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The realm of accounting technology is experiencing a seismic shift as companies strive to establish their dominance amidst growing competition. The question arises: Who will emerge as the frontrunner in this bustling marketplace? The answer lies in understanding the changing landscape and adapting to its demands.

The legal industry has been inundated with an exponential increase in document volumes. Contracts, agreements, court filings, patents, intellectual property documents, legal research, compliance paperwork, and many others flood legal professionals’ desks daily.

In an environment characterized by increasing competition for low-cost loans, the pressure to automate has become a key driver. Online loan providers are under increasing pressure to streamline operations, reduce costs and offer faster solutions to borrowers.

AI systems rely very heavily on data. These are needed by the algorithms to learn, make decisions and gain additional insights. However, like any other technology, AI also harbors potential risks and dangers. Concerns are increasingly being voiced about data protection in particular.

RPA helps companies automate rule-based business processes. However, since classic RPA solutions often reach their limits, more modern technologies such as OCR and IDP become necessary. They make it possible to meaningfully expand the existing RPA system landscape and further increase the automation rate.

It is the ability to learn and improve over time that is at the heart of AI, which requires large amounts of data to train the algorithms. The question often arises whether you can train an AI without data.

Collaboration with start-ups is seen as riskier by some companies. Actually, start-ups offer a multitude of advantages and new opportunities. We list our top five points for working with a startup.

Both OCR and Deep-OCR are used to transfer the content of a document into a machine-readable format. However, the differences in approach and the results are enormous.

Almost all business processes involve documents at one point or another. To make the information actionable for automation, we need OCR, and we need it to be as good as possible.