The deployment of Bots and the ability to extract valuable insights with the help of AI tools have broadened the horizons of knowledge capture, storage and retrieval capabilities
The discipline of knowledge management has been shaped largely by the evolution of information technology over the years and has benefitted a great deal on account of the emergence of matured cognitive tools. In the early years, the focus was to organise and catalogue content and convert limited data available into carefully sorted information and encourage the stakeholders to share their knowhow as well as to motivate them to make use of the repository painstakingly built. Over the last few years the focus has shifted to tapping into external knowledge in addition to the internally generated knowledge on account of the vast amounts of data available from multiple sources thus providing the KM practitioners an opportunity to redefine the scope and impact of KM practice in their organisations.
Now KM is witnessing a new phase where we are experiencing the exciting possibility of new knowledge nuggets being added to the repository not just by its employees and other stakeholders but also through deep learning and machine learning. The deployment of Bots and the ability to extract valuable insights with the help of artificial intelligence tools have broadened the horizons of knowledge capture, storage and retrieval capabilities. The machine-human interface and the ability to mine new knowledge at a rapid pace augmenting the knowledge repository real time as the new facets are discovered, are adding significant value to the knowledge assets of organisations.
Another dimension which is enabling the use of knowledge assets to its fullest potential is analytics. Emanating from the changing consumer behaviour and requirements, the analysis of the buyer behaviour, market phenomenon or technology trends provide deep insights that trigger the charter for new capabilities to be created highlighting the expertise found in certain individuals in the organisation. Such experts can immediately be consulted or asked to act as coaches for others in the organisation to acquire the relevant knowledge or skills from them. What is unique in the current day context is the intelligence the organisation ecosystem has on account of AI tools being able to provide timely pointers to the potential experts based on the analysis of their mail exchanges, participation in the blogs and the subjects researched by them. Further the intelligent Bots could also serve up additional links or provide pointers to the sites from where new knowledge could be resourced and served up to the individuals in the form and style preferred by them.
In this context, we are also finding that the linkage of learning and knowledge management are getting closer than ever before as both functions have started relying upon each other for freshness and contemporary relevance. Repositories built over a period of time are providing a head start for building a smart knowledge ecosystem powered by cutting edge digital tools leading to ease of access and consumption of knowledge nuggets.
With millennials accounting for over 50% of the workforce in most organisations, the style and the duration of learning are also changing rapidly due to learning through tech devices and the acquired habit of accessing the internet for short nuggets on any topic of their choice. As a result organisations are reworking their knowledge repositories to have chunks of short duration content supported by micro learning videos of 3-4 minutes duration by experts, thus ensuring the rich content built over the years is put to effective use. At the same time by tagging their usage patterns it is now possible to add embellishments to the content with ratings and user insights added from time to time. The automatic updation of content in the knowledge repositories in the organisations along with peer ratings and links to other related contents available elsewhere not only keeps the knowledge assets fresh and relevant but by being able to contextualise the content in order to support adaptive learning needs, it is also making the learning experience more productive.
As a result of these developments, the outcomes from the investment in KM function are much more quantifiable and measurable than in the past. In the past, KM practitioners have often struggled to justify how knowledge repositories could be seen as contributing to the wisdom required for decision making. Now the pathways of KM to wisdom and the effective utilisation of knowledge assets at various levels in the organisation are becoming clear to both the practitioners as well as the business heads.
It has been observed in many organisations that KM is no longer a support function whose boundaries are restricted to culling out knowledge objects and building repositories but KM function is becoming the backbone of business operations on the strength of its potential to make a direct impact on the business outcomes. An exciting dimension of the new age KM function is that it has now the added responsibility of identifying the relevant knowledge nuggets from the Bots and coach them on an ongoing basis to become superior performers and learners competing or complimenting the human learners and performers.
Therefore KM function has to redefine its orientation towards defining knowledge objects as it recognises that the challenges in tapping and building knowledge repositories would be very different. In the digital era, KM and L and D practitioners have to adapt to the characteristics of these new channels of expertise and balance the training and coaching methods for both human and machine learning environments where they would be feeding each other and get smarter with every such transaction.
The writer is CEO, Global Talent Track, a corporate training