March 24, 2026

Virtually every healthcare investor today has incorporated AI as a pillar of their investment thesis. For AIF, we see AI as a driver to accelerate progress in the space we invest in by doing three things at scale: processing large volumes of data, detecting patterns across that data, and translating those patterns into recommendations that support decision-making.

This capability matters as demand rises. More than 1 in 5 U.S. adults experience a mental health condition, 1 in 6 children live with a neurodevelopmental disorder, and autism prevalence has increased from 1 in 166 in 2005 to 1 in 31 today. AI gives care teams leverage by helping them move faster from data to action across large populations.

The Pain Points

The system managing this growing demand was built for episodic care, treating conditions through discrete interventions. Spending has climbed to $5.3 trillion (18% of GDP) and is projected to rise faster than economic growth [1]. Meanwhile, administrative load and workforce shortages leave providers with less capacity to coordinate across settings and prevent complications. As reimbursement shifts toward value-based models, those limitations become harder to ignore because payers reward outcomes, not activity.

Infrastructure is also major underlying constraint. Healthcare now generates data at a compound annual growth rate exceeding 35%, yet the vast majority remains unused because systems lack the ability to integrate, interpret, and act on information across the patient journey [2]. 

That is now beginning to change.

Incentives Are Finally Aligning

Under value-based care, business models that improve outcomes at lower cost are rewarded. Providers have incentives to find high-risk patients earlier, manage complex conditions over time, and avoidunnecessary care. Employers push for the same thing because healthcare spend directly impacts benefits budgets and workforce productivity. In this environment, businesses that help teams connect data, coordinate care, and measure outcomes gain a clear competitive advantage. 

This shift creates the perfect conditions for AI to move from experimentation to necessity.

How AI Aligns with the System’s Most Pressing Needs   

• Connecting the Data Layer: Fragmented clinical data remains one of the largest barriers to coordinated care and outcomes-based reimbursement. According to a 2023 NBER working paper [3], broader AI adoption could help reduce U.S. healthcare spending by 5 to 10%, in part by enabling better data integration. Patient information still sits across providers, care settings, and treatment histories, which slows decisions and limits visibility into what actually improves outcomes. Shared data layers and connected workflows give care teams and payers a real-time view of risk, utilization, and results, improving coordination and making outcomes easier to measure. This is a prerequisite for value-based care to scale.

• Expedite personalized treatments: Personalization requires care teams to connect messy, fast-changing information across providers and care settings. AI helps by organizing the data and surfacing what is most likely to matter for a specific patient. This is especially relevant in complex populations. For example, 95% of children with autism have at least one co-occurring medical or psychiatric condition, including anxiety, depression, ADHD, epilepsy, or gastrointestinal disorders. AI can help teams spot patterns across those overlapping needs and adjust care plans sooner, rather than relying on a trial-and-error method. A similar approach benefits chronic disease management. AI tools can track lab results, medications, and symptoms over time to flag risk earlier and reduce avoidable complications. A real-time view of patient status enables care that stays personalized as needs change, which aligns directly with value-based care goals.

• Democratize access to care: Healthcare capacity remains concentrated in major metropolitan areas across much of the world. In the U.S., for example, nearly 96% of counties qualify as mental health professional shortage areas [4], and about 70% of rural counties have no practicing psychiatrist [5]. The effects are widely felt, with families traveling long distances or waiting months for an evaluation.

The pressure to identify patients earlier and manage conditions over time makes the access gap urgent and creates a strong use case for AI. Digital triage and virtual assessment tools extend specialists’ reach beyond geographic limits and move patients through intake faster. Capturing data in a structured manner standardizes workflows and reduces variation in care delivery. Automation of eligibility checks, documentation, and claims lowers administrative burden and allows clinicians to focus on patient care. Together, these capabilities expand access within the existing healthcare framework.

These are interconnected use cases in a system where incentives now favor coordination, continuity, and measurable outcomes.

AI in AIF’s Investment Strategy

The AIF investment strategy is guided by a dual mandate of returns and impact, with priorities centered on coordination, personalization, access, and lifelong support in the areas of behavioral health, mental health, and complex chronic conditions.

When evaluating an investment opportunity, AIF does not treat AI as a standalone theme. AI is underwritten in context, as infrastructure that advances these priorities within scalable healthcare platforms. Since AI now appears in nearly every pitch deck, AIF asks a consistent question in sourcing and diligence: does the use of AI advance one or more of these priorities in a commercially durable way?

The system needs earlier detection, better coordination, and measurable results. AI’s core capabilities, data processing at scale, pattern detection, and longitudinal tracking, map directly to those needs. Applied well, AI supports business models that move measurable outcomes across the priorities that matter most to AIF’s target populations.