How AI Builds Complex Apps for Pharma and MedTech

Many mid-sized companies in pharma and MedTech share the same challenge: processes run on Excel, data sits in silos, and an IT project feels too expensive and too risky. AI changes the equation.

What has changed

Three years ago, a custom application required a classic IT project: requirements specification, tender, development partner, six to twelve months, significant budget. Today, functional applications are built in weeks. AI models handle parts of the development, data pipelines are configured declaratively rather than coded manually, and user interfaces can be generated from descriptions.

This lowers the barrier to entry considerably. It enables a model that was not previously viable: development on a success basis, with no upfront investment from the client.

Fraunhofer IESE describes this shift as a structural break: AI does not act as a tool that speeds up individual tasks, but as an amplifier that changes the entire development process. The prerequisite is that architecture, processes and governance are prepared for it.

Connecting internal and external data

The value of an AI app comes from combining data. Internal data comes from ERP systems, warehouse management, CRM or manually maintained files. External data provides market information, regulatory updates, tender notices or supply shortage alerts.

Technical integration uses standardised interfaces: REST APIs, database connections, file imports. For regulated environments, access rights, audit trails and data processing agreements are built in from the start. The AI model connects the sources, recognises patterns and derives recommendations for action.

The decisive step is not the technology but the process analysis that precedes it. Which decisions are made manually today? Which data exists but is not connected? Where does missing information create effort or risk? The answers to these questions determine whether an AI app delivers a measurable contribution.

Typical use cases

AI-powered apps deliver results wherever data exists but is not connected, and where decisions are made regularly on the basis of that data. Typical applications in pharma and MedTech include:

Demand planning and forecasting with automated order recommendations. Quote generation with pricing logic, discount rules and CRM integration. Invoice verification with OCR recognition and automated validation rules. Technical documentation in line with EU-MDR with versioned storage and audit trail. AI-powered patent research with automated invention disclosure.

In every case, the app handles the repetitive work. Staff focus on decisions that require judgement.

Assessment

AI makes complex apps faster, cheaper and more accessible. For companies that previously had no IT budget for custom solutions, this opens a new path. The prerequisite is a process with recurring character, measurable KPIs and available data. When these three conditions are met, an AI-powered app is realisable in weeks today.

Source: Kelbert, P. / Siebert, J. / Jedlitschka, A.: AI in Software Development: Between Productivity Boost and Trust Crisis. Fraunhofer IESE, October 2025.

The process>app offering makes this approach accessible to SMEs in pharma and MedTech.