AI in Reporting
Generative AI based on so-called Large Language Models has been evolving rapidly. AI is now capable of capturing and processing linguistically coded information, which makes automation of time-consuming tasks, such as text creation, possible. This technical development holds immense potential to increase efficiency and allow effective quality assurance in all areas of reporting.
Automated reporting is either based on internal documents and RAG-technologies, or else on turning simple notes into a coherent report. To achieve this, the process must be customized, and AI must be trained regarding content, structure, and language. Process automation simplifies reporting in that it unifies reports, checks them for completeness, and complies with privacy protection laws. In addition, it becomes possible to further process the resulting reports, e.g. by translating them automatically into different languages.
Keynote «AI in Reporting – Possibilities and Limits»
Reporting requires a substantial amount of resources, so AI-powered process automation promises to alleviate the effort. AI-powered process automation, however, isn’t a merely technical change but indeed a transformation of the entire organization. In her keynote, Danae Perez shows where and how AI is already in use, what opportunities it brings, and which limitations and dangers persist.
Implementation of AI-based Process Automation
Implementation begins with defining future processes and expected results. Afterwards, the model is developed gradually with regular performance tests until it meets the institution’s demands and gets greenlighted to go live. Afterwards, performance must be invigilated to correct faulty processes, if needed.
User Training
Tailored training sessions help employees feed the model with accurate input and check the output regarding content and style. In-house workshops are the ideal format for the staff to learn what to look for and develop an understanding of the best wording. This allows their expertise to go into their reports and ensure their competence grows alongside process automation with lasting effects.
Standardization of Data and Documents
Successful AI strategy requires smart standardized data management and processes. We support standardization from an external perspective by identifying processes and data categories, conciliating them, and aligning them with organizational practice and goals.
Quality Assurance
Both technology and its use require regular checks to ensure the resulting reports meet quality standards. Quality assurance evaluates the performance of the model after its implementation and revises whether the resulting reports meet requirements. At the same time, user experience is evaluated. If needed, we propose specific measures to remedy the shortcomings.
