AI Implementation in the Life Sciences Industry: Survey Results on the Biggest Challenges

Between Ambition and Implementation: Where Life Sciences Companies Currently Stand in Their AI Adoption

Artificial intelligence has firmly established itself on the agenda of many companies. McKinsey describes 2026 as the year of the pilot phase, a trend that can be observed across numerous industries. At the same time, the reality on the ground paints a different picture. Many organizations barely get past initial experiments and pilot projects. AI remains a buzzword in many discussions and stalls before being integrated into scalable processes. Why is that? And why does the life sciences industry, in particular, face these hurdles?

Why implementation is stagnating and what matters most now

This discrepancy is particularly evident in the life sciences and pharmaceutical sectors. The pressure to innovate is high, regulatory requirements are complex, and existing structures have often evolved over time. Although two key themes clearly emerge from the results of our survey, companies identified all four response options as challenges.

Specific Use Cases
Some companies struggle to meaningfully integrate AI into their own context. Ideas often remain abstract or are too far removed from day-to-day operations. As a result, there is no basis for clear prioritization, and the pilot project fails right from the start.

Ownership Between IT and Business Units
Responsibilities are often not clearly defined. Initiatives oscillate between technical and business requirements without clear direction. This leads to inefficiencies and regularly pushes AI implementation off the agenda.

Lack of an Implementation Strategy
This is one of the biggest obstacles. Many organizations start with an idea but without an overarching vision. There is a lack of structure, a business case, and a clear concept of how AI should be used in the long term. As a result, the pilot project leads to frustration and, in many cases, never makes it to rollout.

Scattered data and tools
At the same time, a second key challenge emerges. Data and systems are fragmented in many companies. Information is scattered, formatted differently, and difficult to access. Tools do not interact with one another, making AI use cases inefficient and cumbersome.

The Key Drivers of AI Implementation: A Focus on Strategy and Data

The two dominant themes of our survey are closely related. In both cases, the issue is one of structure and the fact that companies lack a long-term vision.

Lack of an implementation strategy. Providing guidance in the decision-making process

Companies often work on individual projects that make sense on their own. However, without a strategic framework, there is no connection between these initiatives.

Typical challenges include:

• No clear prioritization based on business value
• No shared vision for the use of AI
• Uncertainties regarding regulatory requirements
• AI is approached too holistically, rather than starting with a use case. Keyword: Minimum Viable AI.

The result is many parallel activities without a clear direction and without a single defined use case that can be considered complete. The main problem: there is no clear roadmap for implementation.

Scattered data and systems. Operational realities as the limiting factor

The second major challenge lies in the existing system landscape. Data is the foundation of every AI application, yet this is precisely where the biggest problems often arise.

• Data is scattered across various tools
• Interfaces are either missing or inefficient
• Data formats are neither standardized nor directly analyzable
• Governance and quality vary depending on the source

This fragmentation significantly increases the workload and slows down projects. It means that AI processes still have to include manual steps. At the same time, it affects many other areas of the company. Processes become more complex, decisions take longer, and opportunities for efficiency remain untapped. The question is: How did such a fragmented system landscape come about in the first place?

A structural issue. AI reveals existing challenges

The results show that many of the problems mentioned already exist independently of artificial intelligence. Fragmented systems, a lack of coordination and ownership, and unclear strategic direction are familiar issues in many organizations.

AI amplifies these effects and brings them into sharper focus. They act as direct brakes on digital transformation and create a strategic competitive disadvantage. Companies that therefore improve their fundamentals and systematically dismantle legacy structures will benefit beyond individual AI use cases.

From AI Pilot to Scaling workflow: A Structured Path Forward

To support companies during this phase, we have developed an AI Impact Framework. It helps transform individual initiatives into a clear and actionable structure. The approach is based on three key elements.

Clear step-by-step process
From identifying relevant use cases to scaling up, a structured process with clear prioritization is established.

Responsibilities and change management
Roles are clearly defined, and organizational change is actively supported.

Targeted piloting (Minimum Viable AI)
Focused pilot projects deliver quick results and lay the foundation for further scaling.

Conclusion: Progress requires clarity

The survey clearly shows where companies should start. Strategic direction and a consistent data foundation are crucial for the successful implementation of AI projects.

Those who address these issues in a targeted manner lay the groundwork for sustainable progress and real impact within the company. To support this endeavor, we offer a targeted AI Impact Workshop that provides the right approaches for implementing artificial intelligence into your own processes in a structured and successful manner.

Contact us for a no-obligation consultation to discover where AI can bring value to your business processes.