Drupal and AI: Real Success Stories from Real Projects
After the initial hype around artificial intelligence, a phase of disillusionment has spread across the technology landscape. The early fascination has now given way to critical and pragmatic questions from companies and decision-makers: What is the actual, measurable benefit of AI? How high are the implementation and operating costs? And above all: how do we ensure the security of our data in an AI-driven world?
The talk “Epic things you built with Drupal AI” by Michael "schnitzel" Schmid at DrupalCon Nara provides the decisive answer to exactly these questions. It is tangible proof that integrating AI into Drupal is no longer an abstract promise, but already a lived, value-creating reality.
The central thesis is therefore clear: the debate about Drupal and AI has left the theoretical phase behind. Here we analyze the case studies mentioned in the talk, which prove that Drupal today is the strategic platform for using AI potential safely and efficiently.
The central message: Drupal AI is ready for practical use
Michael wants to sharpen the perception of Drupal AI from an experimental concept to a production-ready, reliable solution. He shows that the technology does not exist only in test systems, but is already solving real and complex problems for well-known organizations in real projects.
His core statement is unambiguous: yes, Drupal AI can be used today. The projects presented by a wide range of companies demonstrate the breadth and maturity of the available tools.
This progress is the result of collaborative work within the Drupal community. The strategic "Drupal AI Initiative" pools resources and expertise from numerous leading companies in the Drupal community, who jointly invest time and money to advance the system’s AI capabilities. Among the projects presented in the talk were, among others, those by:
- 1xINTERNET
- annertech
- Dropsolid
- Liip
- Amazee
This initiative underscores that the further development of Drupal AI stands on a broad, collaborative foundation.
Use cases in practice: how Drupal AI transforms business processes
Increasing efficiency in content and data management
Managing digital content and data is more complex today than ever. Organizations struggle with a flood of information, must review user-generated content for quality and safety, and at the same time comply with legal requirements such as digital accessibility. The following case studies show how Drupal AI offers intelligent and highly effective solutions to precisely these challenges.
Case study 1: 1xINTERNET & World Cancer Day The problem was a high number of spam and low-quality submissions on a platform where users can share personal stories about their fight against cancer. Manual moderation was overwhelmed. The solution is an AI-supported system that automatically moderates content, detects spam, and pre-filters the quality of contributions. A “human in the loop” retains final control. This approach is particularly crucial here, as these are sensitive, personal stories from cancer patients, where a purely machine-based assessment would be inappropriate.
Case study 2: annertech & French telecommunications group A large telecommunications company faced the challenge of verifying license rights for a huge volume of image, video, and audio assets. Marketing teams were unsure which media they were legally allowed to use. The solution is an AI-supported reverse image search integrated into Drupal. This makes it possible to identify the origin of an asset and verify the associated licenses. The system achieved a reliability of 99 %, creating a clear and secure basis for content creation.
Case study 3: Southwark Council A government agency faced the problem of more than 2,000 PDFs that had originally been created for print and were therefore not accessible. Manual conversion would have been extremely time- and cost-intensive. The solution was the use of AI that fully automatically converts these PDFs into complete, accessible websites – including HTML, CSS, and JavaScript. This process is 240 times faster than the manual alternative and represents a massive leap in efficiency in meeting legal requirements.
In summary, these use cases show a clear strategic priority: relieving subject-matter experts of recurring, error-prone tasks in order to shift their focus to higher-value activities. But the technology can go even further and automate core business processes.
Automating core processes and customer interactions
The automation of recurring but business-critical processes is one of the strongest drivers for the use of AI. Here, Drupal AI can play to its strengths to reduce costs, increase efficiency, and at the same time improve customer satisfaction.
Case study 1: Qdos & DB Schenker The transport services company received a large volume of support emails that had to be read, analyzed, and forwarded to the appropriate internal teams manually. This led to long processing times. The solution is an automated email categorization system that analyzes incoming messages using AI, summarizes them, and directly suggests them to the responsible departments. Processing time for complex exceptional cases was reduced from weeks to minutes. In addition, the system generates automated reports that analyze past industry events and provide strategic recommendations for future participation.
Case study 2: FreelyGive & ESP Group Particularly insightful is the case of the ESP Group, implemented by FreelyGive. Here, practical suitability is shown directly from the core of the Drupal community. A railway company was confronted with up to 20,000 compensation claims per hour in the event of train delays. The solution is a system implemented with Drupal AI that automatically verifies 90 % of the claims. This reduced overall processing time by 80 % and led to a demonstrably higher level of customer satisfaction.
Case study 3: Dropsolid & pidpa A water utility received numerous customer inquiries on topics whose answers were already available on the website. To relieve the customer service team, an AI bot was implemented. It analyzes an incoming email request, searches the website for suitable information, and suggests a response to the service employee. The system acts as a supporting tool for the internal team and led to a 15 % increase in efficiency in processing service requests.
Case study 4: Liip & Canton of Basel-Landschaft Citizens had difficulty quickly finding the right information on an information-overloaded government website. The solution was the implementation of a simple chatbot that understands natural-language queries (for example, “How do I register my child for school?”). In its first year alone, the bot successfully answered 50,000 citizen inquiries and received positive feedback from users.
These examples impressively show how Drupal AI not only unlocks internal efficiency potential, but also directly and positively influences external customer satisfaction by accelerating interactions. The following examples show how Drupal with AI even enables completely new use cases.
Drupal as a strategic tool for complex technical challenges
AI fundamentally expands the role of Drupal: from a pure content management system to a flexible and powerful application framework. Drupal becomes the central platform that controls complex, data-intensive tasks in the background and thus solves problems that go far beyond classic website management.
Case study 1: Factorial & Boehringer Ingelheim A global pharmaceutical company faced the Herculean task of consolidating 45 country websites into a single Drupal instance. One of the biggest challenges was identifying similar pages (for example, contact forms) across all old websites for migration. Instead of a manual process, AI was used to analyze all 45 websites and automatically identify semantically similar pages. This meant that a process originally estimated to require 80-95 % manual migration effort was, contrary to expectations, fully automated - an immense saving of weeks of expert work.
Case study 2: amazee.ai For the development of a privacy-compliant, open-source clone of ChatGPT, a robust backend for user management and APIs was required. Instead of developing this from scratch, the team chose Drupal. Drupal operates completely invisibly in the background here and provides its proven core competencies for a completely new type of application. This shows how Drupal’s architecture can serve as a foundation for innovative AI products without having to rewrite the code.
These cases mark a paradigm shift: here, Drupal no longer acts only as a CMS, but as a flexible backend framework that provides its core strengths for completely new, AI-driven application architectures. The examples shown are an invitation to the entire community to get involved.
Conclusion: Drupal is more than ready for the AI age
The success stories presented provide impressive evidence: Drupal and artificial intelligence are no longer a distant future vision, but a field-tested and value-creating combination. The debate has shifted from the question “Whether?” to the question “How?”. The examples show that Drupal is not only compatible with AI technologies, but provides a robust, flexible, and secure foundation for implementing AI solutions pragmatically and profitably - from content moderation and the automation of business processes to the backend for completely new AI applications.
For organizations seeking a strategic advantage in the AI age, Drupal proves to be the intelligent choice. It combines technological innovation with the core values of open source: data sovereignty, cost efficiency, and the invaluable strength of a collaborative, global community. Drupal is not only ready for the future - it is actively shaping it.
