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KM 3.0: KM AND AI

This article appears in the issue June 2017 [Volume 26, Issue 6]
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A key contribution of AI in the enterprise will be to increase the knowledge footprint of the organization’s experience and data streams, so that KM will be not only a departmental practice but also a pan-organization capability.

AI is generally driven by a high-level strategy group, particularly if disruptive aspects of the technology make some of the workforce redundant. To deal with the politics of such issues, efforts should be launched in parallel to re-skill or up-skill the workforce. In all such cases, the company must accept the forces of change—it is better to disrupt oneself than to be disrupted by competitors or upstarts. The field of IT is no stranger to such change, right from the days of COBOL giving way to object-oriented programming.

Trends

The Internet of Things (or Industrial Internet) will open vast new data streams of real-time action and response. AI will help companies tap archival as well as fresh data insights for real-time, rapid-response decisions. Blockchain is still in the early days in the B2C space, but is already making a mark in the enterprise and B2B arena, e.g. verification of contracts. Virtual reality (VR) can transform the learning function and knowledge-sharing experience in organizations, particularly in sectors like healthcare and precision manufacturing.

A key contribution of AI in the enterprise will be to increase the knowledge footprint of the organization’s experience and data streams, so that KM will be not only a departmental practice but also a pan-organization (and even pan-ecosystem) capability. AI-as-a-service (AaaS) is the next wave of SaaS and PaaS evolution. Rather than just “either-or” scenarios, there will also be “and” scenarios of humans and robots working together: “humbots,” “cobots” and maybe even “cyborgs.”

Other emerging frontiers include brilliant software-defined machines, always optimized operations and autonomous networked systems.

Limitations of current AI include less capability to express emotions, originality, empathy, ethics or morality. While the vision of robots taking over the workforce is a common sci-fi-like narrative, the fact remains that it still takes a lot of human effort to make machines smart.

Domain expertise is key for launching AI initiatives. The challenge for knowledge workers is to remain on the curve of lifelong learning to stay relevant and creative in a world where knowledge processes can be increasingly modeled and automated and where the art of the possible continues to leap across new frontiers. We have entered the world where we can ask questions to Siri-like agents; one day, such agents will ask us questions and call us idiots for making dumb mistakes!

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