Artificial intelligence is often discussed as software. In healthcare practices, it is rapidly evolving into core practice infrastructure. AI systems contain institutional knowledge, automate business processes, and influence clinical and administrative decision-making. That evolution creates operational, legal, and financial risks which can significantly affect a practice’s continuity and market value.

The lack of standardization, coupled with the extraordinary adaptability of artificial intelligence, makes it difficult to classify within the traditional legal categories of purchased assets, licensed software, and proprietary intellectual property.

Unlike in those traditional categories where ownership lines are clearly defined over a static product or service, when using AI, a practice may own the data but not the underlying model. It may own the prompts but not the platform on which they operate. It may develop sophisticated workflows while possessing only a limited, revocable license to use the software that enables them. As a result, valuable institutional knowledge may exist inside an AI platform that a practice might not own or fully control or, even worse, cannot legally transfer to a buyer at closing.

Consequently, AI assets present questions that traditional diligence processes are not always designed to answer.

Unlike conventional software, AI systems are rarely confined to a single application or license agreement. Instead, they consist of uniquely layered and iterated responses developed over time, including prompt libraries, custom GPTs, retrieval systems, workflow automations, templates, knowledge bases, and other internally developed processes that may not be exactly replaceable.

When properly implemented, these outputs provide meaningful competitive advantages, but their long-term value depends upon ownership rights and enforceability.

Clarify Ownership Rights Before Introducing AI Tools and Services

The purpose of contractual rights is to create predictable expectations and enforceable certainty. Though it has become easy to gloss over Terms and Conditions for digital assets, it is those terms that establish the level of control and certainty you maintain over your systems. Therefore, every AI implementation decision becomes a long-term business decision affecting operational continuity, legal exposure, and enterprise value.

Establishing certainty requires understanding not only ownership but also your rights to continue operating the system, enforcing contractual rights, transferring the technology in a future transaction, protecting patient information, and preserving the institutional knowledge created through years of use, without interference or interruption.

An appropriate preliminary inquiry should answer the following:

• What combination of AI systems are being used?
• Who owns the underlying data?
• Who owns prompts, workflows, custom GPTs, templates, and AI-generated outputs?
• Which licenses are transferable upon a change of ownership?
• Which contractual restrictions limit continued use after closing?
• Are appropriate Business Associate Agreements in place?
• Where is protected health information stored?
• Is patient or operational data used to train future models?
• Who bears responsibility if the system fails or violates applicable law?
• What operational dependencies exist if a vendor suspends service or experiences a prolonged outage?

Answering these questions is the only way to establish certainty around the systems upon which practices increasingly rely for daily operations and buyers will increasingly emphasize in diligence processes.

Verify Provider Credibility to Protect Ownership Rights and Remedies

Businesses routinely accept online terms without consideration or negotiation because the practical consequences rarely justify individualized review. That assumption changes when the technology becomes embedded in the operation of a healthcare practice, and the providers are being trusted with sensitive data, clinical influence, and practice administration.

Many AI providers are relatively new companies. Others operate through foreign affiliates or maintain their principal operations outside the United States. Some disclaim broad categories of liability, impose mandatory arbitration, limit available remedies, or cap damages at the amount of subscription fees paid. Others simply lack the financial resources to satisfy a significant judgment.

Therefore, when dealing with AI providers, ownership on paper means nothing when those rights are practically unenforceable.

Accordingly, it is necessary to determine the reputation, credibility, and remedial capabilities and incentives of AI vendors.

For example, a provider may agree that a customer owns its prompts, workflows, and outputs. If that provider later improperly discloses confidential information, suspends service, loses critical data, or breaches its contractual obligations, the practical question is not merely whether the contract has been violated. The more important question is whether the practice can realistically obtain an effective remedy.

If that provider has no financial means to pay damages, is hard to find, or is in a different country, the time and expense to locate the provider and obtain an effective remedy will be futile, and the resulting harm may become practically irreversible regardless of the contractual rights available.

Avoid being blinded by speed as you adopt AI. Instead, remain guided by the decision-making metrics you apply to all financial investments you make in your practice, ensuring all vendors and related contractual rights are supported by financially stable counterparties, practical dispute-resolution mechanisms, and realistic remedies.

Decrease Risk by Thoroughly Reviewing AI Assets Before Implementation or Acquisition

The best time to evaluate AI assets is before they become central to practice operations.

A comprehensive AI asset review should identify:

• every AI platform used by the practice;
• ownership of data, workflows, prompts, and outputs;
• licensing and assignment restrictions;
• applicable privacy and regulatory obligations;
• Business Associate Agreement requirements;
• intellectual property ownership;
• indemnification and limitation-of-liability provisions;
• vendor termination rights; and
• operational dependencies that may affect continuity after closing.

Ownership itself may depend upon employment agreements, independent contractor relationships, intellectual property assignments, confidentiality obligations, software license terms, and the particular AI platform used to create or store the material.

Addressing these issues before implementation or acquisition is less disruptive and considerably less expensive than attempting to resolve them after the fact.

Practices that can clearly demonstrate ownership, compliance, operational continuity, and transferability of their AI assets will inspire greater confidence in daily operations and receive higher valuations in the transaction process.

Artificial intelligence has proven to increase efficiency and margins, but efficiency and margins alone do not create enterprise value. Durable value arises only when the systems generating those efficiencies can be owned, transferred, enforced, and relied upon with confidence. As in every other aspect of healthcare M&A, future certainty remains the foundation of valuation and practice continuity.