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Five trends stand out as particularly impactful for 2026. Together, they point to a clear conclusion: success in 2026 will increasingly depend on how well life sciences organizations build strong data foundations, shared semantics, and governance structures that allow AI, analytics, and people to work together at scale.
The life sciences industry is entering a pivotal moment. Advances in technology and efficiency coupled with increasing regulatory complexity and rapidly growing data volumes are converging to reshape how organizations operate, innovate, and compete. For pharma and biotech leaders, the challenge is no longer identifying these forces but understanding how they will materially impact data mangement, decision-making, and long-term value creation.
At ONTOFORCE, we’ve spent nearly 15 years working alongside pharmaceutical and life sciences organizations, helping them navigate data complexity across research, clinical development, regulatory applications, and real-world evidence. That experience gives us a front-row seat to the shifts taking place and a vested interest in how these trends play out for the industry and its players.
Based on what we see across our customer base and the wider ecosystem, five trends stand out as particularly impactful for 2026. While they span AI, regulation, data, and operational speed, they are deeply connected. Together, they point to a clear conclusion: success in 2026 will increasingly depend on how well organizations build strong data foundations, shared semantics, and governance structures that allow AI, analytics, and people to work together at scale.
Below, we explore these five trends and what they mean for the future of the life sciences industry.
Interest in agentic AI and multi-agent systems (MAS) has surged dramatically. Gartner reports a 1,445% increase in MAS inquiries between Q1 2024 and Q2 2025. Clear evidence that organizations are looking beyond single, monolithic AI models.
Unlike traditional AI applications, agentic AI or multi-agent systems rely on collections of specialized AI agents that collaborate to complete complex workflows. Each agent focuses on a specific task, such as retrieving data, reasoning over evidence, or validating outputs, resulting in systems that are more scalable, modular, and efficient.
At the same time, expectations are being recalibrated. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, largely due to unclear business value, rising costs, and potential risks. Poor data foundations and governance can also jeopardize agentic AI initiatives.
Agentic AI is powerful—but only when it is built on reliable, well-structured data and guided by a clearly defined scope. Without strong data foundations and a shared semantic layer that provides context and constraints, organizations risk deploying sophisticated AI agents that amplify inconsistency, uncertainty, or cost rather than delivering meaningful impact.
In 2026, success with agentic AI will not come from experimentation alone. It will come from orchestrating AI agents on top of trusted, contextualized data assets, enabling them to operate predictably and at scale. Organizations that invest in AI-ready data and governance today will be best positioned to translate agentic AI into sustained productivity and enterprise-wide efficiency.
Regulation is no longer a background constraint—it is a central force shaping strategy. In Deloitte’s 2026 Life Sciences Outlook, which surveyed 280 C-suite executives from biopharma and medtech companies, regulation was the most frequently cited trend expected to influence organizational strategy in 2026. Its impact is increasingly felt in how life sciences organizations collect, structure, connect, govern, and report data.
In Europe specially, regulatory initiatives are increasingly influencing how evidence is generated and reused. Programs such as EU Joint Clinical Assessments (JCA), ongoing pharmaceutical legislation reform—including the Critical Medicines Act and the wider EU Pharma Package—and the UK MHRA’s evolving approach to marketing authorization and point-of-care manufacturing all raise the bar for data consistency and data standards.
The cumulative effect is a shift toward continuous, evidence-ready data management. Organizations must be able to:
In 2026, regulatory readiness will depend less on manual documentation and more on connected, well-governed data foundations that allow evidence to be assembled, explained, and reused at speed
Multimodal data is no longer an aspirational goal; it is rapidly becoming standard practice in real-world evidence generation and clinical insight.
Life sciences organizations are increasingly integrating:
When combined effectively, these data sources provide a more complete, longitudinal view of patients and disease progression, enabling deeper phenotyping, improved patient stratification, and more robust evidence generation across clinical development and post-market surveillance.
Learn how AstraZeneca is powering multimodal data reuse with DISQOVER
However, more data does not automatically mean more insight. The real challenge in 2026 is connecting multimodal data into a coherent, explainable view of patients, diseases, and outcomes. Large language models can help bridge structured and unstructured data, but without shared semantics and contextual grounding, insights remain fragmented and difficult to trust. This is where semantic models and knowledge-driven approaches become essential. They provide the structure, context, and traceability needed to turn multimodal data into actionable, decision-ready insight.
As data volumes grow and AI-driven use cases multiply, life sciences organizations are running into a familiar problem: while data may be accessible, it is not always understood in the same way across teams, systems, and applications. In 2026, this challenge is driving renewed focus on the semantic layer.
The semantic layer provides a shared, business- and science-aware view of data. It defines concepts, relationships, and rules once, and makes them reusable across analytics, applications, and AI systems. Rather than forcing every tool, model, or team to reinterpret raw data independently, the semantic layer ensures that everyone is working from a consistent understanding of entities.
This becomes especially critical as organizations adopt agentic AI and multimodal analytics. AI agents can retrieve and process data at scale, but without a semantic layer, they risk producing outputs that are inconsistent, ambiguous, or difficult to validate. A well-defined semantic layer gives AI systems the context and constraints they need to reason reliably, linking data to its meaning, provenance, and intended use.
In 2026, the renewed interest in the semantic layer reflects its foundational capability that enables scalable AI, interoperable data ecosystems, and faster, more confident decision-making.
Speed has always been crucial in the life sciences industry. As the years have progressed, speed and equally important agility are becoming defining factors across the industry’s value chain.
Nearly 48% of respondents in Deloitte’s survey identified accelerated digital transformation as a trend likely to have a substantial impact in 2026, a significant increase compared to 2025. From AI-enabled research to automated analytics and faster decision cycles, organizations are under pressure to move faster without compromising quality or compliance.
This acceleration is especially visible in drug development, where expectations around shortening the path from bench to bedside continue to rise. As innovation cycles compress, organizations that cannot adapt risk being left behind.
In 2026, acceleration is not just about moving faster. It’s about building systems that can sustain speed without sacrificing trust, governance, or scientific rigor.
While these trends may seem distinct, they all converge on a single requirement: strong, integrated data foundations grounded in shared meaning. Agentic AI, multimodal analytics, regulatory readiness, and accelerated innovation all depend on data that is not only accessible, but well-structured, semantically consistent, and trusted.
Industry leaders clearly see AI as a lever for transformation. According to Deloitte, 78% of biopharma and medtech executives interviewed expect AI to play a central role in driving major organizational change in 2026. Beyond technology alone, this shift is also about productivity: 29% of biopharma leaders and 31% of medtech leaders are planning to use AI tools or targeted training to improve workforce efficiency.
However, realizing these gains requires more than deploying new AI applications. In practice, organizations succeed when they focus on:
The biggest disruption opportunity lies not in adopting the latest AI tool, but in preparing data ecosystems where shared semantics allow AI, people, and processes to operate with speed, confidence, and control. Organizations that treat AI as a workforce multiplier, powered by strong data and knowledge foundations, will be best positioned to translate complexity into sustained competitive advantage in 2026 and beyond.
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