New technology means renewed fears of how it might affect your job. Artificial intelligence (AI) has captured the attention of organizations across all sectors — including 80% of CEOs who plan to implement AI into their organization within the next 12 months. Reports suggest “knowledge-based” workers are most at-risk of losing their job to AI.
Knowledge management (KM) relies on working with technology to gather, organize, share, and disseminate information within an organization. While these responsibilities could be performed by an AI, we have previously written about how AI requires knowledge management to be effective at all.
This means the adoption of AI will require a shift from knowledge management to knowledge engineering. We explain what that transition entails in this article.
What is a knowledge engineer?
Knowledge engineers are a new type of custodian of an organization’s knowledge. They are responsible for not just managing that knowledge, but also configuring AI tools to access, utilize and benefit from that knowledge. This includes restructuring content to adapt to the AI’s functionality.
Knowledge managers might have previously compared their role in an organization to a tail trying to wag the dog, but AI is a sea change. Knowledge engineers are now in the best position to understand how to turn an organization’s knowledge into a tailored solution for the optimization of AI.
The need for knowledge engineers stems from the challenges businesses are facing right now as they adopt AI solutions. Organizations want to be able to control where an AI gets its answers from, they want transparency into how the answer was produced, and they need control to ensure security with how their data is being accessed. Achieving this control, transparency, and trust isn’t a problem resolved by better AI but rather through structuring your organization’s knowledge as an infrastructure for AI. The alternative is to accept the weaknesses of more finite products that don’t benefit from true AI.
The first step to transitioning from knowledge manager to knowledge engineer is to prove the value of your KM responsibilities.
Measuring ROI of a knowledge engineer
Knowledge management may appear difficult to quantify, but there is one specific industry that measures its success and failure on KM: contact centers. Support agents augmented by AI spend 80% less time looking for answers and 30% shorter average handle times. These are metrics ingrained in contact centers because they are measured by their ability to resolve knowledge questions quickly, but the same concept can be applied to other business responsibilities.
A well-known McKinsey Global Institute report concluded a searchable record of knowledge can decrease look-up time for employees by 35%. Another global research firm found 32% of respondents believed data management is the top technological inhibitor to deploying AI in their organization. These findings suggest knowledge management is already impacting specific measurable ROIs.
To quantify the ROI of a KM system, consider the Key Performance Indicators (KPIs) it directly affects. In the contact center, these cover average handling time (AHT), first contact resolution (FCR), and average hold time. Also, consider indirect factors like the after-call work time and the number of calls handled, which can be drastically improved by KM.
The numerical reality of KM
When the environment is experiencing tidal shifts, it’s time to provide your organization with value they can understand and measure. At Shelf, nearly 100% of our customers measure the ROI of their KMS implementation. These calculations can include metrics such as number of interactions per month, time saved per interaction, training time for new hires, and the number of articles accessed for knowledge solutions. Demonstrating knowledge’s value in the organization’s success will result in additional support for the role of knowledge engineering.
Knowledge managers are integral to setting up AI for success. They can transition from being apprehensive of AI to becoming knowledge engineers who work hand-in-glove with this technology.
Knowledge engineers have the potential to pioneer how AI accesses, utilizes and benefits from business knowledge, leading the organization into an era of accelerated productivity and efficiency. This is the beginning of the knowledge engineering era — where AI and KM couple to redefine each other’s role in your organization.