The electronic product information (ePI) marks a shift for pharmaceutical companies away from document-based processes and toward a structured, data-driven way of working. As part of the Handelsblatt special publication “Future of Healthcare,” Jessica Giardini, Authorized Officer and Head of the Pharma Division at mt-g, explained in the interview “ePI and AI Readiness: Why Pharmaceutical Companies Should Act Now” why companies should already be addressing this transition, and what role data quality and artificial intelligence play in it.
What ePI Really Means
ePI does not simply replace the printed package leaflet with a PDF version. Instead, summaries of product characteristics, patient information leaflets, and packaging texts are provided in structured, standardized, and machine-readable form.
This format makes product information faster to update, easier to integrate into other systems, and, in the long term, compatible with new applications (such as interactive, AI-supported product information).
The real shift runs deeper than the format change alone: many companies currently manage their product information on a document basis. As a result, changes to package leaflets, translations, or digital channels often have to be entered manually multiple times. With ePI, content can instead be maintained centrally and modularly, reused, and distributed automatically across different systems.
The Move Toward a Data-Driven Pharma Organization
Clinical data, production data, and master data have long been processed in structured form at many companies. Product information, however, has so far remained one of the last major document-centric areas. This changes with ePI: product information will in future be standardized, machine-readable, and reusable worldwide, becoming part of a company-wide data strategy for the first time.
Because product information touches numerous processes (from regulatory affairs and translation to sales and future AI applications), this creates considerable potential for efficiency gains. Companies that recognize their product information early on as a valuable data asset gain a genuine strategic advantage.
Artificial Intelligence: Opportunity and Risk at Once
AI can analyze large volumes of regulatory information, support translation work, run consistency checks, and help assess the impact of changes. However, AI does not solve existing data problems, it exposes them. If the underlying data is inconsistent or poorly structured, AI can adopt and, in the worst case, amplify these weaknesses.
As Jessica Giardini summarizes it: “AI readiness means far more than simply introducing an AI solution.” What matters instead is high-quality, structured data along with clear governance, traceability, and human oversight, requirements that are indispensable in the regulated pharmaceutical environment.
Data Quality as the Real Success Factor
Many pharmaceutical companies hold product data that has grown over years and is often inconsistent, shaped by local adaptations, redundant content, and differing version states. Without harmonization and structuring, the potential of AI remains largely untapped. The decisive question is therefore not which AI solution to deploy, but whether a company's own data is fit for AI use in the first place. Companies that invest early in data quality, governance, and structured content lay the foundation to benefit from both ePI and AI over the long term.
How mt-g Supports This Transition
mt-g supports pharmaceutical companies throughout the transition to ePI, from initial orientation to technical implementation. Because regulatory affairs and IT often bring different perspectives to the table, mt-g takes on a mediating role between regulatory requirements and technical possibilities. As every company's starting point differs, mt-g does not apply a one-size-fits-all approach but develops individual solutions for each organization.
Source: The full interview with Jessica Giardini is available in the Handelsblatt special publication “Future of Healthcare” via Fokus Online.
mt-g.com