AI is empowering life sciences companies to improve productivity and accelerate R&D. The global market for AI in life sciences is expected to surge from $2.14 billion in 2024 to $9.17B by 2032, growing at an annual rate of almost 20%. 

Companies are eager to harness the strategic and financial benefits of AI but are often limited by fragmented implementation. While investing in AI for a singular use case can deliver short-term rewards, this approach misses the broader impact that comes from scaling AI across the entire business. The real opportunity and results lie in a holistic systems approach with a unified, customer-centered strategy across all AI implementations to maximize the return on investment. 

At Slalom, we believe that enabling AI at scale can result in 10x productivity and innovation for most life sciences enterprises. Many organizations are already seeing significant productivity gains with routine task automation, AI-accelerated engineering, and GenAI assistants—just to name a few examples. On the innovation side, paths to acceleration include AI-powered drug discovery and development, in silico clinical trials, and digital twins. 

In 2025, we will also see the rapid rise of agentic AI, in which an AI entity independently takes on a set of tasks and responsibilities based on given inputs. For example, an AI-powered inventory agent can keep sales reps informed of product shortages, proactively suggest solutions like transferring product from another rep’s inventory, and enable compliance with built-in guardrails and triggers to prevent prescription sampling beyond the regular allotment. 

But we cannot build AI skyscrapers on landfills. Scaling AI requires strong data management and governance practices, especially in the risk-averse environment of the life sciences industry, and a clear sense for ROI (beyond year one and into the future). Ensuring data system interoperability and reducing technology debt are both critical for applying AI across operations. Many organizations are optimizing their investments by pairing foundational enterprise data solutions with cutting-edge AI innovations to deliver better outcomes—and get closer to harnessing AI’s full potential across the value chain. 

The shift toward holistic and collaborative AI strategies

• In 2023, 84% of AI transformation efforts were driven by a single leader, while just 5% involved cross-functional teams. 
• In 2024, single leaders for AI transformation decreased to 32%, while cross-functional team leadership surged to 52%. 

Learn more in Slalom’s AI research report.

Initially driven by the need to right-size teams following over-hiring during COVID-19, the trend to “do more with less” has evolved beyond cost-cutting. Companies are now embracing a strategic, value-driven approach focused on optimizing their existing workforce by realigning talent, introducing advanced technology, and shifting efforts to high-impact areas that directly benefit HCPs and patients. 

To achieve this, many organizations are adopting a value operating model to redesign their workflows around specific value streams. By centering efforts on these core streams, companies can prioritize and accelerate tasks that generate the most value. Organizational design plays a crucial role, and life sciences organizations are carefully evaluating their current team structures (often highly siloed and vertical) and reconfiguring talent to focus on end-user value, making the best use of the people they already have. By aligning teams around shared goals, life sciences leaders can foster a mindset centered on outcomes. 

Intelligent automation and AI are central to this transformation, as they reduce administrative burdens and free employees to focus on more meaningful work. This enables employees to use their expertise on tasks requiring human judgment and creativity, making work more fulfilling and impactful. AI, when integrated thoughtfully, can be a powerful complement to the workforce rather than a replacement. It helps reduce cycle times, allowing employees to move from task initiation to completion much faster—and more compliantly and consistently, if well designed. 

As life sciences companies move forward with this technology, value, and person-centric approach, they are building a more resilient and flexible workforce. Organizations that fully embrace this transformation will foster higher customer satisfaction, operational resilience, and employee engagement. In an increasingly competitive market, those who realign their workforce strategies to embrace these tools and operating model will set themselves apart, ensuring they remain agile and innovative. 

With the vast range of therapies now becoming available, the task of staying up to date with current information is critical. In fact, there are 75% more assets available to HCPs and patients than 10 years ago, and the volume of medical knowledge doubles every 72 days. As the complexity grows, medical affairs teams are being elevated to ensure that this information is accessible, actionable, and accurately disseminated. Once considered a secondary function, medical affairs personnel are now stepping into the spotlight to play a key part in linking research, innovation, and patient care holistically. 

AI and automation will be key tools for medical affairs teams as they are elevated within organizations. These technologies can scale the efforts of medical affairs teams to meet the increasing demand for clinical insights and evidence-based information. Generative AI can accelerate the process of producing publications and reports, reducing the time spent on manual tasks and allowing medical affairs teams to focus on more strategic, customer-facing work. By increasing productivity, AI ensures that HCPs and patients get the accurate, evidence-based information they need more quickly. When HCPs have faster access to easily digestible scientific data, they’re more likely to engage and prescribe based on that information—positioning your organization as a leader in delivering timely, impactful insights. 

In addition to the technology transformation, the culture change is required to establish medical affairs as a strategic pillar alongside R&D and commercial operations. With the right tools and a supportive organizational structure, medical affairs can accelerate the flow of critical information and become the essential nervous system that keeps the industry moving forward, ensuring that everyone, from HCPs to patients, is empowered with the knowledge and experience they need and expect.

The rise of medical affairs

• 68% of pharma companies are relying more on medical affairs for clinical data insights.
• AI use in medical affairs will grow 45% in the next five years.
• Investment in medical affairs will increase by 30% in 2025.

Source: C-Suite Strategy

As the pace of scientific discovery picks up, traditional labs are struggling to keep pace with the demand for data-driven research and advanced drug and therapy discovery. By developing better methods for data organization and sharing (e.g., adopting principles of data FAIRification), labs can improve productivity and bring new therapies to market faster than ever before. 

AI and automation are core to this transformation. AI enables labs to streamline workflows, analyze large datasets, and generate insights with speed and accuracy. Automation, such as robotics and advanced instruments, is essential to improving the efficiency of routine tasks, freeing up human capital, and reducing risks related to human error or hazardous material. These technologies rely on high-quality, clean data that is structured for a purpose. Organizations need to break down silos and implement robust data management practices to build a strong foundation of consistent data quality and interoperable systems. 

But the shift to the lab of the future isn’t just about adopting new tech—it’s also about embracing a new way of working. Scientists must move beyond traditional wet lab roles and collaborate seamlessly with dry lab processes, creating a unified approach to scientific discovery. This requires thoughtful change management strategies that empower teams to embrace new technologies and adapt to data-driven workflows. Life sciences organizations must also address the cultural shift needed to fully integrate data as the central asset and support their teams as they adapt to new ways of working. With AI, high-throughput automation, and data management as central components, the life sciences labs of today can transform, unlocking high-impact scientific breakthroughs and better tomorrows for all. 

3 key tools for labs of the future

1. AI & machine learning: Advance in silico paradigm, streamline data analysis, and generate actionable insights

2. Robotics systems: Enhance accuracy, scale routine processes, reduce manual effort, and integrate workflows

3. Advanced bioinformatics, computational modeling, and imaging tools: Enable high-throughput experimentation at an unprecedented scale