Artificial intelligence trends for 2022

Until now, artificial intelligence and machine learning have only been dreams of the future for many companies. But in 2022, the democratization of these important technologies will advance.

In many areas, artificial intelligence and especially the subfield of machine learning (ML) have become indispensable helpers. The amount of data available for AI learning divides the AI world into two: Platform giants collect unimaginably large amounts of data and use it to feed generalized AI methods available for generalized use cases. However, companies can only use these generalized models to a limited extent in the specific context of use. For optimal adjustment, they must train the artificial intelligence with the real data, which is only available in small quantities, and with regard to the respective, usually very specific context. Which three decisive developments in AI and ML will shape the year 2022.

1. Artificial intelligence: more focus on small data and wide data

For a long time, Big Data was mostly indispensable when it came to training artificial intelligence. The problem, however, is that in practice only a few companies and developers have access to sufficient amounts of training data. As a result, much of the business community is largely excluded from the technologies of tomorrow. New trends such as Small and Wide Data are therefore just in time to make AI and ML accessible to smaller companies.

Small Data approaches aim to extract value from smaller data sets with machine learning methods optimized for them using new analysis techniques. Wide Data is about creating synergies from a wide range of different data sources and types to improve the context for AI applications. With these approaches, companies are able to leverage their own treasure trove of data effectively and profitably. A study by the market research company Gartner shows just how interesting the new approaches are: according to this study, around 70 percent of all companies will shift their focus from Big Data to Small and Wide Data by 2025.

2. Intelligent document processing on the rise

The intelligent analysis of documents enables completely new working methods, as companies can digitize and partially or completely automate processes. In this way, processes can be optimized and implemented much more efficiently. Public authorities and large companies, in particular, have vast amounts of data, and new data is added every day. Often, several employees are entrusted with filtering the relevant information from documents that are required for further processing. This takes a lot of time, and the human factor makes for comparatively high susceptibility to errors. Intelligent Document Processing (IDP), i.e. the use of AI-based software for processing documents, is becoming increasingly important and at the same time enables the automation of workflows.

With IDP, companies and government agencies can automate their front- and back-office processes. In particular, application reviews, order acceptance, and updating customer and payment data are prominent application areas for this technology. In addition, IDP software helps with regulatory compliance or product tracking via supply chain systems in retail. Ultimately, the application areas include all text-based work processes.

3. Advances in conversational AI.

Anthropomorphism, the humanization of technology, has always been a major theme in the field of artificial intelligence. At the latest with Siri on the iPhone and Alexa on the smart TV, this phenomenon has become established in everyday life. Experts call these smart, AI-based voice assistants and other dialog systems conversational AI. They will become even more important in 2022.

This technology is a real asset, especially in customer service. In order for chatbots and question answering systems as virtual assistants to be of real help to customers and thus also to companies, there are a number of challenges to be overcome. The AI must correctly interpret, “understand,” and provide answers to customer queries, as well as rely on a human expert as infrequently as possible. Natural Language Processing (NLP) is used to make this experience realistic. The better the conversational AI system works, the more customer inquiries companies can process automatically. This not only saves employee resources but also makes customers less dependent on business hours.

New methods democratize artificial intelligence

“Artificial intelligence and machine learning will increasingly reach businesses in 2022. New methods are democratizing technology and enabling more and more companies to reduce costs through automation. Customers will also benefit from this development because intelligent searches, chatbots, and voice assistants also improve the user experience,” reports Franz, CEO of IntraFind Software AG.

IntraFind Software AG is a provider of enterprise search and artificial intelligence with headquarters in Munich. With its solutions, the software company has been helping organizations of all sizes search, link, and analyze their structured and unstructured data for over 20 years. Regardless of the source in which they are stored. To do this, IntraFind uses AI and machine learning techniques as well as Natural Language Processing. With these technologies, the company not only enables relevant search and analysis results from large data sets but also the automation and digitization of document-based processes.