2026 is shaping up to be a defining moment for life sciences, as leaders across clinical research, MedTech, and biopharma anticipate a decisive shift toward smarter, more autonomous systems. Agentic AI is set to ease long-standing operational bottlenecks, from overburdened clinical research associates to slow, paperwork-heavy trial workflows. At the same time, political and economic pressures are prompting companies to reassess global strategies, favouring more resilient, hybrid models for conducting trials. Industry leaders predict that AI will begin delivering measurable returns by accelerating study startup, streamlining contracting and payments, and enabling on-demand evidence generation for both clinicians and financial stakeholders. Regulatory agencies are also preparing for a new era, aligning frameworks and embracing generative AI to support faster, more transparent review. Collectively, these changes point to a future where clinical and commercial operations become more efficient, data-driven, and continuously adaptive. Here a group of industry leaders share their insights and answer a single question: “What does the very near future hold?”
Michelle Longmire, CEO and Co-founder, Medable
“Clinical development is bottlenecked by manual processes. Clinical Research Associates (CRAs) are overworked, often experiencing burn out. In 2026, Agentic AI will unlock the keys for improved clinical development, starting by alleviating within trial white space and empowering clinical research associates to focus more on sites and the trial operations rather than administrative tasks. To achieve value, agents must be measured against the outcomes they deliver, including end-to-end autonomy. In 2026, we will see agents begin to deliver extremely valuable aspects of the value chain – ultimately enabling full clinical development autonomy with as needed human oversight for key activities.”
Patrick Flanagan, CEO, Veristat
“Tariffs on imported pharmaceuticals and medical components, coupled with rhetoric about bringing trials and manufacturing “back home,” are already prompting sponsors and service providers to re-evaluate their global footprints. While the administration’s intent is to strengthen domestic production and patient participation, these measures introduce new cost pressures and complexities that could ripple through the clinical research ecosystem. The impact may be indirect but meaningful—affecting everything from trial supply chains to site-selection strategies.
“At the same time, the focus on domestic economic growth and potential incentives for U.S.-based research could create opportunities for CROs, site networks, and sponsors that have invested in U.S. infrastructure or flexible, informed trial capabilities. Companies capable of efficiently recruiting U.S. patients, leveraging digital platforms (including AI), and operating within a U.S. regulatory framework will likely see competitive advantages. Conversely, those heavily reliant offshore operations may face rising costs and delays. The sector’s ability to adapt – balancing resilience and insight with cost efficiency – will determine how disruptive these shifts become.
“While political cycles may drive temporary uncertainty, the fundamentals of good clinical research remain global. The continued discovery of applicable scientific innovation, diversity of patient populations, pursuit of personalized and rare-disease cohorts, and the need for international regulatory data ensures that cross-border trials will endure. The likely outcome next year is not a wholesale return of trials to the U.S., but rather a recalibration: a more resilient, hybrid model that strengthens domestic participation without sacrificing global reach. For forward-looking CROs and sponsors, this transition offers a chance to build smarter, more flexible, technology enabled trial ecosystems that thrive regardless of political tides.”
John Chinnici, CEO, Ledger Run
“Next year will be a turning point where AI’s promise in life sciences finally meets pragmatic execution. By targeting the administrative bottlenecks that have plagued clinical trials for decades including contracting, budgeting, and payment processing, AI will begin to demonstrate return on investments and accelerate trials significantly.
“Early estimates show AI can reduce study startup timelines by 15–20% on average, saving millions in overhead costs per global trial. AI has proven it doesn’t need to design the next blockbuster molecule to transform the business of clinical research. The future of clinical trials is not just smarter science, but smarter operations powered by AI that quietly handles the laborious tasks fast and enables researchers to re-focus on supporting sites and patients.”
Mike Monovoukas, CEO and Co-founder, AcuityMD
“In 2026, AI won’t just accelerate product innovation. It will reshape how MedTech companies demonstrate clinical and financial value. On-demand analytics will instantly quantify treatment impact, helping manufacturers prove outcomes for both clinicians and CFOs. Rather than relying on retrospective studies or data, AI-driven models will provide evidence on demand. For forward-thinking MedTech companies, this will mean faster adoption, confidence that they’re aligned to a buyer’s distinct needs, and a competitive edge built on transparency and measurable results.”
Pamela Tenearts, Chief Medical Officer, Medable
“The FDA and EMA have been moving (thoughtfully, but decisively) toward a more aligned, forward-looking set of rules that do more than protect the public; they create room for responsible progress. With the EU AI Act coming into effect in the first half of 2026 and the FDA beginning to deploy generative AI tools to support and accelerate regulatory review, there will be a new regulatory position: guidance that is faster, more data-driven, and anchored in transparency, explainability, and continuous performance monitoring.
“In 2026, there will also be a tiered approach to autonomy. Regulators appear to be increasingly prepared to let AI handle routine, low-risk research tasks with minimal friction, while keeping firm human control over decisions that directly shape safety, ethics, and public trust. That is the right hierarchy. If they stay on this course, international coordination and iterative learning between agencies will not just keep up with AI, they will shape it. The result is substantial: more efficient drug development, lower costs, and more timely, representative access to better therapies. The challenge, which we cannot underestimate, is to ensure that the governance, safeguards, and ethical commitments evolve as quickly as the technology they aim to oversee.”
Philip Poulidis, Co-founder and CEO, ODAIA
“In 2026, AI will finish what digital transformation began: it will automate the consulting layer. Projects that once took months — segmentation, targeting, territory design — will run continuously inside intelligent systems that learn from live data. The cycle of data pulls, analyst hours, and PowerPoint summaries will collapse into real-time models that update themselves. This shift will redefine value creation. Instead of selling time, firms will sell outcomes: strategy, interpretation, and change management built on adaptive software. For biopharma companies, the benefit is speed and control. Insights will be delivered instantly, not quarterly. For service providers, it amounts to a mandate to evolve.AI won’t eliminate consulting; it will elevate it. The winners will be those who move from building reports to building systems and turning expertise into engines that keep learning long after the engagement ends.”
Ravi Ramachandran, Co-founder, Chief Science Officer, Peer AI
“The next phase of automation will connect the data itself. Information that once lived in silos – clinical databases, electronic health records, and real-world evidence – will begin to flow directly into submission-ready formats. Continuous reconciliation will replace months of manual data cleaning as AI systems monitor incoming streams, resolve discrepancies, and flag missing values in real time. Documentation will mirror the true state of the data at every moment. Sponsors will no longer wait for “last patient, last visit” to begin authoring; by 2026, the first prototypes of this regulatory data fabric will link trial operations, medical writing, and regulatory review into a single, traceable thread. As this fabric matures, it will become the backbone of evidence exchange between sponsors and regulators, enabling machine-to-machine review where auditors focus on the underlying evidence rather than just the documents that package it.
“The AI platforms that succeed won't simply generate documents, they will evolve into intelligence layers that translate between data streams and regulatory requirements, maintaining living documentation that updates with the evidence. These agentic systems will transform authoring from a point-in-time activity to continuous orchestration, where compliance becomes a real-time state rather than a retrospective exercise. This shift could also enable innovative trial designs like synthetic control arms, using validated real-world data to augment or replace traditional placebos in rare-disease studies.
“By decade's end, this infrastructure may extend beyond clinical development to create a unified system connecting trial evidence with real-world outcomes.”
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