October 24, 2024
Remember when we were all buzzing about the metaverse like it was going to redefine reality? Yeah, that was just two years ago!
Fast forward to last year, and suddenly generative AI (gen AI) has become the rockstar, spinning up content faster than we can say “machine learning.”
Now, as if we have blinked and missed a beat, we’re already asking, “what’s next?” – Enter Agentic AI, poised to not just assist, but act autonomously…
Could this be the game-changer for Life Sciences? Our expert analysts have found out just what this means for the sector going into 2025 and beyond!
Reach out to discuss this topic in depth.
What is agentic AI?
Agentic AI is an evolved form of AI that creates autonomous agents possessing autonomy, decision-making, and adaptability. The agents can execute tasks in their entirety through natural language-based inputs. They can also set goals independently, plan accordingly, and act to accomplish the targets.
Key characteristics of agentic AI include:
- Autonomy: perform tasks independently
- Reasoning: make advanced decisions
- Flexible planning: adjust plans based on prevailing circumstances
- Workflow optimization: efficiently execute multistep, complex processes
- Natural language understanding: comprehend and follow complex instructions
- Continuous improvement: learn from historical data and feedback
- System integration: integrate with diverse enterprise systems
The winning formula for agentic AI is training the models on diverse datasets with clear and concise instructions.
What does it mean for the life sciences industry?
Life sciences has always been a curious case for any emerging and next-generation technology – as it has always presented a unique challenge when it comes to adopting emerging technologies, whether it was Robotic Process Automation (RPA) a decade ago, cloud computing five years ago, or gen AI more recently, enterprises often display initial enthusiasm, diving into exploratory use cases and early proof of concepts (POCs).
However, as inherent challenges such as regulatory concerns, data privacy, and integration complexities emerge, majority enterprises take a step back and adopt a more cautious approach. This cycle reflects the industry’s general mindset—embracing innovation with enthusiasm, but always tempered by a significant degree of caution
Similarly, the industry is gradually transitioning from a cautious to a more pragmatic approach when it comes to adopting gen AI across various areas.
As enterprises continue to advance in this journey, Agentic AI can act as a powerful catalyst—particularly in targeted areas/segments—by driving efficiencies and accelerating time to return on investment (ROI). By automating decision-making and improving engagement processes, Agentic AI can help organizations realize the full potential of AI adoption faster and with greater impact.
While everyone was buzzing about “top use cases” in 2023, 2024 is all about getting strategic with scaled tech (hello, Gen AI!). Furthermore, just like its predecessor, Agentic AI is set to follow a similar trajectory—but expect this journey to be much faster.
In fact, there are a handful of areas where we predict Agentic AI will make the biggest splash in record time. So, without further ado, here are the top areas where Agentic AI will hit the ground running and deliver results in the short to medium term.
How is it different from other chatbots or conversational assistants?
A key challenge with Agentic AI is understanding how it differs from existing conversational tools, such as chatbots and conversational assistants, which are steadily maturing in their capabilities.
This distinction is not just theoretical but critical, as each technology serves vastly different purposes. The complexity lies in unraveling these differences in both functionality and impact.
To simplify, the table below outlines the fundamental contrasts between chatbots, conversational assistants, and AI agents, with a focus on their technological architecture, autonomy, and practical use in life sciences. By illustrating these nuances, we can appreciate how AI agents go beyond basic interaction to deliver intelligent, autonomous decision-making in dynamic, real-world environments.
What are the challenges?
- Breaking the human barrier and trusting autonomous intelligence: Life sciences leaders and key stakeholders often approach disruptive technologies with caution, given the industry’s complex regulatory landscape and high-stakes environment. Gen AI has gained traction in part because its most successful applications involve a “human-in-the-loop” framework, where human oversight is embedded in AI decision-making processes. This model offers a balance between innovation and control, providing reassurance to organizations that value safety and accountability.
Agentic AI, however, shifts away from this hybrid model by significantly reducing or eliminating human involvement, relying instead on autonomous multi-agent interactions to manage decisions and workflows. For life sciences organizations, this presents a challenge: the need to develop a greater risk appetite and embrace potentially human-less frameworks. Successfully adopting Agentic AI will require not only trust in the technology, but also a shift in mindset, as companies learn to cede control to AI systems capable of operating independently.
- Tactical use case enthusiasm eclipsing long-term strategic execution: The adoption of new technologies in life sciences often follows a pattern of initial excitement, where enterprises focus on specific use cases without fully considering the broader strategic framework. This was evident with gen AI, where enterprises quickly launched pilots across various segments without a cohesive, long-term strategy. Agentic AI faces a similar risk, where organizations may rush to deploy AI agents for isolated use cases—such as patient engagement or health care professional (HCP) interactions—without fully integrating the technology into a comprehensive, scalable architecture. This fragmented approach can limit the long-term value and scalability of AI in life sciences.
- Domain specific training data for agents: AI models are only as good as the data they’re trained on, and in life sciences, domain-specific data is critical. Agentic AI systems require vast amounts of high-quality, structured, and unstructured data to function effectively, whether for patient monitoring, drug discovery, or HCP engagement. However, obtaining and curating training data that is both relevant and comprehensive is particularly challenging in life sciences, where data is often siloed across different systems, protected by privacy regulations like health insurance portability and accountability act (HIPAA), and involves a complex mix of clinical, genomic, and behavioral information. Without access to specialized datasets, AI agents risk underperforming or producing inaccurate results, which could undermine both their efficacy and the trust in their outputs; thus, leading to underwhelming ROI discussions thereafter.
In conclusion, Agentic AI presents a transformative potential for the life sciences industry, pushing the boundaries beyond traditional chatbots and conversational assistants.
However, its adoption will require overcoming industry-specific challenges such as trust, strategic implementation, and the availability of domain-specific training data. As life sciences enterprises gradually embrace this technology, Agentic AI could revolutionize engagement, decision-making, and operational efficiency, but only if organizations are ready to adapt to its autonomous, human-less frameworks.
If you found this blog interesting, check out our blog focusing on The Healthcare Professional (HCP) Engagement Blueprint: Winning Strategies For Building Lasting Connections | Blog – Everest Group , which delves deeper into another topic worked on by our HSL service line.
If you have any questions, would like to gain expertise in Agentic AI and artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Rohit K, Durga Ambati, and Chunky Satija.
