Knowledge remains a critical component when it comes to standing out from the competition. Collaboration between people and machines offers businesses massive potential for their future knowledge management. However, technologies today are making traditional notions of knowledge management obsolete. Organizations need to rethink their handling of data and processes and encourage human-machine interaction to benefit from these changes.
Master & Use the Flood of Data
In the digital age, companies generate and store a veritable flood of data. If you trust the forecasts from Statista, a provider of market and consumer data, data volume per year will amount to at least 175 zettabytes by 2025. Most enterprises are already using their information today, but hardly any are exploiting the full potential.
According to an international survey, although data volumes continue to increase, 55% of corporate data remains unused.
With increasing complexity and mountains of data, it is becoming much more challenging to keep track. Intelligent knowledge management systems can index, analyze, and link existing information across the board and make it available in an appropriately prepared format.
Well-known providers use terms like “Enterprise Search,” “Insight Engine,” “Cognitive Search,” or “AI Search.” Despite the difference in words, they all pursue the same goal: to provide the user with the information needed at the right time. To do this, they use different technologies. These technologies include everything from artificial intelligence (AI), like machine and deep learning, to innovative approaches in speech recognition like natural language processing (NLP), natural language understanding (NLU), and the semantic processing of content that enables human-machine interaction.
But this is only the beginning of the development. The following trends show where the journey may lead.
1. Understand and Meet the Needs of the User
Company information currently resides in numerous different data sources. Much of this data stands isolated in individual applications within individual departments. An essential prerequisite for forward-looking knowledge management is to break down these data silos and intelligently link the available information. Intelligent enterprise search can provide users with relevant information in the appropriate context at the push of a button. The ability to securely query, analyze, and semantically understand all distributed data via connectors can link information and give businesses insights in a graphical format. Users can obtain concrete answers to questions such as “What do we know about a customer?” or “What information is available about a component?” without building up an additional data source.
In this context, the personalization of information is critical in fulfilling the user’s needs. The following factors, among others, are analyzed: the role of the activity, previous actions taken, specific search behavior, and even the emotions that users associate with information. The goal is to personalize the information relevance and build an intelligent assistance system where users can interact naturally to get the exact intelligence they need.
2. People Communicate in Dialogue
Processing unstructured information requires understanding the content. This may seem simple, but building meaningful and purposeful dialogue is one of the most challenging tasks for AI, primarily due to the unparalleled complexity of human language. Dialect, irony, ambiguity – all difficult to capture with rules alone.
Applications with rigid, predefined decision paths are less suitable for complex topics and dialogues. In contrast, AI-based applications create a human-like conversational interaction, i.e., imitate a conversation. Innovative approaches to speech recognition like NLP, NLU, and the semantic processing of content in combination with machine learning have proven highly effective in recent years.
3. AI with Reduced Effort and Great Results: Weak Supervision
AI has had one massive drawback to date: training an intelligent system requires enormous data sets and time-consuming manual work. Weak supervision shows a different way. The innovative method enables systems to learn independently from data sets already available in the company, which don’t have to be prepared diligently in advance. Performance is optimized through the use, reducing the training time exponentially.
At the same time, “Explainable AI” (XAI) is increasingly coming into focus. XAI is about making AI-based decisions understandable and transparent. XAI is still only possible to a limited extent with highly complex models, so the future journey will likely head towards simplification.
4. 360-Degree Holistic Views
In the past, it was necessary to manually compile data and information for specific tasks from different data sources. Now, the holistic preparation of information is standard.
However, results must be individually adapted and presented to the different requirements of employees, positions, and teams. Industry-leading solutions, therefore, already offer the option of simple fine-tuning. Employees can manually make adjustments to the various requirement profiles without going to IT or the solution provider. For this purpose, modules such as layouts, search fields, results, navigation elements, and filters can be combined with zero programming knowledge.
5. Flexibility in Data Preparation
Organizations have many different data sources in use – from on-premises to SaaS environments to cloud services. To enable high-quality information processing, providers often advise their customers to move data to the cloud. This is not always easy, especially in areas where sensitive information or highly specialized applications are available.
Solutions are becoming established whose functions are available both on-premises and in the cloud. Data from cloud applications such as Salesforce, ServiceNow, Office 365, etc., can be processed in the cloud and those from on-premise applications in the corresponding internal data centers.
6. Hyperautomation
Companies must optimize traditional business processes in the direction of flexibility and agility. In this context, automation is often pushed forward. The motivation behind this is not to reduce employees but to free them from routine tasks so that more time is available for other business needs. This development helps companies discover new business areas. However, hyperautomation only works if companies have a solid knowledge management foundation.
Through the use of technologies from AI, it is already possible to modernize processes. While the machine takes over tedious tasks, humans can use their strengths (social interaction, creativity, intuition) in more targeted ways.
