Artificial intelligence (AI) is transforming healthcare rapidly, but successful adoption requires careful coordination between innovation and compliance teams from the earliest stages of any initiative.
As General Counsel and Chief Compliance Officer at Machinify, I see this pattern recur across the industry. A health plan wants to innovate, selects a promising AI solution, scopes the contract, and only then does legal get involved. By the time legal, security, or governance teams are looped into a new AI initiative, key decisions about data access, model training, security, and governance have already been made. And then these decisions often have to be walked back, renegotiated, or reworked entirely. This leads to delays, redlines, and missed opportunities.
Late-stage legal involvement creates friction for all parties—and it’s entirely avoidable.
By involving legal and compliance teams from day one, organizations can avoid rework, reduce risk, and speed up time to value. It’s a strategic shift that anticipates the questions that slow projects down and solves for them upfront.
Why legal is key to moving faster, not slower
When people think about innovation, legal isn’t the first function that comes to mind. But in healthcare, where regulations are dense and data is highly sensitive, success hinges on trust, and trust hinges on compliance.
Most organizations still treat legal and compliance as final checkpoints rather than strategic partners. When these teams are engaged early organizations can avoid rework and accelerate time to value. Early collaboration between legal, compliance, security and AI governance teams helps prevent the need to revisit key decisions later in the process.
The traditional approach creates avoidable delays. It also makes AI initiatives harder to scale, especially when every party has a different interpretation of evolving rules.
Involving legal earlier sets out a foundation that makes it easier to move fast later because the most important barriers have already been addressed.
The risk of waiting: regulatory complexity is only growing
Today, the risks of late-stage legal review are higher than ever. Here’s why:
1. AI and privacy rules are evolving in real time
From Health Insurance Portability and Accountability Act (HIPAA) to state-level consumer privacy laws and upcoming AI-specific regulations, the healthcare legal landscape is increasingly fragmented. Many of these rules don’t clearly address AI use cases, especially around model training, output ownership, and secondary data use.
Much of the current data-sharing regulatory framework was not designed with AI in mind. It speaks to a record being given from one human to another, but it doesn't actually have any regulatory thought process around the learnings that come from that. Legal teams can interpret this.
2. Fast Healthcare Interoperability Resources (FHIR) deadlines are approaching
By January 2027, as currently scheduled, CMS’s Interoperability and Prior Authorization final rule (CMS-0057-F) will require a unified, API-based data exchange format across payers and providers. This will change how medical data is structured, accessed, and audited, and it will require updates to how contracts handle data use, access permissions, and patient rights.
3. Fear of penalties is chilling innovation
Payers and providers are often less afraid of sharing data and more afraid of sharing it the wrong way. Without clear guidance or standardized processes, many choose inaction over risk.
For example, I see a lot of hesitation around information blocking following the final rule from the Office of the Inspector General (OIG) in 2023 that allows for hefty fines against covered “actors” under the 21st Century Cares Act. Nobody is information blocking for competitive advantage. They’re doing it because they are so afraid of running afoul of the regulation.
As a result, innovation stalls, good ideas get stuck in lengthy review cycles, and both payers and members suffer.
Three ways to involve legal from the start and speed AI adoption
Bringing legal and compliance into the earliest stages of AI projects doesn’t have to be difficult.
Here are three practical ways to make it happen:
1. Equip cross-functional teams with legal context early
Legal shouldn’t just be the department that marks up contracts. Legal should also be an educator. Before a request for proposal (RFP) goes out—and certainly before an implementation begins—sales, partnerships, product, and operations teams should be trained on the key compliance principles that apply to AI in healthcare.
At Machinify, we’re invested in this by making sure our people are so acutely aware of how we utilize AI in our products and services that when we’re talking at the outset, that is top of mind.
That internal alignment reduces the chance that we hit deal-breaking issues late in the process. And it builds trust with partners and clients who expect their vendors to be legally sophisticated.
2. Treat legal as a design partner, not a reviewer
When legal joins the table at the start, we can help shape agreements, data models, and product architectures that scale without constantly reworking them later.
For example, AI systems often need to learn from historical data to improve over time. But unless that training is covered in the data use agreement and explicitly permitted, payers may be unable to legally support it.
That’s a structural problem—one that can be solved through intentional contract architecture. If we moved past the idea that this is a penalty-based compliance check and actually justified why we are doing it and how it helps, it would be a lot easier to implement.
Legal can ensure the right opt-ins, consents, data minimization, and usage terms are built in from day one. That reduces risk, accelerates procurement, and sets the stage for scalable deployments.
3. Modernize your data sharing agreements
Contracts are often the biggest source of delay in healthcare innovation. Many were written before FHIR, before the rise of large language models, and before anyone seriously considered AI-assisted decision-making in claims processing.
Today, those same agreements may unintentionally block innovation, even when the technology is safe and sound.
The contracts that governed yesterday's electronic medical record (EMR) integrations aren’t built to support today’s AI models. And as technology and policy evolve, we are going to see open-ended rules with very few guardrails.
We can either wait for regulators to fill in the gaps or we can lead by designing new standards ourselves.
That starts with modern, flexible data use agreements that clarify:
- What types of data can be shared
- For what purposes
- Under what security, privacy, and retention requirements
- With what kinds of auditability and revocation rights
Getting this right early unlocks long-term value that extends beyond one-off implementations to a growing network of AI-driven use cases.
The payoff: faster procurement, smarter governance, better outcomes
When healthcare organizations bring legal into AI planning from the beginning, they can see measurable benefits:
- Shorter contract cycles: Organizations can reduce time spent negotiating data rights, model usage terms, and risk indemnities whenlegal has already addressed them proactively.
- Lower implementation costs: With the right access and agreements in place, engineering teams can spend less time navigating unclear boundaries, which can help reduce overall implementation costs.
- Improved trust: Providers, regulators, and members may be more likely to support AI initiatives when they know that privacy and compliance were priorities from day one.
- Stronger regulatory positioning: Organizations that engage early with CMS, state regulators, and industry groups may be better positioned to help shape and navigate evolving rules.
Leading healthcare AI compliance together
AI has the potential to help payers improve accuracy, reduce administrative waste, and make care more affordable, but it won't succeed without stakeholder confidence.
Legal is central to building that credibility.
Moving away from reactive risk mitigation to proactive legal partnership opens the opportunity for healthcare organizations to unlock the speed, scale, and safety needed to make AI a lasting force for good.
Machinify partners with healthcare payers to modernize their technology so that every claim is paid right the first time, on time, every time. To learn how to get it right the first time, follow our series.
The content on this page is subject to our Terms of Use.
