Artificial intelligence for efficient business processes
In the wake of digitalization, the speed at which the framework conditions for companies are changing is accelerating dramatically. Innovative products and services are flooding the market, frequently being replaced just as quickly by “better” ones. New competitors are turning traditional industries on their heads and pitting themselves in the race against more established companies.
To remain competitive going forward, it is crucial to ensure that internal processes run as efficiently as possible. In the context of digitalization, however, the requirements for business processes are changing, too. Some traditional workflows are being rendered obsolete by end-to-end automation, while others are being elevated to a new standard of quality. Processes not only have to be cost-effective and efficient; above all, they need to be agile, flexible, customized, and forward-looking.
Continuously questioning established business processes, as well as a company’s capacity for transformation, are thus becoming increasingly decisive factors for success. This calls for AI-based technologies that make it possible to locate, interpret, and process information efficiently so that tangible answers to specific questions can be gained from it. This enables businesses to continually adapt their business processes and procedures to fluctuating market demands and changing underlying conditions.
Comprehensive overview thanks to insight engines
One option for implementing intelligent and digital business processes is provided by so-called insight engines. Insight engines are intelligent AI-based search solutions that are designed to find, analyze, link, and extract information from a variety of data sources. While from the user’s perspective they are virtually indistinguishable from traditional search engines, their functionality exceeds conventional solutions several times over thanks to the power of artificial intelligence.
When a search is initiated, the system queries all of the various data sources, including specialist applications, the intranet, the cloud, CRM systems, databases, and archives, and semantically analyzes the corresponding content.
All the views and information generated by the search query (“connecting the dots”) are augmented with conclusive supplementary knowledge and made available to the users in a clearly structured format in accordance with their individual access rights. This provides users with a comprehensive and personalized overview of the topic in question or the digital twin.
A digital twin is a virtual representation of a real object. In other words, physical products, systems, and even processes are reproduced as counterparts in digital form. With the help of AI and machine learning, entire processes can be simulated to gain new insights. Which is why the concept of digital twins is gaining momentum well beyond the industrial sector.
The resulting 360-degree view makes it possible to identify complex correlations quickly, to display them in a clearly structured way, to simulate changes, and thus to evaluate potential impact and responses in advance. Using insight engines provides the optimal foundation for making business-critical decisions and paves the way for transforming business processes.
Understanding requests – the challenge of natural language
In order to fully leverage the extracted facts with all their correlations, the technology needs to adequately understand and correctly interpret the user’s requests. Computers can handle formal languages quite efficiently. This involves languages like XML, SQL, and PHP, which transfer information without ambiguity. Natural, human language, however, is a much different matter. Human language is complex, unstructured, frequently ambiguous, and continually evolving.
Yet advanced technologies are already able to examine and recognize patterns and structures in data. This ensures that users can submit their search queries in natural language, that the solution understands them correctly, and that the user’s intention is correctly identified, processed, and responded to accordingly. Intelligent technologies, so-called natural language processing (NLP) and natural language understanding (NLU), work in the background to accomplish this.
Insight engines, on the other hand, make use of even further methods of AI, in particular deep learning. As a result, the insight engine is constantly expanding its knowledge – it learns from past experience. User behavior, interactions with search hits, saved searches, and similar factors provide the foundation. This knowledge enables insight engines to classify the relevance of the information for individual users or for entire groups. For subsequent searches, the solution knows exactly which results are more relevant and displays them proactively according to priority.
Ready for the future
Efficient processes are among the most essential cornerstones for a company’s competitiveness. Inflexible and rigidly defined processes fail to provide the necessary agility needed to remain competitive in a rapidly changing market.
Today’s businesses can harness new technologies to generate key competitive advantages and ultimately transform business processes and workflows to ensure that they remain business-relevant in the future.