Winning Enterprise B2B in the AI Era: A Field Guide to AI Transformation

Two people. Two months. $8,000.

A consulting firm quoted $4.75 million and two years to build half of it. This is the field guide to why the small team wins now, and how to make your company the one that does it.

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Free to read with Kindle Unlimited. Also in paperback and hardcover. First edition, November 2026.

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Eight things this book will say to your face

• Intelligence is a commodity. Your competitors can rent the same AI models you can, at the same price, this afternoon. The model stopped being the advantage. What you do with it is the only advantage left. (Chapter 1)
• Your buyer already left the room. Buyers research and shortlist with AI before your sales team knows there is a deal. Asked what most influences their shortlist, they rank salespeople last. (Chapter 2)
• AI is not ChatGPT, and your chatbot is not an agent. Half of the money is chasing a fifth of the value. The book teaches you what you are buying in plain language, so that no vendor can fool you. (Chapters 3 and 4)
• Cheap tokens, expensive AI. The price halved while usage grew twenty-five times. Cost per token is the wrong number. Cost per completed task is the one your CFO should manage. (Chapter 5)
• Your pilot is a waiting room. Six months in, nobody has said yes and nobody has said no. Kill every pilot older than 90 days that has no decision attached to it. (Chapters 6 and 16)
• The consulting quote was accurate. For the consulting firm. Feed any project into a pyramid of partners, analysts and day rates, and a two-year, multi-million plan comes out. I helped build the full scope with one colleague for about $8,000. (Chapters 8 and 10)
• Speed beats persuasion. C.H. Robinson took a freight quote from three days to three minutes. The customer did not get a better pitch. They got an answer before your competitor opened the email. (Chapter 9)
• Redeploy before you cut. One in five service leaders cut headcount on the promise of AI. A third of those are already walking it back. People who think a tool is there to replace them do not feed it their best knowledge. (Chapters 7 and 14)

Customer first. Business second. Technology third.

The one rule under every chapter. Companies that run it in the opposite order build things no user asked for, and then call the result an adoption problem.

What you'll learn

The evidence is public 2025 and 2026 data, shown in charts, with every source in the notes. When a number comes from a company that sells the product it measures, the book says so. When the evidence is weak or missing, it says that too.

Cases include C.H. Robinson, Ingram Micro, Caterpillar, Siemens, IKEA, Rexel, Holcim.

• Why nine in ten companies use AI and so few get paid for it, and what the winners redesigned.
• Why cost per completed task, not cost per token, is the number your CFO should manage.
• How to take one workflow from pilot to production: customer first, business second, technology third.
• How to get legal, security and IT to say yes, with a fast lane instead of a review queue.
• Where margin hides in pricing and service, and how to measure the leak.
• What happens to the seller when buyers research with AI and send machines to buy.
• Why you should redeploy before you cut, and how to keep your best people.
• What to do over the next twelve months, quarter by quarter, from where you sit.

Three charts from the book

• Figure 10.3: One problem, two answers. The author's project against the consulting proposal for the same need. Source: the author's own experience. The internal figure is about $2,000 in tokens plus one salary for two months; the author's own time is not counted.
• Figure 9.2: Three days became three minutes. C.H. Robinson, time to return a freight forwarding quote. Source: C.H. Robinson, 26 October 2026. Bars are drawn to scale, which is why the "after" bars are almost invisible.
• Figure 1.1: The price of a fixed level of intelligence keeps collapsing. Cheapest way to reach the same capability, US dollars, logarithmic scale. Source: Epoch AI, inference price trends (March 2025) and "The plunging price of thought" (September 2026).

The chapters

• Part I, Wake up: 1. Intelligence Is Now a Commodity; 2. Your Buyer Already Left the Room
• Part II, Know what you're buying: 3. AI Is Not ChatGPT; 4. A Jagged Frontier, Moving Fast; 5. Cheap Tokens, Expensive AI
• Part III, Why companies fail: 6. Pilot Purgatory; 7. Six Ways to Burn Money; 8. The Hype Machine
• Part IV, How winners win: 9. Speed Beats Persuasion; 10. One Workflow at a Time; 11. The Seller Becomes a Validator; 12. Margin Is Hiding in Pricing and Service; 13. A Way to Say Yes; 14. Redeploy Before You Cut
• Part V, What comes next: 15. Machines That Buy, Machines That Build; 16. Your Twelve-Month Plan, by Seat

Field guides you can use on Monday

• Field Guide A: The Stack, by Name. What to shortlist, layer by layer, and the question to ask at each one.
• Field Guide B: The Team That Leads It. Who should lead, and why business sense beats the org chart.
• Field Guide C: One Workflow, on Paper. The spec, the test set and the risk tier for one real workflow.
• Field Guide D: The Money and the Measures. First-year budget arithmetic and the formula behind every number.
• Field Guide E: Build It Before You Buy It. Test the build-versus-buy question yourself, in one afternoon.

Free on this site, book or no book

• AI Opportunity Map

  The tool the book uses to choose the first workflow. It makes you compare AI against simpler fixes before you build anything.

• Executive AI mandate checklist

  The questions a leadership team should settle before it funds an AI programme.

• Operating Loops

  Short AI workflows for real sales and marketing tasks, with worked examples.

A twelve-month plan for your seat

• Q1, See: You know where customers wait, what your data looks like, what AI you already run, and you have picked one workflow.
• Q2, Prove: That workflow is rebuilt, shadow-tested and live inside limits, with one number moving.
• Q3, Repeat: Two more workflows are live, built faster than the first, and approval has become a checklist.
• Q4, Compound: Freed hours are redeployed, the gains are shared, and next year's list is chosen from evidence.
• Chief executive: Your job is to make it safe and fast to change how work is done. You won't choose the models. You'll decide whether a two-person team with a working system gets a hearing or gets buried. First quarter: Ask for three things: the list of AI systems already running, the age of every pilot, and the share of AI spending tied to a workflow with an owner. Use AI yourself, daily, on real work. The mistake to avoid: Announcing a headcount target before a single workflow has been proven.
• Sales, marketing or service leader: You own the workflows, so the rebuild method belongs to you. First quarter: Mystery-shop your own company and time the answers. Run your buyers' twenty questions through the AI assistants, many times each. Measure the gap between list price and invoiced price. The mistake to avoid: Pointing AI at volume. More emails to people who didn't ask is one of the six ways to burn money.
• Manager: The evidence says you matter more than the software. Where a manager strongly supports AI use, 78% of the team uses it frequently. Elsewhere, 44%. First quarter: Have three people log a week in half-hour blocks and sort it into gathering-and-typing versus judgment-and-trust. Sit with your team for two days and write down how the work is really done. The mistake to avoid: Staying quiet about what this means for your people's jobs. They've already done the arithmetic, and silence confirms the worst version.
• Specialist or seller: You have less authority and more freedom than anyone above you. Nobody has to approve your learning. First quarter: Use a frontier AI model every working day on a real task. Build your own small exam: ten tasks from your job, with what a good answer looks like. Learn where it fails. The mistake to avoid: Waiting to be trained. Of frontline AI users, 66% get little or no guidance.
• Founder: This book has been describing your market. Large companies are being quoted millions and years for things that now cost thousands and weeks. You can sell into that distance. First quarter: Pick one workflow in one industry where customers wait and the answer is checkable. Sit inside three customers until you can write its spec better than they can. The mistake to avoid: Pricing so low that a large buyer decides you aren't serious.

This book is for you if

• You are a chief executive who has to decide where the money goes and cannot tell the real thing from the hype.
• You lead sales, marketing, pricing or customer service in an enterprise and you can feel the ground moving.
• You are a manager or a specialist who sees what is possible and is tired of hearing no.
• You are a founder who suspects the gap between what enterprises need and what they can build is a business. It is.

Skip it if

• You want model internals, code or a list of prompts. This book is written for the business side.
• You want to hear that buying licences is a strategy.
• You are looking for a futurist. I am a practitioner, and the book is written from that chair.

Read the introduction free: A Confession Before We Start

I used AI to write this book. You should know that before you read a word of it.

My name is Juan Beltrán, and this is how the book was made.

AI did the research legwork. It found the studies, pulled the numbers and traced them back to where they came from. Where it couldn't open the original, the notes say so. It helped me brainstorm, and it argued with me when my ideas were weaker than I thought. It built the charts and handled the production work that turns a manuscript into something you can hold or download. It also wrote the English.

Spanish is my mother tongue. I work in English every day, but writing a whole book at the level a reader deserves is a different skill, and I don't have it. For most of my career that would have been the end of this project, or it would have meant paying a ghostwriter to guess at what I meant. This time I could say what I think in my own words and have it come out in clear English. I pushed on it, chapter by chapter, until it was entertaining to read, well structured and useful, because a business book that bores you has failed.

The knowledge in these pages is mine, and so are the vision, the ideas, the opinions, the experiences and the projects you'll read about. I decided what this book argues and what it leaves out. I gave the direction at every step, and I answer for every claim in it.

Some authors would hide all this. I'm putting it on the first page for two reasons.

The first is that you deserve to know. Chapter 13 tells companies to disclose when their customers are dealing with AI, and I'd be a hypocrite to exempt myself.

The second is that this book is its own argument, and I'd rather show it to you than describe it. A commercial guy from the Spanish-speaking world, with a day job, produced a researched, illustrated reference book in his second language. A few years ago that took a publisher, a research team, a translator and an editor. If that makes you slightly uncomfortable about what your competitors can now do with a small team, good. Hold on to that feeling, because you'll need it.

Who is talking to you

I've spent more than 17 years in the trenches of marketing and sales, in the place where technology meets the business and the two usually fail to understand each other.

I trained as a computer science engineer. Later I did an MBA, and after that a master's in data science, because I kept ending up as the person in the room who had to translate between the people who build systems and the people who have to sell with them.

Half of my career was in B2C retail, where you learn fast because the customer tells you every day whether you got it right. The other half has been in B2B industrial, where a single deal can take a year and involve a dozen people, and where "we've always done it this way" is treated as a strategy.

I'm telling you this so you know what kind of book you're holding. I'm not a professor or a futurist, and I don't run an AI lab. I'm a practitioner who has launched the projects, sat through the steering committees and watched good ideas die in approval meetings. This book is written from that chair.

One of those projects is public. At Holcim I was the business lead and main sponsor of an AI assistant that lets construction customers order cement by message. McKinsey was our implementation partner, and published an interview with me about the first pilot, in Spain, in February 2025, and Chapter 10 tells the story, including the customer who tried to order soup.

I keep writing about this work, and I publish the tools and checklists I use, at juanbeltran.ch. When a chapter leaves you wanting the next step, look there.

Why I wrote it

I wrote it because I keep watching smart companies make one expensive mistake.

Intelligence has become cheap. The ability to analyze, write, plan and build software used to be the scarcest thing in a business, and now anyone can buy it for a few dollars. Your buyers already have. They research and shortlist with AI before your sales team knows there's a deal.

Most enterprises have reacted by buying licenses and changing nothing else, and they wonder why the results don't come. I helped build a working system, two of us in two months, and watched it get rejected because it was built too fast and too cheaply to look serious.

One rule sits under every chapter, so I'll state it here. The customer comes first, whether that customer buys from you or works for you. The business comes second, as a consequence of listening to the customer. Technology comes third, as a consequence of understanding the other two. Companies that run it in the opposite order build things no user asked for and then call the result an adoption problem. Technology is there to deliver value to a person. It isn't there to close an IT ticket.

I think the people who see this moment clearly have the biggest opportunity of their careers in front of them. I also think most of them work inside organizations that are designed to stop them. This book is for those people.

Who it's for

It's for you if you're a CEO or an executive who has to decide where the money goes and can't tell the real thing from the hype.

It's for you if you lead sales, marketing, pricing or customer service in an enterprise and you can feel the ground moving.

It's for you if you're a manager or a specialist who sees what's possible and is tired of hearing no.

And it's for you if you're an entrepreneur who suspects that the gap between what enterprises need and what they can build for themselves is a business. It is.

How to use it

The book has five parts. Part I shows what has changed. Part II teaches you what AI is, in plain language, so that no vendor can fool you. Part III takes apart the reasons companies fail. Part IV is the playbook, built from the companies that are winning. Part V looks at what's coming next, including the moment AI moves from the office to the factory floor. Five field guides at the back name the technologies to shortlist, lay out the team, walk through one workflow on paper, give you the budget arithmetic and the formulas, and show you how to test the whole argument yourself in an afternoon.

Each chapter makes one argument, and some of them may annoy you. The evidence comes with it, in charts. Then the chapter ends with something you can use: a test, a checklist, a template or a calculation.

I made three promises to myself about the evidence, and I'll make them to you. I use the most recent data I can find, and I tell you where it comes from. When a number comes from a company that sells the product it's measuring, I say so. And when the evidence is weak or missing, I tell you that too, even when a stronger claim would have made a better headline. You'll see me do it in the first three chapters.

You can read the book straight through or go directly to the chapter that matches the problem on your desk. Either way, read it with a pen. Argue with it. Where your experience says I'm wrong, trust your experience, and then go and measure it.

This book will make some of you uncomfortable, and that's on purpose. I'd rather you were uncomfortable for a few hours here than for a few years in a company that didn't move.

Chapter 1 starts with the best tool I ever saw a company throw away.