A Guide to Understanding the AI Maturity of Your Life Science Organization
Explore how biotech and pharma companies can navigate AI investment with strategic approaches to accelerate clinical development and avoid costly pitfalls.
In today's rapidly evolving artificial intelligence (AI) landscape, biotech and pharma companies face significant uncertainty when it comes to investing in capabilities to accelerate research and clinical development. While AI holds immense promise for revolutionizing clinical trials and operational efficiency, the path to successful implementation is fraught with potential pitfalls. Failure to invest in AI can leave companies lagging competitors, but misguided investments in the wrong tool, product, team, idea, or partner can result in wasted resources and lost ground to competitors.
As Aristotle opined, "Knowing yourself is the beginning of all wisdom." For life science and pharma organizations, understanding their current capabilities, strategic goals and risk tolerance is crucial for navigating the complexities of AI investment and ensuring long-term success in this dynamic environment. By understanding these features organizations can characterize their current ‘AI Maturity’ and consider how they will harness the growth opportunities provided by the constant advancements in tools and technology.
AI Investment: A Game of Risk and Reward
To understand the critical importance of this combined approach, let’s first look at what each method is and what it offers.
Although AI and machine learning have a rich history in the pharmaceutical and life sciences industries, the explosive growth in the capabilities of large language models and Generative AI technologies in the past few years has led to a groundswell of excitement, investment, and expectation. Recent high-profile cases have demonstrated the dual-edged nature of AI investments in the industry. Some partnerships have shown significant promise, for example in leveraging AI to optimize clinical trials and enhance personalized medicine approaches.
However, not all investments yield positive outcomes. There have been notable instances where AI-driven solutions in oncology and other fields resulted in costly failures, with projects being shelved after spending millions without achieving the desired results.
This disparity of outcomes underscores the importance of a considered and strategic approach to investment in AI. Companies must thoroughly assess their AI maturity and align their AI development philosophy with their overarching business objectives to forge a unified and organized strategy. Understanding one’s current capabilities and limitations is crucial for making informed investment decisions.
Pioneers, Fast-followers, and Planners: Three Strategic Approaches to Adopting AI
In navigating the complex landscape of AI investment, companies can be categorized into three strategic approaches: pioneers, fast-followers, and planners. Each approach has distinct characteristics, investment philosophies, and pros & cons.
- Pioneers: Pioneers are the trailblazers in AI investment, dedicating substantial resources to research and development (R&D) and proof of concept experimentation. These organizations are willing to take significant risks, embracing failure as a stepping-stone to market-leading capabilities. While this approach demands heavy investment and almost inevitably results in numerous setbacks, pioneers often reap the rewards of being the first to market with innovative solutions.
- Fast-followers: Fast-followers prioritize the identification of key trends in the industry, often opting to partner with established industry leaders once AI tools and technologies become more stable. Rather than investing heavily in in-house innovation, fast followers focus more on leveraging existing solutions to enhance their operations. This approach allows them to minimize risks while benefiting from the proven successes of pioneers and early adopters.
- Planners: Planning organizations take a cautious approach, focusing on organizing their data and processes to be AI-ready. These companies prepare to capitalize on AI technologies once the dominant tools and providers have emerged and understand the value of having your data in order before investing in cutting-edge tools. By being well-prepared, they can subsequently quickly and effectively integrate AI solutions with minimal disruption and risk. Taking a planning approach de-risks AI adoption, reducing the likelihood of investing heavily in a tool or partner which will not deliver a return on investment.
Charting a Course Through AI Strategy and Implementation: The Key Questions to Ask Yourself Before Embarking on an AI-powered Transformation
Data analytics provides the backbone for understanding the intricate behaviors, preferences, and needs of HCPs and patients. In an environment characterized by uncertainty and the potential for both significant successes and failures, AI partners must serve organizations across the maturity spectrum. The right partners will guide teams to answer these questions thoughtfully and strategically on a global enterprise scale.
Organizations need to understand and interrogate their innovation philosophy, AI strategy and overarching technology strategy across the axis of risk and innovation.
As one of the leading global pharmaceutical service providers, Syneos Health has invested in AI to enhance their clinical delivery model; including development of capabilities to enable better study design, operational acceleration and predictive power/analytics. Syneos integrated operating model combines the expertise of clinical, consulting and technology experts to define and deliver innovation and value aligned with sponsors development priorities.
- How are you leveraging expertise? To best support sponsors in the specialized realm of pharmaceuticals and life sciences, it is essential for an AI partner to bring a deep understanding to the challenges facing sponsors and a proven track record of using AI solutions to accelerate clinical development.
- How are you fostering organizational readiness and change? A key pillar in successful AI adoption emphasizes the importance of organizational readiness and change management. Ensuring that a company’s people are at the center of their AI strategy is crucial, regardless of their position on the risk and innovation spectrum. The right AI partner will be able to help teams prepare for AI adoption, fostering a culture of innovation and use case/investment prioritization to ensure a seamless transition through comprehensive change management practices.
- Have you assessed your business system landscape, adoption and data strategy? Most business systems are transactional in nature and do not have intelligence built in. They are often engineered to align with functional needs, which leads to a fragmentation in process and the data landscape across R&D. It is important to assess the end-to-end processes and supporting data when identifying AI opportunities that could accelerate activities related to de-risking study design, enabling continuous oversight or reducing time to submission.
- What are your plans for capitalizing on the promise of AI? The rapidly changing AI landscape presents both significant challenges and opportunities for pharma and biotech companies. High-profile successes and failures underscore the importance of strategic AI investment. Categorizing companies into pioneers, fast-followers and planners provides a framework for navigating AI investments.
- Are you sure AI is the right solution for you? Although AI-powered solutions are becoming increasingly popular, there are still many instances where traditional data science tools and methodologies are more effective, cheaper, simpler, more robust, more secure and can even provide higher performance. AI tools like large language models (LLMs) are powerful but can’t provide solutions for all business problems. Considering the full spectrum of possible solutions when tackling a business problem is still critical for success.
Navigating the AI landscape requires a balanced approach, integrating strategic foresight with granular practical considerations. Companies must continuously evaluate their position and their readiness to adopt AI, considering both the risks and rewards.
By understanding their unique capabilities and aligning AI investments with their broader business goals, life science and pharma organizations can mitigate potential pitfalls and maximize the transformative potential of AI. Partnering with knowledgeable and experienced AI providers can further enhance their journey, ensuring they remain competitive and innovative in a rapidly evolving industry.
Ready to harness the power of AI?
Contact us to learn more about how our in-house AI and machine learning teams can support AI adoption for your life sciences organization.
Contributors
Kiera Mehigan | Director, R&D Advisory
Rachel Belani-Barker | Managing Director, R&D Advisory
Ben Phillips | Consultant, R&D Advisory
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