Key trends, technologies, and actionable strategies that will redefine laboratory and research operations procurement in 2026
Global laboratory and research procurement is moving beyond traditional cost and supply management to address increasingly complex operational challenges. Modern laboratories face workforce shortages, rising skill gaps, and a volatile supply chain environment that make simple transactional sourcing inadequate. At the same time, the growing emphasis on personalized medicine and precision therapies requires labs to operate at higher throughput with greater flexibility. Procurement teams are seeking solutions that not only ensure consistent supply but also enable operational transparency, data-driven decision-making, and sustainability.
Integrated platforms that combine Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), robotics, and cloud-based analytics are rapidly becoming the industry standard. These connected ecosystems enable seamless coordination across instruments, consumables, and workflows, enhancing reproducibility and optimizing resource utilization. Currently, 60-70% of global pharmaceutical and biotech laboratories have implemented LIMS–robotics–cloud integration [1], achieving 10–25% reductions in reagent consumption in high-throughput settings [2].
Rising labor costs and persistent skills shortages are also accelerating the adoption of automation in labs to improve efficiency and reduce error rates [3]. Procurement is transitioning from traditional cost control to strategic leadership leveraging flexible models such as Robotics-as-a-Service, modular integration packages, and multi-vendor partnerships to balance efficiency with operational agility. The following blog outlines automation adoption trends, emerging technologies and procurement implications shaping laboratory operations and the future of connected digital labs in 2026 and beyond.
Role and adoption of emerging technologies in laboratory operations
Laboratory operations are being reshaped by the convergence of advanced technologies, evolving regulation, and innovation-driven business models. Agentic artificial intelligence, robotics, cloud computing, and sophisticated informatics are enabling labs to automate complex workflows, handle rapidly expanding data volumes, and generate actionable insights at speed. In parallel, regulatory expectations around data integrity and ESG (Environmental, Social, Governance) performance are becoming more stringent. Figure 1 provides an overview of emerging technologies shaping digital laboratories.
AI adoption among large biopharma companies is nearly three times higher than that of smaller firms, with 67% of large companies leveraging AI [4]. Mid-sized life science companies, such as growing biotechs and mid-tier pharmaceutical firms, are currently in an intermediate stage of automation adoption: approximately 78% are utilizing some form of AI, yet only around 20% report full integration across their operations [4]. The majority of these mid-sized companies (41%) are still progressing through partial implementation phases [5].
Many small biotechs see the value of lab digitalization – nearly 70% are expecting that data platforms, software, and connected instruments will improve research quality. Only 3% of small biopharma companies have reached an advanced level of AI readiness, compared to 14% of their larger counterparts, underscoring structural differences in investment capacity, data and talent availability. Moreover, 62% of biopharma companies consider sustainability a top priority over the next 5 years [6], [7]. Figure 2 depicts automation adoption rates across industries to contextualize laboratory automation.
Future-focused trends:
As scientific, regulatory, and operational complexity increases, laboratories are undergoing a fundamental transformation. The convergence of digital technologies, automation, and sustainability priorities is redefining how research is executed, scaled, and governed. The following trends highlight the key forces shaping the next generation of laboratory environments.
- AI-driven automation and robotics are optimizing lab workflows by boosting throughput, improving reproducibility, strengthening compliance, and reducing labor dependence and errors.
- IoT-enabled instruments and smart consumables deliver real–time operational visibility, driving 10–15% reductions in reagent waste and measurable cost efficiencies [8], [9].
- Cloud-based, modular lab models are enabling global collaboration and scalable approaches such as Lab-as-a-Service, improving flexibility and cost control.
- Miniaturization and microfluidics are accelerating experimentation while lowering reagent use and supporting sustainability goals.
- ESG-driven procurement and energy-efficient automation are influencing vendor selection, pushing suppliers toward recyclable, low-emission, and circular design solutions.
- Customized automation, diversified supply bases, and outcome-based contracting are strengthening resilience and aligning investments with GLP/GMP (Good Laboratory Practice/Good Manufacturing Practice) and R&D priorities.
- System integrators are emerging as critical partners, ensuring interoperability, standardized validation, and faster deployment across multi-vendor lab ecosystems.
Together, these trends define a future where laboratory procurement leaders play a pivotal role in enabling efficient, compliant, and innovation-driven research ecosystems leveraging digital technologies to deliver value, mitigate risk, and scientific advancement.
| Trend | Technology Focus / Key Features | Operational Impact | Strategic Value |
|---|---|---|---|
| AI-Driven Procurement | AI for supply chain, predictive analytics, automated sourcing, smart workflows | 35% reduction in inventory levels; 15% lower logistics costs; 22% higher operational efficiency; mitigates 80% of low-inventory risks | Data-driven decision-making, risk mitigation, cost containment, and supply chain resilience |
| Robotics & Automation | Collaborative robots (cobots), automated liquid handling, sample management, LIMS/ELN integration | Up to 40% reduction in process cycle time; 70–90% decrease in error rates; 30–50% throughput increase; up to 70% fewer workplace accidents | Improves productivity, reduces errors, controls labor costs, and enables scalability |
| IoT, Smart Consumables & Real-Time Monitoring | IoT sensors, smart consumables, predictive maintenance | 10–15% reduction in reagent waste; up to 20% higher equipment utilization; 40% less downtime | Enhances transparency, compliance, proactive maintenance, and resource efficiency |
| Cloud-Enabled, Modular & Remote Platforms | Cloud/edge analytics, modular automation | 30–50% faster site rollouts; 25–40% IT cost reduction; lower travel and labor overhead | Enables scalability, multi-site collaboration, flexible budgeting, and agile innovation |
| Miniaturization & Microfluidics Integration | Microfluidics, miniaturized assay systems | 70–90% lower reagent usage per experiment; ~40% reduction in labor costs | Supports sustainable R&D, cost-efficient screening, and customized experimentation |
| Sustainability & Energy-Efficient Automation | Energy-efficient automation, recyclable materials | 20–30% reduction in energy use; 30–50% less waste | Aligns with ESG goals, reduces environmental footprint, and lowers long-term costs |
| Diversified Sourcing & Risk Mitigation | Regional sourcing, AI-based risk tools, automated supplier audits | Backup suppliers cut shortages by over 50%; 30–35% faster procurement cycles | Strengthens supply resilience, compliance, and ecosystem transparency |
| Customized Procurement & Solution Tailoring | Co-developed automation solutions, outcome-based contracts, precision sourcing | 20–25% improvement in SLA adherence; 10–20% cost savings vs. catalog-based sourcing | Drives innovation alignment, cost control, and regulatory compliance |
| Integrator Model & Ecosystem Partnerships | Multi-vendor integration, standardized validation | 15–20% lower integration costs; 25–40% faster technology deployment | Reduces complexity, accelerates adoption, and improves vendor coordination |
| ESG Factors in Automation Vendor Selection | Sustainable design, energy efficiency, recyclable materials, ESG scoring | 10–25% reduction in long-term costs; 91% of procurement teams monitor ESG risks | Supports sustainability goals, regulatory compliance, and investor expectations |
Sources: Secondary Articles and Beroe Analysis
The year ahead for lab procurement
The automated laboratory systems market is poised for significant growth over the next 5-10 years, driven by increasing adoption of innovative solutions and shifting consumer preferences are expected to expand market size showing the fastest growth. Integrated platforms combining Laboratory Information Management Systems (LIMS) and Electronic Lab Notebooks (ELNs), robotics, and cloud analytics are becoming standard, enabling seamless workflows, reproducibility, and resource optimization amid workforce shortages and supply volatility. Emerging technologies e.g., AI, IoT, robotics, cloud platforms and microfluidics are redefining operations for precision medicine and ESG priorities, with adoption varying by company size from large pharma leaders to emerging small biotechs leveraging modular solutions. Despite strong momentum, growth may be constrained by regulatory shifts, macroeconomic pressures, and emerging alternatives. Procurement teams will play a critical role by enabling flexible sourcing models, including Robotics-as-a-Service, supplier diversification, and strategic partnerships to mitigate risk and accelerate R&D outcomes.
References
[1] Pistoia Alliance, “Lab of the Future Survey Results 2023,” Pistoia Alliance, 2023.
[2] McKinsey & Company, “Digitization, automation, and online testing: The future of pharma quality control,” McKinsey Insights, 2019.
[3] Medical Technology Schools, “Interview: Clinical worker shortage,” MedicalTechnologySchools.com, 2025
[4] Pharmaphorum, “Biopharma’s AI rally: Readiness, not hype, in 2025,” Pharmaphorum, 2025.
[5] RSM US LLP, “Middle market is confident about AI despite early-stage adoption challenges,” RSM Insights, 2024.
[6] Pharmaceutical Technology, “Sustainability in biopharma: Collaboration and technology light the way forward,” Pharmaceutical-Technology.com, 2024.
[7] M. Maleki, “The future of AI-driven laboratory automation in life sciences and pharma,” LinkedIn Pulse, 2025.
[8] R. Anitha and A. Parthiban, “AI-IoT-graph synergy for smart waste management: a scalable framework for predictive, resilient, and sustainable urban systems,” Frontiers in Sustainability, vol. 6, 2025.
[9] Bridgera, “IoT waste management: Renewing the face of waste,” Bridgera, 2024.
[10] ZAGENO, “AI in lab procurement: Automation’s role in scientific supply chains,” ZAGENO Blog, 2025.
[11] Manutan, “Artificial intelligence: How does it benefit procurement?” Manutan Blog, 2022.
[12] Standard Bots, “Pharmaceutical robots,” Standard Bots Blog, 2025
[13] WTT Solutions, “Mastering artificial intelligence in procurement: Best practices and top benefits,” WTT Solutions Blog, 2025.
[14] PatSnap Eureka, “Microfluidics vs traditional techniques: Cost and precision,” PatSnap Eureka, 2025.
[15] Roche Diagnostics, “Sustainable laboratory practices,” Roche Diagnostics, 2024.
[16] Agilent Technologies, “Sustainability through lab optimization,” Agilent Technologies.
[17] ZAGENO, “Supplier diversification in life sciences,” ZAGENO Blog, 2025.
[18] Deloitte, “Biopharma lab modernization and digital transformation,” Deloitte Insights, 2025.
[19] Concord, “Procurement cost reduction strategies for 2025,” Concord Blog, 2025.
Author
Dr. Sourabh Mundra – Lead Analyst, Pharma R&D, Beroe
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