Companies see significantly stronger outcomes from AI when they approach it with structured, science‑driven methods instead of treating it as a passing trend. McKinsey’s 2025 State of AI survey shows that only about 6% of organizations qualify as “AI high performers,” meaning they attribute at least 5% of their earnings to AI and report significant value from it. Most crucially, the report found that these leaders tend to treat AI as a disciplined program with governance, workflow redesign, and scaled deployment, not a side experiment.
“Technology, when applied strategically, unlocks exponential growth for businesses,” says Finith Jernigan, Ph.D., founder and CEO of Finith Capital. With a background spanning computational drug design, high-performance computing, and cross-industry technology strategy, Jernigan has built a reputation for solving problems others consider unsolvable. Today, the “unsolvable” often lies in figuring out how to translate AI’s potential into practical, scalable impact.
Jernigan believes the real breakthroughs come from applying advanced computing, simulations, and well-structured experimentation to problems that feel unsolvable. “These are the moments where disciplined technology turns uncertainty into opportunity,” he says.
Start With the Right Problem
Many organizations begin their AI journey by selecting a model or platform, hoping it will reveal opportunities on its own. It’s a mindset Jernigan finds fundamentally flawed. “Too often businesses start with the AI model instead of with the problem. That’s backwards,” he says. “The challenges most worth pursuing are those that are ambiguous, complex, or historically resistant to traditional approaches.”
Drug discovery is an excellent example of this in action. While at Silicon Therapeutics, Jernigan and his team tackled targets with no known chemical starting points that were once considered dead ends for researchers. By pairing simulation technologies with machine learning, they transformed these high-uncertainty challenges into viable programs. Whether in logistics, manufacturing, or finance, the most meaningful value emerges when AI is used to tackle problems that matter, not just problems that are convenient.
Use AI to Simulate, Not Just Predict
Prediction is the most common commercial use of AI, but it’s only the first rung of the ladder. The deeper potential lies in simulation. “Think of AI not just as a crystal ball, but as a wind tunnel for your business decisions,” he says. Instead of forecasting a single future, simulation enables companies to explore hundreds or thousands of potential futures before committing real resources.
This approach can fundamentally reshape strategic planning. In pharmaceuticals, Jernigan’s teams ran expansive simulation cycles to de-risk billion-dollar development decisions. Similar methods can be applied to test supply-chain resiliency, evaluate pricing strategies, or optimize product designs. Simulation converts uncertainty from a liability into an asset by giving leaders a clearer understanding of the decisions that carry the greatest long-term payoff.
Build Cross-Functional Execution Teams
Technology alone cannot create outcomes. Successful AI adoption requires seamless collaboration across disciplines. “AI is not an initiative, it’s a business strategy,” he says, stressing the impact of integrating domain experts, data scientists, and business operators into unified teams that move together from concept to execution.
During his tenure at Psivant Therapeutics, such multidisciplinary teams allowed projects to progress from idea to prototype quickly and effectively. This model stands in contrast to siloed corporate structures, where technical insights often fail to translate into business value. When teams are designed intentionally and encouraged to solve problems collectively, AI becomes a core driver of advantage by ensuring insights move rapidly from concept to real‑world impact.
A Scientific Mindset for Scaling Businesses
What sets Jernigan apart is the scientific discipline he brings to business transformation. His method leans on structure, metrics, and experimentation rather than intuition or trend-following. As an investor and technologist, he looks for profitable companies that are ready for the next level of growth but lack the systems or technical infrastructure to reach it. “The impossible is just the starting point for real innovation,” he says.
For leaders evaluating their next move, Jernigan’s challenge is to identify your toughest issue, then ask what might be possible if you treated it as solvable.
To continue the conversation with Finith Jernigan, Ph.D or learn more about his work, connect with him on LinkedIn or visit his website.
