Document AI uses a variety of machine learning disciplines to validate data, analyze documents and help humans extract information from those documents in meaningful ways.

Document AI is a powerful tool for automating data processing tasks that used to require human intervention. It also helps businesses improve productivity and reduce costs.

1. Automated Documentation

In the world of business, documentation is a key part of nearly all operational processes. It helps businesses capture, store and organize critical data that is vital to their operations and compliance.

In order to successfully extract and consume information, however, businesses need a solution that automates the process of document processing across different forms, formats, and industries. This can be done through document AI software that automates the ingestion, auto-categorization and extraction of information into a system of record.

Traditionally, automated document processing relies on preset rules to determine the best way to extract data from each document. That’s not ideal for organizations that deal with a wide variety of document types and formats.

Unlike traditional rule-bound automation, intelligent document processing uses artificial intelligence (AI) that can react to documents in a much more flexible way. This allows AI to understand the document’s format, and then extract and transfer data from it in a much more efficient manner.

2. Personalized Documentation

Personalized documentation is the new buzz on the block and there’s no denying that. Using machine learning to mine your data and present it in a more user-friendly format will improve productivity and reduce costs for your organization. This is all the more true when you consider the fact that most organizations have employees scattered across multiple locations – some even on the other side of the globe. AI can make all of this possible. Embedding AI in your enterprise is the best way to go about it. To get the most out of your AI initiatives, you need a robust roadmap, a solid plan of action, and a good deal of trust and communication. You can find it all under one roof at SAP AI Business Services.

3. Transparency

Transparency is the ability to explain the decisions made by AI systems and how they are influenced. Knowing these things can give users more agency in their interactions with AI systems, increasing their trust and confidence.

The field of Explainable AI (XAI) has shown considerable promise in improving transparency for AI-based systems by explaining how complex models produce their outcomes. However, many XAI approaches still lack metadata and context information that help to explain how these models were developed.

One potential solution is to use provenance documentation. This enables an explanation of how data were collected, whose ownership and rights were affected, and where the steps in the data analysis process were traced.

Moreover, transparency demands that companies respond to queries and concerns from stakeholders in a timely manner, with attention to individual cases. This is particularly important in situations where AI systems are used to make decisions about individuals. The European Union’s General Data Protection Regulation, for example, gives individuals the right to request access to personal data.

4. Reusability

Using reusable AI components allows developers to quickly bring applications and systems to market without wasting time on new development. This helps keep overall costs down and delivers high levels of customer satisfaction.

Reusable AI models and engines are essential for AI solutions to be effective and scalable. However, reusing these components requires careful planning and design.

For example, a deep-learning model built to scan shelves to make inventory orders is likely to be reused many times, but it must be properly trained and analyzed for each use case. If not, it can be a waste of resources.

Data reusability is an increasingly important goal for many organizations. To truly achieve reusability, however, companies must invest in modern data management platforms that can limit fragmentation and enable the flexibility to reuse data for a variety of modes of use.