Beyond regulation: 4 AI trends transforming life sciences technology in 2026
Artificial intelligence (AI) is becoming embedded into the fabric of quality, compliance, and production systems and shaping how teams interact with software, how processes are managed, and how work is delivered.
The era of experimentation is giving way to one of enterprise integration, where scale, safety, and alignment with regulation matter more than novelty. Four shifts in particular stand out as likely to define how life sciences technology evolves in 2026.
1.
Greater adoption of AI and agentic processes
In the coming year, life sciences organisations will demand more from AI – it will no longer be sufficient for systems simply to respond; users will expect them to act. With the expansion of consumer-grade AI tools, employees now come into the laboratory or manufacturing line with expectations of responsiveness, adaptability, and decision-making that mirror their personal devices. Organisations must therefore move towards agentic capabilities – systems that can plan, decide, and execute workflow steps within clearly defined governance and compliance frameworks.
At the same time, this shift introduces new governance imperatives. It will require stronger oversight frameworks, rigorous audit trails, and robust security controls to ensure that autonomous behaviour remains compliant and traceable. In life sciences, where quality and safety are non-negotiable, the competitive advantage will flow not from the raw power of the models, but from how seamlessly such systems integrate with Gapp, 21 CFR Part 11, and supplier-quality ecosystems.
AI adoption in life sciences is accelerating, but at a more measured pace than in other industries. While McKinsey reports that 88% of organisations globally use AI in at least one business function, adoption in life sciences is lower. Failure rates remain high, with studies cited by Forbes showing 95% of AI initiatives fail to scale beyond pilots when implemented as generic, horizontal tools. In regulated environments, success highly depends on vertical, domain-specific AI that embeds compliance, traceability, and governance from the start.
2.
Robotics entering the corporate environment
While software automation has been well established in pharmaceutical workflows, 2026 will mark a turning point for physical automation in the corporate life sciences environment. Robotics is increasingly appearing in manufacturing, logistics, and laboratory settings, creating a truly hybrid workforce where humans, robots, and AI-driven systems co-exist.
This convergence presents fresh challenges for connectivity, data security, and infrastructure design. The network becomes the production line and every sensor or actuator must meet the same reliability, provenance, and traceability requirements as a critical IT system. In this context, successful organisations will adopt a cross-disciplinary design approach ensuring mechanics, electronics, and digital controls evolve together, rather than in isolation.
Robotics has long been embedded in biopharma manufacturing, particularly in areas such as aseptic filling, materials handling, and high-throughput processing. What is changing is its role. In 2026, we’ll see the move from scripted, predefined robotic routines to AI-driven autonomy, where robots increasingly make context-dependent adjustments based on real-time data. This shift is reflected in sustained investment across the sector. The pharmaceutical robotics market is projected to reach USD $471.44 million by 2034, growing at a CAGR of 8.5%. That growth points to a broader expansion of robotics into laboratories, logistics, and enterprise operations, where intelligence, connectivity, and compliance must evolve together.
3.
Cross-platform AI replacing single-platform solutions
In 2026, AI in life sciences will no longer be confined to a single application or department. It will span multiple domains, drawing data and triggering processes across clinical, regulatory, quality, manufacturing, and supply chain systems. Organisations will expect agents to operate fluidly between environments, handling tasks that span ERP, LIMS, QMS, CRM, and more.
This movement from single-platform silos to cross-platform ecosystems challenges both architecture and compliance. Data that once sat safely within one system will now need to be shared securely across multiple platforms, elevating access control, encryption, and traceability to strategic imperatives. Integration layers and APIs will become as critical as the models themselves – the connective tissue that enables intelligence while preserving the integrity of compliance frameworks.
As AI adoption grows, life sciences organisations are moving away from single-platform intelligence toward multi-system orchestration. Research shows that 94% of life sciences leaders expect AI agents to be essential across operations. This signals growing demand for AI systems that operate across ERP, QMS, LIMS, and other regulated platforms. As a result, integration layers, APIs, and governed connectivity are becoming strategic priorities, often as critical as the models themselves.
4.
AI changing software development through vibe coding
The way life sciences software is built is undergoing a major transformation. Generative AI, combined with agentic development environments, is shifting how applications are created, deployed, and maintained. Rather than writing every line of code, developers will increasingly set intent, defining the ‘vibe’, logic, or outcome they require and let AI generate bespoke components in real time.
This accelerated development will enable rapid customisation and faster go-to-market for tools tailored to unique clinical or regulated manufacturing workflows. At the same time, the role of human engineers will shift to oversight, governance, and risk assurance. In a sector where validation, version-control, and audit trails are built into the development lifecycle, governance of generated code will become as important as the code itself.
Dot Compliance is already seeing this evolution, for instance. About 60% of the code in recent product releases was AI-generated or AI-assisted, accelerating development cycles and enabling rapid iteration. Looking ahead, AI-generated code is expected to dominate software creation, with industry leaders projecting that more than 95% of code will be produced per task or prompt within five years.
The 2026 outlook
The life sciences sector is entering a period where innovation must work hand in hand with compliance. As AI becomes embedded across quality, manufacturing, and regulatory systems, the focus will shift from experimentation to reliability and validation. Success in 2026 will depend on how effectively organisations apply new technology within proven, transparent, and compliant processes.
About the author
Doron Sitbon is the founder and CEO at Dot Compliance, a provider of AI-powered electronic quality management and compliance solutions for the life sciences industry. Sitbon is a dynamic and highly accomplished entrepreneur with over two decades of global executive leadership experience in the enterprise SaaS solution industry.
