A traditional IT investment ROI model misses the true value of AI in pharma. A proper methodology must account for operational efficiencies (e.g., time saved in clinical trials, where each day costs millions) and intangible benefits like improved data quality, competitive advantage, and institutional learning.
Related Insights
DBS quantifies AI impact not by cost savings, but by the incremental revenue generated from AI-driven customer "nudges." Using rigorous A/B testing, they track the lift from these interactions, reframing AI's value proposition from an efficiency tool to a revenue growth engine, targeting over a billion dollars.
The true ROI of AI lies in reallocating the time and resources saved from automation towards accelerating growth and innovation. Instead of simply cutting staff, companies should use the efficiency gains to pursue new initiatives that increase demand for their products or services.
Standardized benchmarks for AI models are largely irrelevant for business applications. Companies need to create their own evaluation systems tailored to their specific industry, workflows, and use cases to accurately assess which new model provides a tangible benefit and ROI.
While AI holds long-term promise for molecule discovery, its most significant near-term impact in biotech is operational. The key benefits today are faster clinical trial recruitment and more efficient regulatory submissions. The revolutionary science of AI-driven drug design is still in its earliest stages.
Box CEO Aaron Levy argues the focus on AI's return (R) is misplaced. The real leverage is making the initial investment (I) so low that companies can pursue projects previously deemed too expensive or risky, from custom software for small firms to new R&D initiatives, thus creating new value.
Measuring AI's value by hours saved is misleading for law firms, as it can imply lower revenue. The true ROI comes from what lawyers do with that saved time: pursuing more complex strategies, conducting deeper analysis, and spending more time with clients—high-value work previously constrained by time.
Snowflake's former CRO offers a pragmatic view of AI, calling it a 'task automator.' He stresses that for enterprise adoption, AI tools can't just be 'cool.' They must deliver a clear return on investment by either generating revenue or creating significant cost savings, like the 418 hours per week saved by their support team.
When leadership demands ROI proof before an AI pilot has run, create a simple but compelling business case. Benchmark the exact time and money spent on a current workflow, then present a projected model of the savings after integrating specific AI tools. This tangible forecast makes it easier to secure approval.
While healthcare companies widely use AI for cost savings and R&D efficiency, it has not yet translated into measurable revenue or earnings growth. For equity investors, there are easier, more direct ways to invest in the AI trend, making healthcare a poor proxy for the theme until its financial impact becomes clear.
While AI provides operational efficiency, its most profound value lies in enabling tasks that were previously impossible due to scale, like instantly rewriting 10 million pages of web content after a terminology change. This capability transcends traditional ROI calculations.
