Why reporting often fails at the interpretation stage
In recent years, many companies have made substantial investments in data platforms, analytics solutions and business intelligence. The good news is that transparency is no longer a technical challenge in many areas. Key performance indicators are readily available, dashboards can be updated almost in real time, and information from a wide variety of sources can be consolidated.
However, the success of this development has given rise to a new problem. The more data that is available, the harder it becomes to maintain an overview. Many managers do not start their working day with a dashboard, but with meetings, emails, consultations and operational decisions. Whilst the relevant information is available, there is often not enough time to analyse it, evaluate it and translate it into concrete actions.
This is precisely where the real challenge of modern reporting processes lies. Key performance indicators provide transparency, but they do not make decisions. It is only through proper interpretation that figures become relevant insights and insights become concrete courses of action. In many companies, this interpretation is still carried out manually. Reports are analysed, anomalies assessed, comments formulated and results prepared for different target groups. After all, what is relevant to financial control is not necessarily relevant to a manager’s decision-making.
At the same time, complexity continues to increase. Data comes from an ever-growing number of systems, business models are changing more rapidly, and economic conditions are becoming more volatile. This increases the need for clear guidance. The key task today is therefore no longer simply to provide information. It is far more important to deliver the right insights to the right people at the right time.
Why AI can fundamentally change the way information is distributed
The discussion about generative AI often focuses on chatbots or intelligent search functions. Yet reporting, in particular, holds further, often underestimated potential.
AI can not only find information, but also summarise, structure and formulate it in a way that is easy to understand. Furthermore, it is capable of analysing large volumes of data, identifying anomalies, contextualising trends and deriving concrete recommendations for action from them. This opens up the possibility of expanding reporting to include additional levels: from the mere presentation of key figures to automated interpretation, decision support and even recommendations for action.
Instead of having to read through extensive tables and identify trends themselves, users receive a clear summary of the key findings. Anomalies are highlighted and relevant information is prioritised. Reporting thus no longer merely answers the question of what has happened, but also helps users understand why a trend is relevant and where action is required.
Insights can be automatically tailored to specific target groups and made available to all relevant decision-makers. This allows for significant scaling of processes that previously often had to be commented on, coordinated and distributed manually. Technical analyses not only reach individual recipient groups but can also be made available consistently and with minimal effort throughout the entire organisation.
This shifts the focus. Less time is spent analysing figures, leaving more time for the actual task at hand: making decisions. AI does not replace the specialist expertise of the controlling or management functions, but rather enhances their impact. It helps to make relevant knowledge available more quickly, provide guidance and ensure that important insights reach the very places where decisions are made based on them.
How a reporting problem gave rise to the idea for Kompass
At Materna, too, the question arose as to how the flow of information to managers could be improved. The Controlling department had a wealth of management information at its disposal. At the same time, it became apparent that interpreting and communicating this information involved a considerable amount of effort.
This challenge gave rise to Kompass – the commentary and performance assistant.
The idea behind it is as simple as it is effective. Rather than simply providing key figures, they are automatically analysed, annotated and translated into plain language. This is based on structured data from the existing reporting and BI environment. On this basis, the solution generates brief explanations and recommendations for action tailored to defined target groups.
As a result, managers receive not just figures, but a direct assessment of the relevant developments. Information reaches those who need it without the need to analyse extensive reports first.
What we have learnt during development
However, the introduction of AI into reporting is not merely a technological challenge. The business context is of paramount importance.
A key insight from the project is that good results do not stem solely from powerful language models. Equally important are high-quality data, clear rules for interpretation and transparent processes. After all, the quality of the results can only be as good as the quality of the underlying information.
This aspect plays a particularly crucial role in reporting. Users must be able to trust that the information provided is robust and factually correct. This trust is not generated by the AI itself, but by a reliable database and clearly defined business guidelines.
That is why Kompass does not replace subject-matter expertise. Rather, the solution helps to make existing knowledge available more quickly and consistently. Subject-matter responsibility remains with the experts who understand and can contextualise the data, key performance indicators and interrelationships within the organisation.
This also changes the role of AI. It does not become a decision-maker, but rather an assistant. It takes on routine tasks of analysis and data preparation, whilst responsibility for evaluation and the derivation of measures remains with the specialist departments and managers. This creates a synergy between human expertise, reliable data and AI-supported assistance, which can sustainably improve the quality and scope of reporting.
The future belongs to context-aware systems
Kompass is an example of a development that goes far beyond traditional reporting. In future, companies will increasingly rely on systems that not only provide information but also present it in the right context.
The added value will then no longer stem solely from transparency, but from providing guidance. Particularly at a time of growing data volumes, the ability to prioritise relevant information and communicate it clearly is becoming a decisive competitive factor.
The next stage in the evolution of information distribution therefore does not lie in even more dashboards or additional key performance indicators. It lies in the intelligent combination of data, specialist knowledge and artificial intelligence.
Conclusion
Modern companies have plenty of data. What is often lacking is an efficient way to quickly derive the right insights from it. The greatest challenge has long since ceased to be access to information, but rather its interpretation.
With Kompass, Materna has demonstrated how AI can provide support precisely at this point. The solution turns key performance indicators into clear guidance and transforms reporting into an active contribution to decision-making. This highlights the direction in which information distribution could evolve in the coming years: away from the mere provision of information, towards genuine decision support.
