From AI Ideas to Working Use Cases

Sep 24

2 min read

Germany | Global life sciences organisation

How a structured AI use-case programme turned growing business demand into working solutions without creating a permanent burden on central engineering teams.

The Challenge

Interest in AI was growing quickly across a Germany-based life sciences enterprise, but execution was lagging behind. Business teams were bringing forward more and more ideas for automation and AI-enabled solutions, often with the expectation that these could reduce manual effort and support a wider cost-saving agenda.

At the same time, practical experience with general-purpose AI was still limited outside specialised research environments. Business teams lacked budget and implementation knowledge, while central engineering teams were already committed to other priorities. Promising ideas repeatedly stalled before they could be tested.

The Approach

A structured AI use-case programme was created to break that cycle. The objective was not to build a permanent central development factory, but to provide enough support to move selected ideas into working solutions, create visible examples and build confidence across the organisation.

The programme was aligned with an existing cost-reduction initiative and developed together with Finance. Evaluation was intentionally kept simple: each proposal needed a clear one-page business case with enough quantitative and qualitative information to understand the problem, the expected improvement and who would take responsibility.

· Around 20 initial ideas were reviewed and five were selected to move forward.

· Use cases were prioritised where value could be understood, data was available and the solution could later be owned by the business rather than creating a permanent dependency on central engineering.

· Microsoft Copilot Studio and Azure AI capabilities were used within the organisation’s existing enterprise environment and security controls.

· Different external partners and funding routes were used depending on the use case, including vendor-supported and internally funded delivery.

· Human-in-the-loop principles were applied wherever AI generated recommendations or draft content.

Examples of Use Cases

Selected projects covered very different business needs, demonstrating that the model could work beyond a single function.

· An HR assistant providing location-specific guidance on topics such as leave, sickness policies and working arrangements.

· Automation of incoming handwritten operational records, including information relating to storage conditions and checks.

· A knowledge solution supporting the preparation of draft responses to questions from external authorities while retaining human review and approval.

Governance by Design

Governance was embedded from the beginning rather than added after development. Solutions were kept inside already established enterprise platforms and security environments, reducing additional technical complexity.

Human review remained central. AI-generated outputs were designed to support employees, not to trigger external actions or decisions automatically. This allowed the organisation to experiment while maintaining appropriate accountability in a regulated environment.

The Outcome

Five selected ideas were turned into working solutions. More importantly, the programme demonstrated that enterprise AI use cases could be built successfully within the organisation’s existing environment.

It also created a repeatable way to move from business interest to execution: assess value, select viable ideas, provide targeted support, prove what works and use the results as lighthouse examples. The intention was always temporary enablement - creating enough knowledge and confidence for the wider organisation to take increasing ownership over time.

Capabilities

AI Use Case Strategy · Business Case Development · AI Governance · Copilot Studio · Azure AI · Vendor Management · Business-Led Innovation