Trendspotting: Artificial Intelligence, Impact of Global Policies, Data Harmonization
By Bio-IT World Staff
January 6, 2026 | To kick off 2026, we spoke with industry experts and leaders in the Bio-IT World community about what they expect and look forward to in the new year. As anticipated, artificial intelligence (AI) will continue to innovate and is expected to “take center stage in drug development” and “will shift from a supportive role to an essential, integrated part of drug creation,” according to David Lazerson of Briya.
With AI’s help, “2026 will feel less like a ‘slow-pipelines’ world and more like a ‘smart-pipelines’ world,” predicted Andrew Mackinnon of Medable. Agentic AI systems “will autonomously propose targets, run virtual experiments, optimize protocols, monitor safety signals, and surface decision-ready recommendations,” but “humans won’t be removed from the process. They’ll shift into higher-value oversight roles,” he said.
As we move forward, there are increasing concerns surrounding how global socioeconomic policies will affect the industry. Allison Cuff Shimooka of TransCelerate BioPharma is expecting “uncertainty will shape every decision,” which will force industry leaders to “weigh innovation against risk and preserve momentum while navigating an unpredictable global landscape.”
The result of all this AI innovation and adaption could result in much better data management. Mark Lambrecht of SAS says, “Data orchestration harmonizes life sciences.” In other words, “as life sciences move toward personalized medicine, we are no longer dealing with isolated data points. Instead, in 2026 and beyond we will orchestrate high-quality, continuous data streams from digital biomarkers, genomics, imaging and clinical laboratories.”
Here are the full trends and predictions, including more on AI, drug development, global policies, cancer treatments, and more. -- the Editors
Jason Bryant, VP of AI & Data, ArisGlobal
2026 will be the year of AI orchestration; this will be the new differentiator: The challenge now extends beyond model selection and single-model performance—toward connecting models, data, and systems across domains and participants. This is about AI platform engines becoming a control layer: coordinating GenAI, agentic AI, and deterministic logic to reduce complexity and unify domains.
Courtney Noah, VP of Scientific Affairs, BioIVT
We’ve seen the benefits of assessing omic biomarkers to get a more complete picture of factors leading to disease onset and progression: With this massive number of data points (potentially in the billions), it becomes difficult for traditional statistical methods to handle the scale and complexity effectively. I believe machine learning and AI will allow us to scale through integration and multi-modal analysis. To address the complexity of biology and disease progress, AI/ML can identify hidden relationships and create more comprehensive predictive signatures. We’ve begun to see innovative AI-based biomarkers across a variety of disease indications including lung cancer, cardiovascular disease and neurodegenerative disorders with a strong focus on applications in preventative medicine and early disease detection.
David Lazerson, CEO and Co-founder, Briya
AI takes center stage in drug development: In 2026, AI will shift from a supportive role to an essential, integrated part of drug creation. While AI has already improved discovery, study design, and operational efficiency, the next few years will see AI-driven insights directly shaping mechanism identification, dosing strategies, and patient stratification. More importantly, regulators are beginning to accept AI-generated evidence as part of formal submissions, laying the groundwork for a future class of “AI-Approved” therapies that have been strengthened by continuous, high-quality evidence collection.
Giovanna Prout, CEO, Countable Labs
Next-generation sequencing created the roadmap for using genetic information for research studies, and in clinical testing, 2026 will be an inflection point: Amid budget constraints in both research and clinical contexts, continued progress in genomics will depend on tools that expand access to highly accurate information while reducing costs, complexity, and bioinformatics challenges. This need is especially critical in emerging areas like minimal residual disease testing, where speed, sensitivity, and affordability are essential. Countable PCR technology will meet this moment with highly sensitive, direct DNA and RNA quantification and a simple counting-based multi-target PCR workflow that will finally democratize access to the genome.
Steve Gens, CEO, Gens & Associates
As AI becomes more integral to the work people are doing, the priority extends beyond matters of governance: We’re at a point now where companies need AI-performance-toolkit skills. Just as Excel, Word, and PowerPoint skills became critical to work 20 years ago and, during the pandemic, people suddenly had to become proficient in Teams or Zoom. It’s the same with AI now. People will need to be able to use it effectively in their daily working lives.
Boyang Wang, Founder, Immortal Dragons
I expect organ replacement to move meaningfully closer to reality: Gene-edited donor animals are improving, graft survival is increasing and regulators are beginning to engage more seriously with xenotransplantation pathways. Work on growing human organs inside animals is advancing past early proof-of-concept stages, and progress on bioprinted vasculature may remove the final structural barrier preventing fully engineered tissues from reaching patients.
On the therapy side, I anticipate the rise of reversible gene modulation. Instead of permanent edits, technologies like epigenetic switches could allow conditions to be treated with tunable, multi-dose approaches. He also expects more precise delivery — into the nose for brain disorders, into the eye for local retinal disease, or into skin tissue without viral vectors — to make genomic therapies feel safer and more controllable. And with AI-powered wet labs, he sees biology becoming more predictable. Robotic labs linked to foundation models are dramatically shortening discovery timelines, and the first practical “digital twins” for metabolism and immune aging may finally reach early clinical pilots.
Jehee Suh, CEO, Inocras
On the research front, our priority is turning comprehensive whole-genome data into discoveries that translate quickly to the clinic: Working with leading institutions such as the Broad Institute, we’re building federated analyses and shared benchmarks that go beyond what panel data can reveal, with complex rearrangements, genome-wide signatures, and non-coding drivers that open new targets and trial paths. Whole genome sequencing provides investigators with a single, comprehensive substrate that minimizes re-testing and enables continual reinterpretation as evidence evolves. This is how we compress the cycle from biomarker discovery to validated, clinically actionable insights.
Ali Jannati, Director of Cognitive Science, Linus Health
As brain-health technology adoption increases, it will accelerate the development of disease-modifying therapies for neurodegenerative conditions but also redefine prevention as a viable endpoint: The next competitive advantage will go to organizations that embed early detection technologies into their R&D strategy, ultimately transforming how new therapies and preventive interventions are discovered and developed.
Hyung Heon Kim, CEO and President, MetaVia
GLP-1 therapies have transformed the pharmaceutical landscape, evolving from diabetes treatments into a powerful new category driving the future of metabolic health: Their success has elevated obesity from a secondary focus to one of the industry’s most dynamic areas of innovation and investment. Yet, as the market expands, an important gap is becoming clear: about 20% of type 2 diabetes patients cannot tolerate or do not respond to GLP-1 therapies. The next wave of innovation will address these unmet needs through combination and oral approaches. Companies are exploring mechanisms that pair GLP-1 activity with GIP, glucagon, or GPR119 agonists to enhance efficacy and tolerability. Oral formulations also hold promise for improving access, adherence, and cost efficiency compared with injectables. Future differentiation will hinge on factors such as tolerability, speed of titration, and dosing convenience. Many current options require months to reach therapeutic levels, a barrier the next generation of therapies aims to overcome. The next stage will focus on smarter, more patient-centric therapies that build on GLP-1s’ success while expanding treatment possibilities for diabetes and obesity.
Aaron Galaznik, Chief Medical Officer, MDClone
One of the most important innovation trends in biomedical research is the growing use of high-fidelity synthetic data to accelerate discovery while protecting patient privacy: By enabling teams to explore patterns, refine models, and validate approaches without accessing identifiable data, synthetic data removes traditional bottlenecks in multi-site collaboration. This creates a faster, more scalable pathway for translational research and individualized medicine.
Andrew Mackinnon, Global Executive General Manager, Medable
2026 will feel less like a “slow-pipelines” world and more like a “smart-pipelines” world: In 2026, expect the transformation of drug development from a predominantly human-driven, sequential process into a continuously learning, agentic-AI-supported pipeline. Instead of researchers and clinicians manually generating hypotheses, designing studies, reviewing data, and coordinating decisions across long cycle times, agentic AI systems will autonomously propose targets, run virtual experiments, optimize protocols, monitor safety signals, and surface decision-ready recommendations. Humans won’t be removed from the process. They’ll shift into higher-value oversight roles, validating AI-generated options and steering strategy while AI handles the labor-intensive, multi-step analytical and coordination work. The result will be a funnel that’s faster, more adaptive, less linear, and increasingly self-optimizing, marking the first true structural redesign of the R&D model in decades.
Orla Cloak, CEO, Minaris Advanced Therapies
The next era of biosafety testing will be driven by far more precise and digital approaches: Next Generation Sequencing (NGS) assays, real-time analytics, and rapid microbial detection are already changing how quickly and confidently therapies can be safely released. An equally critical element is establishing a regulatory acceptance path for innovative assays, which involves early engagement of key stakeholders and the provision of evidence-based data demonstrating their suitability for the intended purpose. In 2026, the testing partners and customers who lean into these modern platforms will not only shorten timelines, they’ll strengthen the scientific trust behind every batch that reaches patients.
Michael Grosberg, VP of Product Management, Model N
Over the next 24 months, pharmaceutical manufacturers will successfully shift the national discourse from list price comparison to a more nuanced perspective of patient value and market access: This will enable manufacturers to prioritize strategies that accelerate access, streamline distribution, and reduce administrative delays, rather than continuing to manage the gross-to-net bubble. We’re going to see a slowdown in new drug availability outside the U.S. as companies grow more selective in where they launch. The complexities of U.S. pricing will start to spread globally. This could be the moment the rest of the world begins to ‘catch’ our reimbursement problems.
Jesse Mendelsohn, SVP, Centers of Excellence, Model N
Anything that expands access is good, but programs like TrumpRx may not move the needle on access in a meaningful way: For most patients with insurance, these drugs could already be accessible and usually cheaper through their plans. Direct-to-consumer options and discounts will help a small subset of patients, such as those who are underinsured or can’t get coverage yet can afford the discounted price, but it won’t transform access at scale. Tariffs and politics are only a small driver of the surge in U.S. manufacturing. The real accelerator is drug discovery. AI, personalized medicine and connected therapies are driving a new innovation cycle, and high-end manufacturing is following. And that’s a key reason why we’re going to keep seeing those cranes and biotech hubs rising across America.
Lars Hartvig Kristiansen, Vice President of Product Innovation and Strategy, Molecular Devices
The increasing complexity of biology used in drug discovery, and the ever-growing bank of data, are making automated workflows and AI-driven software critical to laboratories: AI simplifies the operation, simplifies the data analysis, and helps guide the lab. Fifteen years ago, the scientific expertise required in the lab was exceptionally deep. Today, the solutions that we—and others in the industry—are bringing to market allow researchers to redirect that expertise toward broader scientific innovation. Much of the manual labor can now be automated, imaged perfectly, and driven by AI to secure more consistent, reliable results.
Christian Henry, CEO, PacBio
In 2026, the next major shift in genomics will come from the arrival of a truly affordable long-read genome: Researchers need genomes that are both highly accurate and affordable enough to power large population studies, AI model development, and emerging clinical opportunities. With high-quality HiFi genomes inclusive of methylation data now available for less than $300, PacBio has made the economics of accessing more accurate and complete genomic data more favorable than ever. At this price point, we envision researchers turning to HiFi data to deliver deeper insights that power the next wave of genomic discovery—from pangenome research studies to cancer research and rare disease clinical testing.
Ross Meyercord, CEO, Propel Software
Agentic AI ends the era of standalone software: Next year will mark the tipping point for connected intelligence. Software platforms that extend data and workflows across the enterprise will dominate, while isolated tools will fade into irrelevance. Agentic AI is already proving that productivity breakthroughs come from collaboration, between systems as much as people. The next generation of AI agents won’t live inside individual apps. They’ll communicate, coordinate, and act across entire tech ecosystems, turning fragmented processes into fluid, intelligent networks. In this new era, standalone software simply won't be able to compete. The demise of remaining on-premise software will accelerate, leaving just 15% of those companies over the next three years. SaaS is far from dead; its resurgence coexists with AI agents. In 2026, the winners will be those who combine the agility of AI agents with the reliability of SaaS to deliver measurable business value. SaaS brings the workflows, governance, and guardrails that enterprises demand, while AI agents extend productivity and speed. One without the other falls short, but together, they set the new standard for enterprise software.
John Cogan, COO, Qinecsa
The real impact of AI will be the biggest strategic theme for 2026, which use cases will bring actual ROI so we can get away from the smoke and mirrors and focus on the reality: This does not surprise me as we’ve had 18 months of hype and PoCs, and now it’s time to do the math on investment versus return.
David Gosalvez, Chief Strategy Officer, Revvity Signals
In 2026, life-science R&D will cross a meaningful inflection point as AI-augmented molecular design becomes not just a promising capability but the default mode of early discovery: The winners will be the organizations that deliver the predictive power of models directly into scientific context—embedded into electronic notebooks, analysis platforms, and design workflows—so chemists and biologists can act on high-confidence insights without leaving their workspace. At the same time, the industry will increasingly recognize that the true competitive advantage lies not in algorithms but in the training data behind them. As the benefits of collaborative model improvement begin to outweigh long-held concerns over data sovereignty, the industry will shift toward Federated Learning: a framework where pharma companies can benefit from collective intelligence without ever sharing raw data. By the end of 2026, federated approaches will move from pilots to standard practice, enabling secure cross-company model refinement and accelerating how the industry innovates.
Veronica DeFelice, Director of Biologics, Sapio Sciences
In 2026, natural language AI co-scientists will become trusted collaborators in the biologics lab: Scientists will interact with their digital notebooks using simple conversational prompts to locate data, access protocols, or record observations while working at the bench. Voice-activated AI assistants will enable scientists to query experimental results hands-free during active bench work. This eliminates the traditional friction of gloved hands navigating touchscreens or switching between physical and digital workspaces. These capabilities, built into AI Lab Notebooks, will bridge the gap between experiment and documentation, ensuring that every step is traceable without interrupting the workflow. The same AI systems will support surfacing relevant prior experiments or suggesting compatible reagents as procedures unfold. By bringing natural language interfaces directly into the scientific workspace, laboratories will move closer to fully connected, continuous research environments where information flows as easily as conversation.
Mark Lambrecht, Global Head of Health Care & Life Sciences, SAS
Data orchestration harmonizes life sciences: As life sciences move toward personalized medicine, we are no longer dealing with isolated data points. Instead, in 2026 and beyond we will orchestrate high-quality, continuous data streams from digital biomarkers, genomics, imaging and clinical laboratories. The promise of multimodal analysis – from genome-wide association studies to polygenic risk scores – depends on robust data engineering that can harmonize and contextualize these complex signals. Expect to see significant investment in the joining of the discovery and clinical analytical data fields.
Allison Cuff Shimooka, Chief Operating Officer, TransCelerate BioPharma
Uncertainty will shape every decision: 2026 will be defined by uncertainty—geopolitical instability, volatile global markets, and shifting health policies will influence every choice pharma companies make. From clinical trial planning to pipeline priorities, leaders will increasingly weigh innovation against risk, trying to preserve momentum while navigating an unpredictable global landscape. Shrinking health budgets — from defense spending in Europe to Medicaid and NIH cuts in the U.S. — will force companies to prioritize ruthlessly. Cost vigilance and operational efficiency will be paramount as sponsors manage R&D pipelines in a tighter financial environment
Gilad Almogy, Founder and CEO, Ultima Genomics
The opportunity to generate large-scale datasets at lower costs is driving an emphasis on generating the right foundational training data at scale: Flagship efforts with the Chan Zuckerberg Initiative, the Arc Institute, and the UK Biobank are examples of a first wave of large-scale initiatives to assemble high-resolution, multiomic and longitudinal datasets that can make AI clinically useful. The mandate for 2026 is to prioritize scalable and affordable data generation to enable more foundational dataset generation and standard development that in turn can drive better model and algorithm development.
Michelle Bridenbaker, COO, Unbiased Science
In pharma all teams from manufacturing to medical affairs are going to be working to embed and expand the use of AI in their organizations: We have seen a massive move to transform the way we work starting in 2025, but in 2026, those of us that have begun in earnest the journey towards AI are beyond pilots and finding ways to build better use cases for AI, running more successful project pilots and moving towards more sustainable, AI driven solutions and transformation of how we work. This trend is here to stay, and we all must embrace this change as our resourcing & staffing models are getting leaner, our profit margins tighter, and our workloads are expanding—we must do more with less and AI is the only foreseeable way forward.
Kristofer Mussar, COO, VectorBuilder
In-vivo CAR-T moves from concept to credible path: We are seeing two routes converging in this regard. Firstly, targeted lipid nanoparticles that program T cells in the body. Secondly, lentiviral vectors retargeted to T-cell receptors for direct in-vivo transduction. There’ve been reports of T-cell–specific LNP systems for nucleic acid delivery that generate CAR-T cells in vivo, as well as targeted LNPs for mRNA delivery to T-cell subsets. These show early but real steps toward bypassing ex-vivo cell handling, cost, and access barriers. In parallel, retargeted (pseudotyped) lentiviral vectors aimed at CD3/CD8 have shown improved T-cell specificity in animal models, with comprehensive reviews mapping the state of envelope engineering and safety questions ahead. I’ll be cautious in my optimism, though. Progress will be tangible, but regulators will expect disciplined risk control (insertional safety, off-target transduction) before “off-the-shelf” in-vivo CAR-T becomes routine.
Justin Lavimodiere, Senior Director, Veeva LIMS
Agentic AI lab assistants will drive connectivity and speed: Labs will move beyond chatbots to embed agentic lab assistants that connect highly specific tasks in a regulated environment. QC labs are turning their attention to the efficiency potential of AI agents and steering effort toward activating them across people and process. However, the technology ecosystems in QC labs are fragmented and paper-based processes persist. Companies will modernize and consolidate systems, standardize data and workflows, and integrate quality assurance to reap the productivity gains of QC-specific AI. Lab analysts will work alongside agents capable of starting workflows, summarizing outcomes, and observing and analyzing trends. This will advance proactive risk management by identifying issues early on and driving right first-time execution. The outcome will be a highly effective and efficient QC lab where people and agents work together to shorten batch cycle times.
Jiansheng Wu, Head of CRO Services and SVP, WuXi Biologics
I anticipate a major shift toward multispecific antibodies engineered for improved tumor penetration and more effective blood–brain barrier (BBB) crossing: The field is shifting toward smaller bispecific and trispecific formats that use scFvs, VHHs, and peptide modules to reduce molecular size while maintaining half-life through advanced Fc engineering. This trajectory will be accelerated by integrated AI platforms that are evolving from single-task tools to multi-omic systems unifying genomics, proteomics, structural modeling, and biomolecular behavior. This convergence will enable multispecific antibodies with deeper tumor access, more reliable BBB transit, and new opportunities for diseases previously considered beyond the reach of biologics.