State of the Art-ificial Intelligence

Overview:

• Artificial intelligence (AI) investments in drug discovery are expected to yield the greatest return on investment (ROI) as AI-powered software offerings are driving up more than 50% in cost/time savings and shortening development timelines.
• Integration of AI tools across the health care sector is increasingly essential to remain competitive as biosimulation and automated bioprocessing are streamlining AI adoption.
• Our proprietary surveys and trackers suggest growth in health care AI investments will outpace traditional research and development (R&D) spending through 2029.
• Lucrative discovery partnerships should persist, with direct-to-consumer (DTC) software offerings best-positioned to capitalize on cross-sector AI tailwinds in fiscal year 2025 and beyond (FY25+).

The TD Cowen Insight

AI is disrupting established health care research, diagnosis (Dx) and treatment paradigms as next-gen software offerings and automation drive cost/time savings and shorten development timelines. AI and adjacent tools should see steady tailwinds in FY25+ as R&D spending prioritizes lower labor costs. DTC offerings are especially well-positioned while data catalysts still drive AI stocks with wholly owned drug pipelines.

AI-Driven Innovation in Health Care Sector Shows Strong Results

We previously dubbed 2025 as "The Year of AI", and few if any sectors stand to benefit more from the AI revolution (now in full swing) than health care. Nearly every aspect of our health care ecosystem is ripe for AI-driven innovation with a rapidly expanding cohort of public and private companies already developing targeted computational products to address one or more of them. For years, AI models and algorithms have promised unprecedented savings and access to therapeutic targets and markets that have been out of reach, but in the absence of paradigm-shifting late-stage clinical data readouts, some investors have remained skeptical that the technology is evolved enough to warrant the hype.

AI-based offerings across the health care sector are largely designed to drive their own growth with continued training and re-training of models expected to yield increasingly better results over time. We agree that late-stage clinical readouts (and potential approvals) of AI-powered drugs over the next three to five years will be critical moments in the evolution of this space. However, as detailed herein, AI tools being applied before and beyond late-stage clinical trials are already showing transformational impacts on R&D spending and development timelines. Recent breakthroughs in the ability of both open-source and proprietary data-trained models have reignited the pace with which new offerings can be rolled out with multiple companies now confirming preclinical development timelines being cut in half and clinical trials enrolling over twice as fast.

We expect drug discovery and development will yield some of the greatest ROI for AI applications as broader adoption of end-to-end software and biosimulation offerings continue to drive deeper cost/time savings and improve workflow efficiencies. These structural changes are already beginning to challenge the traditional contact research organization (CRO) model with AI efficiencies calling into question the continued demand for traditional staffing-based outsourcing services.

Our key opinion leaders (KOL) checks and investment trend analyses also suggest we are still in the early stages of AI's impact on other health care paradigms with next-gen tools to optimize clinical trial design and recruitment, automation/bioproduction, diagnostics and preventative wellness all gaining traction. Lower operating expenses and faster delivery times remain top priorities for companies across the sector particularly as government funding uncertainties and international trade tensions loom large. Thus, we expect widespread efficiency mandates will add further momentum to industry demand for AI-powered savings with AI investments likely to outpace R&D spending on more traditional life science tools for at least the next three to five years.

What Is Proprietary?

Our Clinical Trial Scorecard analyzes hundreds of confirmed clinical trials and preclinical discovery programs from selected private and public companies broken down by therapeutic area, program status, and drug modality to allow investors to better track the landscape of AI-driven drug discovery. Our extensive diligence with industry experts includes survey data from biopharma R&D leaders and investors as well as proprietary trackers for publicly disclosed M&A and private investments in health care AI.

AI Projected to Generate Positive Returns for Multiple Healthcare Sectors

We see use cases for AI in health care in the following, and more:

• drug discovery and development,
• wet lab automation,
• clinical trial recruitment/optimization,
• bioproduction,
• medical data analytics/support,
• diagnostics/preventative wellness

Across all segments, we estimate the total addressable market (TAM) of the fiscal year 2024 (FY24) for AI in health care of approximately US$67 billion growing at a 2024 to 2029 CAGR of approximately 22% to approximately US$183 billion estimated by fiscal year 2029 (FY29) with numerous opportunities for upside. This includes a US$48 billion estimated, US$35 billion estimated, US$90 billion estimated, and US$10 billion estimated 2029 TAM from drug discovery, health care automation, medical data analytics/support, and clinical diagnostics respectively.

We expect the rate of penetration across different segments will vary with physicians adopting AI-driven medical data analytics likely being some of the fastest uptake. Our diligence also suggests approximately 47% of drug discovery work could incorporate AI by 2029 with uptake in health care automation and clinical diagnostics also expected to grow between now and 2029.

The proportion of total R&D budgets allocated to AI investments across surveyed biopharma companies is expected to grow from 10% in 2024 to 16% of total spend by 2027. AI-powered drug discovery is estimated to drive cost/time savings of more than 25 to 50% over time which could deepen even more as model algorithms evolve and training data sets become increasingly complex. AI-driven research models that leverage the lab-in-a-loop infrastructure are expected to drive their own growth and evolution that significantly outpaces traditional wet lab trial-and-error approaches. Our diligence and analyses suggest investments in health care AI will grow through at least 2029 despite spending contraction across the sector with demand for AI offerings that allow customers to see how spending money can help them save money.

What To Watch

Numerous clinical and preclinical data readout catalysts from public and private AI drug discovery companies in 2025 to 2026 offer an unprecedented opportunity to assess a wide array of AI-powered clinical assets across multiple therapeutic indications. We also expect the number of AI drug discovery readouts will grow significantly over the next 5-plus years as new and existing companies advance additional AI-powered assets into the clinic. Additional AI-focused updates from the White House should also drive sentiment across different AI segments in tech and health care alike. Beyond clinical data readouts, quarterly earnings updates with management commentary on the demand for DTC AI and adjacent software offerings will also be a critical indicator of industry expectations for the increasing adoption and growing use cases for these platforms across the sector.

Stock Conclusions

AI players able to demonstrate competitive time/cost savings alongside workflow efficiency improvements for their customers, partnered programs, and/or wholly owned drug pipelines will be best positioned to outperform peers. We expect AI drug discovery and development collaborations will persist as demand for these platforms grows across the sector. Companies with differentiated DTC software offerings are especially well-positioned to disproportionately benefit from continuing cross-sector AI tailwinds in FY25+. Conversely, stocks for companies developing their own internal pipelines will also be driven by upcoming clinical data readouts as investors still view them as "biotechlike" where their future value hinges on the commercial opportunity for the drugs they produce and/or develop internally.

Widespread integration of AI robotics, machine learning, and AI-compatible automation also provide key points of differentiation in other tools segments and bioproduction to give companies an increasingly competitive edge versus peers.