New knowledge is being created rapidly due to advanced technology and increased interactions. It arises in various settings like meetings and events. Knowledge comes in different formats and can be stored in various workspaces. It has value that can be enhanced through reliable sources and offers a competitive advantage.
Current landscape
Knowledge creators are still practicing classical frameworks and processes to create new knowledge. This new knowledge comes in product documentation, user manuals, standard operating procedures, and so on within organizational settings. Knowledge creators play a support role as enablers so that organizations’ customers benefit by accomplishing tasks using the documentation.
Knowledge creation teams are often viewed as costs rather than revenue drivers. Even though a few frameworks and metrics are available to quantify the value of the knowledge creator’s team, it is harder to associate the direct evidence of activities accomplished by documentation with business outcomes metrics. At scale, some of the business value metrics justify the investment into knowledge management practices, yet many organizations are not making the right move.
The value stream of the knowledge creators ends once the customer finds a relevant knowledge base article. However, the customer value stream begins from this stage. The real outcome that the knowledge creators’ team should chase is
How does documentation help to accomplish a task?
How does documentation help customers to self-serve?
The data to calculate the above metrics needs sophisticated engineering effort to collect and curate the required data. The main drivers hindering technology adoption are hallucinations in GenAI responses and GenAI’s inability to provide a reliable approach to curating information. More importantly, technical writers are resilient to change, and legacy product vendors are slow to introduce GenAI capabilities inside their products. Technical writers undertake many activities that can be automated without much manual effort. Modern-day customers need to access information quickly and more importantly ability to accomplish tasks using the discovered knowledge even quicker! Customers are getting familiar with using ChatGPT-like interface and prefer conversational design in many products.
Drivers of change
The are three forces at play in the current market landscape that drive change in the knowledge base domain
Technology driver: Automation and intelligence will be abundant across different industries, and the cost of intelligence will go down dramatically. Given the advancements in GenAI technology, new tools are available to solve many business problems in newer ways. Knowledge Management practices are getting disrupted as old practices are either being automated with intelligence or made obsolete.
Customer behavior: The shift in customer behavior is disrupting User Experience (UX) of how knowledge will be consumed and used to produce business outcomes. Customers wish to accomplish complex tasks quickly with human-in-the-loop. Also, they want to complete simple tasks more autonomously utilizing a reliable knowledge base as the source.
Knowledge creation patterns: The way new knowledge is being created is also facing disruption as businesses are trying to reduce value lead time. The quicker the value is realized, the quicker they can capture and monetize the value. This fundamental rule is now becoming a mantra from knowledge creators. The GenAI will accelerate knowledge creation from multiple sources and many practices in knowledge management will be taken over by the GenAI tool.
Anyone who creates new knowledge and uses knowledge will want a new way of better knowledge creation process and utility of the new knowledge respectively.
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