Understanding future of agentic AI in pharma: QnA with Puneet Kacker

“In a world of talkers, be a thinker and a doer.”

The difference between generative AI and agentic AI can be rightly explained by this quote. While gen AI is like a thinker helping you ideate and create, agentic AI is both a thinker and a doer. In fact, it goes one step further by initiating and completing tasks and taking decisions autonomously.

In the pharma and life sciences industry, this capability plays a key role than one might think of. Imagine using a tool that not only researchs but also manages complex workflows. To get more clarity on agentic AI, we spoke with Puneet Kacker, an industry expert and passionate advocate for innovation. He is actively involved in promoting the use of advanced AI technologies in pharma, healthcare, and life sciences, and in driving better and faster solutions to the world. We gained valuable insights into how this next-generation technology is transforming the pharmaceutical industry and how we can work using it to achieve better results.

Here’s the blog that shares his thoughts on Agentic AI and its future in pharma and life sciences industry.

1. What is agentic AI and how is it different from generative AI?

Generative AI is a model to create novel content, including text, images, audio, video and code, quite efficiently. Primarily, it is an instruction-based AI model that is built on large data sets. Hence, it has a strong capability to understand language prompts and generate high-quality outputs.

On the other hand, agentic AI works on a goal-oriented model that operates on high-level instructions, and the agents trigger specific actions. The model uses memory and offers mission-oriented solutions. It can be understood as a human decision-based system that mimics actions typically performed by humans.

A quick example: Gen AI can create an email, while Agentic AI can write, send, track the response, and proceed to send follow-ups

2. What are the key challenges pharma companies should overcome to successfully adopt agentic AI into the industry?

The pharmaceutical industry has been adopting new technologies at a relatively slower pace. It’s not reluctance, but more due to highly regulated sensitive data involved. Agentic AI holds promise to transform certain workflows; however, there are certain reservations due to the LLM limitations in healthcare. Certain tests results have experienced LLMs to be unsafe and hasty. Hence, one should not rely completely on agentic AI for sensitive decisions.

Another challenge is domain knowledge and operations in pharma; specifically in R&D, clinical trials, and regulatory aspects.

That said, agentic AI can play a role in specific pipelines related to innovation discovery, but it needs to mature significantly. And even when it does mature, it is still just a tool. If competitors or other companies also adopt similar tools, then they may all end up generating a similar set of results.

The key for pharma companies is how to retain human talent as-is, while capitalizing on agentic AI systems in a way that both can be plugged together to bring out the best of innovation.

For example, pipelines that collect data routinely, highlight issues in clinical trial data, convert data, transform it, and save it into databases. These routine jobs with complex pipelines can be handled better by using agentic AI. So, in the next 5 to 10 years, these agentic AI systems are expected to become reliable and mature, building a rich storehouse for pharma and similar industries.

3. What measures should be taken for smooth collaboration between human experts and agentic AI agents?

Collaboration is critical for the pharmaceutical industry, and human intervention is non-negotiable. Agents work autonomously and it’s difficult (even unethical in some cases) to trust their decisions without cross-checking. Even a minute error in AI-led judgment could lead to a big impact, especially in clinical trials and drug discovery domains where the stakes are too high. Science always believes in understanding the “why” and “how.” Though agents might have logical reasoning behind their outputs, innovation driven by the scientific mindset and human judgement, by core capability, is necessary.

Thus, human experts in the pharma industry would require more attention on building awareness about how agentic AI systems work. It’s also essential to ensure that any output produced by the agentic AI system that’s going out of the expected or acceptable range should be flagged.

Scientists should understand how the system works and where customization is required. In the current scenario, scientists are crafting prompts and communicating with AI agents, which is a part of shaping innovation. But if AI handles everything, it would be challenging for scientists to make changes that align with their knowledge and intent.

To conclude, a lot of learning is required to excel and capitalize on the agentic AI system.

4. What core capabilities make agentic AI suitable for solving the pharma industry’s complexities?

Agentic AI can support scientific processes and due diligence, especially in forming hypotheses and bringing relevant data to scientists in less time.

A lot of curations, which have traditionally been done manually, can now be handled faster using AI. However, it comes with a lot of reservations. If mistakes in the system go unidentified and are released, they could have detrimental effects.

So, human oversight, with domain knowledge and technological expertise, will continue to play a critical role. Routine tasks such as data collection, data cleaning pipelines, and preparing data for analytics can be handled effectively by agentic AI systems.

That said, there are some reservations about its use in clinical trials, especially in handling patient data, monitoring disease progression, and clinical trial recruitment. So, strong human oversight will always be necessary.

5. What key factors should pharma companies consider when implementing agentic AI?

Figure - Key factors agentic ai

6. Will agentic AI find greater adoption in project management domains within the pharmaceutical industry?

Agentic AI does have a lot of potential since the data is structured, and the rules are well defined.

It has the capacity to consolidate data from multiple sources and coordinate complicated schedules. That said, agentic AI cannot likely replace project coordinators. It works best when collaborating with human decision makers who can bring in the right judgment.

Organizations will require people who are domain experts and also have a strong understanding of advanced technologies like agentic AI. These individuals can customize solutions, flag issues, and take control when needed. It’s just like a self-driving car; there’s always a manual override option. Likewise, in project management, human oversight will always be essential. Leaving critical operations solely to agents could risk project failure or even damage a company’s reputation.

Conclusion

While agentic AI holds immense scope for speeding up processes in the pharmaceutical industry, it’s clear that human oversight along with deep human intelligence will remain the key elements. Dr. Kacker strongly believes, “the future is AI with human, but not AI vs human.”

Pharma faces constant innovation challenges, with new diseases and evolving problems that require adaptable, domain-specific expertise. AI might solve specific problems efficiently, but human capability is what brings flexibility, context, and foresight. The future of pharma, therefore, lies in a strong, complementary partnership between human expertise and machine intelligence, working together to drive meaningful progress .—a progress filled with hope and support for patients around the world.