From AI Pilot to Global Investment Decision

Germany | Global life sciences organisation

How a global life sciences organisation moved from early AI experimentation to an evidence-based enterprise investment decision.

The Challenge

A Germany-based global life sciences organisation was facing growing workload pressure while trying to limit additional hiring. Employees were also working across an increasingly complex system landscape, making information harder to find and day-to-day work less efficient.

As generative AI emerged, the organisation wanted to understand whether Microsoft 365 Copilot could create enough practical value to justify an enterprise-wide investment. The question was not simply whether the technology worked, but where it could be used effectively, how employees would adopt it and whether the economics remained attractive under realistic conditions.

The Approach

A structured global pilot was designed with several hundred employees across functions and regions. The participant group was deliberately broad, combining interested users, digital champions and employees with different levels of digital maturity.

The programme focused on practical adoption and evidence rather than technology hype. The work covered use-case identification, pilot design, community engagement, vendor coordination, implementation support and executive decision material.

·         Relevant business use cases were identified and tested across different functions.

·         Employee feedback and perceived time savings were measured through structured surveys.

·         Microsoft funding was secured and an implementation partner was selected and managed.

·         A scenario-based business case was developed using different levels of adoption and productivity improvement rather than assuming that every employee would benefit equally.

·         Executive materials translated the pilot results into a clear investment decision.

A Realistic View of ROI

One of the most important findings was that AI value was unevenly distributed. Some employees reported significant time savings, others saw more modest benefits and some used the technology very little.

Instead of treating this variation as a weakness, the business case modelled it explicitly. The central question became whether the investment would still generate sufficient value if only part of the workforce became highly productive users. Under realistic adoption assumptions, the model indicated a break-even period of approximately two years.

Decision Under Uncertainty

During the evaluation period, Microsoft presented a commercially attractive enterprise offer, creating a clear decision point. Leadership therefore had to balance the evidence from the pilot, the economics of the offer and the strategic value of building internal AI capability early.

The rationale went beyond short-term productivity. Broad access to generative AI was also seen as a way to build practical experience across the organisation and accelerate learning while the technology was still developing quickly.

The Outcome

The organisation approved an enterprise-wide global rollout of Microsoft 365 Copilot. The decision was supported by pilot evidence, a realistic scenario-based business case and a broader strategic view of AI adoption.

Rather than restricting access to a narrow set of roles, the rollout was designed around broad accessibility so employees across the organisation could discover where generative AI created value in their own work. The result was not simply a software deployment, but an enterprise-level decision to build AI capability at scale.

Capabilities

AI Strategy · Business Case Development · Enterprise AI Adoption · Change Management · Executive Alignment · Partner Management