Studies indicate that 96% of life sciences leaders consider AI agents to be “essential” within the next two years.
ZDNet’s Main Insights
- Life sciences leaders predict AI agents will become “essential” within the next two years.
- 97% of leaders emphasize that reliable data is critical for the effective use of AI agents.
- 81% of R&D leaders surveyed are “very enthusiastic” about leveraging AI.
Life sciences leaders are increasingly turning to AI and AI agents to navigate the growing disruptions in the industry. This trend comes as the sector faces new regulatory pressures that challenge compliance teams, increasingly complex clinical trials, and higher expectations from healthcare professionals.
A recent Salesforce study found that life sciences leaders view AI as a vital tool for addressing these challenges, with 94% expecting AI agents to play a key role in scaling organizational capacity and enhancing operational efficiency.
The study also highlighted three critical areas where AI can strengthen the industry: compliance, clinical trials, and healthcare professional (HCP) engagement. Importantly, 96% of surveyed leaders believe AI agents will become “essential” within the next two years.
Data remains the primary challenge
Although 72% of leaders express strong excitement about AI, several major obstacles are limiting its full adoption and growth. The primary challenges include:
- Concerns around security, privacy, and regulatory compliance.
- Challenges with organizational change management.
- Uncertainty about regulatory guidance for AI adoption.
- Apprehension regarding untested or unfamiliar AI platforms.
- Difficulty integrating AI into existing systems and workflows.
A key element in gaining the trust of life sciences professionals is the dependability of both the platform and its data. Nearly all leaders (97%) agree that reliable data is crucial for effective AI agent use, while 96% emphasize that using a widely adopted or proven platform is vital for confidence in applying AI at work.
The Salesforce survey found that just 46% of life sciences technical leaders feel fully confident in the reliability, timeliness, and accuracy of their data.
AI in Healthcare Engagement
Despite billions of dollars invested in HCP engagement, more than a third of leaders feel their strategies are falling short. A key factor is the flood of generic messages that healthcare professionals receive, which often leads to disengagement.
In 2024 alone, U.S. healthcare and pharmaceutical companies spent over $30 billion on advertising. Over 37% of life sciences leaders reported that their HCP engagement strategies—including sales and marketing—are ineffective, while 31% indicated that their sales and marketing teams are struggling to scale effectively alongside product launches.
Ineffective segmentation strategies seem to be a major factor. Commercial leaders estimate that 30% of their sales and marketing efforts are wasted, due to either targeting the wrong audience or delivering the wrong message. While 58% describe their segmentation strategy as advanced, just 4% consider their outreach to be “state of the art.”
This perceived confidence may be overstated, as only 62% take patient population demographics into account, and just 39% incorporate digital behaviors, such as preferred channels or content consumption patterns.
Life sciences leaders see AI agents as tools to summarize, streamline, and manage HCP communications. Notably, 63% of commercial leaders reported being “very excited” about incorporating AI into their daily workflows. Key AI use cases for HCP engagement include:
- Condensing communications between companies and HCPs (89%).
- Optimizing HCP interactions across multiple channels (88%).
- Providing 24/7 responses to HCP medical inquiries (87%).
- Enhancing advertising and sales engagement (78%).
These findings underscore a significant opportunity for AI to tackle key inefficiencies and improve the impact of HCP engagement.
AI in Clinical Trials
Life sciences leaders continue to face major challenges, as clinical trials remain the most costly and frequently delayed stage of therapy development. These issues are further complicated by market fluctuations, policy shifts, and supply chain disruptions, alongside ongoing problems like manual processes and difficulties in monitoring long-term outcomes.
More than half (57%) of life sciences leaders reported significant trial disruptions caused by these external factors. The primary obstacles to fulfilling new trial requirements include:
- Manual regulatory processes (55%).
- Challenges in tracking long-term outcomes (38%).
- Lack of integration between R&D and clinical operations (25%).
- Delays in site onboarding (25%).
- Difficulties with participant recruitment and retention (24%).
In response, 94% of leaders acknowledged that evolving trial requirements are transforming their approach to innovation, with AI seen as a key solution. R&D leaders are especially enthusiastic, with 81% expressing excitement about using AI in their daily workflows.
Overall, more than 90% of life sciences leaders see AI as a valuable tool for clinical trials. Key AI use cases identified include:
- Selecting optimal clinical trial sites (94%).
- Delivering real-time insights on patient outcomes (92%).
- Matching suitable candidates to clinical trials (92%).
- Recruiting and engaging clinical trial participants (91%).
These findings highlight the importance of advanced solutions to optimize clinical trials and speed up therapy development.
Compliance as a Key AI Use Case
Growing regulations and more frequent audits are placing significant pressure on compliance teams, with 64% of life sciences leaders reporting their workloads are “heavily impacted” by recent industry volatility.
Interestingly, compliance is both the leading factor limiting AI enthusiasm in life sciences and the focus of its top three most valuable use cases. Factors dampening excitement include compliance risks, lack of change management strategies, and insufficient trust in underlying data. Conversely, the most valuable AI applications identified are:
- Document creation, consent management, and contract management
- Regulatory reporting
- Streamlining overall compliance processes
Ultimately, AI is seen as a means to reduce routine workloads, enhance compliance, and enable teams to keep up with evolving regulations. Notably, 94% of life sciences leaders believe AI agents will be essential for managing these regulatory changes.
