The rapid pace of AI implementation in life sciences requires action now to anticipate and manage risks. As best practices, applications, regulations, and risks evolve alongside AI technologies, life sciences companies will need to stay vigilant as they continue to leverage these tools across the enterprise.
Hear What Life Sciences Leaders Have to Say: Download the Report
In our inaugural report on the impact of AI in the life sciences industry, Arnold & Porter surveyed 100 senior executives and department heads specializing in AI, risk management, legal, data science, quality assurance, regulatory affairs, compliance, cybersecurity, marketing, and digital strategy. The respondents, who played a leading or supporting role in decision-making around AI implementation, represented various sectors within the life sciences and healthcare industries.
6 Key Findings That Show How AI Is Transforming the Life Sciences Industry
AI implementation is still in the early stages for many life sciences companies — but adoption is accelerating. Three-quarters of respondents began implementing AI within the last two years; only 5% started more than five years ago. However, 86% of those now in the process of implementing AI tools are planning to deploy them in the next two years or less
Life sciences companies are adopting AI across the product lifecycle. Almost eight in 10 life sciences companies surveyed are using or planning to use AI in R&D, with significant segments planning to or already implementing AI for manufacturing (62%), marketing (45%), regulatory (42%), and compliance (29%) functions.
Intellectual property (IP) presents significant concerns for life sciences companies using AI. Nearly three-quarters of respondents have a high level of concern about facing AI-related IP issues in the upcoming year; only 3% express no concern at all.
AI is transforming product discovery and design. Approximately half of respondents have explored leveraging AI to optimize the product discovery and design process, citing improved efficiency and faster time-to-market for new products as key benefits. Sixty-three percent are using or plan to use AI-driven testing and simulations.
AI is optimizing marketing and sales strategies. A high majority of respondents report that AI-driven initiatives to optimize sales strategies are highly effective in the product commercialization and stewardship phase
Governance appears to be lagging behind AI implementation. Just over half of respondents currently using AI have put AI policies and standard operating procedures in place; even fewer have completed regular AI audits or assembled cross-functional teams to promote safe and effective use
Defining AI In Life Sciences
Artificial intelligence (AI)
AI leverages technology to emulate human intelligence in performing tasks. It has a broad range of applications in life sciences, ranging from product discovery, diagnostics, and virtual assistants to predictive analytics tools for personalized medicine and enhanced product commercialization.
Generative AI
Generative AI refers to algorithms or neural networks capable of identifying patterns and structures within data and generating diverse content types, from audio to text to images.
Machine learning (ML)
ML is a subset of AI and the foundation for both predictive and generative AI. It enables the development of intelligent systems by allowing algorithms to learn from vast and complex datasets without explicit programming, thereby facilitating tasks and advancements.
Predictive AI
Predictive AI employs statistical algorithms to forecast future events or trends by analyzing historical data patterns, thus facilitating informed decision-making and proactive interventions to optimize processes.
Security by design
Security by design involves integrating security measures into the initial design and development stages of a product, system, or application, prioritizing the inherent inclusion of security features rather than retroactively adding them.
Hear What Life Sciences Leaders Have to Say: Download the Report
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