Why is execution becoming a bigger challenge for life sciences organisations in 2026?

Scientific innovation is accelerating, but organisations are operating with tighter talent pools, more complex portfolios, and higher regulatory expectations. Delivering results now depends on how well teams plan, coordinate, and execute work at scale.

How are workforce shortages impacting R&D and clinical delivery?

Persistent skills gaps are making it harder to staff programmes consistently. Many organisations are being forced to prioritise initiatives more carefully and find better ways to understand capacity, demand, and skills across their portfolios.

Why is data infrastructure such a critical focus right now?

Life sciences teams generate vast amounts of data, but fragmented systems limit visibility and slow decision-making. Connected, reliable data enables better prioritisation, forecasting, and governance across complex programmes.

What is holding organisations back from getting real value from AI?

Most challenges are operational rather than technical. Unclear ownership, inconsistent data, and disconnected workflows make it difficult to move AI from pilot projects into everyday use.

How are advanced therapies changing the way programmes are managed?

Cell and gene therapies introduce new levels of coordination across manufacturing, clinical development, and regulation. Managing multiple assets in parallel requires stronger planning, dependency tracking, and risk oversight.

How can project and portfolio management software help under these conditions?

PPM platforms give organisations a single view of priorities, resources, timelines, and risks. This visibility helps leaders make faster decisions, balance constrained resources, and maintain oversight without adding administrative burden.

What should life sciences leaders look for in modern project management tools?

Tools should support governance and compliance while being easy for teams to use day-to-day. Adoption, data consistency, and portfolio-level visibility are just as important as technical features.

Life Sciences Trends: 6 Shifts Reshaping Pharma & Biotech Strategy in 2026.

The life sciences sector is entering 2026 under growing pressure — to innovate faster, operate more efficiently, and deliver increasingly complex therapies at scale. Scientific ambition remains high, but the conditions for execution are changing. Talent shortages, data fragmentation, rising regulatory demands, and the shift from experimental technologies to enterprise-wide adoption are forcing organisations to rethink how work gets done.

This guide outlines the key trends shaping the life sciences landscape in 2026 — from workforce constraints and AI execution challenges to advanced therapies, decentralised trials, and governance-first technology strategies. Together, these trends highlight a clear message: success will depend not just on breakthrough science, but on the operational foundations that support it.

Key Takeaways

Execution is becoming the true differentiator. Scientific innovation remains strong, but organisations that succeed in 2026 will be those that can reliably execute across complex, global portfolios under increasing pressure.

Talent constraints are structural, not temporary. Workforce shortages in critical roles will persist, making visibility into capacity, skills, and priorities essential to sustaining progress.

Data quality and connectivity matter more than volume. Fragmented systems limit the value of digital investment; clean, connected, and accessible data is now a competitive asset.

AI advantage depends on operational readiness. Moving from pilots to impact requires clear use cases, strong governance, and data foundations that support repeatable execution.

Advanced therapies demand operational scale. As cell and gene therapy pipelines expand, success hinges on managing complexity across manufacturing, regulation, and delivery without slowing innovation.

Decentralised trials and wearables increase both reach and complexity. These models improve patient access and data richness, but require stronger controls to maintain quality and compliance.

Governance and adoption must advance together. Regulatory demands are rising, and technology must support structured oversight while being intuitive enough for teams to use every day.

Workforce Pressure and Resourcing Constraints

The life sciences industry is at a crossroads when it comes to talent. Demand for skilled workers across research, clinical operations, regulatory affairs and advanced technologies is climbing, but the available workforce isn’t keeping pace. In the UK alone, one industry forecast suggests the sector will need as many as 145,000 new and replacement workers by 2035 to sustain growth and innovation [1]. Across Europe and the U.S., thousands of job postings remain unfilled, particularly in specialised roles like clinical research, data science, and biomanufacturing where skill requirements have rapidly evolved [2].

At the same time, the hiring market has become more complex. Broad industry reports show employment in life sciences can fluctuate — with job growth in some regions and segments, and slowdowns or layoffs in others [3]. Many organisations are struggling to recruit for emerging capabilities connected to digital transformation and modern therapeutic platforms, creating a mismatch between what the market needs and who is available to do the work [4]. As technologies like AI, data analytics, and advanced biologics become essential, securing the right people — and keeping them — will be one of the most defining operational challenges for 2026.

Data and Digital Infrastructure as a Differentiator

Life sciences organisations are generating more data than ever but having data isn’t the same as using it well. Industry research showed that more than 80% of life sciences executives believe data and digital technologies will be critical to future success [5], yet many companies still struggle with fragmented systems and inconsistent data standards.

Digital infrastructure is becoming more of a strategic asset — than simply a support function. According to a recent Deloitte industry benchmark, companies with mature digital capabilities are more likely to hit key development milestones on time, compared with peers relying on legacy software and manual tools. As the volume and velocity of data continue to grow, the ability to access clean, connected data will increasingly separate leaders from laggards in 2026.

AI Moving from Experimentation to Execution

AI in life sciences has moved past the “proof of concept” stage. What was once a buzzword is now being embedded into real processes. From drug discovery and patient stratification to manufacturing quality checks and supply planning. 68% of US life sciences sectors say AI is fundamentally reshaping their business [6]. This shift reflects a broader industry expectation that AI is now essential to compete.

At the same time, there’s a clear gap between ambition and execution. Many companies still struggle to operationalise AI because of limited data readiness, unclear use case prioritisation, and lack of cross-functional governance. As investment continues in 2026, life sciences organisations that can bridge this gap — turning AI from isolated pilots into repeatable, measurable workflows — will have an operational edge in discovery, development, and delivery.

Cell and Gene Therapies Move Toward Scale

Cell and gene therapies are moving beyond niche use cases and becoming a core part of many life sciences pipelines. More than 30 therapies have already been approved by the FDA, with many more advancing through late-stage trials across oncology, rare disease, and autoimmune conditions. As these treatments scale, organisations are facing new delivery challenges. Cell and gene therapies rely on complex manufacturing models, strict chain-of-custody requirements, and close coordination across clinical, regulatory, and supply teams. Industry investment reflects strong confidence in their future, with global cell and gene therapy investment reaching $15.2 billion in 2024, even amid broader market uncertainty [7].

As more organisations move from single-asset programmes to full therapy portfolios, the challenge shifts from scientific feasibility to operational scale. Companies preparing for 2026 are focusing on how to manage parallel development timelines, regulatory milestones, and manufacturing readiness without slowing progress or increasing risk.

Decentralised Trials and Wearables Gain Momentum

Clinical trials are becoming more flexible as organisations look for better ways to reach patients and gather data. Decentralised and hybrid trial models reduce reliance on physical sites by combining remote monitoring, digital tools, and at-home participation. This approach is helping companies improve recruitment and retention, particularly for studies involving rare diseases or hard-to-reach populations.

Wearable devices are playing a growing role in this shift. Sensors that track activity, heart rate, sleep, and other physiological signals allow teams to collect data continuously rather than relying on periodic site visits. The global wearable medical device market is projected to exceed more than $168 billion by 2030, driven by adoption in clinical research and remote patient monitoring [8].

As these approaches expand, organisations are focusing on how to manage higher data volumes, maintain data quality, and meet regulatory expectations — all while keeping trials simple for patients to take part in.

Governance, Compliance, and Adoption-First Technology

Governance and compliance pressures continue to rise across life sciences, driven by tighter regulations, faster innovation cycles, and more global operating models. Organisations are managing expanding oversight requirements across clinical development, manufacturing, data usage, and AI adoption. In Deloitte’s 2025 Life Sciences Executive Outlook, over half of non-US executives cited regulatory change as a key factor shaping strategy, reflecting how policy and compliance considerations are influencing investment and execution decisions heading into 2026 [9].

At the same time, attention is shifting toward whether technology is actually used as intended. After years of complex rollouts that delivered limited value, life sciences leaders are prioritising tools that support consistent processes, clear accountability, and everyday adoption across teams. Industry research shows that nearly 70% of digital transformations fail to meet expectations, often due to poor user adoption rather than technical gaps.

As governance demands grow, organisations are looking for technology that supports structured oversight without slowing teams down. Adoption-first approaches — focused on clarity, usability, and alignment with how people work — are becoming just as important as compliance itself.

How Project Portfolio Management Software Is Helping Life Sciences Organizations

Life sciences companies are increasingly adopting PPM platforms like Cora to align strategy, execution, and governance across the product lifecycle. By replacing siloed tools with a single operational platform, organizations gain the visibility and control needed to deliver complex portfolios.

Workforce Optimization Under Talent Constraints

Cora provides real-time visibility into resource capacity, demand, and skills, allowing leaders to prioritize critical programs and prevent over-allocation. Scenario-based resourcing replaces spreadsheets, helping organizations deploy scarce expertise where it delivers the greatest impact.

A Single Source of Truth for Data-Driven Decisions

Cora centralizes plans, budgets, risks, milestones, and outcomes across R&D and clinical portfolios. Executives gain portfolio-level oversight, while teams retain detailed asset and trial-level visibility—enabling faster, more confident decision-making.

AI-Ready Data Foundations for Scaled Execution

Cora creates a structured, AI-ready data foundation by connecting clean project, cost, resource, risk, and schedule data in one platform. This enables advanced analytics, predictive insights, and AI-driven planning while maintaining governance, consistency, and control.

Managing Complex, Data-Intensive Programs at Scale

Cora supports coordination across advanced therapies and decentralized trials through integrated schedules, milestone tracking, and dependency management. By linking timelines, vendors, risks, and deliverables, organizations maintain oversight, anticipate bottlenecks, and scale portfolios without compromising compliance or speed.

Built-In Governance with an Adoption-First Approach

Cora embeds audit-ready governance directly into daily execution with time-stamped changes and centralized documentation. Clear workflows and role-based views drive adoption, ensuring compliance requirements are met without slowing teams down.

Preparing for the Future of Life Sciences

The life sciences sector stands at an inflection point. The convergence of AI, advanced therapies, digital health technologies, and evolving regulatory frameworks is creating both unprecedented opportunities and complex challenges.

Organizations that succeed in 2026 and beyond will share common characteristics: they will scale AI strategically, redesign how work is done, align investments with defensible sources of value, and maintain the workforce capabilities needed to execute on ambitious scientific agendas.

Portfolio visibility and governance will separate leaders from laggards. Companies need consolidated views across global portfolios, standardized processes, and adoption-first technology that teams actually use