Traditional most form of stacking all relevant information together is to store in binders. All physical form of information was earlier created and collaged in binders that were labelled for categorization. Vast information was stockpiled in rooms full of binders covering pigeon shelves.
With advancement of technology and introduction of first wave of computerization, documents found a place in soft copies. All information relevant to a particular topic was collected at one place in form of various files. These files were digitally stacked in folders for classification.
Document management system was a great step ahead of folders and floppies when organizations started to realize the archive value of information. Communication and globalization were both evolving thus making inter departmental circulation of information an inevitable pillar for basic operations.
Content management system was a giant leap in evolution of knowledge as it was now that organizations realized the projection and importance of data. Data had to procured and analysed but above all stored safely. CMS was based on fact that content is not just document but a collection of data, information, analysis, reports, and much more.
Knowledge management system became an important element of corporate interaction thus stretching the horizons of information beyond document and data. KMS facilitates cloud storage, and circulation of knowledge among internal, external, and intermediary parties to communication.
Arrival of ‘big data’ concept has completely changed traditional practices of creation, curation, and circulation of knowledge. Knowledge base must now be crisp, concise, and correct. Digital aging of users has made it mandatory for organizations to have 24×7 connectivity providing seamless and personalized experience in form of knowledge distribution.
Intent based knowledge delivery connects all portals of communication on an agent’s system. It is thus easy for an agent to remain on same screen, same platform, yet receive all tickets raised, search information related to them and solve the queries respectively.
Zero touch is a concept of eliminating any form of human contact with the customers. Intense training through Machine Learning is required to make AI support capable of reading user complaints and solving the query by either knowledge dissemination or by direct guidance.
Operations and quality supervisors receive notifications and feedbacks from various sources. They can be in form of comments, feedback form reply, or as posts over social media. AI knowledge base gathers and processes them all into deriving a unified and actionable conclusion.
With SDN (Software Defined Networking), internal and external communication of knowledge is facilitated but intent base is required to train your machine into understanding of the same. AI thus recognizes interests of organization and works in accordance with the same.
Contact centers are essentially required to move ahead with time. Using AI connectivity and knowledge base, full fledged virtual contact centers can easily be built using knowledge base and communication software to keep agents updated and connected.
AI feature is efficient enough to convert the dialogues exchanged on call into captions thereby quickly scanning through it and sorting out questions put up and answers to the same using in depth search through knowledge base thus speeding up call resolution process.
Agents at contact centers follow a patterned approach to communicate with the callers. Bots are trained to use similar tones and read problem through query posted. Chatbots resolve customer queries using relevant knowledge articles by identifying keywords, thus deflecting tickets.
With operations growing and expanding over digital platforms, data too is increasing by volume and size. AI knowledge base can easily and intelligently use the filters it is programmed with to sort, analyse, and classify data thus managing big data under supervision with accuracy.
With the help of AI, it is possible for self service platforms to keep solutions ready by anticipating queries and categorizing them. This helps in providing customers with appropriate solutions as soon as problem arises in real time helping to close complaints thus lowering down overall time.
AI sorts query as it is typed in using keyword with a Google like elastic search. Questions so formed are then reconciled against the probable answers organization’s knowledge base has. A list of all potential queries is made based on the number of hits.
Self service portals help the users and website/app visitors to know exactly which section do they have to visit and how to implement solutions so achieved. It reduces overall time taken and complexities faced by customers thereby building trust and confidence in quality.
With implementation of AI knowledge base, AI & ML trained bots, and customer service automation, burden of number of tickets, consistency of information, speed, and quality can all be delegated. This reduces manpower requirement generating financial gains.
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