What is driving the market for Canadian AI businesses

Businesses built around artificial intelligence, whether that means a company selling an AI-powered product, one using AI as a core part of its operations, or one built around a proprietary model or dataset, are one of the newest and least standardized categories in the small and mid-sized business market. Many of these businesses are young enough that they do not yet have the multi-year financial history buyers typically rely on in more established categories, and the traditional yardsticks used to price a small business, several years of steady seller discretionary earnings or EBITDA, often do not apply cleanly to a company still in an early growth phase.

Why the usual valuation questions get harder to answer here

In a more established business, a buyer can generally look at several years of consistent financial performance and build a reasonable picture of what a new owner should expect going forward. Many AI businesses have not existed long enough to offer that, and even where they have, rapid change in the underlying technology and competitive landscape makes historical performance a weaker predictor of what comes next than it would be in a slower-moving industry. That does not make these businesses unsellable, but it does shift buyer attention toward different questions: the strength and defensibility of the underlying technology and data, the durability of any customer relationships already in place, and how much of the value depends on a small technical team that may or may not stay on after a sale.

The diligence questions that matter most in an AI business

• Who actually owns the intellectual property behind any proprietary model, tooling or dataset, including confirming it was properly assigned by founders, employees and any contractors who worked on it
• Where training data came from and whether its use is properly licensed or otherwise permissible, an evolving area of law that a buyer’s counsel typically reviews closely
• How dependent the product or service is on a small number of technical staff, since losing key engineers or researchers after a sale can undermine the business more directly than losing an owner would in a more traditional company
• Reliance on third-party AI platforms, models or infrastructure providers, and what happens to the business if pricing, access or terms from that provider change
• Data privacy and security practices, since AI products often process large volumes of personal or sensitive data subject to federal privacy law
• Whether claimed capabilities and performance have been independently verified rather than taken at the company’s own description

Why financing looks unusually thin for this category

AI businesses typically hold almost no traditional collateral, no real property, limited equipment, and inventory that does not really exist in the traditional sense, which means conventional asset-based lending and programs built around financing tangible business assets generally do not fit this category well. Most of the capital behind AI businesses to date has come from equity investors rather than lenders, and a buyer expecting to finance an acquisition with a conventional small business loan is likely to find that path narrower here than in almost any other category discussed in this series. That pushes most AI business transactions toward equity-heavy structures, strategic acquirers absorbing the technology and team into a larger organization, or specialized investors comfortable underwriting technology risk rather than cash flow history, and a first-time buyer approaching this category the way they would a more conventional small business acquisition is likely to be surprised by how differently the financing conversation goes.

Talent retention and equity-heavy compensation add their own risk

Compensation in many AI businesses leans more heavily on equity and less on cash salary than in more established industries, which is one more reason key technical staff represent a retention risk a buyer has to think through carefully: an acquisition can trigger vesting terms, change-of-control provisions or simply a moment where a key researcher or engineer decides to leave for a competing opportunity, and a buyer generally wants to understand those terms and talk to key people directly before relying too heavily on their continued involvement. The regulatory environment around artificial intelligence is also still developing in Canada and globally, and specific rules around data use, algorithmic transparency and related obligations continue to evolve, which is one more reason this article does not attempt to state current legal requirements as settled fact. A buyer evaluating an AI business is generally better served by current advice from counsel familiar with the specific technology and data involved than by relying on a general description that could be outdated by the time it is read.