The Brad Jacobs Playbook, Updated for AI

There’s a business book that keeps coming back to me lately. It’s called How to Make a Few Billion Dollars by Brad Jacobs.

The premise sounds almost too simple. Find industries that are boring, profitable, and running on outdated technology. Come in, modernize the operations, and prove that the improved tech stack actually works. Then find other businesses in the same industry and deploy the same improvements across all of them. Aggregate the revenue. Sell the combined entity at a higher valuation multiple than the individual businesses would have fetched separately.

Jacobs ran this playbook in waste management, trucking, and logistics. Industries that don’t show up in TechCrunch. Industries where the biggest innovation of the decade was switching from fax to email. And he built billions doing it.

I keep thinking about what this looks like right now, with AI changing the modernization math entirely.

The Hair Salon Moment

A few months ago I gave a talk to a room full of hair salon owners.

I showed them how AI could automatically respond to text messages from clients, handle appointment reminders, and pick up the phone when nobody at the front desk was available to answer.

The room was standing-room only. People in the back were craning to see the screen.

And the thing that stuck with me wasn’t their excitement. It was their surprise.

They didn’t know any of this was possible.

These aren’t naive people. They run real businesses — managing staff, handling cash flow, dealing with difficult clients, navigating the economics of a service business that lives on razor-thin margins. They’re smart, they’re capable, and their tech stack hadn’t meaningfully changed in ten years.

That gap — between what’s now technically possible and what these business owners actually know about — is exactly where Brad Jacobs built his career.

Why the Playbook Works

The core insight in the Jacobs model is that “boring” industries are often boring precisely because they’ve been ignored by technology people.

Tech talent tends to cluster around consumer apps, enterprise software, and fashionable verticals. The waste management company doesn’t get the software engineer. The trucking dispatcher doesn’t get the product manager who could redesign their entire workflow.

So these industries accumulate a tech debt that compounds quietly over decades. The businesses are still profitable — sometimes very profitable — but they’re leaving enormous efficiency gains on the table.

The modernization opportunity isn’t about making these businesses do something radically new. It’s about closing the gap between what they’re doing and what best practices look like. New routing software saves 15% on fuel. A better CRM stops leads from falling through the cracks. Automated reminders cut no-shows in half.

None of this is glamorous. All of it moves the numbers.

What AI Changes

The original Jacobs playbook required real capital to execute. You had to acquire businesses, hire teams to deploy and customize enterprise software, run the change management process, wait for ROI to appear. The modernization phase alone could take a year and cost hundreds of thousands of dollars.

AI changes that math significantly.

Backend optimization that used to require an enterprise software team now happens in weeks. An AI-powered phone answering system. An automated appointment reminder workflow. A system that flags which clients are overdue for follow-up. A reporting tool that surfaces the week’s key numbers without anyone having to pull them manually.

Each of these used to require specialized vendors, long implementations, and expensive monthly contracts. Most can now be built in days with modern AI tools for a fraction of the cost.

This means the gap between “outdated business” and “optimized business” is narrower than it’s ever been — and cheaper to close.

The Edge Isn’t Building. It’s Seeing.

Here’s what matters most about this playbook: you don’t have to be a developer to execute it.

The bottleneck isn’t building the technology. It’s identifying the right opportunity — finding the business that’s profitable but stuck on old systems, understanding which parts of the operation AI can actually improve, and having the relationships to bring capital and technology together.

I’ve spent the last few years building AI systems for clients across different industries. Healthcare, real estate, professional services, retail. I can see pretty quickly now where the gaps are. Where the workflows are repetitive enough to automate. Where the data exists but nobody’s looking at it. Where a $300 per month AI tool could replace a $60,000 per year hire.

That pattern recognition — knowing what AI can do, knowing where to apply it — is the edge. Not the code.

The people who will do well with this playbook aren’t necessarily the engineers. They’re the people who are “dangerous enough” to understand the technology and know how to find the places where it fits.

The Target Industries

When I think about where this works best today, a few sectors keep coming up:

Medical practices — psychiatry offices, specialist clinics, and similar practices are often profitable and running on scheduling software from 2010. They have repetitive patient communication workflows, complex intake paperwork, and front desk tasks that burn expensive staff time on administrative work AI can handle.

Legal services — elder law, estate planning, small business legal — sectors where the clients are consistent but the admin load is heavy. Intake forms, document prep, client communication, billing. Most small law firms are still doing this manually.

Local service businesses — HVAC, plumbing, electrical, cleaning services. These businesses are usually profitable and almost entirely unoptimized on the tech side. A modern AI stack for customer communication and scheduling alone can change the economics meaningfully.

What these sectors share: they’re not broken. They’re profitable. The owners are capable. But they’re running operations that haven’t changed much in a decade, and nobody in their world has shown them what’s now possible.

The Roll-Up Phase

The real leverage in the Jacobs model comes in the second half: once you’ve proven the tech stack works in one business, you find others in the same space and deploy the same improvements.

The first implementation is the hard one. You’re figuring out the workflow, customizing the tools, training the staff, running the change management. That takes real time and energy.

The second and third implementations are mostly execution. The playbook already exists. You’re just deploying it into a new business that has the same fundamental structure.

That’s how you go from “I helped one hair salon modernize its operations” to “I’ve deployed this stack across twelve salons in the region and the combined entity has the revenue profile of a serious business.”

Aggregate revenue. Sell at a higher multiple than any individual business would have gotten. Exit.

Brad Jacobs did this with waste trucks. The same logic works with AI and the local service businesses that haven’t been touched by it yet.

The Practical Starting Point

If this model interests you, the first move isn’t acquisition. It’s proof of concept.

Find one business in an industry you understand — ideally where you already have relationships. Go deep on their operations. Build the AI systems that actually move their numbers. Document everything.

Once you have a working deployment that demonstrably improves the business, you have two things: a proof of concept, and a template you can replicate.

That’s the starting point for everything else.

The boring businesses aren’t boring anymore. They’re just waiting for someone who sees the gap.