What are the key challenges in implementing AI in KM, and how can they be addressed?

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

The use of artificial intelligence (AI) in knowledge management (KM) has moved from experimentation to implementation¹, but key challenges in this implementation remain.

A new paper² published in the journal Technological Forecasting and Social Change explores these challenges and how to address them. This research, carried out by Mojtaba Rezaei, involved three methodologies: a literature review on KM and AI’s challenges, a Delphi study with domain experts, and confirmatory factor analysis (CFA) across four KM processes. Data from retail sector professionals validated the challenges identified by experts.

1. Limitations of previous studies

Rezaei begins by looking at how the challenges of implementing AI in KM have been addressed in previous research, including that of Jarrahi and colleagues³ which was summarised in a previous RealKM Magazine article⁴.

While these studies reflect the growing interest in understanding AI’s role and challenges in KM, Rezaei contends that they suffer from three significant limitations. First, they are mainly theoretical, lacking validation evidence and practical applicability, which limits their relevance to real-world organisational contexts. Second, they focus narrowly on specific challenges, such as ethical issues or trust, while failing to address the broader challenges that span the entire KM lifecycle. Third, their scope is restricted by prioritising knowledge sharing, leaving critical gaps in understanding how AI can integrate seamlessly across all KM processes, including knowledge creation, storage, and application.

Rezaei advises that this absence of holistic analysis and actionable insights underscores the need for more comprehensive and evidence-grounded research to bridge the gap between theory and practice in this domain. The paper addresses this critical gap by identifying and analysing the key implementation challenges of AI in KM, providing a comprehensive overview to guide organisations in managing the complexities of adoption.

2. Findings of the new study

The findings of Rezaei’s study unveil a comprehensive landscape of challenges posed by integrating AI within KM. These challenges have been categorised into three distinctive domains: technological, organisational, and ethical.

2.1. Technological challenges

Within this category, six distinct factors have been identified, each representing a significant hurdle. These factors include data quality and availability, scalability, transparency and explainability, reliability and robustness, security and privacy, and algorithm complexity.

2.2. Organisational challenges

Four key factors constitute this segment of challenges. These encompass resistance to change, lack of expertise, interdepartmental collaboration, and budget constraints.

2.3. Ethical challenges

In this domain, five significant factors emerge. These include bias, accountability, privacy, transparency, and lack of job security.

3. Challenges for specific aspects of KM

3.1. Knowledge creation

For knowledge creation, the predominant challenges include resistance to change and lack of job security. These challenges necessitate a focus on overcoming cultural and organisational barriers while providing job security to employees for the effective implementation of AI in knowledge capture.

Resistance to change often stems from a fear of the unknown or a perception of a threat to existing processes and job roles. Addressing this requires fostering a culture that embraces change and innovation, which can be achieved through effective communication, training programs, and demonstrating the tangible benefits of AI integration to the staff. Involving employees in the transition process and offering reassurance and support can reduce resistance and create a more collaborative environment.

Emphasising AI’s augmentation aspect can address concerns over job security and its potential to replace human roles. By offering opportunities for upskilling and reskilling, organisations can ensure that their workforce remains valuable contributors in an AI-driven environment.

3.2. Knowledge storage

In knowledge storage, the most significant hurdles include a lack of expertise, budget constraints, and privacy concerns. Tackling these issues necessitates securing critical knowledge and assets for effective data management and storage, as well as taking steps to alleviate budget restrictions and resolve privacy issues.

Budget constraints highlight the financial challenges faced during AI adoption. These can be mitigated by exploring funding options like grants, partnerships, or cost-sharing models and prioritising AI projects based on their potential impact and feasibility.

Privacy concerns underline the importance of data protection and privacy in AI-KM integration. Establishing robust data governance policies, stringent security measures, and compliance with data protection regulations can alleviate these concerns and foster a more secure environment for data management.

3.3. Knowledge sharing

In knowledge sharing, the paramount challenges are security and privacy and transparency and explainability.

Security and privacy concerns are paramount in AI-KM integration. Addressing these challenges involves implementing robust security protocols, regular audits, and possibly employing data encryption technologies to maintain confidentiality, integrity, and data availability.

Transparency and explainability challenges require adopting explainable AI (XAI) technologies and practices to make AI models more understandable and interpretable for users, fostering trust and understanding. Navigating these obstacles in knowledge sharing is vital for promoting trust and understanding among stakeholders while ensuring secure and responsible data management.

3.4. Knowledge application

Rezaei identifies two main challenges for the effective implementation of AI in knowledge application: lack of job security and budget constraints.

The lack of job security in knowledge application emphasises employee concerns about AI technologies potentially replacing human roles. Mitigating this challenge involves focusing on the complementarity of AI and human skills, demonstrating how AI can augment human capabilities rather than replace them. Investing in employee development and providing opportunities for skill enhancement can bolster job security and foster a positive perception of AI integration.

Budget constraints in the knowledge application point to financial limitations during AI adoption. Addressing this involves strategic financial planning and prioritising AI projects based on their potential impact and alignment with organisational goals. Exploring various funding options, such as partnerships, grants, or crowdsourcing, can alleviate budget constraints and ensure optimal resource allocation.

3.5. Overlapping challenges

Specific challenges transcend the boundaries of the different aspects of KM, showcasing their general applicability and significance.

Security and privacy stand out as critical concerns, consistently presenting high factor loadings across all four KM aspects. This accentuates the vital data security and privacy needs, irrespective of the specific KM aspect. Safeguarding sensitive information and ensuring confidentiality is paramount, given the data-driven nature of AI applications in KM.

Similarly, the challenge of lack of job security holds substantial weight across all four aspects, signalling the widespread apprehension among employees regarding the potential impact of AI on their job roles. Ensuring job security is crucial for the employees’ mental well-being and is integral for maintaining a productive and motivated workforce. By emphasising the complementary role of AI, organisations can alleviate these concerns and foster a more inclusive approach to AI implementation.

Budget constraints also pose substantial challenges in three of the four aspects, emphasising the need for organisations to have adequate resources and financial backing to deploy AI in KM successfully. Economic and financial considerations play a crucial role in determining the feasibility and sustainability of AI projects. Hence, strategic financial planning and exploration of various funding avenues become essential.

However, it is noteworthy that some challenges exhibit a more pronounced presence in a specific aspect. For instance, knowledge creation grapples predominantly with resistance to change and a lack of expertise, reflecting the cultural and knowledge gaps that need to be bridged for successful AI integration. Knowledge storage, on the other hand, is significantly affected by a lack of expertise and privacy concerns, highlighting the need for skilled professionals and robust data protection measures.

4. Implications for KM practice

Rezaei advises that the study findings underscore organisations’ need to develop a nuanced understanding of the multifaceted challenges organisations face when implementing AI-based KM systems, to formulate targeted mitigation strategies. These challenges encompass technological, organisational, and ethical dimensions.

Practical measures may include enhancing data quality and accessibility, ensuring algorithmic transparency and explainability, and addressing ethical concerns such as bias and privacy protection.

Rezaei advocates for developing tailored strategies to address challenges specific to each aspect of KM – knowledge creation, knowledge storage, knowledge sharing, and knowledge application – and the unique challenges associated with each. For instance, while data quality and availability may be paramount in knowledge creation, transparency and explainability might be critical in knowledge sharing.

Furthermore, Rezaei states that the study highlights the interdependence of challenges within individual aspects of KM. Addressing one challenge can potentially have cascading positive effects on other challenges within the same aspect of KM. For example, mitigating expertise shortages in knowledge storage may concurrently alleviate privacy concerns. This finding underscores the importance of adopting a holistic approach that accounts for these intricate relationships, moving beyond isolated problem-solving that may yield sub-optimal outcomes.

Article source: Mojtaba Rezaei, 2025. CC BY 4.0.

Header image source: Mohamed Hassan on Pixabay.

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