“One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin”. Metamorphosis, by Franz Kafka
This is a story of a fictional Knowledge Manager who has been experiencing his Gregor Samsa moment of late! Let’s call him A.
Over the past few months, A has been struggling! The phrases AI and RPA have robbed him of a good night’s sleep and he keeps wondering if he would wake up one morning and be told that his skills around networking, fostering collaboration, transforming data into stories and most importantly stimulating conversations would no longer be considered as essential skills for practising KM. A is at his wits end thinking if he needs to reskill himself or if it is time for him to test new waters.
Throughout his KM career, A has been an ardent fan of LinkedIn and had used it a lot to introspect and decipher new concepts. He turns to his old friend and within a few minutes, extracts insights to motivate himself. The two articles that catch his attention and imagination are RPA to AI: the intelligent automation journey and Knowledge Management That Makes a Difference.
The RPA to AI article is an eye opener for him. He understands that we are on a journey to automate intelligence using robots. The first stage of the automation process sounds familiar to him as he has been involved in process optimization activities that aimed at cutting costs and increasing efficiencies and accuracy. Rather than feeling apprehensive, A starts to feel energised as he realizes that this brand of KM is not alien to him. Over the years, he has evolved from a backend data collector and aggregator to a business analyst who attempts to provide knowledge based solutions based on stakeholder needs ONLY. He realizes that the first generation Robot could be his friend after all!
A takes cautious steps towards the second stage which comes to him as a huge surprise. Despite his best attempts, A has never been able to extract insights from unstructured data like emails, scanned images etc. due to human and technology limitations. He had to resign to the fact that “We are drowning in information but starved for knowledge” (Megatrends by John Naisbitt). Now he feels optimistic realizing that the Cognitive Robot in stage two can help him create patterns from repetitive processes around inputs and outputs. It strikes him that the second generation Robot using natural language processing (NLP) and machine learning could accelerate the process of experiential learning within an organization.
Moving on to the third stage, A feels like seeing himself in the mirror. The intelligent chatbot or the third generation Robot appears to have assumed his persona that he has painstakingly developed over the years. A has always strived to enable interaction with his stakeholders by steering them in the right direction in their quest for knowledge. However, a few years ago, he did start to acknowledge that the process takes disproportionate amount of his time and despite his best efforts he can help a limited set of stakeholders only. He had secretly started hoping for a magic wand powered solution that could automatically learn from conversations, improve over a period of time and take over his mantle of human GPS in the KM ecosystem.
Approaching stage four, A begins to feel cautiously optimistic about his chances of remaining relevant in the KM discipline in the foreseeable future. He is completely bowled over though as he comes to terms with the real power of artificial intelligence in this stage. He recognizes that at this stage the robot would be able to mimic human intelligence and would comfortably be able to turbo-charge mission critical activities. And going by the same analogy, the robot should be able to address the requirements of extracting and then documenting the critical knowledge of experts quite easily. It would indeed be the Holy Grail for KM!
A realizes that this stage is still largely conceptual and it is likely to take considerable amount of time before it becomes a reality. Realizing that he would be able to earn his bread for a few more years after all by complementing his skills and embracing the robots from stages 1-3, he ponders “will there be no human KMs when stage four finally takes off”.
He flicks over to the second article “Knowledge Management That Makes a Difference” gives him hope. Digging deep into his own experiential learning, patterns created over the years and supported by the golden principles highlighted in the article, he arrives at a conclusion that stage four would still need humans who would facilitate the art of collaboration and sharing. They may not be called Knowledge Managers but as “the Bard” had once said “What’s in a name?”
And here are the golden principles that have been borrowed directly from the original article:
Connection before content
We learn when we talk
Knowledge is created and shared in conversation
Learn in small groups integrate in the large group
Asking opens the door to learning
Experts are used to stimulate thinking not to provide answers