Illustration showing a developer building an assurance case for her project.
ABCs of KMArtificial intelligenceTools & methods

GenAI-KM organizational readiness self-assessment tool

Bruce Boyes28 May 2026
718 4 minutes read

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

To help organizations to have a sound basis for their deployment of generative AI (GenAI) in knowledge management (KM), the GenAI-KM organizational readiness self-assessment tool below works across five strategic dimensions.

The tool converts each strategic dimension and sub-dimension into targeted assessment questions that managers and KM practitioners can use to evaluate their organization’s readiness for GenAI adoption in KM processes. By investigating each question, priority improvement areas can be identified before or during implementation.

The GenAI-KM organizational readiness self-assessment tool is one of the outputs of a recent bibliometric literature review1 in the Journal of Innovation & Knowledge. Authors Roberto Cerchione, Giuseppe Liccardo, and Renato Passaro analyse 1411 articles related to the topic of Gen AI in KM to provide a complete systematization of this emerging research domain, which reflects that GenAI is causing a revolutionary shift in KM processes. Other outputs from their review include a revised SECI model2.

Why is this tool important?

Artificial intelligence (AI) can greatly assist3 knowledge management (KM) in organizations. However, in recent articles4 in RealKM Magazine‘s long-running AI series, 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 it5, and create work rather than reduce it6. These are very clear indicators that AI is being deployed before the needed governance has been developed and put in place.

The five GenAI-KM strategic dimensions

Five strategic areas to consider regarding GenAI-KM organizational readiness.
Five strategic areas to consider regarding GenAI-KM organizational readiness. Source: Cerchione, Liccardo, & Passaro, 2026.

GenAI-KM organizational readiness self-assessment tool questions

The tool converts each strategic dimension and sub-dimension into targeted assessment questions. By investigating each question, managers and KM 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.

Other articles in RealKM Magazine‘s expansive series on AI in KM, and KM in AI and AI resources library can assist with investigating these questions.

Dimension Sub-dimension Sample assessment questions
Strategic
Alignment		 Internal Knowledge standardization
  • Do we use templates or protocols that GenAI tools can be trained on or follow?
  • Is unstructured content (e.g., PDFs, slides) routinely converted to structured formats?
Human-AI collaboration vision
  • Is GenAI included in strategic KM planning?
  • Do managers understand GenAI’s implications for knowledge workflows?
  • Do we have guidelines defining how GenAI should assist (not replace) human decision-making?
KMS alignment
  • Do we have platforms (e.g., APIs, cloud infrastructure) to integrate GenAI?
  • Are internal knowledge assets stored in consistent and structured formats?
  • Are our KM systems compatible with GenAI inputs and outputs?
Technical
Infrastructure		 Integration
  • Are GenAI tools interoperable with existing KM platforms?
  • Can AI outputs be exported into internal databases or collaboration tools?
Scalability
  • Do we have sufficient computing resources for GenAI experimentation?
  • Is there a roadmap for scaling successful GenAI initiatives?
Data infrastructure
  • Is sandbox access to data available for AI model training?
  • Can small-scale GenAI pilots be quickly deployed and tested?
Data
Governance		 Dataset quality
  • Do we have access to internal and external datasets relevant to our domain?
  • Are these datasets regularly updated, well-documented and audited?
Data policy
  • Is there a data governance policy regulating data usage and ownership?
  • Do policies exist for AI-generated content use and disclosure?
  • Are data quality checks and controls routinely applied?
Data contextualization
  • Is our data labelled or annotated in a way that supports generative tasks?
  • Do we have metadata or ontologies to contextualize information for GenAI?
Human
Capital		 Employee training
  • Do users understand how GenAI outputs are generated and their limitations?
  • Are there training modules on interpreting and validating GenAI results?
  • Are KM staff trained in GenAI prompts, validation, and oversight?
  • Are teams encouraged to try GenAI tools and report learnings?
  • Is there organizational resistance or openness to AI in knowledge work?
Roles and responsibilities
  • Are roles clear for humans when reviewing GenAI outputs?
  • Is experimentation encouraged within clear boundaries?
Establishment of co-creation practices
  • Do collaborative workflows allow GenAI and humans to jointly develop content?
  • Are there templates or structured tasks designed for AI-human cooperation?
  • Are feedback loops integrated into GenAI tools for continuous learning?
  • Is experimentation with AI tools protected from negative performance review outcomes?
Risk
Management		 Ethical frameworks
  • Do we have a policy on responsible GenAI use?
  • Are ethical risks reviewed during GenAI deployment?
Responsibility
  • Are there sanctions or restrictions on misusing GenAI tools?
  • Are employees trained on ethical AI usage?
  • Are informal uses of GenAI mapped and governed?
Reliability and traceability
  • Do we evaluate the reliability of GenAI content before acting on it?
  • Are there review processes to validate GenAI outputs for relevance and accuracy?
  • Are AI-generated insights logged and traceable to their source prompts or datasets?
  • Are escalation protocols in place for misuse or hallucinations?

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

References:

  1. Cerchione, R., Liccardo, G., & Passaro, R. (2026). Artificial knowledge generation: investigating the revolutionary role of generative AI in knowledge management. Journal of Innovation & Knowledge, 11, 100866. ↩
  2. Boyes, B. (2026, June 5). Rethinking the SECI model for GenAI: three perspectives, including one from Japan. RealKM Magazine. ↩
  3. Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. ↩
  4. Boyes, B. (2026, May 22). Disgraceful AI acts are growing. Will the knowledge management community be next? RealKM Magazine. ↩
  5. Gaskell, A. (2026, April 9). How AI-generated “workslop” makes us less productive. The Horizons Tracker. ↩
  6. Angus, D. (2026, April 30). ‘Just looping you in’: why letting AI write our emails might actually create more work. The Conversation. ↩
Tags
AI artificial intelligence GenAI generative AI
Bruce Boyes28 May 2026
718 4 minutes read
avatar

Bruce Boyes

Bruce Boyes is editor, lead writer, and a director of RealKM Magazine and winner of the international Knowledge Management Award 2025 (Individual Category). He is an experienced knowledge manager, environmental manager, project manager, communicator, and educator, and holds a Master of Environmental Management with Distinction and a Certificate of Technology (Electronics). His many career highlights include: establishing RealKM Magazine as an award-winning resource with more than 2,500 articles and 5 million reader views, leading the knowledge management (KM) community KM and Sustainable Development Goals (SDGs) initiative, using agile approaches to oversee the on time and under budget implementation of an award-winning $77.4 million recovery program for one of Australia's iconic river systems, leading a knowledge strategy process for Australia’s 56 natural resource management (NRM) regional organisations, pioneering collaborative learning and governance approaches to empower communities to sustainably manage landscapes and catchments in the face of complexity, being one of the first to join a new landmark aviation complexity initiative, initiating and teaching two new knowledge management subjects at Shanxi University in China, and writing numerous notable environmental strategies, reports, and other works.

Related Articles

GPS navigation

What happens when we outsource boring but important work to AI? Research shows we forget how to do it ourselves

12 Mar 2024
Training room

Lack of workplace training puts AI revolution at risk

6 Nov 2018
What if knowledge exists but nobody knows?

What if knowledge exists but nobody knows? [Forum special series, Artificial intelligence]

8 Apr 2026
An older manager with a younger employee.

Older managers can’t expect younger employees to teach them genAI

26 Dec 2024
Back to top button