The fourth industrial revolution has arrived. The possibilities of AI and how we will benefit from it is mind boggling and beyond imagination of many. It is said that like second industrial revolution resulted in us getting electrified, the fourth industrial revolution will end in us being ‘cognified’. We are getting into a data and insight driven world and it will be interesting to check the linkage between Knowledge management and Artificial intelligence at this juncture so that we leverage AI in a more meaningful way.

To understand the linkage between KM and AI, let us first understand what exactly organizations do with knowledge. Organizations perform different kinds of tasks and their success and competitiveness depends on the maturity in performing critical tasks, as well as where they stand with respect to industry in this. Tasks are performed by employees and machines, who take input information about the task, process the same based on knowledge (know how and know why) and complete the task. A physician collects symptoms, a professor’s input is what was taught in earlier session of the class, an architect needs requirements from the client etc. Hence for any task there is an input in the form of information, then that information is processed using knowledge and output is created.

In the case of humans, they can process large variation in the input information with respect to a task, even if the input information is not clear, they can remove the noise and if they do not have the relevant knowledge to process the information, they do further study, discuss with others, gain further knowledge and work on the information. They apply both know-how (procedural knowledge) and know-why (causal knowledge) as required. In the case of machines, they are pre-coded with rules (Know-how) on how to process the input information. The types of input information that they can process is very well defined. The knowledge (know-how) created to process the inputs are created by humans and used by both machines and humans.

With advent of AI, this relationship between input, processing and output for machines started changing. AI has enabled machines to create their own know-how to transform input to output. As a result AI can take up a wider range of inputs for a task, create their own know how and give output. Through learning they improve their know how and as a result provide better outputs as they learn. Here do note that, the input range does not change much, but for the given set of inputs, output created improves as a result of learning. 

What does this mean for organizations? As mentioned earlier, success or competitiveness of an organization depends on maturity in performing tasks and how they improve upon it. There is a journey towards efficiency and effectiveness that all organizations are forced to undertake, as a result of market dynamics. Underlying this journey is a continuous decrease in complexity with respect to tasks performed, where more and more variables are identified, their relationships are understood.

How does AI impact the way tasks are performed and the learning cycle?

Positive impacts

• Improved efficiency of tasks: Due to their ability to learn and improve, AI driven technology can help an organization improve its task on a regular basis. Given an approach to performing a task, the AI tools can help reach the most efficient approach must faster.
• Expediting learning: AI based technologies if used prudently can help in fast tracking the learning cycle. This is enabled through generating new data and creating insights from the same in the way tasks are performed.
• Knowledge findability and Employee productivity: One of the most popular use cases with AI has been the ability to find relevant content faster. AI can improve search drastically and give employees the information and the knowledge most relevant to them. This in turn will improve employee productivity and overall productivity.
• Human-machine collaboration and Employee productivity: With AI taking up routine and data heavy activities, employees are able to focus on complex activities, which can directly impact overall productivity of the organization and fast track maturity in performing tasks

Limitations

• Cannot improve effectiveness: AI improvement happens at the know how level and they cannot work with causal knowledge. Hence AI technologies on its own cannot innovate and drastically change the approach to perform a task.
• AI cannot leverage existing knowledge: This is another great drawback of AI. AI is data driven and creates insights from data to improve. It is not able to leverage knowledge generated from other sources, bring them together and create a new know how with respect to the task it is performing.
• Dependency on AI algorithms may at times slow down learning: Because know-how evolved by AI technologies are a mystery when deep learning techniques are used, organizations who extensively use AI in their process, without any clear strategy may find their learning cycle slow down with respect to the specific tasks. This is because they are not able to develop any understanding about the tasks they are performing. They will also become heavily dependent on AI vendors for algorithms to perform those tasks.

Hence for organizations to stay competitive in the long run, we need an approach that considers the strengths and weakness of AI and accordingly leverage knowledge. Unplanned application of AI may actually bring down competitiveness of an organization.

About the Author:  Dr. Randhir Pushpa is the Founder and Chief Consultant, Acies Innovations - Bengaluru, Karnataka, India, and a frequent contributor to KMI.  Dr. Pushpa has over 18 years of experience, in Knowledge Management (KM) and Innovation Management. Holds PhD in Management Science with an MBA and Bachelors in Technology.  He is passionate about Knowledge Management and focused on evolving interventions and practices that help align the practice of managing knowledge to business outcomes. Currently focusing on evolving Artificial Intelligence driven tools that can be used to leverage knowledge proactively.