AI to ROI

Ray Rike
  • 4.8 (41)
  • Management
  • Weekly

AI to ROI is a podcast that shares how enterprises translate AI investments into measurable business value. Hosted by Ray Rike, Founder and CEO of Benchmarkit, the show features senior enterprise leaders and AI software executives who share how AI initiatives move from pilots to production, and how ROI is actually measured and achieved. In addition, each week, we publish a bonus episode with AI to ROI Newsletter co-author, Peter Buchanan to discuss the Big Story of the Week. The AI to ROI podcast is the evolution of the original "Metrics to Measure Up" podcast.

  1. 2d ago

    AI & Organization Design - a Return on Intelligence Conversation with Brian Solis, Head of Innovation ServiceNow

    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 Covered Redesign 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.

    AI & Organization Design - a Return on Intelligence Conversation with Brian Solis, Head of Innovation ServiceNow

  2. 5d ago

    The Quest to Create an Auditable, Trackable, and Accurate AI Budget

    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 Takeaways Tracking 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.

    The Quest to Create an Auditable, Trackable, and Accurate AI Budget

  3. Oct 15

    Morally Binding AI Governance Isn’t Legally Binding AI Regulation: AI Safety, Muse, and Data Center Financing

    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.

    Morally Binding AI Governance Isn’t Legally Binding AI Regulation: AI Safety, Muse, and Data Center Financing

  4. Oct 6

    Rogue Agents Are the Big New AI Risk

    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 Subscribe to the AI to ROI podcast, leave a five-star rating, and read the full August 18th edition 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.

    Rogue Agents Are the Big New AI Risk

  5. Oct 6

    The CEO and Employee Role in Delivering AI ROI - with Jeff McMillan, CEO of McMillan AI & Former Head of AI, Morgan Stanley

    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. 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.

    The CEO and Employee Role in Delivering AI ROI - with Jeff McMillan, CEO of McMillan AI & Former Head of AI, Morgan Stanley

  6. Oct 1

    AI Pacing Is So Yesterday: We Need to Treat AI as Critical U.S. Infrastructure

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

    AI Pacing Is So Yesterday: We Need to Treat AI as Critical U.S. Infrastructure

  7. Sep 23

    Six Challenges to Scaling Agentic AI in the Enterprise

    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 stopRead the full September 15 Big Story and subscribe to the AI to ROI newsletter 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.

    Six Challenges to Scaling Agentic AI in the Enterprise

  8. Sep 16

    Building an AI-First organization with Francis Brero, VP AI and Strategy HG Insights

    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. 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.

    Building an AI-First organization with Francis Brero, VP AI and Strategy HG Insights
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4.8
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About

AI to ROI is a podcast that shares how enterprises translate AI investments into measurable business value. Hosted by Ray Rike, Founder and CEO of Benchmarkit, the show features senior enterprise leaders and AI software executives who share how AI initiatives move from pilots to production, and how ROI is actually measured and achieved. In addition, each week, we publish a bonus episode with AI to ROI Newsletter co-author, Peter Buchanan to discuss the Big Story of the Week. The AI to ROI podcast is the evolution of the original "Metrics to Measure Up" podcast.

Information

  • Creator
    Ray Rike
  • Years Active
    2020 - 2026
  • Episodes
    272
  • Rating
    Clean
  • Show Website
    AI to ROI

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