Pricing is one of the most underleveraged strategic levers in B2B SaaS and AI Software. Most companies are getting it wrong. In this episode, Ray Rike sits down with Dan Balcauski, founder of Product Tranquility and a 20-year software industry veteran, to cut through the noise around consumption, usage, outcome, and hybrid pricing models. Dan brings a practitioner's perspective on when to review pricing, who should own it, and how the CFO fits into the equation.

Signs Your Pricing Needs a Review

• Best-in-class companies review pricing at least quarterly -- but review does not always mean change
• Key warning signals include declining net revenue retention and unexpected shifts in win/loss conversion rates
• AI-native companies are iterating on pricing monthly due to rapid competitive dynamics
• Sales cycle length is a practical constraint: a 12-month enterprise cycle limits how frequently you can test and observe pricing changes

The Role of Customers in Pricing Strategy

• Never anchor your pricing strategy entirely to your existing customer base -- they carry inherent bias
• A practical research mix: roughly one-third existing customers, two-thirds prospects
• Existing customers know your real value; prospects only know what you show them -- both perspectives matter
• When introducing a second product, maintain structural similarity in pricing tiers even if the pricing metric differs

Pricing Ownership and Governance

• Below $5M ARR, the founder/CEO owns pricing; above $20M it shifts to Product or Marketing -- the gap in between is where ownership gets dangerously vague
• Product Marketing is best positioned to own pricing because it sits at the intersection of positioning and value communication
• Sales owning pricing is a misalignment of incentives -- "like putting Dracula in charge of the blood bank"
• Best practice is a pricing council with a designated decision-maker, not design by committee

Discounting and the CFO's Role

• Discounting policy is often the easiest and fastest win -- and one of the first places Dan looks with any client
• Enforcement matters as much as policy: without monitoring, no new pricing strategy will ever reach the market as intended
• The CFO plays a dual role -- operational (contracts, billing, deal desk guardrails) and strategic (modeling cash flow and KPI impact when shifting pricing models)
• Caution: A finance-led focus on consistent margin profiles across products can misread how different market segments actually behave

Outcome-Based Pricing: Hype vs. Reality

• Outcome-based pricing is "the future and always will be" -- it is not new, and it is genuinely difficult to execute
• True outcome pricing only works when you are directly in the revenue or savings transaction, as Stripe is
• A more practical frame is output-based pricing -- Intercom's 99 cents per resolved support ticket is a strong example of measuring a clear, attributable unit of value

If you are involved in how best to monetize and price your B2B AI or SaaS product - this is a very valuable listen!

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While the AI headlines obsess over foundation model fundraises and hyperscaler spending, a quieter revolution is generating real, measurable returns. In this episode of AI to ROI: The Big Story, Ray Rike and Peter Buchanan break down why vertical AI companies may be building the most durable and valuable businesses in the history of enterprise software, and why most people aren't paying attention yet.

What's covered in this episode:

• Defining Vertical AI: What separates vertical AI from horizontal tools like Microsoft Copilot or Google Workspace AI, and why the distinction matters for buyers and investors alike
• A fundamentally different business model: Why vertical AI companies target labor budgets (10x the size of enterprise software budgets) rather than IT spend, and how outcome- and consumption-based pricing is replacing the traditional per-seat model
• The funding explosion: Vertical AI investment grew from $8B in 2023 to $22B in 2024 to $42B in 2025, with unicorn counts in the sector jumping nearly 6x in just two years
• Harvey (Legal AI): How this $8B+ valuation company grew ARR from $100M to $190M in just four months by orchestrating multiple AI models across legal workflows and embedding deeply into law firm operations
• Abridge (Healthcare AI): How a cardiologist-founded company reached a $5.3B valuation by turning physician-patient conversations into structured clinical documentation in real time, with deep Epic EHR integration across 150+ health systems
• Sierra (Customer Experience AI): How Brett Taylor's enterprise AI platform hit $100M ARR in just 21 months and crossed the $10B decacorn threshold, raising the question of whether the agent era could produce the first trillion-dollar enterprise software companies
• MaintainX (Industrial/Manufacturing AI):How this maintenance management platform is tackling $1.4 trillion in annual equipment failure costs across 11,000 customers and 11 million assets — with a 34% reduction in unplanned downtime for customers
• Why vertical AI moats are so durable: Proprietary data that compounds with every transaction, embedded institutional knowledge that makes switching costs higher than any legacy ERP migration, and a model architecture that gets stronger as foundational models improve
• Advice for enterprise buyers: Why 2026 is the year to evaluate vertical AI vendors, insist on outcome-based pricing, and start with one workflow before expanding

Interested in reading the details on the Vertical AI industry and trends? Check out the AI to ROI Newsletter providing even more detail by clicking here.

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In this episode of the AI to ROI Podcast, host Ray Rike sits down with Amanda Kahlow, founder and CEO of 1Mind. Prior to 1Mind, Amanda was the founder and former CEO of 6sense, an early pioneer in intent data.

The Vision Behind 1Mind: Amanda founded 6sense to help companies find buyers; she founded 1Mind to close them. 1Mind builds what she calls "go-to-market superhumans", AI agents that take on multiple roles across the full customer lifecycle, from inbound qualification and live demo delivery to deal closing for SMB/commercial accounts, and even post-sale onboarding, upsell, and cross-sell motions.

Why the Buyer Journey Has Fundamentally Changed: Amanda argues that traditional intent data and one-way marketing are becoming obsolete. Buyers no longer follow a linear path of Google searches and form fills; they expect real-time, two-way, solution-oriented conversations, much like they get from interacting with large language models today. The old model of blasting outbound emails or routing inbound leads through a sequential SDR → AE → SE handoff chain is increasingly misaligned with how modern buyers want to engage.

Top Use Cases: How Customers Deploy 1Mind: The most common starting point is the inbound website use case, customers start by placing a superhuman on the website that can qualify a visitor, deliver a personalized live demo, answer deep technical questions, and in some cases take the deal all the way to close, all on first touch. From there, customers frequently expand to the "ride-along" use case, where the superhuman joins every sales call as an always-available AI sales engineer. Human sellers retain control but can call on the superhuman in real time to answer hard questions, surface the right case study or slide, run an integration demo, or ask the qualifying questions (MEDDIC and similar) that sellers often avoid.

Measurable Business Impact: Amanda shares compelling early results from enterprise customers, including a ~40% reduction in sales cycle length (from ~90 days to ~60 days) and a doubling of ACV for deals that passed through the superhuman pipeline versus the traditional pipeline. She attributes the ACV lift to getting buyers to vendor-of-choice status earlier in the cycle, eliminating the need to compete on price. 1Mind also has use cases for existing customer bases — proactively engaging customers about new features to drive upsell and cross-sell, a task that human CS teams increasingly can't keep pace with, given the speed of product development.

How Customers Measure ROI: Amanda is direct: the right measurement framework is revenue impact, not top-of-funnel pipeline metrics. She encourages customers to tie superhuman performance to shortened deal cycles, higher ACV, and bottom-of-funnel revenue influence. She acknowledges there is a maturity curve — some customers start by measuring meetings booked — but the companies seeing the most value are those willing to shift away from MQL-based thinking toward board-level outcomes: revenue growth, lower CAC, and expansion revenue.

Onboarding & Time to Value: 1Mind has invested heavily in its self-serve platform to reduce deployment time from a four-month process to an average of about four weeks today, with some customers going live in as little as four days. All deployments are full enterprise contracts, as 1Mind does not run pilots.

Advice for Leaders on AI ROI Amanda emphasizes that realizing meaningful AI ROI requires a top-down mandate from the CEO. Incremental point solutions can improve efficiency at the margins, but the big needle-movers require new playbooks and organizational willingness to change how work gets done, not just layer AI on top of existing processes.

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On this AI to ROI Big Story episode, our hosts Ray Rike and Peter Buchanan dig into Deloitte's 2026 State of AI Report, a 41-page annual study surveying over 3,300 business leaders on the state of enterprise AI adoption. Deloitte calls it "The Untapped Edge," and Ray and Peter unpack exactly why.

They walk through the report's seven key inflection points from scaling pilots into production and reimagining business processes, to agentic AI, sovereign AI, and physical AI, with a focus on what the data actually means for companies trying to drive real ROI in 2026.

Key topics covered in this episode include:

• Pilot to Production: Why 54% of respondents expect a major leap in production deployment in the next 3–6 months, and why 37% of companies are still making little or no change to existing processes
• Productivity & Revenue: How 66% of organizations report efficiency gains today, but only 20% are seeing actual revenue impact from AI - and what it will take to close that gap
• Business Transformation: Why 84% of companies have yet to redesign jobs around AI, and what that means for long-term competitiveness
• Agentic AI: What the jump from 26% to 74% expected adoption of agentic AI over two years signals, and the top enterprise use cases including customer support, supply chain, R&D, and cybersecurity
• Governance: Why only 21% of companies have a mature governance model for autonomous agents, and what leading companies are doing to build responsible frameworks from the ground up
• Sovereign AI: How 83% of multinational board members view sovereign AI as at least moderately important, and why the US, Europe, and the Middle East are approaching it very differently

Ray and Peter close with a clear-eyed summary of what enterprises need to do now: close the gap between strategy and operational readiness, redesign work with an AI-first mindset, and shift focus from incremental efficiency to genuine strategic reinvention.

📰 This episode is based on the February 19th edition of the AI to ROI newsletter. Subscribe at ai2roi.substack.com

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Is the trillion-dollar AI bet actually going to pay off? In this episode of AI to ROI, hosts Ray Rike and Peter Buchanan tackle the big question head-on: with hyperscalers pouring over $600 billion into AI infrastructure this year alone, enterprises struggling to move pilots into production, and white-collar job postings already falling 16% year-over-year, the anxiety is real and justified. But so is the optimism.

Ray and Peter break down why the same supply constraints slowing AI buildout may actually give companies and workers more time to adapt, why foundation model costs have plummeted 97% since 2023, and how IBM's internally deployed AI has already generated $4.5 billion in productivity savings. From healthcare transcription to AI-native go-to-market tools, the ROI is emerging, but not evenly or quickly enough for most.

What We Cover in This Episode:

• The staggering scale of AI infrastructure spending: The five largest hyperscalers (Amazon, Microsoft, Alphabet, Meta, and Oracle) are on track to spend over $600 billion in CapEx this year, with Oracle committing 57% of its annual revenue and Microsoft 45%, ratios more typical of heavy industrial companies than software firms
• Why the build-out is slower than everyone thinks: Grid upgrade timelines in the US run 8+ years, data center construction is broadly behind schedule, and critical shortages in chips, transformers, skilled labor, and construction materials aren't expected to ease until at least 2028
• The pilot-to-production gap is real: Only 6% of enterprise AI projects are delivering returns within a year, and most organizations lack the frameworks and experience to move from experimentation to operational deployment at scale
• Trust, hallucinations, and governance are still major blockers: Regulated industries like financial services and healthcare face compounding uncertainty, caught between pre-AI regulations still on the books and a patchwork of conflicting state, federal, and international AI policy
• The workforce impact is already being felt : Salesforce cut 4,000 customer support roles, Klarna reduced headcount by 40%, white-collar job postings are down 16% year-over-year, and college graduate placement rates have dropped from 83-88% to roughly 23%, hitting data science, software development, and graphic design hardest
• But the technology itself is accelerating fast: Foundation model costs have dropped 97% since early 2023, the number of available models has grown from 60 to 650, and enterprises are getting smarter about orchestrating multiple models for different tasks
• Real ROI stories are emerging: IBM has generated $4.5 billion in productivity savings from internally deployed AI since January 2023, automating nearly 4 million hours of work annually at $3.50 returned for every dollar invested
• Vertical AI is gaining serious traction: Healthcare AI is the fastest-growing vertical, with one transcription tool alone saving 50,000 clinician hours. Legal, cybersecurity, customer support, and IT operations are all seeing meaningful gains
• The competitive pressure is intensifying: 54% of business leaders in a Mercer study believe they won't remain competitive in five years without AI at scale, and 92% of firms plan to increase AI budgets over the next three years

Why You Should Listen:

If you're a business leader, investor, or professional trying to cut through AI hype and understand what's actually happening on the ground, this episode delivers the balanced, data-driven perspective that's hard to find. Ray and Peter don't just cheerlead or catastrophize; they give you the real picture: where the bottlenecks are, where the returns are genuinely showing up, and why the next two to three years of slower-than-expected adoption might actually be the window your organization needs to get AI right.

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What does it actually mean for an ERP to be AI-native and why does it matter for your finance team?

In this episode of the AI to ROI podcast, host Ray Rike sits down with Santiago Nestares (Santi), Founder and CEO of DualEntry, to unpack the real differences between legacy ERP systems and a ground-up AI-native platform.

Santi shares the origin story behind DualEntry, born from a painful nine-month ERP implementation at his previous company that cost a team of 12 and hundreds of thousands of dollars—and explains why simply adding AI features on top of old database architecture misses the point entirely. AI, he argues, isn't a feature you plug in; it's a design philosophy that must be embedded at every layer of the product.

Ray and Santi dig into one of the thorniest challenges in enterprise finance: the tension between probabilistic AI models and the zero-error standard that accounting demands. Their answer? Deterministic guardrails—approval workflows, permissioning layers, and audit trails—that let AI work freely in draft mode while keeping humans accountable for every posted transaction.

You'll also hear about DualEntry's "Next Day Migration" approach, including how the company uses AI to map and migrate every transaction (not just trial balances) in hours rather than months, giving prospects a live sandbox with their own data before they ever sign a contract.

What You'll Learn

• Why adding AI to a legacy ERP is like "running an on-prem system with a CD on the cloud", and what truly AI-native architecture looks like instead
• The difference between deterministic and probabilistic systems, and why accounting can't afford to get it right only 99.9% of the time without the right guardrails
• How DualEntry's Next Day Migration works: AI-assisted mapping, atomic transactions, and a live sandbox demo using the prospect's own data
• The real ROI of AI-native ERP from eliminating manual categorization drudgery to enabling multi-dimensional segmentation that surfaces hidden pockets of value and risk
• How Dto build audit-ready explainability without being able to explain the AI itself - by tracing every decision back to a human approval
• Why early-career finance professionals are "living the luckiest time" in the professionand how to lean into AI rather than fear it

Episode Topics at a Glance

• 00:00 — Welcome & guest introduction
• 00:51 — Santi's origin story: a nine-month legacy ERP nightmare that sparked DualEntry
• 02:52 — AI-native vs. legacy ERP: what's the real difference?
• 04:48 — Deterministic vs. probabilistic systems explained
• 06:49 — How to identify a truly AI-first platform vs. an AI add-on
• 10:07 — Next Day Migration: using AI to accelerate ERP transitions
• 14:11 — Implementation team design: finance practitioners + forward-deployed engineers
• 17:18 — Measurable ROI: from real-time bank feeds to AI-driven business insights
• 22:52 — AI explainability, audit trails, and the permissioning layer
• 25:06 — Rapid fire: CFO ROI variables, who owns AI ROI, and advice for early-career finance professionals

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Is your company leaving money on the table by not having a Chief AI Officer?

In this episode, Ray Rike and Peter Buchanan dig into groundbreaking research from IBM's Institute of Business Value, spanning 600+ executives across 22 geographies and 21 industries to unpack why dedicated AI leadership is quickly becoming non-negotiable for enterprises competing in today's market.

The numbers tell a compelling story: only 26% of companies currently have a CAIO, yet those that do are seeing 10% higher ROI on their AI investments and are 24% more likely to outperform their peers. And that gap? It's widening. With 66% of existing CAIOs predicting most organizations will have someone in this role within 24 months, the window to gain a first-mover advantage is open — but not for long.

Ray and Peter go deep on some of the episode's most surprising findings, including:

• Who's actually getting hired: 73% of CAIOs come from data-focused backgrounds, but the most effective ones are hybrid leaders equally fluent in business strategy and data science. And 57% were promoted from within, because institutional knowledge often matters more than technical expertise.
• Where they sit in the org chart matters enormously: CAIOs who report directly to the CEO and control the AI budget (61% do) drive far greater results than those positioned as glorified advisors without real authority.
• The hub-and-spoke model delivers 36% higher ROI: companies that pair a centralized AI function with embedded business unit partners outperform those with fully decentralized AI decision-making, giving them both governance and agility.
• Three pillars that make or break a CAIO: measurement tied to real business outcomes, cross-functional teamwork across the entire C-suite, and genuine authority to make tough decisions. Strip away any one of these and ROI suffers.
• What to do if you're not ready to hire one yet : Ray and Peter offer practical alternatives, from AI steering committees to centers of excellence, and explain why accountability can't be an afterthought regardless of your company's size or structure.

They also tackle the growing complexity of managing AI at scale, the average large enterprise is now running 11 generative AI models and why the rise of agentic AI makes centralized leadership even more critical before things become, as Ray puts it, "a hot mess."

Whether you're a Fortune 500 executive or a mid-market leader trying to figure out your AI strategy, this episode is packed with data-backed insights to help you move from AI experimentation to measurable, scalable ROI.

Prefer to read more detail - check out the AI to ROI Newsletter covering this topic at: ai2roi.substack.com/p/the-chief-ai-officer-from-nice-to?r=2ldi4p

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This episode of the AI to ROI podcast, hosted by Ray Rike and Peter Buchanan, explores how leading SaaS companies are surviving the "SaaSpocalypse" - a massive market cap devaluation triggered by the rise of AI. The hosts break down the transition from traditional SaaS to AI-first models, emphasizing that simple "feature bloating" isn't enough; companies must undergo a fundamental "organ replacement" of their architecture, pricing, and culture.

The discussion deep-dives into three success stories:

• Notion: Transformed from a document suite into an agentic execution platform through strategic acquisitions, moving toward autonomous workflows.
• Canva: Democratized design by making AI features invisible and intuitive, resulting in a 700% increase in AI tool usage and massive revenue growth.
• ServiceNow: Leveraged its 20-year history in workflow automation to pivot from seat-based pricing to task-based pricing, using AI agents to orchestrate complex enterprise processes.

If you are a SaaS company executive looking for great examples of how they transitioned to be AI-first - this episode is full of great examples, strategies, and tactics.

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