In the world of life sciences research and development, the potential benefits of artificial intelligence (AI) are tantalizingly close. Yet, many research organizations have been slow to embrace the technological revolution in its entirety. Specifically, while they might leverage AI for part of their process (typically “in model” or predictive AI), they hesitate to think about leveraging improved techniques to review existing data or generate potential new avenues of research. The best way to understand the problem is by using an analogy of an ax versus a chainsaw.
The “Ax vs. Chainsaw” Analogy
Imagine a team of researchers tasked with chopping down trees, each equipped with an ax. Over time, they develop their own skills and processes to efficiently tackle the job. Now, envision someone introducing a chainsaw to the team. To effectively use the chainsaw, the researchers must pause their tree-chopping efforts and invest time in learning this new tool.
What typically happens is one group of researchers, especially ones who have found success with the old methods, will resist, stating they are too busy chopping down trees to learn how to use a chainsaw. A smaller subset will try to use the new tools incorrectly, and therefore find little success. They will state, “this ax stinks.” This is where the early adopters and those most interested in finding new ways of doing things will become the most productive on the team. They will stop, learn how to use the new tool and rapidly surpass those who did not take the time to do so.
In our analogy, it is important to acknowledge the chainsaw, representing AI tools, is not yet perfectly designed for the research community’s needs. Just like any emerging technology, AI has room for improvement, particularly in terms of user-friendliness and accessibility—and especially so for researchers without a background in IT. Dell Technologies is working with multiple organizations to improve tools and increase their usability. However, the current state of affairs should not deter us from embracing AI’s immense potential. Instead, it underscores the need for ongoing refinement and development, ensuring AI becomes an even more powerful and accessible tool in the arsenal of life sciences research and development.
The Potential of AI in Life Sciences R&D as a Catalyst, Not a Replacement
By transcending conventional model predictive AI methods and harnessing the power of extractive and generative AI (GenAI), the discovery of enhanced correlations can uncover remarkable insights concealed within overlooked and underutilized existing data, surpassing the ordinary review process and potentially uncovering new avenues of exploration.
It is crucial to understand AI is not here to replace researchers but to empower them. The technology adoption curve still applies, and we are in the early adopter phase. However, early adopters in the pharmaceutical space can gain a significant advantage in terms of accelerated time to market. AI is wonderful at finding correlation and completely inadequate at causation.
Create an AI Adoption Plan, Select the Right Partners and Measure Your Success
To kickstart AI adoption, research organizations across all of life sciences need to have a structured plan. My recommendation is to start by prioritizing AI adoption into specific targets with clear and well-defined pain points that are addressable. In particular, focus on areas where you can measure success. In addition, be constantly vigilant in ensuring data security. Organizationally, this goes beyond compliance—consider protecting data (and the data about your data) at all times.
In addition, select the correct partners who are here to help you in accelerating adoption. At Dell, we have been working overtime to make sure our solutions and AI and GenAI services align with your needs. We’ve made sure our solutions and services focus on a multicloud, customizable infrastructure that prides itself on your organization maintaining data sovereignty.
Lastly, measure your success and expand. It is important that leadership is involved, that your KPIs meet requirements and that everyone in the organization is comfortable with embracing failure and adapting to find success.
For life sciences research organizations, the key decision is to accelerate adoption of AI across their organization. Embracing AI tools and methodologies is not just an option; it is a necessity to stay competitive in the ever-evolving landscape of scientific discovery and therapeutic development. Dell Technologies is here to help with that process and standing ready to work with your organization wherever you are on your journey. Remember, those who master the chainsaw of AI will undoubtedly become the trailblazers in the field, ushering in a new era of accelerated progress.
