This article presents insights from individual interviews with 27 pharmaceutical and biotech executives on the impact of AI on organizational structures, strategies, and decision-making. Rather than focusing on specific AI applications, we explored how organizations are preparing for AI-driven transformation, with the goal of prompting leaders to assess their readiness for the “New Normal” and build the necessary capabilities for a seamless transition.
AI has swiftly evolved from a niche technology to a transformative force across industries. The combination of rising computing power and Generative AI has propelled AI beyond theoretical potential into broad practical applications, reshaping workflows, decision-making, and competitive strategies.
In the pharmaceutical industry, AI is rapidly transforming data-rich functions such as drug discovery, clinical site management, medical affairs, and supply chain operations. However, despite its growing integration, AI is not yet “the New Normal,” but its broad adoption is inevitable. AI is not a passing trend; it will become an indispensable part of organizational culture and operations.
The rate at which organizations adopt and integrate AI often reflects the strategic intent of their leadership, whether they aim to lead the charge, follow closely, or cautiously observe.
This dynamic mirrors the digital transformation era of the past decade.
AI has the potential to revolutionize processes, but doing so effectively requires a well-governed and strategic approach. To ensure successful adoption, AI initiatives must not be treated as purely technology projects. Their impact on organizational structure, strategy, and culture must be understood to drive meaningful adoption.
Leadership in Guiding the AI Transition
Regardless of whether you lead an organization, a department, or a function, AI adoption requires a balance between enthusiasm and strategic execution. Leaders must capture the excitement for AI’s potential to innovate and gain efficiencies with a clear, measured approach to adoption. Effective leadership involves articulating a future direction that not only highlights AI’s potential benefits, such as enhancing research capabilities and improving patient outcomes, but also acknowledges the challenges of ethical implications and workforce adjustments.
Surprisingly, 40% of the executives we surveyed were not actively leading AI initiatives, despite AI’s inevitability in the industry. This signals a critical leadership gap that may hinder successful implementation. Given AI’s permanence, leadership plays a pivotal role in shaping how their organization embraces and adopts AI to its full potential.
As with any large-scale transformation, leaders must ensure that employees understand AI’s role as an enabler rather than a threat. One executive shared an example in which AI optimized clinical trial recruitment. It was crucial for him to provide training and reassurance that AI is complementing, not replacing, the expertise of his researchers. By setting realistic expectations, prioritizing ethical considerations, and investing in upskilling, leaders can guide their organizations through AI adoption while maintaining trust and engagement.
Ultimately, effective leadership must balance vision with pragmatism to transform AI from a disruptive force into a strategic enabler for the organization.
Governance Frameworks – AI Czar versus Governance Committee?
While much of the AI discussion focuses on benefits, organizations must also address the risks and complexities of long-term technology development, data infrastructure management, and measuring ROI. Many executives emphasized the need for governance structures to navigate these challenges effectively.
Experienced AI leaders emphasized cross-functional governance committees as the best approach to managing AI adoption. These committees ensure that AI initiatives align with business objectives, regulatory requirements, and ethical considerations. In contrast, executives with less AI experience initially advocated for an “AI Czar”, a single leader to oversee AI initiatives. However, organizations that chose this model quickly transitioned to a governance framework to manage AI’s complexity.
In the governance model, AI requesters present their use cases to a committee that evaluates proposals before leadership allocates budgets and resources. This structure ensures leadership prioritizes high-impact, high-ROI applications while avoiding excessive time spent on technical, legal, or ethical concerns.
AI needs a Bi-lingual Organization That Understands “Bio” and “Tech”
Successful AI adoption hinges on collaboration between technical and industry experts. Organizations must cultivate a ‘bi-lingual’ workforce, fluent in both the realities of pharmaceutical and biotech operations (Bio) and the science of AI (Tech).
Not surprisingly, companies are increasingly establishing formal and informal mechanisms to connect AI specialists (data scientists) with domain experts in business functions. This enhanced collaboration results in better-defined use cases for governance boards to review.
Several participants shared that they have initiated “AI onboarding,” offering basic AI literacy programs. These programs, delivered by AI vendors or internal IT teams, help employees understand how AI models work, key terminologies, and potential applications. Such initiatives ensure that teams can effectively articulate problems while leaving the technical implementation to AI experts, leading to more refined use cases and stronger alignment between business goals and AI capabilities.
Bringing AI specialists together with domain experts allows requesters to focus on defining problems while leaving implementation to data scientists. However, one executive raised a critical concern: AI availability may require organizations to rethink their long-term strategic plans, as business questions have traditionally been shaped by what was perceived as possible rather than the full range of AI-enabled possibilities.
Weighing Costs and Benefits of AI: ROI vs. FOMO
To successfully implement AI, companies need to be strategic in their approach, balancing the promise of innovation with the need for responsible, efficient execution. AI promises significant ROI and competitive advantages, but as always, investments must be made strategically, with a realistic understanding of the risks, costs, and timeline for value realization.
The interviews we conducted did not focus on the technical aspects of AI or the detailed applications of various technologies. However, several participants explained, at a high level, the applications and the benefits they are anticipating.
Interestingly, none of the participants were able (or willing) to quantify the benefits they already received from AI, reinforcing the notion that AI is still in the early stages of adoption, definitely not yet the New Normal.
The table below shows a few AI applications organized by the organizational functions we interviewed. Of course, the table was generated with the help of ChatGPT and not with confidential information the interviewees gave us. However, the table illustrates the wide range of examples and represents just the tip of the iceberg of meaningful applications.
The broad range of applications raises the question: Which are most meaningful for the organization?
While ROI is a key driver of AI investments, uncertainty around long-term costs and benefits complicates decision-making. Direct costs, such as software and infrastructure, can be estimated fairly well, however, indirect costs are harder to quantify, including:
- Long-term data preparation and maintenance
- Investment in technical infrastructure
- Upskilling and training employees
It seems that the real cost drivers in the long run will be the creation, maintenance and/or purchase of data. One of the executives put it bluntly, “Data will be King.” Much like the printer industry, where ink, not the printer, drives costs, AI’s real long-term expenses will come from data acquisition, storage, and management.
Although ROI calculations play a role in AI investment decisions, many executives admitted that, in practice, AI adoption is often motivated less by financial metrics and more by competitive pressure, what we call “AI FOMO syndrome”, where organizations feel, they cannot afford to delay AI adoption for fear of falling behind competitors. The lack of rigorous evaluation frameworks is yet another indication that AI is not yet the New Normal.
Decision-Making in the AI Era
One of the most noticeable cultural shifts that comes with AI applications is the increasing emphasis on data-driven decision-making. Many organizations have pushed towards evidence-based decisions for a while with mixed success. However, AI’s ability to analyze vast amounts of data and to generate more and deeper insights is driving companies to ensure that evidence-based decision making is utilized over intuition or tradition. Does this mean AI will make the decision for us? While AI enhances analytical capabilities, it does not eliminate the need for human oversight and contextual judgment.
One executive shared an example from their organization where AI-generated FDA submissions reduced processing time by several months. However, final approval still requires human judgment; someone needs to decide that the created document is indeed ready for submission, and that judgment typically comes from someone who probably oversaw the process previously. The participant pointed out that to a certain extent, the person who decides at the end may need an even higher skillset level because in the past they had multiple team members looking at and working on the submission document.
The executive also raised the concern if AI performs these tasks, how will future employees develop the expertise to oversee them? This challenge, known as “skill atrophy” or “skill degradation”, occurs when people stop practicing a skill because technology performs it for them. To maintain proficiency levels, organizations will need to add training capacities to counter skill atrophy effects.
AI enhances decision-making, but alignment across teams remains a human challenge. Large-scale AI initiatives, such as implementing AI in global supply chains, require cross-functional collaboration and negotiation. Just because “AI said so” does not create alignment among stakeholders. Leaders still have to deal with micro-politics and power games of the stakeholders involved. “Organizational Leadership”, steering people across the entire organization to a unified direction, will remain. The ability to “collectively make sense” of AI-driven insights remains a critical leadership skill, ensuring that AI-driven recommendations align with business priorities.
Cultural Change: Will It Accelerate or Hinder AI Adoption?
AI is more than just a technological advancement; it is transforming the culture of organizations. Preparing for this shift is critical to ensure a smooth and speedy transition to the New Normal. While AI advocates emphasize its benefits, a significant portion of the workforce remains skeptical and concerned. Even among our small sample of 27 executives, we clearly recognized a significant portion of leaders expressing their own worries and preconceived notions of their leadership team members.
Some participants shared that AI is too complex to understand, or prone to errors because of bad data. “Garbage in, garbage out” was a phrase we heard repeatedly. Others worried about AI’s impact on job security, particularly in repetitive, low-skill roles or fields like data analytics. We also heard that AI and data-driven decision-making would diminish the relevance of their job and their contribution to the organization, potentially reducing their sense of organizational belonging. In addition, concerns were raised that AI-driven decision-making would become the domain of a select group of “techies,” shifting power dynamics within the organization.
Cost was another frequently mentioned concern. Several executives indicated that their organizations were hesitant to invest in AI, preferring to be “fast followers” rather than early adopters. However, our discussions revealed that in many cases, the desire to be a “fast follower” was an organizational display-side, while in reality they remain a follower. Many executives that we labeled “anxious bystanders”, seemed to have delayed their own AI upskilling, even when they had accessible opportunities to build AI literacy.
No doubt, if these and other attitudes towards AI persist, it will lead to anxieties or even resistance in organization to a smooth and fast adoption of AI. To counter these concerns, organizations need to demystify AI by providing accessible education and training, and opportunities for safe experimentation with AI. Making AI literacy a core organizational goal would go a long way in addressing fears and misconceptions, even among executives.
Of course, not all executives shared concerns. Several participants talked about how they reinforced the idea that AI is a tool to enhance, not replace, human decision-making. They shared with us what they shared with their employees: “as AI takes over repetitive and routine tasks, you will be able to focus more on higher-value, creative, and strategic activities”.
AI will undoubtedly act as a catalyst for cultural transformation within organizations. Consequently, leaders must strike the right balance by acknowledging and addressing workforce concerns, while highlighting AI’s advantages, such as data-driven decision-making, the reduction of mundane tasks, and the ability to tackle long-standing challenges with innovative solutions.
Conclusion
The “New Normal” of AI is approaching fast. Leaders must act now, not just to implement AI, but to shape its role within their organization. Those who proactively embrace AI, establish strong governance, and invest in AI literacy will lead their organization forward. The question is not whether AI will become standard practice. The question is, are the leaders ready to lead the organization to gain competitive advantages with AI, or at minimum, ensure the company is not falling behind.
Are you ready?
