AI to ROI

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.

For the comprehensive research report, click here, and to subscribe to the AI to ROI newsletter at ai2roi.substack.com.
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