4 Critical Factors You Should Know About Generative AI in Life Sciences

Recent generative AI (GenAI) advancements are now bringing AI and data conversations into the mainstream. In Accenture’s recent Pulse of Change C-suite survey, nearly all C-suite participants said “generative AI will be transformative but only a third have started investing significantly.” It is time to prepare for the future of work with AI.

My career success has not only come from connecting data and dots but also people. I am taking this moment to connect with you and share four of today’s hottest topics surrounding generative AI in Life Sciences. Here’s what you need to know:

1. The risks of using generative AI in Life Sciences

When you are dealing with life-saving products, it can be easy to wait for the perfect solution, approach, technology, and/or team but we must find ways to take calculated experiments to prove new capabilities and bring a spirit of innovation. We should not value perfection over progress.

Today, I am watching many organizations get stuck in their AI journeys. For example, one organization used GenAI to write a report. While 50% to 75% of the project saw near perfect results, the remaining part – traditionally the most scrutinized part – took as much time as previous versions.

There were some critical pieces missing in this experiment including change management and clear communication for the employees typically responsible for the report’s creation.

If you are going to apply efficiency to a process using generative AI, what are you going to do with that efficiency? Think about how you are applying a higher bar for excellence at the same time as you are responsibly implementing AI.

I would also encourage you to watch Fortune’s discussion, Capturing AI benefits: How to balance risk and opportunity, with my colleague, Arnab Chakraborty. These high-level risks also apply to life sciences and require future consideration.

2. How to prioritize generative AI?

When it comes to prioritizing generative AI, I recommend that organizations ask these questions in the boardroom:

• While clearing technical debt, how are we applying generative AI to do it?

• Are we using GenAI to write faster, cheaper, and better code?

• Are we using GenAI to create test strips and automation?

• Are we using GenAI to summarize documents and create better documentation?

These are just some of the key questions. It is important to remember that AI is not separate for an organization’s work. It should be embedded in the smallest projects. To do this, every project should have a checklist that includes ROI, sustainability, governance, security, and GenAI. This ensures a sustainable and holistic approach.

3. The impact of generative AI on jobs

One of the most important aspects of AI talent is the hope that humans will focus on the complex and high impact parts of their roles and retention will improve. Research tells us that if people are amazing at their jobs and we give them generative AI, they will be even more amazing at it. Simply put, AI will create better job satisfaction and higher quality deliverables.  

In Life Sciences, we are asking:

• Can we bring drugs to market faster with AI?

• Can we provide better patient outcomes?

• Can we deliver better customer experience and consumer products?

We will see a new host of jobs crop up but in the long term, we will see a transformation of customer experience and patient experience.

I also want to highlight the importance of Responsible AI again and emphasize that AI is a continuous journey. We are on a technological continuum. As we witnessed with NEDA’s AI chatbot, Tessa, organizations cannot switch on AI and hope for the best. We need to educate and test, not ignore. Policy and regulation will need to adapt and respond.

4. The future workforce

In our post-Covid world, fatigue is still here. High performers are working more than ever. We need a balance and we can find it with AI as a copilot concept.

Accenture’s Pulse of Change survey highlighted that 61% of C-suite said at least half their workers have received AI training but very few (5%) have reached the full workforce.” The next steps for upskilling or reskilling will be critical.

And because of this, a broader talent pool will be created around AI. The idea of an “AI worker” is completely different now than it was a year ago and very different from what it was three years ago. Today, if you’re in the legal department of an organization and advising AI regulations, you are in the AI workforce. If you are in HR and using generative AI to recruit new employees and manage employee expectations, you too are in the AI workforce.

And there is a lot of excitement around the idea of a ‘prompt engineer’ as the next profession. Prompt engineering in the shape and format we are seeing now is a skill we will all need to have. This will be a foundational skill in our toolkit — similar to the ways we use Apple TV and other technology.

The future workforce will need to be a fully diverse, inclusive, and equitable set of AI practitioners. Since unconscious bias can be a reality, a diverse team is an effective solution to closing that gap. In fact, a DE&I focus is one of the most important aspects of our future as we build a happier and healthier world with a Responsible AI approach.

About the Author:

Tracy Ring is Chief Data Officer and Global Generative AI Lead for Accenture’s Data & AI Life Sciences practice.  

For over 20 years, she has developed AI strategies for dozens of organizations, shaped deals for large-scale transformation, and sponsored broader platform ecosystem collaborations. Today, as generative AI transforms Life Sciences, Ring advises CDOs and Chief Analytics/AI Officers to drive impact and strategic value across the business in commercial, research & development, supply chain and enabling areas with a Responsible AI lens. She guides AI solutions in regulatory filings, early drug development and the future of commercial and intelligent supply chain.   

Ring is a passionate leader who is dedicated to empowering and mentoring female technologists while creating and maintaining diverse, inclusive spaces. She is a recipient of the 2020 Top 25 Consultant Leadership award by Consulting Magazine for her work around diversity, equity and inclusion. She is also a founding member of Women Leaders in Data & Analytics (WLDA), an invitation-only cohort of Chief Data and Analytics Officers that focuses on changing the equation for CDOs to work together with CIOs to capture competitive edge using data and AI.   

She holds a Master of Business Administration from Harvard Business School.