When deployed in large organizations, artificial intelligence must meet specific requirements for scalability, content digitization, and data analytics. Enterprise AI is stepping up to solve this. Find out why this step is important, what challenges it poses, and how practical implementation is shaping up here.
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What is Enterprise AI?
Enterprise AI is a specific type of business software that uses sophisticated artificial intelligence to support large companies in their digital transformation. This involves automating processes that require machine learning analogous to human intelligence - for example, the reading of documents, so-called Document Understanding. Operating at this level while meeting enterprise requirements in terms of data processing and content digitization is no easy task for business IT. Enterprise AI can be seen as a technological advancement that addresses these challenges and is superior in its capabilities to many previous AI, cloud computing, and IoT concepts.
Artificial intelligence (AI) includes, among other things Machine Learning as a subset that takes a data- and algorithm-based approach, usually using human annotations to map a learning process. Through this - also Human-in-the-loop approach, AI models are continuously tested and their accuracy improved. Because processes relating to enterprise AI, such as document understanding, cannot be clearly classified in this category system of the two terms, they are used here without clear differentiation. On the one hand, the technologies use complex algorithms and statistical models to process large volumes of data; on the other hand, they also make their own decisions based on these, which imitate human behavior.
How is an enterprise AI platform deployed?
The number of successful use cases is growing rapidly and does not stop at any industry. Enterprise AI helps predictive maintenance of industrial plants, for example, for manufacturing, power generation, and oil or gas extraction. Enterprise AI is also an important tool in finance - for controlling cash flow, trading securities, and even detecting fraud. In countless sectors, supply chains can be optimized, sales maximized, and many processes automated. Generally speaking, Enterprise AI helps wherever big data analytics can improve business performance.
Among many business applications that can be called enterprise AI, Document Understanding is a particularly common process. What is meant by this is the Extraction of information from unstructured and semi-structured documents and the subsequent conversion to structured data. Machine learning helps to classify and sort by document type, to flexibly recognize different elements and to distinguish, for example, date, number, currency and address information. Irrelevant content must also be identified as such. Meanwhile, it should be possible to evaluate the accuracy at any time on the basis of the confidence level. This can be increased by using as much training data as possible and a large number of manual corrections. However, at least two different techniques are necessary so that documents can be analyzed in their entirety by machine:
Computer Vision AI
This dimension of Document Understanding AI focuses on the visual aspects of a document and recognizes geometric information in it with the help of algorithms. This applies, for example, to images, logos, the layout, but also tables. Since countless combinations of these elements are possible in principle, accurate recognition requires a particularly large amount of training data. In concrete terms, however, data such as names or addresses can also be extracted from invoices extract. For a lot of information that cannot be captured well visually, a semantic approach is additionally necessary.
Natural Language Processing (NLP)
NLP uses machine learning to capture linguistic aspects, structure and meaning of text. This involves breaking down the semantic syntax of a text into smaller units, which are then categorized so that the AI can interpret the respective context. In this way, companies are able to automate the processing of large amounts of information that cannot be captured by computer vision or other techniques. Using the example of invoice can be used to determine the type of product or service as well as prices and quantities.
In practice, a combination of such techniques is always necessary for a holistic document understanding of different document types. This often also involves classic OCR software to extend the possibilities regarding text recognition and data extraction.
Enterprise AI platform requirements
Machine learning at enterprise level must meet special requirements. The basic prerequisite is high storage and computing capacity that adapts flexibly to a potentially fluctuating supply of data. Corresponding cloud computing platforms are now available in large numbers - from Microsoft, for example. Particularly important requirements that enterprise AI must also meet concern:
When you have a large business with multiple data sources and internal and external systems, a robust data processing pipeline is even more important. This is where content digitization and document understanding functions come into play. These should be as powerful as possible and reliable in their interpretation. Above all, accuracy, the integration of third-party systems and the correct handling of exceptions are crucial. High scalability and the ability to prioritize data can optimize the process and reduce costs.
The data volume as well as the necessary speed of data acquisition in large companies and corporations are overwhelming. It is not uncommon for hundreds of petabytes or even exabytes from millions of endpoints to be analyzed in near real-time using modeling algorithms. This requires a variety of different analysis methods such as batch processing, stream processing or recursive data processing - all while maintaining the highest security standards. The basic prerequisite for this is extensive data cleansing functions that ensure high data quality at all times.
Many decisions, such as those concerning higher management and strategic financial or product planning, are still reserved for humans. In order to be able to use the results of data analysis for this purpose, versatile monitoring and supervision options are necessary that visualize processes and correlations from the database as quickly as possible. Also important for usability is a short processing time for documents, WebSSo integration, flexible annotation functions, and the possibility of manual corrections.
Enterprise Artificial Intelligence Usage
For companies, success with enterprise AI and cloud computing requires a structural approach that goes beyond deployment for individual projects. After all, the course is to be set for future business success. This is largely determined by the holistic use of machine learning and the early creation of a comprehensive database. This also requires the targeted participation of as many employees as possible in accordance with a human-in-the-loop principle: Regular human annotations help to continuously increase the accuracy, reliability and flexibility of an AI. This puts the AI in a better position to focus on the decisive data fields and make appropriate forecasts. In addition, risks are reduced, personnel efficiency is increased and costs are lowered in the long term.
Enterprise AI in 5 steps
- Set goals
At the beginning, the exact purpose of applications to be introduced should be defined and coordinated with employees who are familiar with the relevant processes. In this way, the potential of automation and its consequences for the company can be assessed.
- Check requirements
The next step is to examine whether the necessary basic requirements in terms of computing power and infrastructure are met and what changes are necessary. It should be carefully weighed up which AI solution is best suited to the goals set. Here, a Enterprise Checklist help
The AI systems must now be integrated into existing or newly developed work processes. It is important to familiarize employees with the new technology and the associated user interface at an early stage.
- Customization and optimization
This is followed by a phase of continuous review and fine-tuning. Not only does the company learn how to handle the new technology better and better, but the technology itself can also adapt to the goals set at the beginning with the help of training data.
In principle, this process never ends, but only decreases in intensity after some time. Regular annotations help the AI to adapt to ongoing changes in requirements as well, which are part of everyday life for large companies. Depending on the goals set, the HITL approach can also be followed right from the start.
Many companies are trying to get this innovation cycle rolling by developing the corresponding technologies completely in-house. What could still succeed with ERP or CRM systems, comes up against its limits when trying to develop universal IoT or AI platforms using open source software and microservices. This approach to cloud computing often proves too expensive, slow and inefficient. Alternatively, a model-driven architecture is an option so that developers do not have to deal with an endless number of data types of an entity.
Ultimately, automated document management based on effective document understanding is proving to be one of the keys to success even for large companies. When selecting the appropriate software, the individual requirements are decisive, which at the enterprise level relate in particular to computing power, data accuracy and quality. In this context, it is also important to dovetail AI and technical expertise in order to make profitable decisions in the long term and to remain economically successful.
High scalability, myriad technologies for content digitization, especially Document Understanding, complex methods for analyzing large amounts of data, flexible UI with versatile visualization methods.
Enterprise AI includes some of the most sophisticated AI technologies, such as computer vision or natural language processing.
Every large enterprise should already be thinking about implementing enterprise AI. Automated analysis of business data offers invaluable added value, without which many companies will no longer be able to compete.