GenAI-KM organizational readiness self-assessment tool

This article is part of an ongoing series looking at AI in KM, and KM in AI.

Artificial intelligence (AI) can greatly assist¹ knowledge management (KM) in organizations. However, in a number of articles in RealKM Magazine‘s long-running AI series, most recently last week², I’ve sounded the alarm regarding bad AI research and practice, including that AI-generated material with hallucinations is being circulated in the KM community. Research is also increasingly showing that badly implemented AI can reduce productivity rather than increase it³, and create work rather than reduce it⁴. These are very clear indicators that AI is being deployed before the needed governance has been developed and put in place.

To help organizations to have a sound basis for their deployment of generative AI (GenAI), the most popular form of AI⁵, a newly published paper⁶ in the Journal of Innovation & Knowledge puts forward a GenAI-KM organizational readiness self-assessment tool. The tool is one of the outputs of a bibliometric literature review analysing 1411 articles related to the topic of Gen AI in KM. As well as the tool, paper authors Roberto Cerchione, Giuseppe Liccardo, and Renato Passaro provide a complete systematization of this emerging research domain, including a revised SECI model which reflects that GenAI is causing a revolutionary shift in KM processes. These further outputs of the paper will be presented in forthcoming RealKM Magazine articles.

Cerchione, Liccardo, & Passaro advise that managers should evaluate integration of GenAI for several purposes:

• GenAI can be used in synthetic dataset production to support training and simulations of diagnostic patterns, especially for managers in scarce or strictly regulated data industries such as healthcare.
• R&D teams should integrate GenAI into early-stage innovation processes and then apply structured validation steps, such as peer review or experimentation, to assess the value of AI-generated insights.
• Knowledge management systems (KMS) should be revisited to support co-creation models where humans and machines can successfully cooperate in knowledge generation. This may involve updating internal taxonomies and retraining staff on how to critically engage with AI outputs. Strategically integrating GenAI into KMS and innovation processes will determine an organization’s ability to remain competitive and adaptive in a rapidly evolving landscape.
• GenAI may also be employed to codify expert knowledge into structured outputs such as reports and learning materials. This may help preserve institutional memory and accelerate employee learning, while mitigating intergenerational knowledge gaps.

Effective organizational integration calls for specifically designed training courses for employees that develop a culture of responsible experimentation, along with the adoption of governance frameworks that define its acceptable use in everyday tasks and processes. Risk mitigation strategies are also imperative, especially to integrate GenAI into decision-making and highly sensitive processes.

From their study, Cerchione, Liccardo, & Passaro have identified five strategic areas on which managers should focus:

The GenAI-KM readiness self-assessment tool below converts each strategic dimension and sub-dimension into targeted assessment questions that managers can use to evaluate their organization’s readiness for GenAI adoption in KM processes.

By investigating each question, practitioners can identify priority improvement areas before or during implementation. For instance, an organization which evaluates itself as poor in ‘data contextualization’ may first prioritize enriching metadata and establishing consistent ontologies before piloting a GenAI-powered knowledge assistant. This approach ensures that readiness evaluation is both systematic and closely related to highly contextualized items, effectively supporting GenAI integration.

Header image source: Assurance (Version 1) in Turing Commons, CC BY-SA 4.0. 

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