AI to ROI

In this special edition of AI to ROI, Ray and Peter step outside the usual format to break down four of the most important stories from the latest AI to ROI News and Analysis edition.

Recording from Bratislava, Slovakia - Ray joins Peter to work through what the White House’s voluntary AI safety pact actually commits the labs to, why OpenAI’s string of safety and disclosure incidents should factor into enterprise procurement, whether small businesses will trust Meta’s Muse with their payment and ad accounts, and why rising Treasury yields and delayed projects are putting pressure on AI data center financing.

Throughout the episode, Ray and Peter translate each headline into specific governance and buying questions for executives.

• The White House AI Safety Pact: Six major AI companies signed a one-page agreement with no penalties or timelines. Ray sees it as a starting point, Peter sees a publicity event, and both agree Congress needs to treat frontier AI more like critical infrastructure.

• External auditors and conflicts of interest: Anthropic named Accenture as its first embedded evaluator. The hosts weigh the value of year-round auditor access against the conflict created by Accenture’s large Claude implementation practice.

• OpenAI’s safety and disclosure problem: The canceled Astra release, more than fifteen incidents in recent months, and slow disclosure on events like the Hugging Face breach. Ray and Peter separate agent containment, which is an industry-wide problem, from disclosure, which is a vendor problem that belongs in contracts.

• Meta Muse for Small Business: Distribution into roughly 200 million small businesses gives Muse a real advantage, but low consumer trust in Meta and Amazon’s decision to block Muse show the friction. The recommendation is to start with reporting and ad drafts and hold off on payment integration.

• Shadow AI inside the enterprise: Employees who get value from an AI agent will keep using it, so companies need a third-party agent policy before dependency sets in.

• Data center financing warning signs: Oracle’s force majeure letter on the New Mexico Stargate campus, a 5.17% ten-year Treasury yield, Bain’s estimate that most required AI revenue must come from markets that barely exist today, Moody’s figure of $2.8 trillion in off-balance-sheet commitments, and NVIDIA’s circular financing deals.

For a deeper analysis of these stories - check out the detailed newsletter edition by clicking here.

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Jeff McMillan, Founder and CEO of McMillan AI and former Head of AI at Morgan Stanley, joins Ray Rike to explain what separates AI programs that deliver measurable returns from those that stall in pilot mode.

Jeff led Morgan Stanley's partnership with OpenAI in 2022, when the firm became OpenAI's first financial services customer, and later ran the firmwide AI function focused on education, infrastructure, repeatable deployment processes and measurement.

The conversation covers the foundation companies need before they scale AI, how to build an accountable business case, the CFO's role in measuring ROI, and how governance can speed adoption instead of slowing it.

What Ray and Jeff Covered

• The AI stack is more than tools and models. Jeff argues most firms are underinvesting in four infrastructure layers: data quality across unstructured content such as PDFs, emails and Slack; a semantic layer that defines customers and products consistently; an AI governance layer that monitors other AI with humans above the loop; and an orchestration layer that will eventually coordinate hundreds or thousands of agents.

• Process redesign comes before AI investment. Many organizations cannot describe how work actually gets done, and senior leaders often do not know the steps in their own workflows. Putting AI on top of a bad process repeats automation's oldest problem.

• The business case starts with a benchmark. Teams should document current state throughput, cycle time, rework rate, and cost, then size the opportunity before building. A 4% efficiency gain is not worth pursuing, and production results need roughly three months of post-deployment data to validate.

• Efficiency only becomes ROI when you redeploy capacity. Creating 30% more capacity across 100 people produces no return if the time is not moved to higher-value work. Ray connects this to Parkinson's Law and to revenue per employee as a macro indicator of AI productivity.

• The CFO owns the measurement framework, not the outcome. Business owners define and capture the benefits. Finance builds the benchmarking framework, teaches teams how to measure and independently validates results, with an auditable record of labor, token and development cost for every project. Jeff recommends the KPI team sit in finance rather than operations.

• Risk-tiered governance and carved-out capacity. A risk-based tiering process matches approvals and monitoring to actual exposure, and a credible owner with both a risk and a commercial mindset keeps use cases moving. Jeff's top recommendation for CFOs and go-to-market leaders is to pick three high-impact, easier-to-deploy use cases and take the best person on the team out of their day job to deliver them.

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This summer, three frontier labs found that their models had hacked real companies without anyone telling them to.

In this episode, Ray Rike and Peter Buchanan walk through the OpenAI, Anthropic, and Meta incidents, why all three traced back to the same third-party testing contractor and the same misconfigured sandbox, and what it means for every enterprise deploying AI agents. They cover the five questions buyers should be asking vendors now, the legal and insurance gaps that leave companies exposed, and why agent risk needs a named owner at the board level.

Key topics covered:

• How OpenAI models with weakened safety guardrails found a zero-day, reached the open internet, and broke into Hugging Face, leaving hundreds of thousands of coordinating notes for each other along the way

• Why Anthropic and Meta experienced nearly identical failures through the same third-party evaluator, and why "sloppy" understates a failure mode that repeated across three labs

• The enterprise readiness gap: 84% of organizations doubt they could pass an AI compliance audit of their agents, and only one in five keeps a real-time inventory of agent risk

• Five buyer questions, including whether vendors should provide safety certification the way SaaS vendors provide SOC 2 reports, and whether standard cyber insurance covers damage caused by your own AI agents

• Why the Computer Fraud and Abuse Act struggles to address AI-initiated attacks, and why contract language and insurance terms will move faster than new legislation

• Practical controls for labs and enterprises: default-deny outbound network access, immutable audit trails, an inventory of every privileged agent, human approval for high blast radius actions, and quarterly board-level agent risk metrics

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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 evaluator

• How 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 crisis

• CrowdStrike 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 activity

• Why 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 2,300

• How the existing 16-sector critical infrastructure framework works, and a proposed tiered oversight model where only the most powerful frontier models carry the heaviest requirements

• What 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 it
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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 it

• How Amazon defines agent reliability through consistency, robustness, predictability, and safety, and why it builds an undo path into every agent

• The 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 projects

• Why 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 weeks

• Orchestration sprawl across multiple vendor platforms, and why workforce resistance is a myth when only 2% of technology leaders report significant employee pushback

• Why every agentic AI pilot should have kill criteria agreed before development starts, with only three valid outcomes: scale, redesign, or stop
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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 Covered

• The 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.

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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 Covered

• Capital 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.

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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 case

• Why 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 concept

• Functional 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 margin

• Token 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 tokens

• Pricing 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 defensible

• Customer 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 initiative

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For the first time, the two leading model companies can be compared on an apples to apples basis. In Q2 2026, Anthropic booked $11.6B against OpenAI's $6.7B, and posted roughly $300M in operating profit while OpenAI's operating loss widened to $12.3B.

Ray Rike and Peter Buchanan unpack what that actually signals to an enterprise CFO signing a multi-year platform commitment, and why one profitable quarter does not settle a market where both companies carry hundreds of billions in infrastructure obligations.

The bigger argument: OpenAI and Anthropic are running near-identical playbooks. Comparable frontier models, comparable pricing, the same enterprise logos, competing coding agents, parallel vertical pushes, and mirrored forward-deployed engineering ventures. When strategy converges, execution becomes the differentiator.

What Ray and Peter cover in this episode:

• The market being chased: $64B in AI model platforms in 2026 per Gartner, agentic coding tools growing from $4B to $30B by 2030, and implementation services from $18B to $76B by 2031
• Claude Code economics, including median enterprise spend tripling from $69 to $219 per month, and why Ray wants to see gross and net revenue retention before calling it durable
• Codex bundled inside ChatGPT, 100,000 migration signups in 10 days, and why signups without retention remain a vanity metric
• The go to market gap: Anthropic's stable commercial leadership bench versus four sales leaders in two years at OpenAI, and what that churn costs in enterprise continuity
• Channel conflict as both companies ship vertical products that compete with the partners embedding their models, and Harvey's move away from Claude as the early warning
• Distribution bets: the Salesforce and Claude Force deal with $300M in committed token spend, Ode at $1.5B, and Deploy Co at $4B
• The insurgents that make this a multi front war: Google's install base and invisible AI distribution, Microsoft pushing lower cost MAI models with 13% orchestration share, NVIDIA's IBM-style ecosystem play from the 1980s, and open-weight models now at 72% of OpenRouter tokens
• A rapid-fire close on the five moves each company needs to make to win

The takeaway

This is not OpenAI versus Anthropic. It is both of them defending against hyperscalers above and open-weight economics below while battling each other for the same enterprise budget.

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Ray Rike sits down with co-host Peter Buchanan to unpack Benchmarkit's August 11th research edition, based on a 396-company survey conducted in partnership with Mavvrik. The conversation moves past the "is AI adoption happening" question and into the harder one: do enterprises actually understand what AI is costing them, and can they see it coming.

• Tracking isn't the same as forecasting. 98% of companies say they track AI costs and 44% call themselves advanced, but only 11% can forecast spend within 10% of budget, and 54% miss their forecast by more than 26%.

• AI costs break down into three distinct patterns: product AI (impacts cost of goods sold and pricing), workflow automation (impacts operating margin), and true agentic AI (non-deterministic, harder to predict, and prone to costly retry loops).

• Data platforms, not LLM tokens, are the top driver of budget misses. 47% of companies cite data storage and platform costs as the leading cause, ahead of token costs, with GPU, network, and human-in-the-loop costs frequently left out entirely.

• Granular attribution remains rare. Only 36% of organizations running agentic workloads can attribute cost by individual agent, and just 29% can attribute AI coding tool spend down to the individual developer.

• The business consequences are real and already happening. 49% of AI product companies have had to reprice, 25% have halted an AI initiative outright, and 40% have had to report an AI cost overrun to their board.

• Multi-cloud and multi-model complexity is compounding the problem. The average company now uses 2.4 models and 68% run hybrid hosted and on-prem AI workloads, adding new layers of capital, depreciation, and orchestration cost that finance often isn't capturing.

• The report closes with role-specific fixes for the CFO, CIO/CTO, FinOps, product leadership, and engineering, with the throughline being visibility first, economic value second, before committing to multi-million dollar AI bets.

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