How to Turn AI Hype into Hard Results in Pharma
Artificial intelligence is starting to generate real returns in the pharmaceutical industry—but only for a select few. A recent survey shows that just 10% of pharma companies using AI are truly capturing its value. What separates these leaders from the rest?
They begin with a clear commercial AI strategy and a roadmap of use cases. They never launch AI solutions without redesigning related processes. This means rethinking how teams function and actively driving behavior change. They also maintain forward-looking AI governance and risk management.
Where Most Pharma Companies Stand
About half of pharma companies haven’t even piloted AI solutions yet. The good news? It’s still possible to catch up. The fastest path forward requires bold ambition. Leaders should aim to set industry standards for AI-enabled teams and personalized engagement with healthcare professionals (HCPs).
This includes using AI to create dynamic content engines tailored for HCPs and empowering field teams with next-best-action recommendations and training tools.
More Than Just Ambition
Ambition alone won’t deliver results. Real ROI demands the right capabilities. This means having the right data, technology, talent, roles, processes, and governance in place.
Successful companies embrace a fail-fast, test-and-learn culture. They experiment rapidly and adjust quickly. By contrast, companies that wait for fully validated pilot results before deploying AI solutions will fall behind.
The Biggest Challenge—and Opportunity
Many large pharma companies have invested heavily in data and tech infrastructure. Yet few have focused on building their capacity for change. This gap is the greatest hurdle and the biggest opportunity.
The future leaders will be those who invest in rethinking how teams work and drive real change in behavior and processes.
Key Steps for Pharma Companies to Extract AI Value
- Define a clear AI strategy: Identify specific commercial use cases and set measurable goals.
- Redesign processes: Align AI deployment with changes in workflows and team behavior.
- Implement strong governance: Manage AI risks and ensure ethical use.
- Foster a test-and-learn mindset: Encourage rapid experimentation and learning from failures.
- Invest in change management: Build organizational capacity to adapt and evolve.
Pharma professionals looking to deepen their AI skills can explore training options to support these changes. For practical AI courses relevant to healthcare and pharma, consider visiting Complete AI Training.
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