Introduction to conversational learning
It’s a simple and key question for every employee as they interact with your organization and the public. Typically, after completing their initial on-boarding training, everyone who has a job question simply asks their peers or manager. But as every job becomes more complex, “skill creep” sets in requiring less people to do more with less time to do it and learn the skills they need. Asking questions is a good thing but to a point. No one wants to appear to ask too many questions for fear of looking like they don’t know what they’re doing. Or worse and more commonly their peer or manager being asked may not know the answer or provide the wrong one.
The new normal today – “Skill creep”
Over the years, jobs in most industries, even those considered unchanging and static in function became more complex, competitive and require more skills to be successful. Think of a restaurant server for many decades only needing to know the same wait staff etiquette of generations past as well as that night’s menu to be great at their job. Today they can’t only rely on those age old skills but need familiarity with the restaurants point of sale system, knowledge of using tablet computers, complete knowledge of the menu in terms of allergies and procedures to answer all customer questions for both the restaurant’s sake of avoiding legal action and their own career. This same skill creep is happening in most every job and industry.
Conversational AI Chatbots: The Rise In Conversational Learning?
According to Forrester, Chatbots for IT Operations and used by all are on the rise. This has spread to Finance, HR and even learning. Companies like ServiceNow have connectors to learning systems so that their personalized chatbot can remind employees its time to take specific training. AI Chatbots combined with advanced RPU (Robotic Process Automation) capabilities from companies like Moveworks or Socrates have taken this further by enabling personalized chatbots that are able to understand the intent of employees questions or requests as a chat message or email and determine the context, intent and their meaning. Knowing employee location and status these systems can answer questions, pull out snippets of text or engage in HR tasks and operations employees would have to otherwise conduct with an HR staff member.
In the past, employees could more easily “tap the shoulder” of a colleague, manager or friend if they had a job or training question. That’s changing. Employees having to deal with Covid stationed at home can’t meet at the water cooler and feel more pressure than ever to limit phone calls for answers. This has pushed chat interactions as the preferred way to instantly get answers to their questions.
LXPs which have of late focused on delivering the right learning and other content to employees are great at letting employees get credit and earn credentials for learning any number of skills. The most advanced also help companies develop a talent marketplace to know who based on newly acquired training credentials is ready to move to another role. Great examples are Degreed and LinkedIn Learning.
Since 2017, Google and many other natural language understanding (NLU) vendors have achieved a 95% rate of understanding for spoken and written language compared to a human. This gave rise to text and voice enabled chatbots to understand what you say or write. This is matched to their capabilities for understanding and indexing any text, document or video, comprehending every second of video and every sentence of text. Paired together, asking a chatbot a question gives employees not only the right answer pulled from existing documents, videos or courses the chatbot can start to hold a conversation to further refine the answer provided. Just look at what Google Lambda is doing for interrogative conversation. Though certainly not a replacement to LMSs or LXPs, companies like ServiceNow, Microsoft and many others will undoubtedly use these as a great interface for employees in realtime to get the answers and content they need to do their jobs better all through a chat or voice interface.
Knowledge management and enterprise search systems like Getguru, Talla, Bloomfire, Coveo and most recently Moveworks have come a long way in understanding the relationship of content to all other content. Auto tagging and AI will take much of the manual curation out of organizing a company’s content. Their focus is on curating in relation to all the rest of an organization’s content and the collective experience of all employees. However, this is not dynamically curated in real-time to specific employee actions and job role and that of their peers within and outside of their organization to improve answer accuracy for a specific job. They lack the high fidelity tracking of everything employees, customers or partners do to interpret a high degree of personalization. This means these systems lack the ability to predict what answers or content snippets users will find most useful at the right moment. Existing systems to date have been most successful for large Healthcare, technology company focused projects utilizing large teams to deploy and tune.
Feathercap is an AI powered answer platform that lets employees ask questions and instantly get the right answers. From SMS, WhatsApp and any web device. For more:
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