from horizontal tools like Microsoft Copilot or Google Workspace AI, and why the distinction matters for buyers and investors alikeA fundamentally different business model: Why vertical AI companies target labor budgets (10x the size of enterprise software budgets) rather than IT spend, and how outcome- and consumption-based pricing is replacing the traditional per-seat modelThe funding explosion: Vertical AI investment grew from $8B in 2023 to $22B in 2024 to $42B in 2025, with unicorn counts in the sector jumping nearly 6x in just two yearsHarvey (Legal AI): How this $8B+ valuation company grew ARR from $100M to $190M in just four months by orchestrating multiple AI models across legal workflows and embedding deeply into law firm operationsAbridge (Healthcare AI): How a cardiologist-founded company reached a $5.3B valuation by turning physician-patient conversations into structured clinical documentation in real time, with deep Epic EHR integration across 150+ health systemsSierra (Customer Experience AI): How Brett Taylor's enterprise AI platform hit $100M ARR in just 21 months and crossed the $10B decacorn threshold, raising the question of whether the agent era could produce the first trillion-dollar enterprise software companiesMaintainX (Industrial/Manufacturing AI):How this maintenance management platform is tackling $1.4 trillion in annual equipment failure costs across 11,000 customers and 11 million assets — with a 34% reduction in unplanned downtime for customersWhy vertical AI moats are so durable: Proprietary data that compounds with every transaction, embedded institutional knowledge that makes switching costs higher than any legacy ERP migration, and a model architecture that gets stronger as foundational models improveAdvice for enterprise buyers: Why 2026 is the year to evaluate vertical AI vendors, insist on outcome-based pricing, and start with one workflow before expandingInterested in reading the details on the Vertical AI industry and trends? Check out the AI to ROI Newsletter providing even more detail by clicking here.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the AI to ROI Podcast, host Ray Rike sits down with Amanda Kahlow, founder and CEO of 1Mind. Prior to 1Mind, Amanda was the founder and former CEO of 6sense, an early pioneer in intent data.The Vision Behind 1Mind: Amanda founded 6sense to help companies find buyers; she founded 1Mind to close them. 1Mind builds what she calls "go-to-market superhumans", AI agents that take on multiple roles across the full customer lifecycle, from inbound qualification and live demo delivery to deal closing for SMB/commercial accounts, and even post-sale onboarding, upsell, and cross-sell motions.Why the Buyer Journey Has Fundamentally Changed: Amanda argues that traditional intent data and one-way marketing are becoming obsolete. Buyers no longer follow a linear path of Google searches and form fills; they expect real-time, two-way, solution-oriented conversations, much like they get from interacting with large language models today. The old model of blasting outbound emails or routing inbound leads through a sequential SDR → AE → SE handoff chain is increasingly misaligned with how modern buyers want to engage.Top Use Cases: How Customers Deploy 1Mind: The most common starting point is the inbound website use case, customers start by placing a superhuman on the website that can qualify a visitor, deliver a personalized live demo, answer deep technical questions, and in some cases take the deal all the way to close, all on first touch. From there, customers frequently expand to the "ride-along" use case, where the superhuman joins every sales call as an always-available AI sales engineer. Human sellers retain control but can call on the superhuman in real time to answer hard questions, surface the right case study or slide, run an integration demo, or ask the qualifying questions (MEDDIC and similar) that sellers often avoid.Measurable Business Impact: Amanda shares compelling early results from enterprise customers, including a ~40% reduction in sales cycle length (from ~90 days to ~60 days) and a doubling of ACV for deals that passed through the superhuman pipeline versus the traditional pipeline. She attributes the ACV lift to getting buyers to vendor-of-choice status earlier in the cycle, eliminating the need to compete on price. 1Mind also has use cases for existing customer bases — proactively engaging customers about new features to drive upsell and cross-sell, a task that human CS teams increasingly can't keep pace with, given the speed of product development.How Customers Measure ROI: Amanda is direct: the right measurement framework is revenue impact, not top-of-funnel pipeline metrics. She encourages customers to tie superhuman performance to shortened deal cycles, higher ACV, and bottom-of-funnel revenue influence. She acknowledges there is a maturity curve — some customers start by measuring meetings booked — but the companies seeing the most value are those willing to shift away from MQL-based thinking toward board-level outcomes: revenue growth, lower CAC, and expansion revenue.Onboarding & Time to Value: 1Mind has invested heavily in its self-serve platform to reduce deployment time from a four-month process to an average of about four weeks today, with some customers going live in as little as four days. All deployments are full enterprise contracts, as 1Mind does not run pilots.Advice for Leaders on AI ROI Amanda emphasizes that realizing meaningful AI ROI requires a top-down mandate from the CEO. Incremental point solutions can improve efficiency at the margins, but the big needle-movers require new playbooks and organizational willingness to change how work gets done, not just layer AI on top of existing processes.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this AI to ROI Big Story episode, our hosts Ray Rike and Peter Buchanan dig into Deloitte's 2026 State of AI Report, a 41-page annual study surveying over 3,300 business leaders on the state of enterprise AI adoption. Deloitte calls it "The Untapped Edge," and Ray and Peter unpack exactly why.They walk through the report's seven key inflection points from scaling pilots into production and reimagining business processes, to agentic AI, sovereign AI, and physical AI, with a focus on what the data actually means for companies trying to drive real ROI in 2026.Key topics covered in this episode include:Pilot to Production: Why 54% of respondents expect a major leap in production deployment in the next 3–6 months, and why 37% of companies are still making little or no change to existing processesProductivity & Revenue: How 66% of organizations report efficiency gains today, but only 20% are seeing actual revenue impact from AI - and what it will take to close that gapBusiness Transformation: Why 84% of companies have yet to redesign jobs around AI, and what that means for long-term competitivenessAgentic AI: What the jump from 26% to 74% expected adoption of agentic AI over two years signals, and the top enterprise use cases including customer support, supply chain, R&D, and cybersecurityGovernance: Why only 21% of companies have a mature governance model for autonomous agents, and what leading companies are doing to build responsible frameworks from the ground upSovereign AI: How 83% of multinational board members view sovereign AI as at least moderately important, and why the US, Europe, and the Middle East are approaching it very differentlyRay and Peter close with a clear-eyed summary of what enterprises need to do now: close the gap between strategy and operational readiness, redesign work with an AI-first mindset, and shift focus from incremental efficiency to genuine strategic reinvention.📰 This episode is based on the February 19th edition of the AI to ROI newsletter. Subscribe at ai2roi.substack.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is the trillion-dollar AI bet actually going to pay off? In this episode of AI to ROI, hosts Ray Rike and Peter Buchanan tackle the big question head-on: with hyperscalers pouring over $600 billion into AI infrastructure this year alone, enterprises struggling to move pilots into production, and white-collar job postings already falling 16% year-over-year, the anxiety is real and justified. But so is the optimism.Ray and Peter break down why the same supply constraints slowing AI buildout may actually give companies and workers more time to adapt, why foundation model costs have plummeted 97% since 2023, and how IBM's internally deployed AI has already generated $4.5 billion in productivity savings. From healthcare transcription to AI-native go-to-market tools, the ROI is emerging, but not evenly or quickly enough for most.What We Cover in This Episode:The staggering scale of AI infrastructure spending: The five largest hyperscalers (Amazon, Microsoft, Alphabet, Meta, and Oracle) are on track to spend over $600 billion in CapEx this year, with Oracle committing 57% of its annual revenue and Microsoft 45%, ratios more typical of heavy industrial companies than software firmsWhy the build-out is slower than everyone thinks: Grid upgrade timelines in the US run 8+ years, data center construction is broadly behind schedule, and critical shortages in chips, transformers, skilled labor, and construction materials aren't expected to ease until at least 2028The pilot-to-production gap is real: Only 6% of enterprise AI projects are delivering returns within a year, and most organizations lack the frameworks and experience to move from experimentation to operational deployment at scaleTrust, hallucinations, and governance are still major blockers: Regulated industries like financial services and healthcare face compounding uncertainty, caught between pre-AI regulations still on the books and a patchwork of conflicting state, federal, and international AI policyThe workforce impact is already being felt : Salesforce cut 4,000 customer support roles, Klarna reduced headcount by 40%, white-collar job postings are down 16% year-over-year, and college graduate placement rates have dropped from 83-88% to roughly 23%, hitting data science, software development, and graphic design hardestBut the technology itself is accelerating fast: Foundation model costs have dropped 97% since early 2023, the number of available models has grown from 60 to 650, and enterprises are getting smarter about orchestrating multiple models for different tasksReal ROI stories are emerging: IBM has generated $4.5 billion in productivity savings from internally deployed AI since January 2023, automating nearly 4 million hours of work annually at $3.50 returned for every dollar investedVertical AI is gaining serious traction: Healthcare AI is the fastest-growing vertical, with one transcription tool alone saving 50,000 clinician hours. Legal, cybersecurity, customer support, and IT operations are all seeing meaningful gainsThe competitive pressure is intensifying: 54% of business leaders in a Mercer study believe they won't remain competitive in five years without AI at scale, and 92% of firms plan to increase AI budgets over the next three yearsWhy You Should Listen:If you're a business leader, investor, or professional trying to cut through AI hype and understand what's actually happening on the ground, this episode delivers the balanced, data-driven perspective that's hard to find. Ray and Peter don't just cheerlead or catastrophize; they give you the real picture: where the bottlenecks are, where the returns are genuinely showing up, and why the next two to three years of slower-than-expected adoption might actually be the window your organization needs to get AI right.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What does it actually mean for an ERP to be AI-native and why does it matter for your finance team? In this episode of the AI to ROI podcast, host Ray Rike sits down with Santiago Nestares (Santi), Founder and CEO of DualEntry, to unpack the real differences between legacy ERP systems and a ground-up AI-native platform.Santi shares the origin story behind DualEntry, born from a painful nine-month ERP implementation at his previous company that cost a team of 12 and hundreds of thousands of dollars—and explains why simply adding AI features on top of old database architecture misses the point entirely. AI, he argues, isn't a feature you plug in; it's a design philosophy that must be embedded at every layer of the product.Ray and Santi dig into one of the thorniest challenges in enterprise finance: the tension between probabilistic AI models and the zero-error standard that accounting demands. Their answer? Deterministic guardrails—approval workflows, permissioning layers, and audit trails—that let AI work freely in draft mode while keeping humans accountable for every posted transaction.You'll also hear about DualEntry's "Next Day Migration" approach, including how the company uses AI to map and migrate every transaction (not just trial balances) in hours rather than months, giving prospects a live sandbox with their own data before they ever sign a contract.What You'll LearnWhy adding AI to a legacy ERP is like "running an on-prem system with a CD on the cloud", and what truly AI-native architecture looks like insteadThe difference between deterministic and probabilistic systems, and why accounting can't afford to get it right only 99.9% of the time without the right guardrailsHow DualEntry's Next Day Migration works: AI-assisted mapping, atomic transactions, and a live sandbox demo using the prospect's own dataThe real ROI of AI-native ERP from eliminating manual categorization drudgery to enabling multi-dimensional segmentation that surfaces hidden pockets of value and riskHow Dto build audit-ready explainability without being able to explain the AI itself - by tracing every decision back to a human approvalWhy early-career finance professionals are "living the luckiest time" in the professionand how to lean into AI rather than fear itEpisode Topics at a Glance00:00 — Welcome & guest introduction00:51 — Santi's origin story: a nine-month legacy ERP nightmare that sparked DualEntry02:52 — AI-native vs. legacy ERP: what's the real difference?04:48 — Deterministic vs. probabilistic systems explained06:49 — How to identify a truly AI-first platform vs. an AI add-on10:07 — Next Day Migration: using AI to accelerate ERP transitions14:11 — Implementation team design: finance practitioners + forward-deployed engineers17:18 — Measurable ROI: from real-time bank feeds to AI-driven business insights22:52 — AI explainability, audit trails, and the permissioning layer25:06 — Rapid fire: CFO ROI variables, who owns AI ROI, and advice for early-career finance professionalsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is your company leaving money on the table by not having a Chief AI Officer? In this episode, Ray Rike and Peter Buchanan dig into groundbreaking research from IBM's Institute of Business Value, spanning 600+ executives across 22 geographies and 21 industries to unpack why dedicated AI leadership is quickly becoming non-negotiable for enterprises competing in today's market.The numbers tell a compelling story: only 26% of companies currently have a CAIO, yet those that do are seeing 10% higher ROI on their AI investments and are 24% more likely to outperform their peers. And that gap? It's widening. With 66% of existing CAIOs predicting most organizations will have someone in this role within 24 months, the window to gain a first-mover advantage is open — but not for long.Ray and Peter go deep on some of the episode's most surprising findings, including:Who's actually getting hired: 73% of CAIOs come from data-focused backgrounds, but the most effective ones are hybrid leaders equally fluent in business strategy and data science. And 57% were promoted from within, because institutional knowledge often matters more than technical expertise.Where they sit in the org chart matters enormously: CAIOs who report directly to the CEO and control the AI budget (61% do) drive far greater results than those positioned as glorified advisors without real authority.The hub-and-spoke model delivers 36% higher ROI: companies that pair a centralized AI function with embedded business unit partners outperform those with fully decentralized AI decision-making, giving them both governance and agility.Three pillars that make or break a CAIO: measurement tied to real business outcomes, cross-functional teamwork across the entire C-suite, and genuine authority to make tough decisions. Strip away any one of these and ROI suffers.What to do if you're not ready to hire one yet : Ray and Peter offer practical alternatives, from AI steering committees to centers of excellence, and explain why accountability can't be an afterthought regardless of your company's size or structure.They also tackle the growing complexity of managing AI at scale, the average large enterprise is now running 11 generative AI models and why the rise of agentic AI makes centralized leadership even more critical before things become, as Ray puts it, "a hot mess."Whether you're a Fortune 500 executive or a mid-market leader trying to figure out your AI strategy, this episode is packed with data-backed insights to help you move from AI experimentation to measurable, scalable ROI.Prefer to read more detail - check out the AI to ROI Newsletter covering this topic at: ai2roi.substack.com/p/the-chief-ai-officer-from-nice-to?r=2ldi4pSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This episode of the AI to ROI podcast, hosted by Ray Rike and Peter Buchanan, explores how leading SaaS companies are surviving the "SaaSpocalypse" - a massive market cap devaluation triggered by the rise of AI. The hosts break down the transition from traditional SaaS to AI-first models, emphasizing that simple "feature bloating" isn't enough; companies must undergo a fundamental "organ replacement" of their architecture, pricing, and culture.The discussion deep-dives into three success stories:Notion: Transformed from a document suite into an agentic execution platform through strategic acquisitions, moving toward autonomous workflows.Canva: Democratized design by making AI features invisible and intuitive, resulting in a 700% increase in AI tool usage and massive revenue growth.ServiceNow: Leveraged its 20-year history in workflow automation to pivot from seat-based pricing to task-based pricing, using AI agents to orchestrate complex enterprise processes.If you are a SaaS company executive looking for great examples of how they transitioned to be AI-first - this episode is full of great examples, strategies, and tactics.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, our host Ray Rike sits down with Jacco van der Kooij, founde and CEO, Winning by Design, to discuss the real-world deployment of "Jack" - an AI SDR that has spent the last 12 months redefining the front line of Go-To-Market (GTM) strategy. Jacco shares the "AHA" moments and hard truths discovered while moving beyond human constraints like slow response times and inconsistent qualification. Discover how treating an AI SDR (agent) as a "system" rather than a "product" initially led to 2,030 conversations, 100% CRM capture, and a $200,000 deal.Episode SummaryWinning by Design set out to prove that if AI can handle the high-risk, high-empathy role of an SDR, it can work anywhere in GTM. Over the course of a year, their AI agent, Jack was built on a foundation of 1mind logic and Clay enrichment. The agent evolved from a simple chatbot into a trained GTM operator.Key Highlights:Breaking Human Constraints: The project addressed critical issues like burnout, limited global coverage, and poor CRM hygiene that even the best human reps struggle to maintain.The "AHA" Moments: Jacco details how the team realized Jack shouldn't just "chat" but perform industrial-scale qualification while supporting buyers in their buying journey.The Power of Iteration: Initial surprises, such as a low 8% email capture rate, were overcome by designing a better "value exchange" rather than just tweaking prompts.Tangible Results: After refinement, email capture jumped to 20%, MQL conversion rose by 36%, and the system successfully captured nearly 9,000 SPICED answers.The Ultimate Do’s and Don’ts: Success requires anchoring the agent in a GTM system and iterating weekly; failures stem from treating AI like an unstructured chatbot or deploying without clear ownership.The Do'sDesign the value exchange first: Ensure the AI provides something useful to the buyer before asking for informationEarn the next step: Focus on providing enough value to merit the next stage of the conversationAnchor the agent in your GTM system: AI should scale a pre-designed, structured system rather than an improvised process.Start narrow, then expand: Focus on one specific motion and one outcome before attempting to scale.Iterate weekly: Small, frequent changes to the system drive the most significant gains in performance.Focus on the buyer's journey: Design the experience to help the buyer buy, rather than just helping the seller sell.The Don'tsTreat AI like a chatbot: Avoid unstructured "chatting," as it kills conversion rates; focus on industrial-scale qualification instead.Chase volume over quality: Remember that activity is not the same as a healthy pipeline.Hide the AI behind humans: Be transparent about using an AI agent to build trust with the buyer.Deploy without ownership: AI implementation is a Go-to-Market responsibility, not just an IT project.Expect AI to fix a bad process: AI will not fix poor GTM design; it will only expose and amplify existing flaws.Point AI at unstructured data: Do not simply point the AI at a massive folder of research; start with specific, high-quality training materials.If you are considering deploying agentic AI into your Sales organization and process, this episode is full of great insights, experiences, and measurements for your AI investment in Sales.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Are we witnessing a productivity revolution or the greatest labor displacement in history?In this detailed episode of AI to ROI, Peter Buchanan and Ray Rike break down the "Great AI Jobs War," a period of massive upheaval where corporate gleefulness meets workforce anxiety. They move past the "AI washing" to find the real metrics that define success in the age of intelligence.Key Discussion Points:The Historical Context: Ray draws parallels between the AI revolution and past disruptions like the Industrial Revolution, the cotton gin, and the assembly line, noting that AI is moving with a magnitude and speed never seen beforeThe "Mother May I" Productivity Gap: While 47% of S&P 500 companies now discuss AI in earnings calls, only 10% are seeing meaningful ROI, leaving a "staggering" 56% of companies getting "little to nothing" out of their implementationsThe Million-Dollar Employee: A deep dive into Klarna’s radical transformation—reducing headcount from 5,000 to 3,000 through attrition while doubling revenue to reach the "magic number" of $1.1 million in revenue per employeeThe War on Early Careers: Why entry-level IT hires have plummeted from 25% to 7% of all hires, and the "structural problem" of junior roles requiring 2–3 years of experience because AI is now doing the "digital grunt work"Blue-Collar as the "Gold-Collar" Future: Why the CEO of NVIDIA suggests young people skip computer programming for mechanical trades, and how salaries for AI-related construction and electrical roles have doubledCustomer Service Autonomy: How Bank of America's "Erica" handled 2 billion interactions with a 98% resolution rate in under 44 seconds, signaling a massive shift in how businesses handle scaleActionable Insights for Leaders:Measure Revenue Per Employee: This is the ultimate metric for AI productivity.Bake AI Aptitude into Hiring: Every new white-collar job description should require "AI curiosity" and applicable tool skills.Strategic Augmentation: The goal isn't just headcount reduction, but using the "free cash flow" from AI efficiencies to build a war chest for growth and sales.To read more details and subscribe to the AI to ROI Newsletter for more data-driven strategies on turning AI hype into bottom-line results.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In the second episode of AI to ROI: The Big Story, Ray Rike and Peter Buchanan analyze the critical transformation required for traditional SaaS companies to become AI-first organizations. With the SaaS industry generating $273 billion annually, the hosts warn that incumbents are under "two-front" attack: internal refactoring of legacy systems and external disruption from hyper-efficient AI-native startups.Key Highlights of the Episode:The "SaaS to AI" Pivot: Ray compares the current shift to the on-premise-to-SaaS transition of 20 years ago, noting that today’s change requires a fundamental rewrite of an operating culture rather than just adding "AI veils" like prompt enginesThe Rise of AI-Native Efficiency: Peter highlights companies like Lovable and Cursor, which have achieved hundreds of millions (or billions) in valuation in under a year with minimal staff, challenging the traditional SaaS model of linear employee growthThe Shift in Financial Metrics: The hosts discuss the new economic reality: forgetting 80% gross margins in favor of a 50-65% range to account for high token and inference costs. Success will depend on the "COGS to CAC" model, offsetting higher infrastructure costs with dramatically lower customer acquisition costs via AI automationA Roadmap for Success: To fight back, SaaS incumbents must re-architect around outcomes rather than features. This includes leveraging their "crown jewel data" and status as systems of record to build decision intelligence layers that AI-native startups lackThe HubSpot Success Story: Ray details how HubSpot successfully scrapped its 2023 roadmap within weeks of ChatGPT’s launch, shipping AI-native products in under 90 days and moving toward a "Results-as-a-Service" futureAdvice for Leaders and Employees: Ray suggests that CEOs must re-engineer every department to be AI-first, while employees should commit to learning new AI tools every month to remain employable in an increasingly automated landscape.Listen to the full episode for a deep dive into how to avoid becoming an "orphan" SaaS company in the age of Agentic AI.You can read the newsletter edition covering this topic by clicking here.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the AI to ROI podcast, host Ray sits down with Drew Laxton, CFO at Outreach, to explore the profound transformation of sales technology and financial metrics in the age of AI. Drew shares the strategic reasoning behind his return to Outreach, driven by a conviction that the company is uniquely positioned to lead the next era of agentic AI and automated revenue workflows.The conversation goes beyond the hype, offering a masterclass in how finance leaders must adapt to a software landscape that is moving from seat-based subscriptions to consumption-driven models. Drew provides an inside look at how Outreach is re-engineering its own financial playbook to account for the high compute costs and non-linear revenue growth associated with AI.Key discussion points include:The "Personal Productivity" vs. "Financial ROI" Debate: Why the initial wave of AI efficiency must eventually translate into higher quotas and lower OpEx to satisfy the board.Maintaining Margins in an AI-Native World: A deep dive into the "triumvirate" of Product, Engineering, and Finance that manages gross margins as compute costs replace traditional SaaS overhead.The Metric Recalibration: Why traditional SaaS snowballs don't work for AI, and how Outreach is using "spend-as-truth" to normalize data for NRR and CAC calculations.Agentic AI in Action: How Outreach's "revenue agents" are replacing manual prospecting with autonomous, data-tuned interactions that learn from previous customer engagement.Some Key Insights and Quotes pulled from the conversation with Drew:On the "Boring" Wins of AI: While many look for revolutionary shifts, Drew emphasizes the value in automating the mundane:"A lot of the AI tools that I’ve seen so far... there's kind of boring outcomes that are very impactful... like our QA process within the coding side has very much streamlined."On the Changing Economics of SaaS: Drew acknowledges that AI-native products fundamentally alter the 80%+ gross margin expectations of the past decade:"We do need to bring gross margin into our understanding of SaaS tools because it's just not the same... You've got to be more efficient on the go-to-market side to make the economics work."On the Rise of Consumption Pricing: The shift to variable pricing means the "snowball" metric of the past is no longer sufficient:"What is your ARR has become a lot more challenging question than it used to be... consumption is not linear on these products. It’s kind of zero, very little, and then a lot."On the Importance of Usage Over Revenue: In a variable world, product utilization becomes the primary indicator of a healthy business:"Product utilization... becomes a core signal to retention. It's not just revenue anymore, it's utilization month by month... spend is truth."Advice for Aspiring CFOs: For those looking to reach the C-suite in the AI era, Drew suggests one primary trait:"Be curious. Just be curious about everything... Ask questions, get time with the CFO or the leaders of the various organizations... wanting to understand their business has only benefited me."See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In the first episode of the AI to ROI: Big Story podcast, our co-hosts Peter Buchanan and Ray Rike discuss the emerging importance of Context Graphs in AI Software.Why are context graphs suddenly being called a trillion-dollar opportunity in enterprise AI? In this inaugural episode of The Big Story, hosts Ray Rike (CEO of Benchmarkit) and Peter Buchanan (Managing Partner of New Plan) dive into the "glue technology" that fills the missing gap in the AI stack.While traditional knowledge graphs tell you what happened, context graphs reveal the why! Context Graphs capture the decision traces, policy constraints, and precedents that make AI agents truly auditable and trustworthy. From preventing "data breakage" in regulated workflows to revolutionizing supply chain quality control, discover why context is the key to moving AI from experimental pilots to reliable production.Key TakeawaysThe Why Behind the Action: Context graphs provide the connectivity that agentic AI lacks, recording who made a decision and under what specific constraints.A Trillion-Dollar Value Add: Industry leaders believe context is a massive economic value driver for companies in the era of AI.Beyond Knowledge Graphs: Moving from simple data points to decision lineages that explain the "why" behind an event.Real-World Use Cases: Deep dives into data governance at firms like Vanguard and Prudential, and quality control in the automotive supply chain.The Vendor Landscape: Discussion on current players like Atlan, Neo4j, and Writer, and why tech giants like Microsoft and Salesforce are the "lurkers" to watch.Timestamps00:00 Introduction to the AI to ROI podcast series.02:40 Defining Context Graphs: The missing gap in the AI stack.04:15 The Trillion-Dollar Opportunity: Economic value vs. market size.06:30 Knowledge Graphs vs. Context Graphs: Moving from "what" to "why".09:20 Who should care? Roles from the CEO to the Chief Risk Officer.12:45 Use Case 1: Data Governance and preventing "downstream breakage".15:10 Use Case 2: Unifying the Go-To-Market (GTM) stack.18:30 Use Case 3: Supply chain visibility and automotive quality control.21:40 The Market Map: Current leaders and "Lurker" strategies from Big Tech.25:50 Executive Summary: Things every leader must do right now.Context graphs are a key component to turn Agentic AI software vision into Agentic AI explainability!Read the AI to ROI Newsletter on Substack to dive deeper into this week's Big Story!Subscribe at: ai2roi.substack.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to the first episode of AI to ROI, the newly re-imagined evolution of the highly successful Metrics That Measure Up podcast. In this launch episode, our host, Ray Rike sets the stage for a new era of conversations focused on turning artificial intelligence from hype into measurable business outcomes.The inaugural guest is Todd Olson, Founder and CEO of Pendo, who joins the show for an interactive, unscripted discussion on how companies should measure the real impact of AI agents inside modern SaaS and cloud organizations. Together, they explore how AI-driven “digital workers” are reshaping productivity, workflows, and operating models across the enterpriseKey topics include how companies can measure the performance and business impact of AI agents, the emerging metrics that define agent adoption and activation, and why connecting usage data to tangible outcomes like time saved, cost reduction, and revenue impact is critical for ROI. Todd also shares his perspective on outcome-based pricing, why it remains rare in AI-native software today, and what must change for it to scale.The conversation wraps with a forward-looking discussion on SaaS and AI convergence, as agents increasingly appear on org charts and product roadmaps, followed by practical advice on developing AI competencies for the next generation of business leaders.If you care about moving beyond AI experimentation toward measurable economic value, AI to ROI is your new go-to podcast.Subscribe, rate, and follow AI to ROI on your favorite podcast platform.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The world of FP&A is having its day in the spotlight. New pricing models, a new focus on near real-time business planning, the increased focus on balancing revenue growth and profitability, coupled with the dynamic impact that AI is having on the SaaS market, are all making the role of FP&A a more strategic asset. Albert Gozzi, is the founder and CEO of Aleph, a modern FP&A platform and company that recently raised $29M in their Series B financing from Khosla Ventures.During today's episode, our host, Ray Rike is joined by Albert Gozzi, Founder and CEO Aleph, to discuss the strategic future of FP&A including:The vision behind founding AlephThe evolution of AI in FP&AFP&A’s role in developing corporate strategyGrowth strategies being used in a crowded categoryIf you are a finance leader, FP&A professional or fellow B2B SaaS founder with a product purpose built for the Office of Finance, this conversation with Albert Gozzi is full of unique insights, ideas and opportunity to make your Financial Planning and Analysis organization a strategic asset!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Think about the unparalleled growth that OpenAI has experienced since the public introduction of ChatGPT on November 30, 2022. Now, think about being in finance, or being the controller who had to scale their financial processes, including closing the books to keep up with a company that has scaled from less than $100M in revenue to $10B+ in less than 3 years!?!?That was the situation that Sowmya Ranganathan, Former Controller OpenAI and CEO, Lumera found herself facing in the early part of 2023.During today's conversation with Sowmya, our host Ray Rike discusses several important lessons and use cases of AI at OpenAI in the Finance department including:Why there was no existing playbook for scaling Finance in a company like OpenAIChallenges in one of the fastest growing software companies of all timeUnderstanding compute expenses is critical to understanding the financial performance today and tomorrow at an AI companyWhy excel could not work at the scale of OpenAI (1M+ rows)Using OpenAI to enable finance to write the python code to write the statistical model to analyze financial dataWhy historic analysis is not a good place to start for forecasting in a hypergrowth, compute intensive companyLeveraging AI in the Financial close process - leveraging data warehouse information to build a repeatable processTracking GPU costs in real-time throughout the month - not an excel scale requirementHallucinations are a real concern - but once your AI is encapsulated as standard code - the concerns are minimizedHuman review on any stochastic model is a best practice - such as contract data fields from signed contracts to establish billingA long description would not do the conversation justice - so jump in and be ready to pause the audio to capture the highlights.Sowmya has been in financial leaderships at Square and Rippling in addition to OpenAI, so she has a very unique perspective on not scaling finance to meet the unparalleled growth at OpenAI, and two other hyper growth companies. If you are interested in learning about real-life stories of using AI in Finance at the world's largest AI software company - this episode is a must listen!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Christina Ross is the founder and CEO of Cube. Prior to founding a SaaS platform purpose built for everyone of those companies still using Excel (majority of companies) for business modeling but want to enhance the collaboration of business budgeting, planning and performance across the company, Christina was a corporate audit executive at GE, financial transformation consultant at Deloitte and a multi-time CFO at companies including Rent the Runway, Criteo and Eyeview.During today's episode, Christina and our host, Ray Rike discuss multiple aspects of the Financial Planning and Analysis role, department and the strategic opportunity for FP&A to materially increase the impact on business strategy and performance. Topics we discussed include:How the experiences at larger companies including GE and Deloitte shaped her view on the strategic role of FP&AThe biggest challenges facing CFOs with the FP&A function todayThe role of FP&A in business strategyThe impact of FP&A on business performanceIf you are a CFO, FP&A leader or even the CEO this episode is full of great insights and ideas on how to increase the business impact of your FP&A function!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Exit Ready Analytics is a concept that any B2B SaaS company CFO and CEO should become familiar with before entering into any potential strategic company sell initiative, deal due diligence and/or data room preparation.During this episode, Will Sullivan, Managing Partner at Predictive Analytics Partners discusses his experiences from over $30 Billion in strategic acquisitions across 20 transactions. Topics discussed include:Exit Ready Analytics - the when, what, and howThe differences between a strategic Chief Revenue Officer and a Head of SalesWhen to hire a CRO and their responsibilitiesHow to bridge the CRO and CFO relationshipIf you are a CEO, CFO or CRO in a B2B SaaS company that is either considering a strategic sell process and/or want to increase the strength of the CFO and CFO relationship - this episode has something for you!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Imagine leading Finance for a company that has made 31 acquisitions over the last six years. Then, imagine the challenges of having near real-time visibility into a recently acquired company to ensure the forecast accuracy that a Private Equity firm expects from their portfolio companies, specifically from their CFO.That is exactly the environment that Josh Schauer, CFO at insightsoftware, operates in every day! During today's episode, we discuss three main topics that are part and parcel to achieving near real-time insight into the data, performance metrics, and trends required to drive financial decisions - quickly. Those topics include:The challenges with fragmented data for financial decision-makingMoving from historic to real-time data for Financial decision-makingDeveloping a process to quickly integrate acquired companies into your financial systemsModifying budgets based on actual performance insightsIf you are considering private equity as a potential exit strategy, or are part of a company that is growing through acquisition, this is a must-listen-to episode!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Casey Woo, Founder and CEO of the Operators Guild has a very interesting journey, from being a West Point cadet, a Harvard graduate, an investment banker at Goldman Sachs, a multiple-time CFO and now the founder and CEO of the Operators Guild, and General Partner at Fog Ventures. With this background Casey has been able to experience and identify the critical role of the "scaler" in companies.During the conversation with Casey we cover multiple topics including:The role of the scaler versus specialist in businessThe personality traits of a scalerHow process and performance interact from a scaler's perspectiveHow the Operators Guild became a community of scalersIf you have ever felt that one department, one role and doing the same thing day over day was not fully leveraging your skill set and talents - this conversation is thought provoking and inspirational.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Melissa Howatson is the CFO of Vena Solutions, a $100M+ ARR cloud-based financial planning and analysis (FP&A) platform that helps companies streamline budgeting, forecasting, reporting, and financial modeling, with a strong emphasis on Excel integration.During the episode, we covered multiple topics with Melissa including:Latest trends in B2B SaaS FP&AAI in Finance - the importance of change managementMetrics that Matter at > $100M ARRMeasuring the Impact of a podcastThe 30-minute conversation hit upon multiple key trending topics including: 1) how FP&A is evolving as a strategic business partner to the other key functions; 2) why the CFO needs to lead a culture of experimentation with AI; 3) how EBITDA increases in importance as a company scales and; 4) how to measure the impact of a company sponsored podcast!If you are an aspiring CFO, or a CFO looking to scale your company beyond $100M ARR or are interested in how a world-class CFO came to be this conversation has something for you!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marcos Rivera is the founder and CEO, Pricing I/O. Marcos has a long career as a B2B Software operating executive and now leverages that experience to help B2B SaaS and AI-Native companies optimize monetization, pricing, and packaging.9 ingredients for a winning pricing strategy9 psychology concepts for pricingDifferences between SaaS and AI pricingThe value of pricing frameworksMarco was the head of pricing and packaging at Vista Equity, one of the top Private Equity firms in the B2B SaaS industry - an incredible foundation to see how leading companies leverage pricing as a strategic growth lever.Marcos started by sharing the key ingredients to developing a winning strategy, explains all nine, and highlighted why he believes the top four are most critical:Knowing the compelling value that our software deliversEstablish a clear market positionPricing that builds trustCase studies and ROI proof Have a pricing point of viewConsistent pricing messagingData-Driven pricing insightsSocial proof and testimonialsOngoing price optimizationAnother key topic discussed was the 9 psychological concepts for pricing, including:Halo effectLoss aversion (FOMO)Social proofConfirmation biasScarcity effectMere exposure effectAnchoring effectAuthority biasGoal gradient effectIf you are responsible for creating, testing, refining, or selling B2B SaaS or AI-Native products - this episode is a great way to understand the "why and how" of pricing - not just the what!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Shane Murphy-Reuter, GTM President at Calendly, has been part of multiple B2B SaaS companies during their hypergrowth phase, including Webflow, ZoomInfo, and Intercom. He recently joined Calendly to integrate the Go-to-Market functions and continue to find new opportunities to increase growth and growth efficiency. Shane is responsible for creating a more seamless customer experience across each stage of the Calendly customer journey. During today's episode, the discussion covers a wide variety of topics including:The Primary Role of the GTM Executive in a PLG CompanyThe key inflection points in scaling a PLG companyHow to evolve a brand - from the buyer’s perspective How to build an integrated GTM team in a PLG companyOne of the key aspects of this conversation is that we dive deep into how to leverage and apply B2C best practices in a B2B and PLG environment - at scale. Another key insight here is the importance of becoming a multi-product company and evolving the brand of a primarily self-service, single-product company.If you are evaluating or recently transitioned to an integrated GTM organizational structure that begins and ends with the customer experience in a Product-Led Growth environment - this conversation is for you!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Josh Aharonoff, better known as "Your CFO Guy" and the founder of Might Digits, a consultancy specializing in accounting, finance and fractional CFO services. Josh has amassed 450,000+ followers on LinkedIn, which is extremely rare for anyone, especially someone who caters to the corporate finance community!During Josh's appearance on the Metrics that Measure Up Podcast, he and our host, Ray Rike discuss a wide variety of topics including:The Role of the Fractional CFOWhen to consider an internal VP Finance or CFOWhy excel is a CFOs best friend…or NOTBuilding a LinkedIn following of 450K+ - the business case and the processIf you are a small or medium size business CEO, and are interested in when it might be the right time to bring in a VP Finance or CFO, or a finance professional looking to scale-up your personal brand, want to enhance your excel skills or considering starting your own business this conversation with Josh is chalked full of great insights, ideas and best practices!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dan Miller, CFO at RightRev, has been at the center of Usage-Based Pricing, having served as CFO at Fastly and previously as VP of Finance and General Manager at NetSuite. During today's episode, Dan and Ray discuss how Usage-Based Pricing and AI Outcome-Based Pricing are impacting ARR Reporting and Revenue Recognition Management.During today's episode, Dan and Ray cover several emerging trends in SaaS and Native-AI companies including:How variable pricing models impact revenue recognitionHow does the evolution of Outcome-Based pricing impact revenue recognitionHow AI is and will impact the Office of FinanceA few key takeaways from the episode that are worthy of a deeper dive and listen include:Understanding how contract modifications impact revenue recognition policy, process, and reportingBlended offerings including a fixed fee + usage are great for customers - but hard to manage revenue recognitionHow token and credit-based pricing impacts revenue recognition and gross profit calculation and reportingThe strategic impact and tactical challenges of transitioning from pure subscription or hybrid Usage-Based PricingIf you are a CFO or SaaS executive looking to better understand how pricing trends are challenging existing revenue recognition processes, infrastructure, and automation - this episode is a great listen!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

There is a famous saying that goes something like this "Half of my advertising (marketing) investment is wasted, the trouble is I do not know which half". This quote is credited to John Wanamaker over 100 years ago, and many marketers feel the same in 2025!Pranav Piyush is the founder and CEO, Paramark and they are attempting to make this quote not quite as relevant or correct in the future. Paramark is a marketing measurement and optimization platform designed to help businesses understand the true impact of their marketing efforts across various channels. By leveraging advanced statistical methods and machine learning, marketing and finance teams are better enabled to make data-driven decisions with confidence.During today's conversation with Pranav, we cover a wide array of topics including:Aligning Marketing Investment to Outcomes - that matter to a CFOThe concept of incrementalityHow to anticipate and measure channel specific diminishing returnsBrand vs Performance measurementsThe top 3 metrics a CMO should be sharing with their CFOIf you are B2B Marketing leader responsible for budget and delivering an ROI on that budget, or a CFO looking to better understand how to measure the ROI on marketing expenses, this episode is a great listen.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

B2B Marketing and AI Trends are evolving rapidly in 2025, and who better to discuss those trends with than Sydney Sloan, Chief Marketing Officer at G2 - the leader in B2B Software reviews!During today's episode we discuss a wide array of topics with Sydney including:Marketing Budget Allocation for B2B tech companies in 2025Peer reviews and their impact on B2B SaaS purchasesThe growth in AI - as measured by categories and vendors with G2 reviewsThe opportunity to exploit the power of AI for B2B MarketersIf you are in the B2B SaaS industry, a B2B SaaS Marketing executive or a B2B SaaS customer this episode has something for you!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The Data and Sales Intelligence category includes a list of well known players such as ZoomInfo, Seamless and Apollo, and are now joined by new entrants like Clay and Lusha. But where does a pure play Data (Sales) Intelligence provider like People Data Labs fit - their CEO, Brian Eisenberg who has grown through the ranks at People Data Labs from Data Engineer to CEO provides his insights in how the Sales (People) Intelligence category is evolving.During today's episode, our host Ray Rike discusses multiple topics with Ben including:The top challenges customers are facing with today’s Data Intelligence solutions?How does People Data Labs ensure they remain compliant with the evolving data privacy laws and vendor specific Terms of ServiceHow will next generation Data Intelligence solutions address the current challengesBen’s personal career journey - from Data Engineer to CEO in 7 yearsIf your B2B SaaS or technology company uses Sales Intelligence Data to feed your outbound machine - this conversation is a must listen!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Bill Koefoed is the CFO of OneStream which went public in 2024 after being acquired by KKR in 2021. The CFO journey from being a Private Equity owned company to preparing for an IPO and beyond, while also transitioning from a perpetual license model to a subscription business is a fascinating experience and story.During this episode, Bill and Ray discuss a wide variety of topics and experiences during this CFO journey including:How the role of CFO changes in a Private Equity majority owned B2B SaaS companyThe lessons learned in transitioning from a perpetual license to a B2B SaaS subscription modelThe preparation required to take a B2B SaaS company publicHow technology has changed the Office of Finance and the CFO roleThe journey to becoming a B2B SaaS CFO - the Bill Koefoed pathBill mentioned that he had previously been the CFO of a Private equity-owned company. Once you have the first experience under your belt, your reputation as a Private equity-experienced CFO will be the access ticket to the next CFO position.Bill highlighted the importance that pricing plays when first starting the transition from perpetual to subscription. The cross-over or break-even point was targeted at 5 years, which essentially says that beginning in Year 6 the benefit of operating in a subscription business model materially increase.Bill shared the metrics that he prioritizes, and he started with the 98% Gross Revenue Retention Rate which highlights their priority and focus on customer satisfaction. In addition, Net New ARR, Net Revenue Retention and how much of Net New ARR is coming from "new customers" versus customer expansion. In fact, new logo acquisition is a top focus for 2025, including going from 1,600 customer to 10,000 plus new customers.  New customer ARR contributes about 60% of the total new ARR.What customer acquisition efficiency metric does Bill use - he really likes LTV:CAC Ratio and the CAC Payback Period which they currently stand at 24 months - but that is with a $340K ACV!If you are a CFO in a B2B SaaS company, or are an executive leader looking to evolve into a private equity acquisition or initial public offering - this conversation with Bill Koefoed, Chief Financial Officer at OneStream is an enlightening conversation that covers a broad variety of insights, experiences and inspiration!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Aviv Canaani is the Chief Revenue Officer at Datarails who recently transformed their Go-to-Market motion to be primarily inbound from the traditional outbound motion. During today's episode Aviv and our host, Ray Rike dive deep into multiple GTM strategies and measurements including:Top performance measurements for a B2B SaaS CROThe catalyst for transitioning to an "inbound GTM motion"The ROI for an Inbound vs Outbound GTM motionLeveraging Social Media to build awareness with the Office of FinanceMeasuring ROI in content and media investmentsAviv was initially the head of Marketing and then took overall responsibility for Sales, Marketing, and Customer Success. Soon thereafter, he quickly realized that they needed to increase the efficiency of their Go-to-Market investments and associated processes. But before we dive into the transition from a 90% outbound strategy, we discussed the top metrics for CROs.First, Aviv highlighted that CAC efficiency, as measured by the CAC Payback Period (CPP) where they have decreased the CAC Payback Period by 50% is a TOP metric for CROs. One of the first topics we discussed was the primary "input signals" to decrease the payback period. One of the things Aviv highlighted is that increasing the quality of leads that are provided to AEs was a good first place to start. The ultimate goal was that AEs could spend the majority of their time on selling and closing opportunities, versus doing cold outbound prospecting.Another key tactic was to ensure he had a very predictable way to know for each dollar investment in Go-to-Market, what the expected outcomes as measured by new customer ARR could be generated. Using a "waterfall" methodology, Aviv knows that for every dollar of Marketing spend what are the predictable outcomes as measured by meetings, opportunities, new customers, and the associated new ARR.By having a predictable model, Aviv can go to the CFO and confidently show what the ROI is for every dollar invested in Marketing, they can begin to allocate more to brand building which will have more impact in a few quarters versus just measuring the short-term ROI on Demand investments.Next, we dove into the transition from a primarily outbound GTM motion to primarily an inbound GTM motion. First, in 2022, even though SDRs were hitting their "meeting goals" they were not converting to customers. As a result, they increased the focus on "high intent" leads which increased the efficiency of the GTM investments. One of the primary measurements they used to validate the inbound focus, they found a 3x-4x higher win rate, and a shorter sales cycle all leading to increased GTM efficiency.What is the primary source for 90% of new ARR coming from inbound? First, they brought on a team of B2C Marketing professionals who used paid search, paid media, and social media strategies to drive higher intent inbounds. This even included the use of Instagram and TikTok...to reach the Office of Finance! In addition, they focused on SEO and even a podcast to get their brand and message in front of finance executives. Now that the brand has been enhanced through the media investments will over time also increase the efficiency of the outbound activities.Another strategy was to divide outbound and inbound SDRs, and in fact, a majority of the outbound SDRs are now located in the Philippines which maintained effectiveness and increased efficiency as measured by outputs (New ARR) versus inputs (SDR investments).If you are a B2B SaaS CFO or GTM leader, this conversation with Aviv is full of ideas, insights, and successful experiences in evolving the GTM playbook!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This episode of the “Metrics that Measure Up” Podcast features James D. Wilton, Author of Capturing Value - The Definitive Guide to Transforming SaaS Pricing and Unshackling Growth and Managing Partner, MonevateDuring our conversation we covered four primary topics with James:Actual Value versus Perceived Value - which matters mostPrice Metric Evaluation CriteriaInnovative Monetization StrategiesMeasuring a Customer’s Willingness to PayIf you are considering changing your existing pricing model and/or introducing new pricing for either an existing or new product - like a new AI module, this conversation is loaded with great ideas and insights into the different pricing models being used in the B2B SaaS industry!!!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.