Many companies want to put AI to productive use, but they quickly realize that success does not depend solely on AI technology. What matters most is whether relevant data can be found, whether processes have been understood, whether systems can be integrated, and whether it is clear what information may be used for which use case. Before AI can deliver real value, therefore, there must be transparency regarding data sources, the system landscape, process logic, responsibilities, and technical requirements.
A medium-sized company was planning to implement its first AI applications for sales, customer service, knowledge management, and document processing. The various departments had many ideas: automated analysis of customer inquiries, intelligent search within technical documents, support for creating quotes, summarization of emails, and better utilization of internal knowledge.
At the same time, it was unclear what data was actually available for these purposes. Information was scattered across ERP, CRM, DMS, SharePoint, file servers, email inboxes, Excel spreadsheets, and specialized applications. In some cases, data was unstructured, out of date, or known only to individual employees. Access rights, interfaces, and responsibilities were also not consistently defined.
The goal was to systematically prepare the company for concrete AI use: What data is available? Where is it located? What is its quality? Which use cases are realistic? Which systems need to be integrated? And what organizational prerequisites must be established?
Liquam conducted an AI Readiness Assessment to systematically evaluate the company’s internal AI capabilities. In the first step, relevant AI opportunities were identified and prioritized in collaboration with the business units. The focus was not on general trends, but on specific operational challenges: recurring customer inquiries, manual document review, time-consuming research, scattered expert knowledge, media discontinuities, and long processing times.
Subsequently, the necessary data sources were analyzed. Liquam identified which information is available in ERP, CRM, DMS, SharePoint, file systems, email inboxes, databases, or specialized applications. Among other factors, data quality, timeliness, structure, access options, interfaces, responsibilities, and legal frameworks were evaluated. Based on this, an AI-ready roadmap for the company was created.
It shows which use cases are realistic in the short term, which data sources can be used for them, what preparatory work is required, and which issues must first be addressed from an organizational or technical perspective. The result was a prioritized roadmap: ranging from quickly implementable AI pilots and necessary data and interface measures to a target vision for the company’s own AI platform.
