TL;DR

• Selling a small business is slow, manual, and structurally broken.
• AI-native platforms can compress timelines, lower costs, and rebuild SMB M&A by automating discovery, preparation, and diligence.

You’ve spent 30 years running a family HVAC business in Kansas. You know your customers by name, your kids grew up sweeping the shop floor, and you’ve finally decided it’s time to retire.

But here’s the catch: selling your business takes two years on average. You’re bombarded with confusing spreadsheets, endless back-and-forth emails, and a broker who charges the kind of fee that will eat into your profit from a sale that will be the largest of your lifetime, but small potatoes for them.

It’s a little like trying to sell your used car through Sotheby’s. The system simply isn’t built for you.

This isn’t an isolated problem. Millions of small business owners are stuck in this same maze. The market is massive in size but invisible to the traditional players. It’s not that there aren’t willing buyers or sellers; it’s that the process is considered too manual, too slow, and too expensive to work at scale.

A Market Hidden in Plain Sight

When I worked in investment banking, I saw firsthand how the model was built. Deals were bespoke, expensive, and *endlessly* manual. As a junior banker, I spent nights buried in spreadsheets, redlines, and diligence checklists. That machinery works for a $200m software company. But apply it to a $3m local business, and the gears grind to a halt. The math collapses.

That mismatch leaves a massive gap that is more than a nuisance. It leaves millions of small business owners without a real path to exit and buyers without an efficient way to find them.

The numbers tell the story. Small businesses make up 99.9% of U.S. firms and employ nearly 46% of the private workforce. Over 12 million small businesses are expected to change hands in the next 10 to 15 years as ownership turns over from one generation to the next. Yet, 70% of owners lack a succession plan, and most of those transactions will fall under $5m in enterprise value, a segment we’ve established that banks structurally cannot serve.

The average time to sell a small business today is 18 to 24 months, and many of those deals collapse along the way because of poor discovery, mismatched expectations, or a lack of support.

Instead of professional guidance, owners are often left with fragmented broker networks, DIY processes, or the decision to never transact at all. The result is not just inefficiency. It is a structural failure.

A Perfect Storm of Supply, Demand, and Capability

Three powerful forces are colliding at once to unlock this overlooked market.

First, there is a record supply of businesses that are ready to be sold, driven by generational turnover in ownership.

Second, there is a growing class of modern acquirers, from search funds and solo capitalists to family offices, all actively seeking deal flow.

Third, new technical capabilities make it possible to automate previously manual workflows, match buyers and sellers with precision, and compress time-to-close without hiring armies of analysts.

Tasks that once required weeks of human effort can now be automated. Preparing a confidential information memorandum, cleaning up messy financials, or running diligence Q&A used to demand whole teams. Today, AI agents trained on financial, legal, and operational data can generate transaction materials, identify anomalies, or even simulate diligence exchanges.

What was once too manual, too slow, and too expensive can now be reimagined as agentic infrastructure. This is not incremental tooling. It is a first-principles redesign of the investment banking model.

Selling a Small Business Still Feels Like 1999. AI Can Bring It Into the Future

Think about how you shop for a house today. You don’t flip through a binder of grainy MLS printouts like it’s 1999. You pull up Zillow, sort by “3 bedrooms, under $500k,” and maybe a few other dream features – and immediately get real-time listings with photos, comps, and mortgage calculators. Meanwhile, small business M&A still looks like that old binder. Owners and buyers are forced to piece deals together through PDFs, cold emails, and patchwork broker networks.

Most small business owners do not have a CFO. They have never been through a sale. They are not building a data room. They are running a business, keeping customers happy, managing staff, and trying to stay profitable. Selling that business well is a full-time job on its own, which means owners often end up in outdated systems or underpowered broker arrangements. Key steps like financial preparation, buyer outreach, and diligence are still handled manually, often over spreadsheets and email.

Buyers are not in a much better position. They spend months manually sourcing leads, evaluating companies without clean financials, and juggling diligence through inboxes and PDFs. The result is a fragmented, high-friction market where deals stall, founders miss opportunities for exits, and buyers miss out on opportunities.

Here is where AI comes in. Imagine Zillow-level discovery combined with TurboTax-style preparation, layered with a digital analyst who never sleeps. That isn’t AI/VC buzzword word salad, it’s the promise of an AI-native M&A model. Instead of armies of junior bankers building CIMs and scrubbing financials, software agents can handle the grunt work: generating deal materials, flagging anomalies, and running Q&A loops. Buyers and sellers can finally focus on the human side: trust, negotiation, and the decision to shake hands, while the workflow hums in the background.

In short, what once felt like 1999 spreadsheets and inbox chaos is starting to resemble a modern marketplace.

An AI-Native Model for SMB M&A

The most important companies in this space will be those that vertically integrate the transaction process and serve both buyers and sellers through a software-first platform.

These platforms will accelerate transactions by automating tasks like CIM generation, buyer targeting, Q&A workflows, and diligence prep. They will improve match quality by using structured data, intent signals, and buyer-seller profiling to pair the right parties together. They will expand access by lowering fees, offering intuitive onboarding, and enabling asynchronous workflows, so even smaller businesses can get professional-grade support while buyers gain structured, qualified opportunities.

What We Believe

To back this market, we hold a few core convictions.

• The M&A stack for small businesses needs a complete rebuild. Legacy systems and broker networks were never designed for this segment. New platforms must rethink the entire experience from first outreach to final close.
• Workflow will beat headcount. The next generation of advisory will not rely on junior analysts. It will rely on automation, standardization, and smart infrastructure. Discovery, not diligence, is the major unlock. The biggest inefficiency in SMB M&A is not in execution but in surfacing the right deal in the first place. Whoever solves for signal at scale will win the market.

This market is not small. It’s only been hard to reach. The long tail of M&A has been underserved because it could not be profitably accessed. With the right infrastructure, it becomes one of the most dynamic segments of private markets.

We’re Still Early

There are already experiments at the edges. Some platforms are vertical-specific. Others are helping solo buyers run smarter searches or build workflows around diligence. A few are experimenting with broader ecosystems. But what is still missing is a true end-to-end platform that combines automation, data, and infrastructure to support SMB M&A from first-touch to final close.

We believe the next category-defining platforms will come from this space. If you’re building one of them, that is the opportunity we are most excited about. We would love to meet you, reach out to Tarun or Eliza.

This article is for informational purposes only and does not constitute investment advice. Views expressed represent the opinions of Jump Capital. Jump Capital may have investments in or pursue investments in the technology sectors and companies discussed. References to specific companies do not constitute investment recommendations.