Right now, using AI means something completely different depending on who you ask.
At home, this may mean relying on AI to help us answer day-to-day questions. I’ve used it to plan summer vacations, help design our new interior paint colors, and help with the kids’ homework (yes, we all do it!).
At work, the gaps between users grow. For some, using AI means having a chatbot help write emails and summarize meetings, while for others, it’s building custom agents, automating workflows, or generating in minutes what might otherwise take hours to build manually in Excel or other programs. Adding to the disparate meanings is the fact that in the pharmaceutical and biotech world, “AI” has been used interchangeably with terms like machine learning models, translational insights, digital twins, or advanced simulation environments.
None of these perspectives are wrong, they simply reflect where each person sits within a rapidly evolving AI ecosystem. However, with growing pressure on individuals and teams alike to expand AI skills, lacking a shared definition creates a risk of building disconnected capabilities rather than connected intelligence.
I have seen this over the last decade or more with companies looking to upskill in their modeling and simulation platforms to support translation discovery and clinical research. It requires a coordinated effort to allow teams to work together to implement the best solution for their company. Similarly, everyone is now racing to upskill in AI and develop their own interpretation of what that means without always considering how those technologies communicate, integrate, or support the wider scientific process within their companies.
That seems like a missed opportunity.
Right now, organizations have the chance to incorporate AI in an ecosystem framework, where technologies, data, scientific models, and people work together seamlessly. Efficiency in our industry comes from creating environments where information flows freely, where insights can be trusted across teams and disciplines, and where AI enhances decision-making rather than complicating it.
However, to make this a reality, leaders must first create and communicate a shared vision of the purpose and goals of AI use within their organizations. Otherwise, they will continue to be plagued by inconsistent and duplicative applications and agents, running up unnecessary costs without realizing the full benefit their technologies can offer.
Here is my vision for your consideration, a vision that is shared by all of us at Simulations Plus: I believe the future of AI in modeling and simulation will be defined by connectivity and interoperability. We need platforms and ecosystems that bring together mechanistic science, simulation technologies, data-driven AI, and human expertise into a coherent framework.
AI should not sit outside the scientific process as a separate layer of technology. It should exist within it, acting as a co-scientist, supporting and enabling better decisions across discovery, development, and clinical research.
The organizations that succeed over the next few years will not simply be the ones with the largest datasets who are adopting AI the fastest. They will be the ones that build connected ecosystems capable of turning complex science into faster, more confident decision-making and ultimately delivering better therapies to patients in a reliable and trusted way.
If you are interested in learning more about how to implement an AI-powered ecosystem, I’m happy to chat.