Doron Sitbon, CEO of Dot Compliance, outlines four developments set to change how life-sciences organizations design, deploy and govern intelligent systems in the year ahead.
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 organizations 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. Organizations 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 behavior 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 organizations 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 organizations 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. Organizations 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 organizations 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 customization 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. About 60% of the code in recent product releases was AI-generated or AI-assisted, accelerating development cycles and enabling rapid iteration. In the latest Dottie release, the Vibe Analytics agent generates code in real time to perform statistical analysis and present insights based on the user’s conversation. This demonstrates a move from static AI-assisted coding to dynamic, intent-driven code generation. 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 organizations apply new technology within proven, transparent and compliant processes.
