How pharma commercial teams can move past barriers to achieve meaningful AI adoption

Brett's learnings

1. We're not yet in a post-AI adoption phase

The high failure rate of enterprise AI pilots shouldn’t discourage use by life sciences commercial organizations. These early efforts have largely been experimental, and it’s unrealistic to expect perfection. Studies show that once people move past the pilot stage, they begin to see results. The challenge is getting to an organizational level of adoption.

2. AI adoption is uneven across the enterprise, and mindsets around it vary

AI may already be used in advanced ways in some parts of an enterprise, even as its use remains limited elsewhere. An analysis showed that patterns of AI adoption run counter to common assumptions about age and department and that people approach adoption with different mindsets. These range from the "ROI-er" who insists on seeing the numbers to the "Swamped" who feels overwhelmed by one more thing to learn. A person can cycle through different AI states. Meeting people where they are can help overcome these hurdles.

3. Showing results shifts AI adoption from 'push' to 'pull'

Organizations can build demand for AI by consistently highlighting wins. As colleagues see it deliver value, their attitude may shift from "should I adopt?" to "if I don't, will I be at a disadvantage?" Seeing others succeed may encourage team members who are initially resistant to try AI-driven alerts, for instance.

4. Advanced AI becomes more accessible as technical barriers come down

Advanced AI, including agentic tools, is shifting from something reserved for technical specialists to something anyone can use. Graphical interfaces and low- and no-code platforms are making that possible. This opens the door to experimenting with AI and bringing it into daily work.

The bottom line:

Achieving enterprise-level AI adoption among biopharma commercial teams takes more than successful pilots. A segmentation strategy— i.e., tailoring the approach based on an understanding of individual mindsets—can help overcome resistance and encourage meaningful AI use throughout more organizational layers. Additionally, making wins visible and encouraging experimentation are other strategies found to help. As AI budgets become a bigger focus, a customized approach may take on more importance.