Licensing 2026: Why the Human Touch Still Matters

Artificial intelligence can draft a licensing agreement in seconds. But can it recognize a good deal, protect a small business, or turn an invention into a profitable commercial relationship? That remains the job of the licensing professional.

A Young Profession Faces an Old Question

Licensing is a relatively young profession. The Licensing Executives Society (LES), founded in 1965, has now passed its 60-year milestone. During those six decades, licensing has evolved from a specialized activity largely associated with patents and industrial technology into a critical business strategy encompassing software, biotechnology, brands, digital content, artificial intelligence, and data.

Yet one fundamental principle has not changed: An invention or technology does not have to be manufactured or marketed by its owner to become profitable.

In Chapter 6 of my book, Managing Technology for Profit, An Evergreen Small Business Guide, I emphasized that technology may pay best in someone else’s company. That observation is even more relevant in 2026.

Small businesses rarely possess all the capital, manufacturing capacity, distribution networks, or market access needed to commercialize every promising innovation. Licensing allows them to participate in markets they could never enter alone.

What has changed dramatically is the speed at which licensing opportunities can be identified, evaluated, documented, and negotiated.

Artificial intelligence is rewriting the licensing playbook. But it is not replacing the need for a playbook—or the judgment required to use one.

Technology May Still Pay Best in Someone Else’s Company

The central licensing decision remains a choice between two paths: develop and commercialize technology yourself, or permit another business to exploit it in exchange for compensation.

Neither path is automatically superior.

A small technology company might earn more by licensing its patented process to an established manufacturer than by attempting to build a factory. Conversely, licensing too early, too broadly, or exclusively to the wrong partner may sacrifice much of the technology’s future value.

AI can help model these alternatives, research potential licensees, and compare royalty structures. It cannot independently determine the business owner’s appetite for risk, the reliability of a prospective partner, or the strategic importance of retaining control.

Those are business judgments.

Artificial Intelligence: A New Tool in the Licensing Shop

In the original chapter, I described the importance of preparing for licensing and having a plan before entering negotiations.

That advice deserves renewed emphasis.

Today’s AI tools can assist with tasks that once consumed hours or days of professional effort. They can produce preliminary confidentiality agreements, identify provisions in patent licenses, compare software licensing terms, and summarize complex documents.

They can also help prepare negotiation checklists, develop alternative royalty scenarios, and flag apparent inconsistencies between contractual provisions.

Consider five common licensing activities:

1. Confidential disclosure agreements (CDAs). AI can draft confidentiality obligations, identify exclusions, and propose disclosure periods. But it may overlook whether sensitive know-how can realistically be protected after disclosure.

2. Patent licenses. AI can suggest provisions covering territory, field of use, exclusivity, sublicensing, and royalties. It may fail to appreciate how narrowly or broadly the licensed patent claims actually protect the commercial product.

3. Software licenses. AI can assist with permitted uses, user restrictions, warranties, maintenance, and ownership provisions. But it may miss open-source obligations, cybersecurity exposures, or the consequences of combining proprietary and third-party code.

4. Technology licenses. AI can draft milestone payments, technical assistance obligations, and improvement clauses. Yet it may not recognize the practical difficulty of transferring undocumented manufacturing know-how.

5. Data and AI licenses. AI can propose rights relating to data access, training, reuse, model outputs, and confidentiality. But the underlying questions of data ownership, privacy, lawful use, and competitive advantage demand careful human analysis.

In every case, AI can produce language that sounds convincing while failing to protect the intended commercial bargain.

A well-written agreement is not necessarily a well-negotiated agreement.

That distinction may be the most important lesson for the next generation of licensing professionals.

Don’t Play the Licensing Game Without a Plan

A licensing agreement is not simply a legal document. It is the written expression of a business relationship and an allocation of opportunities, obligations, and risks.

Before negotiating, the licensor should understand what is being licensed, why the other party wants it, how the licensee expects to make money, and what each party must contribute to achieve commercial success.

AI may help answer some of these questions. But it can also create an illusion of preparation.

For example, imagine a small business that has developed an innovative sensor and receives an offer from a large international manufacturer.

An AI-generated agreement proposes an exclusive worldwide license, a modest upfront payment, and a royalty on net sales.

The document looks professional. The language is polished. The definitions appear comprehensive.

But what if the manufacturer never introduces the product?

What if the license covers markets the manufacturer does not serve?

What if the definition of net sales allows deductions that substantially reduce royalties?

What if the manufacturer develops an improved version that competes with the licensed technology?

And what if the small business has surrendered its right to license anyone else?

The problem is not the quality of the drafting. It is the absence of a sound commercial strategy.

An experienced licensing professional might negotiate performance milestones, minimum annual royalties, field-of-use restrictions, audit rights, termination provisions, and rights to improvements.

Those provisions could determine whether the transaction becomes a continuing source of income or an expensive missed opportunity.

The New Licensing Professional: From Drafting to Deal Design

For emerging licensing professionals, AI presents both a challenge and an opportunity.

Historically, junior professionals learned by researching agreements, preparing drafts, reviewing precedents, and supporting negotiations. AI now performs portions of this work with remarkable speed.

That creates a danger: professionals may learn to generate documents without developing the judgment needed to evaluate them.

The most valuable licensing skills in 2026 are therefore not limited to knowing what contractual provisions say. They include understanding why those provisions matter.

A licensing professional must be able to recognize the economic drivers of a transaction, assess intellectual property strength, identify competing commercial interests, structure incentives, and anticipate what happens when a relationship does not proceed as planned.

The professional must also understand negotiation.

A computer can propose a royalty of 5 percent. It cannot reliably tell whether the other party would accept 7 percent in exchange for exclusivity, or whether a lower running royalty combined with minimum payments and milestones would produce a better outcome.

AI can compare alternatives, but it cannot assume responsibility for the commercial judgment.

The successful licensing professional of tomorrow will not compete with AI on the speed of document production. That professional will compete on the quality of decisions.

The Licensing Agreement That Looked Perfect

One of the most effective ways to understand the limitations of AI is to examine an agreement it has generated.

Imagine asking an AI system to prepare a license for a patented medical device. The resulting agreement may contain all the familiar headings: grant of rights, royalty payments, confidentiality, warranties, indemnification, and termination.

At first glance, the document appears complete.

Closer examination may reveal that the license is exclusive but imposes no commercialization obligations; that royalties are payable only on products specifically identified by the licensee; that the agreement grants broad sublicensing rights without meaningful reporting requirements; or that ownership of future improvements is ambiguous.

Each problem might be hidden within otherwise plausible contractual language.

An experienced licensing professional asks not merely, “Is this clause standard?” but, “What happens to my client if this clause is exercised, ignored, or disputed?”

That question separates document preparation from professional judgment.

For small businesses, it can also separate licensing success from failure.

Routes Still Vary on the Road to Royalties

In Chapter 6, I emphasized that there is no single route to licensing income.

That remains true in 2026.

A business may negotiate an upfront license fee, running royalties, minimum annual payments, development milestones, sublicensing revenue, equity participation, or combinations of these approaches.

New technology creates additional possibilities. Data licensing may involve access-based or usage-based fees. Software agreements may use subscriptions or transaction-based pricing. AI-related arrangements may distinguish between rights to use a model, access training data, deploy outputs, or commercialize applications.

These opportunities also create new risks.

Who owns improvements generated using licensed technology? Can confidential information be used to train an AI model? Can a licensee retain data-derived insights after termination? How should revenue be measured when the licensed technology is only one component of a larger AI-enabled service?

These are not merely drafting questions. They are questions about ownership, control, competitive advantage, and future value.

And the answers may change as technology and regulation evolve.

A Practical Licensing Checklist for 2026

For small businesses contemplating a license, I would update the original chapter’s shop manual with seven essential questions:

• What do I own? Verify the intellectual property, know-how, data rights, and third-party restrictions before offering a license.

• What am I giving away? Define the technology, territory, field of use, exclusivity, duration, and sublicensing rights.

• How will I be paid? Examine royalty bases, deductions, milestones, minimum payments, and audit rights.

• What must the licensee accomplish? Establish commercialization obligations, performance milestones, and consequences for nonperformance.

• What happens to future improvements? Address ownership, access, grant-backs, and independently developed technology.

• What could go wrong? Consider infringement, confidentiality, data misuse, regulatory compliance, liability, and termination.

• Who has checked the AI’s work? Require informed commercial and legal review before relying on generated provisions.

AI can help prepare this checklist. It should not be the final authority on whether the answers are acceptable.

The Next 60 Years of Licensing

As LES enters its seventh decade, the profession faces a transformation as significant as the expansion from traditional patent licensing into software, biotechnology, and digital technology.

Artificial intelligence will make some licensing tasks faster, cheaper, and more accessible. Small businesses that previously could not afford extensive preliminary analysis may gain access to useful research and drafting assistance.

That is a genuine opportunity.

But greater access to contractual language does not automatically produce better transactions.

Indeed, the ease of generating sophisticated documents may increase the risk that inexperienced businesses sign agreements they do not fully understand.

The licensing profession therefore has an important educational responsibility. It must help business owners distinguish between obtaining a document and negotiating a deal, between identifying a potential royalty and creating a dependable revenue stream, and between transferring rights and building a productive commercial relationship.

For younger professionals, the message is equally important: learn to use AI, but do not allow it to replace the development of fundamental licensing skills.

Learn intellectual property. Learn business economics. Learn how products reach markets. Learn how to negotiate. Learn how to recognize risk. Above all, learn how to ask the questions that a computer may never think to ask.

Sixty years after the founding of LES, the essential purpose of licensing remains unchanged: bringing ideas, technologies, and commercial capabilities together to create value.

The tools have changed. The opportunities have multiplied. The speed of business has accelerated.

But the central lesson of Chapter 6 remains as relevant as ever:

Technology may pay best in someone else’s company—but only when the licensing deal is structured to make that happen.

In 2026, AI can help write the license. It still takes human expertise to make the license work.