Hype is everywhere. From technology to the latest and greatest lifestyle trends, what’s “hot” today can be “ice cold” tomorrow. That’s simply life in the hyper-accelerated world in which we live.
Generative Artificial Intelligence, or Gen AI, is no exception. Yes, a lot around Gen AI started as hype, with promises of dramatic changes in how we work, the data we work with, and who is in charge: humans or machines.
So how refreshing it truly is to ponder not the hype, but the real, revolutionary breakthroughs created by Gen AI.
Today, smart life sciences companies – hungry for data and innovations to improve patient care – are learning like never before. They’re exploring the benefits of using Gen AI, with Large Language Models at its core, to dramatically improve how work gets done and to make better decisions more quickly than ever.
At Axtria, we began advocating in 2010 for a dramatic increase in the use of data within the life sciences industry we serve. Early on, we found that data analytics (today’s AI) was only as good as the data upon which it was based. Said another way, if you had bad, unreliable, or outdated data, the decisions you made would be just as bad, unreliable, or outdated. It was simply “garbage in, garbage out,” as Axtria’s President and CEO, Jassi Chadha, stated in a recent article in BioSpectrum.
Clearly, winning the data battle is job one. That’s what makes it so refreshing to see real advancements – credible, insightful, reliable Gen AI technologies – becoming so prevalent in so many places when you get this right.
Early on, clients wanted to jump on the Gen AI bandwagon. But, as in many innovations, they didn’t know what they didn’t know. Sure, they had access to formal research and reports – the “structured” data in the world of Gen AI. But they didn’t know, at first, that the mother lode of insights comes from “unstructured” data. In our work with life-sciences clients, that could mean things such as doctor’s notes, recordings from patient examples, and unusual yet insightful data that doesn’t fit into a simple spreadsheet, all ripe for a Gen AI model’s picking.
Today, the embrace of Gen AI is as real as it gets. The hype has moved on to other areas. In fact, Gartner has issued a fascinating report about this, called Predicts 2024: Generative AI Brings New Value to Life Sciences.
In its report examining real impacts Gen AI is bringing to the industry, Gartner cited several observations of particular importance regarding the use of Gen AI in the selling process and the anticipated rise of virtual assistants:
- Life science organizations are embracing virtual assistants (VAs) to address the need for efficiency in a competitive market and the evolving dynamics of remote work, which is reshaping sales representative interactions and workflows.
- By 2026, use of virtual assistants in 30% of life science organizations will decrease the number of sales representatives while accelerating business growth.
- By 2027, 25% of life sciences organizations will implement IT solutions that enable holistic sustainability decisions across the entire product life cycle. In the commercial organization, human-centered AI will connect patients, providers, and partners through conversational interactions. Virtual assistants with emotional intelligence will help unlock the potential of sales teams, making every touchpoint smarter, more meaningful, and effective.
- With the rise of specialty and rare disease treatments, life science sales representatives face heightened pressures. They must be knowledgeable in therapeutic nuances, navigate competitive market dynamics, and identify trends in complex purchasing environments. Additionally, they are tasked with strategizing account interactions with diverse stakeholders and serve as the primary liaison for HCPs.
- In therapeutic areas with limited clinical differentiation, life science marketers are realizing that relying solely on the therapeutic product is no longer sustainable. The emphasis is now on delivering additional value beyond mere clinical advantages.
- The role of sales representatives is no longer just about selling. It’s evolving, with a growing emphasis on collaboration with diverse internal teams, adoption of data-driven strategies, and synchronizing across digital and analog customer touchpoints. Consequently, as the complexity and breadth of the role have expanded, it has become too demanding for many sales representatives to effectively do everything well.
There’s a lot we’ve learned in today’s world of Gen AI within the life sciences industry. The discussion has only just begun. From our experience in providing technology and domain expertise, we’ve developed some specific insights of our own that are already helping our life sciences clients. These include:
- Your Gen AI platform is only as good as the data you feed it. Ensure the best and most complete information is going in, so you can get the best, actionable information upon which to make important decisions that directly impact patients and improve operations.
- Most information is good information. It may just be incomplete. In our work, we focus on populating Gen AI platforms for clients with “Generative AI-Ready Datasets,” or GRDs. No more garbage in or out. We share more ways to build robust, generative AI-ready datasets that deliver real, measurable results.
- For one global client focused on improving medical care, we’ve already collaborated to help them leverage voluminous data into easily accessible datasets. Their Gen AI platform is now speeding decision-making, expanding access to information previously deep in company archives, and exciting employees with new transformative tools. In fact, their IT organization is experiencing a spike in requests for versions tailored to individual work groups!
- As Gartner points out, marketing organizations will unlock AI qualities that create smart “virtual assistants” to help even the sharpest sales teams. In our view, relegating certain tasks to these “VAs” will free humans to focus on more strategic, value-add activities that serve customers better and drive new efficiencies. A great goal: Leverage the positive aspects of Gen AI to create “super reps,” working smarter, using technology to help them determine a “next best action” while traveling between appointments, and boosting reliance on data.
- We realize that in our industry, decisions our clients make can be life-impacting. That’s why it’s critical that we collaborate to drive the best decisions possible, especially when addressing rare diseases. Given their unique challenges – fewer patients across which to amortize costs, higher development expenses, and more – a data-first approach can help patients who might have been overlooked in the past.
In the end, Gen AI platforms are only as good as the time, care, and data that power their efficacy. Bad data not only drives bad decisions, but it can also create wrong answers to critical queries asked by users. These “hallucinations” can emerge when “stale” information has grown irrelevant or incorrect. There is a solution: Deploy advanced frameworks like Retrieval-Augmented Generation, or RAG, which access external knowledge to supplement internal data. We share more on managing, and avoiding, hallucinations.
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Decoding AI Hallucinations: Unmasking The Illusions Of Generative AI
Ironically, Generative AI’s promise to drive us forward into the future – managing data, working smarter, and getting super-fast access to information – is only as good as the engineering put to work in organizing and manipulating the vast treasures of structured and unstructured data from the past.
The good news is that this work is real and no longer hype.
Gartner, Predicts 2024: Generative AI Brings New Value to Life Sciences, Reuben Harwood, Animesh Gandhi, Michael Shanler, Jeff Smith, 10 January 2024.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
