Editor’s Note: This is part three of a three-part series. The first segment discusses how learning organizations need to better integrate learning capabilities within business operations in order to strengthen their value proposition. The second segment discusses how emerging technologies, such as Tin Can, natural language processing, learning machines and analytics can help develop Smart Learning Systems. This segment discusses integrated learning in tomorrow’s workplace.
New technologies change work environments, enabling individuals to leverage their organization’s knowledge resources when they need them. As a result, organizations must start preparing themselves so that they can engineer an environment that records workforce transactions such as, but not limited to, viewing, writing, assembling, answering and conversing, across multiple technology platforms including computers, tablets and telephones using all sorts of enterprise applications.
Recording transactions is the first step and together with analytics, allows organizations to correlate behavior and specific resources with outcomes so that they can continue to improve workplace environments by giving prominence to products, tools and strategies that are working. By today’s standards this represents a significant improvement, but by future standards it remains crude.
Through natural language processing and machine learning, business will be able to extend quantitative and qualitative analytics to optimize knowledge transfer across the entire system. Consider the following sample questions and ask yourself if today’s corporate learning environment addresses them:
- Who needs help right now?
- Who is our leading expert?
- What resources do our top sales people use and how?
- What are arguments for and against a particular point of view?
- Who has a new idea that looks promising?
- How are social networks impacting organizational performance?
- How well am I performing my role?
The technologies to answer such questions have emerged and organizations that are able to integrate and leverage them will be at a competitive advantage. Such companies are able to automate the analysis of corporate content to determine both the nature and quality of submissions when employees transact. By considering other points of associated data that include employee attributes, the context of submission, and other corporate records, companies can instantaneously extrapolate meaning from each worker transaction. The results of which can be used to prompt a system response, such as alert the employee, send specific information resources or set up a meeting with an advisor.
As an example, consider a client service representative at a future call center. Our rep is on the telephone with a customer who is making an inquiry about a promotion. The call is being analyzed by a natural language processing and learning machine. A quick assessment of records that include past cases handled by the rep, as well as training and corporate communications that the rep received, has helped our system to determine that this representative has no basis for dealing with this inquiry, and so information immediately flashes on said representative’s screen to assist. As people increasingly transact using technology, more information can be gleamed to assess expertise and provide support.
These learning technologies signal advancements in organizational learning capabilities that will ultimately enable the accreditation of informal learning similar to that of structured learning. However, unlike today’s system that certifies knowledge at completion of a program and perhaps requires recertification periodically, this system maintains an ongoing account of expertise based on an individual’s everyday performance. As such, accreditation will be transient and always reflects a person’s knowledge at the time of inquiry — A doctorate acknowledged some years earlier, may not be recognized in the future if the person owning the degree does not continue to demonstrate expertise.
Further, these technologies signal a time when organizations can start moving away from scientific management approaches adopted during an industrial era (when the only way to optimize a system performance was to put in place a large administrative overhead embodied by layers of management). Today’s technologies can be put in place to help coordinate the system, minimizing overhead and enabling workers to perform real-time. Such thinking is evident in gamification approaches that strive to set clear performance targets, provide tools that empower workers and real-time feedback systems that motivate and guide people towards success. It is also reflected in the application of complex-adaptive system engineering which establishes rules at a transactional level to enable real-time system changes to address emerging situations.
Today’s organizations are embarking on new opportunities to redefine how they achieve their targets and it is certain that human performance enablement will be an important element of business design. Learning leaders need to recognize this opportunity and start down the road of transformation early enough to secure their position in this space.