New book from The Enterprise Edge® Founder & CEO Mark Vigoroso
coming in H1 2027!

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There's a moment most enterprise leaders know well. The technology contract is signed. The implementation partner is onboarded. The steering committee has met. The slide deck promises transformation. And then, somewhere between the kickoff and the go-live, something goes wrong - not dramatically, not all at once, but in the slow, expensive way that enterprise technology has always gone wrong. Adoption stalls. Timelines slip. The business case quietly stops being referenced. And the organization moves on to the next initiative, carrying just a little more cynicism than it had before.

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This book is about why that keeps happening -  and why the various breeds of enterprise AI make it both more likely and more consequential than ever.

 

We are living through the most significant shift in enterprise technology since the internet. AI isn't a feature upgrade or a new software category. It's a fundamental change in what computers can do, how decisions get made, and where competitive advantage comes from. Every major enterprise software vendor is embedding it. Every hyperscaler is racing to own it. Every board is asking about it. And most organizations, despite enormous investment and genuine ambition, are struggling to convert that potential into measurable business value.

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The question isn't whether AI works. It does. The question is why so many organizations can't seem to make it work for them - at speed, at scale, in ways that show up in the numbers that actually matter.

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The answer, it turns out, has very little to do with the technology.

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Value Physics - the buy-side companion to Revenue Physics - is built around a single unifying idea: that the laws governing how AI value moves through an organization behave like physics. There are forces that accelerate value. There are forces that create friction. There are organizational structures that add mass - the kind that slows everything down. And there is velocity: the speed at which a company can move from investment to outcome. These forces interact in predictable ways. Understanding them doesn't just explain why transformation programs fail. It tells you exactly where to intervene.

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The core Value Physics equation is this: 

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Enterprise AI Value = [(Acceleration − Friction) / Mass] × Velocity

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It sounds deceptively simple. But it contains a diagnosis of almost every enterprise AI failure you've ever witnessed. Too much friction from legacy systems, undefined process ownership, and political resistance. Too much organizational mass from layers of governance, customization debt, and competing incentives. Too little velocity because the foundational work - clean data, clear processes, coherent architecture - was never done before the AI tools were turned on. The technology lands on an organization that wasn't ready to receive it. And the result is exactly what you'd expect from the physics: the value doesn't move.

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This book is organized in six layers - System, Strategic, Architecture, Capital, Operations, and Workforce - because that's how enterprise AI actually fails. It doesn't fail in one place. It fails in the seams between functions, between incentives, between the people who buy technology and the people who have to live with it. The CFO signs off on a platform the CIO didn't choose. The COO inherits a deployment the CHRO's workforce wasn't prepared for. The board asks about AI risk while the CISO is still trying to get a seat at the procurement table. Each layer has its own failure modes. And each one requires its own discipline.

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The System Layer starts where most books don't: with the buying system itself. Enterprise software fails before go-live not because vendors lie and not because technology doesn't work, but because the conditions for value were never established in the first place. Misaligned incentives. Undefined process ownership. Vendor theater masquerading as due diligence. Transformation optimism bias that confuses signing a contract with achieving an outcome. These aren't new problems - but AI makes them dramatically more expensive, because the gap between what AI can theoretically do and what an unprepared organization can actually absorb has never been wider. This section introduces the Value Physics framework, makes the case for speed-to-value as the only north star worth navigating by, and lays out what it actually means for an organization to be AI-ready. The answer centers on the Clean Core Imperative - the principle that AI readiness and architectural discipline are the same thing. You cannot build fast on a foundation that was never solid.

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The Strategic Layer is for the leaders responsible for direction: the CEO and the board. It addresses the question that should be at the center of every AI investment conversation but rarely is - are we using AI to expand our competitive position, or are we using it to make our existing complexity more expensive? AI deployed without strategic clarity doesn't just fail to create value; it multiplies the dysfunction it lands in. This section also introduces a frame that most enterprise AI strategies miss entirely: the external value opportunity. Organizations spend enormous energy on internal automation and operational efficiency while overlooking the fact that AI embedded in customer-facing products, partner ecosystems, and revenue-generating services can generate returns that dwarf anything achievable through cost reduction alone. The flywheel of enterprise AI value has an inside and an outside. Most organizations are only spinning half of it.

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The Architecture Layer is where the technical reality lives - but this section is not written for technologists alone. It opens with a clear taxonomy of AI types and what each one actually means for deployment timelines, cost profiles, data requirements, and failure modes. Predictive AI, generative AI, reasoning models, agentic systems, computer vision, embedded ERP AI, fine-tuned domain models, RAG architectures - these are not interchangeable, and treating them as if they are is one of the most expensive mistakes enterprise buyers make. From there, the section moves through data as economic infrastructure, integration as a strategic function, platform selection criteria, the rapidly expanding AI security risk surface, and cloud economics. It closes with a chapter on AI observability and production monitoring - the operational discipline most enterprises discover they need about six months after they should have built it, when models start drifting, outputs start degrading, and no one has the instrumentation to know why.

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The Capital Layer gives the CFO the frameworks that standard financial governance was never designed to provide. Enterprise AI has fundamentally changed the economics of technology - from predictable license fees to variable inference costs, from per-seat pricing to consumption-based volatility, from five-year amortization schedules to token usage that can spike overnight. The CFO who applies traditional software procurement logic to AI investment will consistently underprice the risk and overestimate the return. This section covers AI cost volatility strategy, the true economics of building versus buying versus orchestrating versus embedding AI into existing vendor platforms, tech debt decommissioning as a value-creation strategy, and the AI FinOps discipline that general cloud cost management was never equipped to handle. It concludes with a Value Measurement System - the instrumentation framework that has to be built from day one, because the single most common failure mode after go-live isn't a bad product. It's the absence of any coherent way to know whether the product is working.

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The Operations Layer belongs to the COO, and it begins with a principle that sounds obvious but is almost universally violated: you cannot automate what you cannot see. Process transparency is not a prerequisite for digital transformation in some abstract sense - it is a hard requirement for AI deployment that doesn't amplify existing dysfunction at scale. This section covers deployment and scaling discipline across the full spectrum of human-led, AI-augmented, and AI-executed workflows, with specific attention to the governance requirements that agentic AI introduces when systems start taking actions without waiting for human approval. It also addresses, with more candor than most books in this space allow, what happens when AI fails - not the theoretical failure modes, but the real ones: the ERP blow-ups, the stalled pilots, the CRM collapses from over-customization, the hallucination rates that crossed tolerance thresholds and couldn't be rolled back because no one had built the rollback. And it provides the unified AI Governance Operating Model that most organizations are currently avoiding - the one that answers the question nobody wants to own: who is actually responsible when the AI gets it wrong?

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The Workforce Layer is the one that determines whether everything else sticks. Technology transformations don't fail because the software doesn't work. They fail because the people who are supposed to use it don't - because the change management was an afterthought, because the incentives weren't realigned, because AI change fatigue is real in organizations that have lived through wave after wave of transformation and have learned, rationally, to wait it out. This section covers human and digital labor design, the emerging talent architecture of the AI enterprise, and the reskilling economics that determine whether capability gets built or continuously bought. It closes with the chapter that matters most for regulated industries and anyone with meaningful legal exposure: Ethical AI in the Enterprise. Not as philosophy. As liability. Bias in hiring AI, lending AI, healthcare AI, and insurance AI isn't an abstract concern about fairness - it's a regulatory exposure with teeth, a litigation risk that is growing, and a reputational risk that can move markets. The CHRO and General Counsel share accountability for it whether they've formalized that accountability or not.

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The book closes with reference tools designed to be used, not shelved: such as a structured vendor due diligence framework for evaluating AI platforms, implementation partners, and embedded ERP AI; a regulatory reference map covering the full landscape from the EU AI Act to US sector-specific regulation across financial services, healthcare, and beyond; and a comprehensive Value Physics glossary that gives every member of the leadership team a shared vocabulary for decisions they're currently making in different languages.

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There's a version of the AI era that looks like this: an arms race of capability with no coherent strategy, pilots that never scale, vendors that capture value while customers absorb complexity, and organizations that mistake activity for progress until the window closes. A lot of companies are living that version right now.

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And there's another version - one where leaders understand the forces at work, build the foundations that let value compound, align their organizations around outcomes instead of implementations, and emerge from this period with durable competitive advantage that is genuinely hard to replicate.

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The difference between those two versions isn't the technology. It's the understanding. The physics, as it turns out, always win. The only question is whether you're working with them or against them..