AI and Strategic Decisions: From Automation to Decision-Making

This shift is particularly significant for document-centric systems such as archives and document management systems, where AI unlocks entirely new possibilities. Beyond automating processes, AI empowers companies to make smarter strategic decisions by generating data-driven predictions and recommendations. In this blog post, we explore how AI enhances archive systems and why an archive is the ideal foundation for this innovative technology.

Conventional IT systems in digital transformation are built on rule-based operations using if-then logic. The successes of digitalization in recent years have shown that this logic can already automate many processes and make them more intelligent and efficient. However, this logic does have its limits: it can only solve tasks that fall within its specific, predefined parameters. On the other hand, AI-driven processes can handle much more complex tasks. They are designed to incorporate new information into their decision-making. This is possible because AI does not make decisions based on a predefined logic but rather on training with real data. Simply put, AI-based systems can solve more complex problems.

The Archive: From Storage to Data Hub

Archives were once physical spaces within companies where documents were filed and stored. However, with digitalization, their role has evolved significantly. Modern archiving systems now serve as highly functional digital data repositories, offering secure storage and user-friendly management. With our years of expertise in archiving, we understand that businesses see this functionality as the foundation of digital archiving solutions. In practice, these systems primarily automate the digital storage of documents while also providing intuitive management tools that allow users to quickly access individual files when needed. 

However, archives can be much more than just storage. They hold vast amounts of valuable data, which is why we refer to the archive as the heart of data. With the power of artificial intelligence, these hidden insights can be uncovered and put to use. One key advantage is the sheer volume of information stored in archives – both structured and unstructured – often spanning long periods. This makes them an ideal foundation for identifying patterns, analyzing trends, and generating forecasts. In addition, the centralized nature of an archive system, with its uniform data structure, enhances AI models by providing easy access to diverse datasets, ultimately leading to more accurate and meaningful results. 

How Archives Are Transformed by AI

Using artificial intelligence in archives is a complex process that takes place on various interconnected levels. Some of the associated processes are already in use today, while others are under development or being planned. 

Automation of data organization

The first essential step in integrating AI into an archive is automating data capture. At first glance, this may seem like a purely functional task, but it is fundamentally important for the effective use of AI. AI algorithms rely on high-quality data to generate reliable results. Before AI can be seamlessly integrated into business processes, the data foundation for training AI models must be as robust as possible. AI also becomes indispensable for data maintenance, as it can automatically recognize, classify, and analyze documents. 

AI models are already being used for text recognition (OCR) today. AI significantly improves the quality of text recognition in scanned paper documents, providing companies with a much higher-quality data foundation. In this data generation process, AI also facilitates the extraction of metadata from both analog and digital documents by automatically reading information such as date, sender, or contract details. It can then categorize documents based on content, identifying them as invoices, contracts, or protocols. 

Analysis and linking of data

For an archive to be more than just a data repository, the stored data must be analyzed in a second step. With the help of artificial intelligence, it becomes possible to conduct comprehensive, real-time data analysis with the aim of identifying overarching patterns within individual documents. To achieve this, document data is examined for recurring structures. This approach enables you to recognize specific payment cycles or identify irregularities. 

Furthermore, AI can link this data based on logical criteria. This allows AI to merge various pieces of information from different documents and sources to create a comprehensive picture of a business transaction – for example, by linking electronic invoices with the corresponding supply contracts. 

Preparation of forecasts and recommendations

An AI-powered archiving system has even more potential: it can go beyond mere analysis and generate forecasts about future events based on collected data. For example, an AI system could analyze business data to provide insights into potential supply chain bottlenecks and when they might occur. Another scenario for AI’s analytical capabilities is in risk assessment – such as detecting fraud, identifying financial risks in contract clauses, or predicting potential payment defaults from business partners. AI will also become indispensable in monitoring legal and compliance regulations. Ultimately, AI can even be used to suggest recommended actions for decision-makers. For example, evaluating archive data could give rise to placing greater emphasis on certain aspects when renegotiating a contract or specifically optimizing a part of a business process. 

The Takeaway: AI-Powered Archives Will Be a Key Strategic Advantage for Companies 

The integration of AI into document-centric systems offers companies numerous benefits that go beyond simply boosting efficiency. AI automates data generation processes, improves data quality, and creates the basis for fast data-driven decisions in companies. Some of the use cases described are admittedly still under development – and it will be some time before they are seamlessly integrated into actual business processes. 

What is certain, however, is that the day will come when companies that use AI for strategic forecasting and analysis will gain a real competitive advantage from doing so. This could be because they are more agile and can respond better to market changes, or because AI enables them to identify risks earlier, implement compliance requirements more reliably, and meet regulatory requirements with fewer resources.