Life sciences investors must carefully consider each of the following areas to thoroughly evaluate the strength of AI-enabled investment targets and the solutions they create:

1. Build internal AI competency with the right external partners

To improve AI competency internally, investors should form AI diligence teams including the right set of both internal company leaders and external experts – from legal and policy consultants to academic and research partners – to validate technical claims, stay current on regulatory changes, and establish actionable AI compliance programs.

As AI proves ROI, investors will likely be asked to vet more technologies, more quickly and will need to take affirmative steps to create a constructive pipeline of investments to maintain market leadership.

In addition, investors need to consider the technology in context. If potential customers are not clear on how to safely operationalize the technology they may take a pass, especially as they seek to manage competing calls to pilot AI tools. The so-called “human in the loop” safeguard only acts as one when the humans in the loop understand how to use the technology, what it does well, and what it doesn’t do well, and have clear picture of monetization, scalability, and the end user of the tool. This takes time, training, and practice.

Operators and investors who engage early with the right legal experts, regulators, licensing and professional boards, and clinician and patient representatives can more proactively manage long-horizon compliance and operational risk, while ensuring the business is competitively positioned for growth.

2. Know the pathways to reimbursement

The healthcare industry’s reimbursement models and associated coding systems play a critical role in determining whether and how an AI-driven clinical service or product, or medical device, can be reimbursed, which directly influences market adoption, revenue predictability, and investor confidence. For AI developers, early alignment between product design and existing or emerging coding pathways is essential to ensure that their tools can be integrated into clinical workflows and billed appropriately by providers.

AI investors and targets must closely track code-development activity, assess whether their use cases fit within existing codes or require new code applications, and generate the clinical evidence needed to support coding, valuation, and coverage decisions. Engaging reimbursement experts and legal counsel early can help structure evidence-generation plans, partnership strategies, and regulatory positioning to avoid reimbursement barriers that could materially limit scalability or valuation.

Beyond CPT code developments, companies preparing to commercialize their product through Medicare may benefit from The Centers for Medicare & Medicaid Services (CMS) New Technology Add-on Payment (NTAP) program. For medical device and drug manufacturers, earning NTAP status can make it easier for hospitals to adopt their technology, improving patient access to cutting-edge care while easing the financial burden for adoption on healthcare providers.

2. Elevate data governance and IP diligence

Sophisticated AI cannot compensate for poor data quality, lackluster security protocols, or weak data governance. Investors and targets must regularly scrutinize data quality, security, and completeness, as well as the strength of model-training processes. Critical questions include where the training data come from, if the data agreement allows a company to monetize systems and tools trained or built using the data, how the data are cleaned and validated, and whether independent evaluations confirm model performance.

AI investors must have a deep understanding of a target company’s data infrastructure, including the robustness of existing data governance frameworks and assurances for IP defensibility. This includes knowing how an organization is securely managing sensitive data and working with protected health information (PHI), maintaining interoperable systems, and navigating emerging regulations around AI policy, transparency, validation, and ethical use. Investors must also examine the lifecycle risk of a target: how its AI use cases may evolve, what markets will support adoption, the biggest opportunities for strategic alignment/misalignment, and what true scalability looks like.

From a legal perspective, this means investors and targets should proactively engage legal counsel early to structure data rights, contracting frameworks, and compliance controls that will withstand diligence scrutiny. Firms that can demonstrate traceable model lineage, defensible risk-management processes, and audit-ready documentation will be significantly better positioned in competitive financing or acquisition processes.

3. Prioritize strategic and operational execution

Having a flashy algorithm no longer cuts it as AI innovation. In the competitive life sciences sector, investors are looking for AI solutions that provide clinical or science-backed utility with a realistic path to scale.

For their part, investors should target companies that have a demonstrated ability to integrate AI into scalable, regulated business models. AI targets should have disciplined operational infrastructures – robust QA processes, documented model-governance systems, and cross-functional regulatory, clinical, and data-science oversight – demonstrating that they can mature from experimentation to compliant, repeatable, enterprise-grade delivery.

4. Embrace collaborative ecosystems

Investors should prioritize AI companies that have demonstrated an ability to operate within – and actively cultivate – robust collaborative ecosystems. In today’s regulatory and commercial environment, no organization can scale healthcare AI solutions alone. Companies that strategically align themselves with the right partners are better positioned to secure data access, clinical validation, and compliance infrastructure necessary for sustainable growth.

Strong indicators of this capability include the formation of durable data-sharing agreements with respected health systems, payors, or research institutions; participation in high-credibility research alliances that can support evidence generation and regulatory acceptance; and exploration of co-development or commercialization partnerships that extend reach and reduce the burden of go-to-market execution.

From a legal and regulatory standpoint, these collaborations serve as an important risk-mitigation mechanism. Well-designed agreements can clarify data rights, allocate liability, streamline compliance obligations, and create a more defensible posture before regulators and contracting partners.

For investors, backing companies that intentionally build these ecosystems not only reduces execution and regulatory risk, but accelerates innovation cycles, enhances credibility with oversight bodies, and strengthens the pathway to scalability and broad market adoption. In a landscape where policy, reimbursement, and clinical integration are evolving rapidly, companies with established and well-governed partnership networks will be best positioned to lead.

Bottom line

AI is a strategic capability, reshaping every layer of the life sciences industry. All of this means that the trend line for AI adoption and investment in the life sciences sector is likely to be directionally upwards, but with lots of fits and starts obscured in this trend. Investors who pair strong internal awareness with disciplined legal, regulatory, and technical diligence will make better decisions, avoid hype-driven traps, and position themselves to capture durable value in a fast-moving market.