How Machine Learning can Usher in a New Paradigm of Organizational Learning

What is Organizational Learning and Machine Learning and how they Work Together

With organizations automating most of their core and noncore processes and using algorithms to power their applications and drive their operations, one of the key facets of knowledge economy firms, organizational learning can move into an entirely new paradigm.

To explain, organizational learning happens as a systemic process where reusable knowledge artifacts help employees learn from the experiences of their peers and coworkers for each iteration of the organizational processes and this in turn, helps them to avoid what is known as “Reinventing the Wheel”.

In other words, as organizations acquire knowledge and learn from each round or iteration of their core processes, they actualize efficiencies from this learning and derive synergies from the past experiences of their employees.

Now, let us turn to how Machine Learning can help Organizational Learning. To start with, Machine Learning is a term used to describe how automated algorithmic systems learn in the same manner in which humans learn from past experiences.

Indeed, the beauty of machine learning is that once programmed, the systems and software automatically detect patterns and identify broad based indicators out of raw data. In turn, this learning is used to augment the work done by the machines which in turn, enhances their efficacy and effectiveness.

Thus, machines begin to “learn and think” like humans which is the premise behind this concept. Though scientists agree that we have not yet reached the stage where machines mimic or even surpass humans, the possibilities are breathtaking and the opportunities are many.

How MK Enabled Organizational Learning can usher in a New Paradigm

So, how can machine learning help organizations usher in a new paradigm in learning? For one, they can replace the existing KM or Knowledge Management Systems and help organizations derive more benefits in terms of lesser time taken to identify patterns and indicators.

Next, machine learning is hugely beneficial to organizations that do not have the resources to invest in expensive KM systems.

Third, ML or Machine Learning enabled software can easily help rookies and novices enhance their learning and knowledge of mission critical projects and more importantly, let them access the entire Knowledge Artifacts Library without having to browse through the collection individually.

In other words, Meta Data or the Higher Lever Learning needed to have a Bird’s Eye View of the Entire KM Database with the added advantage of micro insights and macro patterns being made available to all employees in the organization.

Thus, ML enabled KM Systems would help organizations synergize in the Man Machine Interfaces and help them reap the efficiencies from the economies of scale and the benefits from the synergies as mentioned earlier. In turn, this can lead to a Paradigm Shift in the way organizations manage their Learning and KM systems.

Limitations of Current ML Technologies and the Threat of Redundancies

Having said that, it is also the case that ML alone cannot be the solution for all KM related problems in the organizations.

For instance, as mentioned earlier, ML capabilities are still in early modes and there is some distance to cover before they can be used to enhance learning and KM capabilities in organizations.

Moreover, recent reports suggest that ML powered software makes some egregious mistakes as far as detecting patterns and indicators among data clusters is concerned.

Third, the prospect of ML enabled KM systems and Learning Modules replacing the Human Element needs some circumspection as job losses and the redundancies of the employees in the overall KM and Organizational Learning Paradigm can defeat the very purpose for which they have been deployed.

Moreover, organizational learning is a group activity that gains from debate and discussion and the prospect of solitary learning through ML systems is something that can derail the objectives of organization wide learning.

How ML Enabled KM can Become a Game Changer for Innovative Firms

On the other hand, there is enough evidence to suggest that at the present moment, ML enabled KM systems can indeed make a significant difference to the way in which Organizational Learning happens.

The most critical and crucial advantage is that ML enabled KM systems work very well in large and very large organizations.

In our experience, we have found that organizations that have employees in the thousands and the hundreds of thousands can indeed benefit from the New Paradigm of ML Driven Organizational Learning.

Indeed, given the fact that such organizations like Infosys, IBM, and Microsoft often struggle with organizing their KM artifacts and managing their learning systems, it is obvious that ML can help such organizations in ways that were never thought of before.

Moreover, geographically dispersed organizations can benefit immensely from a centralized and automated KM system that bridges the distance gap and shortens the lead time for information to become knowledge and for data to become useful as patterns are identified and indicators are tagged.

Recent research suggests that organizations such as 3M are investing extensively in ML studies to identify how it can benefit their organizational learning systems.


Lastly, organizational learning for most corporate entities is a noncore process that does not figure prominently in their priorities.

On the other hand, it is absolutely critical for Knowledge Economy firms whose ability to innovate and be inventive makes the difference between success and failure.

Thus, ML driven organizational learning can definitely help them outpace the competition and stay ahead of the curve.

To conclude, while ML enabled KM systems are still in their infancy, their potential to be game changers and usher in new paradigms in organizational learning are immense.

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