AI to ROI

When Dario Amodei published his essay calling on frontier labs to pace their releases, the leaders of the largest competing labs endorsed it within a weekend, and Microsoft followed on Monday.In this episode, Ray Rike and Peter Buchanan examine what "pacing" actually means, why each lab defines it differently, and why self-governance by frontier labs leaves enterprises, governments, and emerging AI players without a seat at the table. They then make the case for formally designating frontier AI as part of U.S. critical infrastructure, with government-hired evaluators funded by lab fees.Key topics covered:Anthropic's three-step pacing plan, including outside evaluators with employee-level access and a commitment of up to $1 billion over five years for Accenture to serve as an independent evaluatorHow the Anthropic (FAA model), Google DeepMind (FINRA model), and OpenAI (IAEA model) approaches differ, and why Cohere's Aidan Gomez compared the arrangement to the bond rating agency cartel that preceded the 2008 financial crisisCrowdStrike threat data showing 88% of observed exploits landed within 48 hours of disclosure, with AI agent activity generating 2.5x the event volume of human activityWhy the government is under-resourced to respond: NIST's AI standards center runs on a $15 million budget against an estimated $84 million need, and CISA headcount has fallen from roughly 3,400 to about 2,300How the existing 16-sector critical infrastructure framework works, and a proposed tiered oversight model where only the most powerful frontier models carry the heaviest requirementsWhat enterprise executives should do now: plan for exploit windows measured in hours, monitor agent activity separately from human activity, protect model weights, and build 72-hour incident reporting capability before regulation requires itSubscribe to the AI to ROI podcast, leave a five-star rating, and let us know which stories, topics, and guests you would like us to cover next.Read the full September 22nd edition 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.

Deloitte research shows 85% of enterprises are working on agentic AI use cases, yet only 5% have put one into production that delivers meaningful ROI. In this episode of the AI to ROI Big Story, Ray Rike and Peter Buchanan walk through the six challenges that separate the enterprises scaling agentic AI from those stuck in pilot mode: data readiness, production reliability, governance, cost visibility and ROI measurement, orchestration, and workforce readiness. Drawing on research from Gartner, Deloitte, McKinsey, KPMG, VentureBeat, and the Benchmarkit and Mavvrik 2026 State of AI Cost Governance report, they share how Amazon, FedEx, Lowe's, Cisco, Walmart, and Petrobras are approaching each challenge, and why the winners treat agentic AI as an operating model problem before a technology problem.Covered in this episode:Why clean data and a shared semantic layer are the foundation, with Google finding agent accuracy above 90% when data is standardized compared to 60% to 70% without itHow Amazon defines agent reliability through consistency, robustness, predictability, and safety, and why it builds an undo path into every agentThe governance gap: 85% of IT teams believe every agent is accounted for, but only 42% can say who owns them, and only 13% of enterprises believe they have adequate governance for the agent volumes Gartner projectsWhy 98% of enterprises track AI infrastructure spend but only 11% can forecast it within 10%, and why cost per successful outcome is the metric that matters, illustrated by Uber exhausting its annual AI budget by April and Petrobras finding $120 million in tax savings in three weeksOrchestration sprawl across multiple vendor platforms, and why workforce resistance is a myth when only 2% of technology leaders report significant employee pushbackWhy every agentic AI pilot should have kill criteria agreed before development starts, with only three valid outcomes: scale, redesign, or stopRead the full September 15 Big Story and subscribe to the AI to ROI newsletter 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.

Francis Brero, VP of AI Strategy at HG Insights, joins Ray Rike to unpack what it actually takes to move a established software company toward an AI first operating model. Francis founded MadKudu, which was acquired by HG Insights, and was given a mandate to infuse AI into both the product and the internal operating processes. The conversation covers how HG Insights defines AI first, how the company organized around it, who owns execution across the functional groups, and who is accountable for the return on those AI investments.What Ray and Francis CoveredThe buyer is becoming an agent. Francis rebuilt the product assumption from an analyst consuming a data file to an agent consuming data on demand. Shipping an MCP interface was the first build in his first three weeks, and it opened the door to every customer already standing up agentic go-to-market systems.Velocity is the separator between legacy and AI native. An agentic software development lifecycle changed shipping pace, and Francis makes the case that bolting an engine onto a bicycle only gets you so far before you have to build the motorcycle.Pricing has to be re-architected for agent discoverability. Unique, high value data assets get priced down so an agent will actually reach for them, commoditized assets absorb more of the price, and data delivered through MCP is leased for a single workflow rather than sold into the customer warehouse. Both changes alter the shape of gross margin.The $5, $50, $500 decomposition test. Every job to be done gets broken into atomic tasks, and each task gets a price the business would pay to outsource it. Five dollar tasks get automated. Francis notes the hardest part is that most operators have never decomposed their own work that far.Centralized ownership of AI ROI. Francis owns the leading indicator and the productivity gain, the functional leader still owns the lagging indicator, and the two present the business case jointly to the CFO and CEO. Centralization is also the control point that prevents engineering spend from exploding when everyone gets model access.Two lessons learned. Operationally, he moved to second order process redesign before the organization understood first order automation, and had to reset to crawl, walk, run. On the product side, AI generated ten times more code and therefore more total defects, until he introduced adversarial review across different model families rather than same family self review.If you are finding value in these conversations, please subscribe on your favorite podcast app, leave a five star rating, and connect with Ray Rike on LinkedIn to suggest future guests.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Our co-hosts, Ray Rike and Peter Buchanan, open this AI to ROI: Big Story edition with a confession. Reviewing their own newsletter coverage over the past several months, more than half of the Friday news and analysis stories were about AI model companies; another twenty to thirty percent covered semiconductors, data centers, and SaaS to AI incumbents; and AI native application companies accounted for less than five percent. That imbalance is the starting point for the question behind this episode: where are the killer AI native application companies, and what is standing between them and the breakout status their funding levels imply?What Ray and Peter CoveredCapital concentration is setting the narrative. An August analysis from The Information found Anthropic and OpenAI now capture 89 cents of every dollar spent across the 35 largest AI startups, up four and a half points year over year. Ray pushes back on the forecast of a one trillion dollar AI software market by 2030, noting that SaaS took roughly twenty years to build a three hundred billion dollar base and the full cloud stack took twenty years to reach roughly eight hundred fifty billion.A three-front squeeze on the application layer. Model companies are moving up the stack because the model itself will not be the durable moat, the same way Oracle and the client-server database vendors moved into applications in the 1990s. Systems of record and data platforms are re-architecting as AI-first and buying what they cannot build fast enough. And coding agents have made build versus buy credible again, with McKinsey reporting 32 percent of organizations have already decided against purchasing at least one software product or feature because they could build it internally.COGS is the new CAC. AI native applications running on third-party models are delivering 50 to 65 percent gross margins rather than 80 percent, which pulls capital away from customer acquisition and raises the dependence on outside funding. Usage-based pricing amplifies the problem when the pricing architecture and guardrails were not designed to protect margin.Retention is still experimental. Some AI native application companies report churn between 25 and 45 percent, roughly triple a mature SaaS benchmark, which reflects how little is deeply embedded yet and how low switching costs remain in the early departmental deployment.The visibility math. Tool Radar tracked roughly 11,500 mentions across 387 tech media sources between February and August. Only seven percent of the more than 10,000 software products tracked received any coverage at all, and ChatGPT alone accounted for close to 22 percent of the sample.What the breakouts have in common. Harvey built for legal depth, moved onto a purpose-built model to fix its cost structure, and staffs 40 to 50 percent of pre-sales with people from the legal industry. Abridge went narrow on clinical documentation. EvenUp prices against recovered damages rather than seats. Fieldguide earned the AIUC-1 certification and uses audit firms as a distribution channel. The five traits Peter pulls out of those cases include embedded domain context, an expensive workflow worth solving, proprietary context accumulated from customer interaction, expansion from single task to full system, and outcome-aligned pricing.Ray closes with the position he will stake his reputation on. The AI native applications that win will own complex multi step workflows and the data around them, make the economic value obvious and tightly coupled to the pricing model, and make the underlying model the least interesting part of the value proposition.If you are getting value from these episodes, please subscribe to the AI to ROI podcast, give us a five star rating, and reach out to Ray Rike on LinkedIn if you are an AI native application company or an enterprise executive with an AI success story to share.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ketan Karkhanis, CEO of ThoughtSpot joins Ray Rike to make the case that the AI conversation has been stuck on models, tokens, and pilots when it should have started with a KPI. Drawing on his time building Einstein Analytics and running Sales Cloud at Salesforce, and now leading ThoughtSpot, Ketan lays out how enterprises move from AI aspiration to measured outcomes by rewiring the operating model, not the org chart.Topics covered in this episode:Every AI investment conversation is an ROI conversation. Why AI projects should begin with the KPI to be improved rather than the model to be deployed, and why "AI saves you two hours a day" is the lazy version of the value caseWhy pilots are where value goes to die. The case for starting with the customer and working backwards into process redesign, and ThoughtSpot's Spot30 program that targets one measurable outcome in 30 days instead of an open-ended proof of conceptFunctional KPIs as the buildup to income statement impact. Financial ROI is a derivative of functional gains, so the practical path runs through metrics like NPS, average deal size, cycle time, and DSO before it reaches revenue and marginToken anxiety and the gross margin problem. Ray shares benchmarking data showing 49% of software companies embedding generative AI had to reprice to protect gross margin and 25% halted an AI initiative over cost overruns. Ketan explains ThoughtSpot's credit-based pricing and the architecture behind it, using LLMs only for intent resolution rather than as a wrapper that resells tokensPricing predictability for the budget holder. Why CFOs do not need certainty, they need a unit of consumption they can forecast, and why tying pricing to the customer's own business model is what makes the spend defensibleCustomer success as the ultimate AI to ROI metric. How ThoughtSpot renamed customer success to customer outcomes and FDEs to AI outcome managers, opens every staff meeting with a five-metric customer review, and assigns a C-suite owner to every AI initiativeFollow the AI to ROI podcast on your favorite podcast app, and connect with Ray Rike on LinkedIn to suggest future guests.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.