AI to ROI

Brian Solis, Head of Global Innovation at ServiceNow, joins Ray Rike to explain why AI strategy is an organizational design challenge, not simply a technology deployment.The conversation moves beyond making today's work faster to ask what companies could do differently with AI. Brian and Ray discuss the gap between AI ambition and execution, why end-to-end workflows need redesign, and how leaders can turn individual productivity gains into measurable business value.What Ray and Brian CoveredRedesign the work before adding agents. Customer journeys cross functions, but systems, data, budgets, and metrics often do not. Brian explains why connecting people and processes is part of the work, alongside governance, accountability, security, and observability.AI maturity is more than adoption. Drawing on ServiceNow's Enterprise AI Maturity Index, Brian discusses the distance between experimenting with AI and changing how work flows across the enterprise.Iteration and innovation need different goals. Improving today's work can free capacity, but the return depends on what happens next. Brian's two-mode approach pairs efficiency gains with deliberate investment in new work, customer relationships, and growth.Measure outcomes, not activity. Ray and Brian connect return on intelligence to revenue per employee, output relative to human effort, and the economic value created when people and agents work together.Leadership starts with better questions. Brian shares lessons from his work with executives and his book, Infinite Company, on learning, unlearning, and designing organizations around outcomes rather than the traditional org chart.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.

Companies have AI spend tracking in place, but very few can forecast it. In this Big Story episode of AI to ROI, Ray Rike and Peter Buchanan walk through the October 13 newsletter, “The Quest to Create an Auditable, Trackable, and Accurate AI Budget.” The discussion draws on Benchmarkit’s research with Mavvrik covering 396 organizations, which was recently cited by the Wall Street Journal and Apollo.The headline finding is that tracking is not the same as forecasting. Nearly every company tracks AI infrastructure costs, yet only 11% forecast AI spend within 10% accuracy, and more than half miss by over 26%. The miss affects the business directly. 62% of respondents say unexpected AI costs materially changed a business decision in the past year, about one in four stopped an initiative, and 48% of companies that embed LLMs in their products had to raise prices.Ray and Peter cover the budget overruns at Uber, Rippling, and Amazon, along with the shared pathologies behind them: misaligned incentives like token maxing, usage that went unforecasted and untracked, and no measurable ROI. They also share results from their own model test. A model that cost $0.15 per attempt ended up costing almost $57 per completed task once failures, retries, and human rework were included.Key TakeawaysTracking is not visibility: Most companies see spend at a summary level, but only about a third track the cost of the agents they have in production, and 46% have no real-time alerting for overruns.Six cost layers: A full AI budget covers tokens and inference, AI infrastructure, agents, AI management applications, labor, and the growing verification tax.Price the finished task, not the attempt: Completion rates, retry multipliers, and human rework drive total cost far more than list token prices do.A six-step CFO framework: Calculate the full cost of a finished task, trace every dollar to an owner through chargebacks, set guardrails and automatic stops, govern workflows rather than people, and audit invoices every quarter.The verification tax is real: In a survey of 6,000 senior executives, 90% saw no measurable AI productivity impact, in large part because of the labor needed to review and correct AI output.The bottom line: measure AI cost at the most granular level possible, and always tie that cost to the economic value of the outcome.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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

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 CoveredThe 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.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.

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 wayWhy Anthropic and Meta experienced nearly identical failures through the same third-party evaluator, and why "sloppy" understates a failure mode that repeated across three labsThe 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 riskFive 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 agentsWhy the Computer Fraud and Abuse Act struggles to address AI-initiated attacks, and why contract language and insurance terms will move faster than new legislationPractical 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 metricsSubscribe to the AI to ROI podcast, leave a five-star rating, and read the full August 18th 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.