Systematic Literature Review (SLR): Knowledge Management (KM)

Processes and Artificial Intelligence (AI)

Nada S. AlMuzaini, Boyka Simeonova and Mat Hughes

School of Business, University of Leicester, University Rd, Leicester LE1 7RH, U.K.

Keywords: Knowledge Management (KM), Artificial Intelligence (AI), Knowledge Management (KM) Process,

Knowledge Creation, Knowledge Sharing and Knowledge Utilisation.

Abstract: The purpose of this systematic literature review is to identify the gaps and limitations within Knowledge

Management (KM) processes through the lens of Artificial Intelligence (AI). Using a systematic literature

review methodology, 42 academic articles were identified and analysed through content analysis to examine

how KM processes are addressed within AI-related research. The studies were thematically coded and

categorised to uncover prevailing patterns and insights. The review finds that the integration of AI into KM

is still in its nascent stages, with fragmented and evolving research. Five core themes emerged from the

analysis: (1) AI and human collaboration, (2) Trust and ethics, (3) Ingenuity, (4) Organisational performance

and (5) Information security. Each theme highlights both opportunities and challenges of AI within KM

processes. In addition, the review identifies limitations within each theme and offers suggestions for future

research. This paper provides a comprehensive overview of how AI intersects with KM processes and

demonstrates the value of applying a systematic literature review to organise and explore this emerging area

of research.

1 INTRODUCTION

Knowledge is recognised as a key organisational

resource, with organisations actively orchestrating

knowledge assets and processes to create, share and

apply knowledge effectively (Olan et al., 2022, Alavi

et al., 2024). Individuals exchange both tacit and

explicit knowledge through experiences, skills,

documentation and formal communication (Nonaka

and Takeuchi, 1995). The role of Knowledge

Management (KM) is to manage these knowledge

resources efficiently (Jarrahi et al., 2023) and to

facilitate the KM processes, namely knowledge

creation, sharing and utilisation, which are essential

for achieving a competitive advantage in today’s

complex environments (Heisig, 2009).

KM includes various processes, which involve a

set of systematic functions designed to manage an

organisation's knowledge effectively and enhance its

performance (Sabherwal and Becerra‐Fernandez,

2003). These KM processes include knowledge

creation, acquisition, assimilation and sharing across

the organisation, enabling informed decision-making

https://orcid.org/0000-0001-6859-558X

and intelligent action (Heisig, 2009). To meet KM

objectives, these processes help establish a

framework that supports the effective creation,

sharing and utilisation of knowledge by both the

organisation and its members (Dalkir, 2023). Heisig

(2009) emphasised that the success of KM depends

on the interplay between KM processes and

contextual factors such as organisational culture,

technology and leadership. For KM to be effective,

these processes must work together in harmony.

Heisig (2009) also identified the most common KM

processes as knowledge creation, sharing, utilisation,

identification and acquisition. These processes

interact in a continuous cycle, influencing one

another in an emergent rather than hierarchical

manner (Alavi and Leidner, 2001).

Knowledge creation refers to the generation of

new knowledge from both explicit and tacit sources

(Nonaka, 1994). Knowledge sharing involves the

exchange of information, ideas and experiences

among individuals, teams and employees (Jarrahi et

al., 2023). Knowledge utilisation is the application of

knowledge after it has been created and shared,

AlMuzaini, N. S., Simeonova, B. and Hughes, M.

Systematic Literature Review (SLR): Knowledge Management (KM) Processes and Artiﬁcial Intelligence (AI).

DOI: 10.5220/0013685000004000

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 2: KEOD and KMIS, pages

237-246

ISBN: 978-989-758-769-6; ISSN: 2184-3228

237

transforming it into action and value creation. (Wiig,

1993). Knowledge identity refers to a data analyst’s

self-perception of their knowledge, shaped by past

experiences, current practices and future aspirations.

This identity influences their ability to apply analytics

for problem-solving effectively (Intezari et al., 2022).

Knowledge acquisition is the process of identifying

knowledge from internal and external sources and

transforming it into a format that can be internalised

or used effectively within the organisation (Gaines,

2004).

A Knowledge Management (KM) model outlines

how knowledge is processed across departments

within an organisation. This process facilitates

knowledge flow and provides a framework for

enhancing collaboration, teamwork and

organisational learning (Heisig, 2009).The following

section presents two KM models.

Nonaka’s model of knowledge creation (Nonaka,

1994; Nonaka and Takeuchi, 1995) highlights the

dynamic interaction between tacit and explicit

knowledge within individuals and organisations. It

consists of four modes: socialisation (tacit to tacit),

combination (explicit to explicit) , externalisation

(tacit to explicit), and internalisation (explicit to tacit

knowledge) (Nonaka and Takeuchi, 1995). The four

modes of knowledge creation can be utilised to

enhance an organisation's KM strategies, drive

innovation, and foster learning with individuals and

groups (Nonaka, 1994).

To manage organisational learning through KM

processes, Alavi and Leidner (2001) created a model

emphasising the role of KM processes. Their model

included knowledge creation, storage/retrieval,

transfer and application. Knowledge creation

encompasses the internal and external development

of new knowledge for the organisation. Knowledge

storage and retrieval involve storing knowledge

within an organisation to be effectively retrieved for

future use. Knowledge transfer includes sharing

knowledge within the organisation through formal

meeting documentation and databases. Knowledge

application is applying the knowledge to achieve

organisational goals effectively in decision-making

processes.

KM models illustrate how knowledge is managed

and utilised at various stages within an organisation.

The use of core KM processes such as knowledge

creation, sharing and utilisation is fundamental for

understanding how knowledge is effectively

exercised in organisational settings (McAdam and

McCreedy, 1999). These processes also support

organisations in creating, sharing and applying

knowledge to enable better decision-making and

more informed strategic choices in competitive

environments (Rollett, 2012).

Technology such as artificial intelligence (AI) has

transformed organisational contexts by offering new

opportunities to enhance knowledge management

(Jarrahi et al., 2023). For instance, AI impacts KM

processes by generating new insights from explicit

knowledge and assisting with problem-solving

(Alavi et al., 2024). It can also extract, analyse and

share knowledge from various data sources, thereby

leveraging an organisation’s knowledge assets and

enhancing decision-making by converting data into

actionable insights (Benbya et al., 2021, Rezaei et al.,

2024). This encourages employees at all levels to

collaborate, share and utilise information and insights

produced by A(Sumbal et al., 2024). For example, AI

tools such as generative AI (GenAI) can act as co-

creators alongside humans to support task execution,

creativity and innovation (Benbya et al., 2021).

This provides a deeper understanding of how

knowledge flows through the organisation and how it

is embedded in both individual cognition and

collective practices (Alavi et al., 2024, Alavi and

Leidner, 2001). However, it is also disrupting

traditional KM processes by analysing large datasets

to generate knowledge more rapidly than

conventional methods (Peters et al., 2024, Anthony et

al., 2023). Despite these benefits, AI also presents

risks, including algorithmic bias, ethical concerns and

the potential erosion of tacit knowledge (Benbya et

al., 2024, Alavi et al., 2024, Anthony et al., 2023).

These risks underscore the importance of retaining

expert insights within KM frameworks. (Alavi et al.,

2024, Alavi and Leidner, 2001)

Although AI is increasingly integrated into

business functions, its role within KM remains

empirically underdeveloped. There is limited

research exploring how AI influences the core KM

processes of knowledge creation, sharing and

utilisation (Anthony et al., 2023, Peters et al., 2024).

Given the limited availability of existing studies, this

research adopts a systematic literature review (SLR)

to examine and understand the current state of the

literature on AI within KM processes, with a specific

focus on knowledge creation, sharing, and utilisation.

The central research question guiding this study

is: What is the current state of the literature on AI

within KM processes, particularly in relation to

knowledge creation, sharing, and utilisation?

Through the systematic analysis of 42 academic

articles published in high-quality journals, this study

aims to identify key themes, highlight research gaps,

and propose future directions for both theory and

practice.

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The following section presents a systematic

literature review to understand the gaps in KM and AI

and includes a research theme that emerged from the

literature review.

2 METHODOLOGY

To identify and understand research gaps in AI within

the field of KM as it relates to organisational aspects,

this systematic literature review (SLR) aims to

explore both known and unknown areas of KM

processes and AI. The SLR was conducted following

the methodological framework suggested by

Tranfield et al. (2003), as themes were created to

identify, code and analyse the articles.

The search strategy encompassed a thorough

examination of a primary database using only the

Web of Science, as this database provided the

necessary journals for this search. The search was

conducted using the following keywords:

“knowledge management”, “artificial intelligence”,

“knowledge transfer”, “knowledge sharing”,

“knowledge creation”, “knowledge utilisation”, or

"knowledge utilization” and “knowledge application”

(presented in appendix) Within the research, only

journals were selected for ranking in the ABS list with

greater than or equal to two stars with selected fields,

including Information Management, Innovation,

General Management (Ethics, Gender and Social

responsibility), Organisation Studies, and

Entrepreneurship.

Articles were excluded that particularly on

domains such as R&D, construction knowledge, audit

technology, profit performance, gender, medical

logistics, marketing analytics, grant prediction,

scientific mobility, virtual teams, and invention

growth were excluded.

The search initially identified 109 articles. After

carefully evaluating each article's title, this number

was reduced to 62. After reviewing the abstracts to

assess their alignment with the research objectives,

only 42 articles were considered relevant (shown in

appendix).

The following section reviews five themes of SLR

to provide a deeper understanding of the gaps in the

interdisciplinary field of KM and AI, as well as the

connection between them.

Table 1: Keywords assembly structure.

Keywords Search

1. “artificial intelligence” and “knowledge

management”

2. “artificial intelligence” and “knowledge transfer”

3. “artificial intelligence” and “knowledge sharing”

4. “artificial intelligence” and “knowledge creation”

5. “artificial intelligence” and “knowledge

utilisation” or “knowledge utilization"”

6. “artificial intelligence” and “knowledge

lication”

Figure 1: Flowchart of the sample selection process, as per

(Kahale et al., 2021, Mengist et al., 2020).

2.1 Findings: Current State of the

Knowledge in the Discipline of AI

in KM Processes

The analysis utilised the classifications that emerged

from the citation analysis. All 42 papers were

classified independently according to 5 themes; each

theme's details are explained below.

The results show a need for more empirical

studies within the KM and AI disciplines. Of the 42

articles reviewed, the majority were quantitative

studies (11) focusing on statistical analysis, followed

by qualitative studies (8) providing deeper insights

into experiences. Additionally, three mixed-method

studies provided a clearer understanding of KM and

AI. The review also included conceptual frameworks

(3 articles) and literature reviews (17 articles), which

were primarily based on editorial literature reviews (4

articles), literature reviews (3 articles), or systematic

literature reviews (10 articles), identifying existing

knowledge and highlighting future research gaps in

KM and AI.

Systematic Literature Review (SLR): Knowledge Management (KM) Processes and Artiﬁcial Intelligence (AI)

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The articles used the following theories:

knowledge-based view, resource-based view,

dynamic capability theory, relationship marketing

theory, systems theory and emerging theories related

to knowledge augmentation and knowledge hiding.

Table 2: Summary of themes and sub-themes.

Themes

Sub-

themes

Description

No.

Article

1) AI-

individual

collaboration

Models

and

organis

ational

knowle

dge.

Articles view AI as

a co-creator

working alongside

the individual and

consider AI to have

numerous

cognitive skills

necessary to

improve

productivity and

create new

knowledge for

organisations

rocesses.

) Trust and Ethics

Articles raise

concerns about AI

in relation to trust

and ethic.

) Ingenuity

Innovati

solution

s and

creativit

Article views

ingenuity within

organisational

contexts as

creativity and

innovative thinking

in problem-

solving, providing

new opportunities

to improve

organisational

erformance.

4) Performance

Articles view ways

to improve the

organisation's

erformance.

5) Information Security

Articles mention

information

security within AI

systems to prevent

knowledge from

leakage.

Theme 1: AI–Individual Collaboration (16 articles)

These articles view AI as a co-creator working

alongside the individual and consider AI to have

numerous cognitive skills necessary to improve

productivity and create new knowledge for

organisational processes. The sub-themes are the KM

model and organisational knowledge.

AI integration into the KM model, articles discussing the

KM model with the integration of AI (6 articles).

Articles mentioning the KM model include KM

processes using AI. The literature reviewed by Jarrahi et al.

(2023) and Benbya et al. (2024) used the KM model for

knowledge creation, sharing, storage/retrial and application.

Jarrahi et al. (2023) recommended utilising AI to

support KM processes. The article underscores the

collaboration between AI and humans in organisational

settings, emphasising that AI should enhance employees'

skills rather than replace them. The article's main argument

is that AI will enhance employees' expertise, provide a large

amount of data and improve the efficiency of KM processes.

On the other hand, Benbya et al. (2024) explained the role

of GenAI and its implications for knowledge and creative

work in fields such as design, writing, and innovation.

GenAI is helpful for creativity and knowledge creation.

Additionally, GenAI can work alongside employees to

answer their enquiries, provide feedback, enable them to

share knowledge, foster learning experiences, and aid the

organisation's knowledge in decision-making and strategic

planning.

GenAI is integrated within the KM model, particularly

in the view of Alavi et al. (2024), who

recommended

using the KM processes of knowledge creation,

transfer, utilisation, storage, retrieval and articulation

of explicit implementation. GenAI helps to facilitate

more comprehensive knowledge, and organisations

must carefully manage technology integration with

human insight to ensure ethical and accurate KM

practices.

Nonaka's model of knowledge creation integrated

human intelligence into AI processes to enhance

learning and adaptability while augmenting

organisational knowledge, thereby creating a

"recursive theory of knowledge augmentation”

(Harfouche et al. (2023). The theory extends the

traditional models of organisational knowledge

creation with the integration of AI; with the

knowledge creation model, the author used five new

models of knowledge augmentation: Alimentation,

Aggregation, Amplification, Reflection, and Fusion.

This mode explains the collaboration of humans and

AI reclusively to feed AI with human knowledge,

learn from AI outputs, and interact between humans

and AI to ensure that knowledge is explained and

refined within the organisation.

AI is transforming organisational knowledge.

Ardito et al. (2022) used absorptive capacity theory in

a systematic review to explore how AI transforms

KM in organisations, focusing on how knowledge is

acquired, assimilated, transformed, and exploited

across stages. Zhang et al. (2025), in their systematic

review, explain how AI is influencing each dimension

of the SECI model of knowledge creation (presented

by Nonaka and Takeuchi (1996)), highlighting how

AI accelerates the conversion of tacit to explicit

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knowledge, enriches knowledge integration, and

ultimately enhances innovation, decision-making,

and organisational performance.

Organisational Knowledge: articles discussing

how AI changes organisational knowledge (10

articles).

AI, particularly ML systems reduce the need for

human interaction and organisations must understand

the learning capabilities of the systems to align them

with their strategies (Sturm et al., 2021). For example,

ML has the potential to provide information for sales

personnel to help with sales decisions, market

knowledge, and assist with visual and written content

(Paschen et al., 2020). Intelligent bots in KM have

numerous important functions in collaborating with

other stakeholders and supporting employees in their

daily tasks. For example, mobile messaging apps

implement bots to monitor conversations and provide

recommendation services (Kane, 2017). Chin et al.

(2024) explains in a conceptual paper, helps to

recognise how organisational knowledge is shaped by

the interaction between AI and human intelligence in

managing cross-cultural knowledge. It aims to

understand how humans and AI collaborate, conflict,

and co-create knowledge in the context of

humanitarian logistics.

Two studies (Shollo et al., 2022, Olan et al., 2022)

showed the importance of RBV in investigating how

businesses can create value and achieve a competitive

advantage by leveraging AI. From a qualitative

perspective, a study by Shollo et al. (2022) achieved

an understanding of the business's capabilities using

AI, as it helps the organisation to create new

knowledge for the firm, make better decisions, and

add value to the business strategies. This highlights

how AI can orchestrate AI capabilities to create and

sustain value within the organisation's strategies.

From a quantitative perspective, Liu et al. (2024)

explored the influence of AI on the speed of

internationalisation for firms from emerging markets,

focusing on Chinese firms. The results show that AI

has a positive impact on organisations' processes,

improving their competitive advantage by global

standards. However, research also noted that firms

based in their home country, where reliance on

market knowledge and practices may hinder the use

of AI for international growth, may also benefit from

AI. This provides a new perspective on how AI can

help firms’ KM emerge in new global markets, create

a competitive advantage, and provide valuable

insights by learning from the feedback.

There are numerous methods for investigating

feedback loops in AI. One approach is to explore the

role of knowledge brokers, who translate tacit

knowledge into explicit knowledge by coding the

individuals' insights and experience, leading to new

knowledge within the AI system to facilitate decision-

making processes (Waardenburg et al., 2022).

Fowler (2000) discusses how integrating human

intelligence with AI can enhance problem-solving

and user acceptance. Additionally, the research

examines how AI systems codify tacit knowledge into

actionable steps by finding, filtering, formatting,

forwarding, and providing feedback knowledge to the

Human-in-the-Loop AI, thereby supporting human

decision-making. Kumar (2025) demonstrates the

development of a dual-loop framework that

influences the use of AI within KM to support better

decision-making and continuous feedback. This

framework encourages human–AI collaboration in

the practice of automated, augmented, and supported

decisions.

Limitations: While the existing literature

provides insight into how AI affects organisational

knowledge, there is still a need for broader research

to understand its overall impact on knowledge-related

practices. Further studies are needed to explore the

evolving relationship between AI and human roles,

including its influence on workplace dynamics,

information flow, and the reliability of knowledge

within organisations.

Theme 2: Trust and Ethics (10 articles): These

articles discuss AI in relation to trust and ethics.

Creating tacit dimensions for AI is challenging.

Sanzogni et al. (2017) highlighted four aspects that

would have implications for research, which other

researchers have since explored.

Firstly, there are ethical challenges to the

knowledge of AI (Sanzogni et al., 2017). Benbya et

al. (2024) discuss several ethical and practical

concerns related to copyright infringement when

GenAI creates text and other content, potentially

using copyrighted material without permission.

Additionally, GenAI can produce misinformation,

which may lead to employees becoming overly reliant

on AI. Issues of accountability and ownership are also

raised, particularly regarding who holds rights to AI-

generated content. Research by Rezaei et al. (2024)

studied the ethical challenges in decision-making

processes within AI in knowledge sharing, which

helps to validate ethical challenges in the e-retail

landscape. The author noted that privacy and data

protection challenges, bias and fairness, and

transparency and explainability are particularly

significant in influencing decision-making processes.

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Secondly, knowledge sharing in KM is

challenging to measure with AI (Sanzogni et al.,

2017). However, AI technology helps to facilitate

explicit knowledge sharing rather than employees

hiding the knowledge (Abubakar et al., 2019). Recent

research has used AI to help capture explicit

knowledge related to knowledge hiding. For example,

managers could use AI to assess fairness among

employees, allowing them to address issues quickly

before they escalate (Abubakar et al., 2019). In this

instance, AI has a positive impact by increasing the

transparency of knowledge. Additionally, Di Vaio et

al. (2020) discussed the role of AI in advancing

sustainable business models through KM systems,

noting that AI has the potential to support knowledge

sharing across different countries. However, AI raises

concerns about privacy and transparency, and policy

support is needed to address these issues.

Thirdly, reliance on AI may reduce some social

interactions, as AI-driven work activities will

influence employees’ social relationships by shifting

interaction dynamics (Sanzogni et al., 2017). Jarrahi

et al. (2023) and Benbya et al. (2024) while AI can

assist in KM processes, it will reduce the social

interactions and aspect of knowledge exchange

knowledge between individuals. Evenly, AI supports

collaborative roles like decision-making, and

organisations need to have concerns about ethics,

safety, and accuracy (Chanda, 2024).

Lastly, the relationship between AI and KM will

affect the power relations in society, understanding

the design of AI systems in terms of who designs and

manages the knowledge sharing (Sanzogni et al.,

2017). Trust in AI and knowledge sharing can be

hindered when users lack an understanding of the AI

systems. Transparency is essential to build user trust

and facilitate effective knowledge sharing (Rezaei et

al., 2024). The Trust Paradox highlights that a lack of

transparency and public understanding of AI systems

can hinder AI adoption. Addressing trust issues

through factors such as reliability, transparency,

association, reciprocity, and accountability can help

balance knowledge and power, enabling broader trust

in AI-generated knowledge (Nylund et al., 2023).

Additionally, Chowdhury et al. (2022) examined the

organisational dynamics of employee AI-

collaboration in knowledge sharing, showing a

positive relationship between employees’ trust in AI

systems and improved business performance.

Limitations: The existing studies acknowledge

that tacit knowledge remains challenging to capture

and integrate within AI systems. However, many of

these studies are limited in scope, often focusing on

specific industries or relying primarily on quantitative

methods. There is also a noticeable gap in the

literature concerning ethical considerations related to

AI in KM. Broader and more in-depth qualitative

research is needed to gain a deeper understanding of

these ethical challenges and their implications for AI-

driven knowledge processes.

Theme 3: Ingenuity (10 articles): The articles view

ingenuity within an organisational context as the

innovation and creativity to solve problems and

create new opportunities that impact organisational

performance. The sub-themes include innovative

solutions and creativity.

Innovative solutions: Articles view innovative

thinking in creating new problem-solutions to

improve organisational performance (7 articles).

AI offers new opportunities for enabling

knowledge sharing among businesses that foster open

innovation. Broekhuizen et al. (2023) investigate the

challenges and opportunities of using AI in managing

open innovation to enhance creativity, collaboration,

and innovation between organisations. Hence, by

creating new knowledge, AI helps the innovation

strategy of firms by implementing shared knowledge

to construct the creative process within open

innovation. Additionally, AI can be viewed as a tool

comprising a set of techniques that benefit both

customers and firms. The article emphasises the need

for further research to understand how AI tools can

support the development of more innovative KM

practices within organisations (Marvi et al., 2024).

AI can help with knowledge sharing, innovative

communication, and facilitating knowledge-hiding

on why employees are hiding knowledge in the

organisations (Abubakar et al., 2019). Implementing

AI can improve KM practices by facilitating

communication processes within the organisation, as

currently, there is no empirical research on

understanding how communication practices between

AI and the organisation’s KM (Iaia et al., 2024).

AI acts as a tool to improve the KM of the

organisation, as AI allows the acquisition, storage and

utilisation of the data and knowledge within the

organisation to help the employees with decision-

making and take the required training to improve the

employees’ skills and knowledge on how to use AI

effectively, as noted by Oppioli et al. (2023).

Additionally, AI supports firms in achieving

competitive advantages, which impacts the financial

and non-financial performance (Bahoo et al., 2023).

AI such as ChatGPT can reshape KM: Sumbal et

al. (2024) conducted a case study focusing on

academics in Hong Kong to study how ChatGPT is

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reshaping the KM paradigm. The research

highlighted how ChatGPT is useful for checking

knowledge bases, specifically explicit knowledge.

Additionally, the article demonstrates how data and

information help to improve decision-making and

promote collaboration within different levels of the

organisation. Tacit knowledge can be supported by

knowledge sharing through ChatGPT, as it serves as

a knowledge base for creating and disseminating

knowledge.

Creativity: Articles view AI creativity

as providing new opportunities for organisation (3

articles).

AI provides many creative solutions for managing

knowledge. These include changing the dynamics of

knowledge sharing on social media platforms, as AI

offers innovative solutions by enabling quick

responses and reducing wait times (e.g., through

chatbots) (Ghouri et al., 2022). Similarly, Saviano et

al. (2023) explained that integrating AI with customer

service would influence the customer's handling

of emotional and complex customer needs.

Additionally, Botega and Silva (2020) studied AI to

support KM in selecting creativity and innovation

techniques during new product development, and

noted that AI systems can improve creativity and

innovation by providing more creative knowledge to

the employees.

Limitations: While AI shows promise in

enhancing innovation and creativity, the current

literature provides limited insight into its broader

impact on organisational knowledge culture and

leadership practices. There is also a gap in

understanding how AI influences human factors

across different roles, organisational contexts and

sectors. Further research is needed to investigate

these areas in greater depth and better understand the

broader implications of AI within organisational

settings.

Theme 4: Performance (10 articles): These articles

view ways to improve the organisation's

performance.

One benefit of AI is that it solves complex

problems and supports knowledge sharing for

organisational performance. A recent study by Olan

et al. (2022) investigated the role of AI and

knowledge sharing on organisational performance.

Results showed a positive relationship between AI

knowledge sharing on organisational performance

and organisational innovation, which can help

employees make decisions and provide new

opportunities. AI has the potential to improve team

knowledge through sharing the mong among

employees and (Zhang et al., 2024) and supporting

smarter, more efficient, and sustainable practices for

organisations that invest in knowledge sharing

systems (Al Halbusi et al., 2025). Similarly, Rodgers

et al. (2023) found suggest that AI algorithms

strengthen the transfer of knowledge and influence

decision-making within the organisation. Armenia et

al. (2024) demonstrate that AI helps companies

gather, store, and utilise knowledge more effectively,

which improves decision-making and maintains

competitive advantage.

AI can be integrated in various ways, such as by

incorporating it into human-computer technology.

Additionally, it can be used to assess employee job

satisfaction and performance, as noted Li et al.

(2022), demonstrating positive impacts in these areas.

Also, AI can be integrated into banking systems to

encourage efficient knowledge to support decision-

making, as explained by Mohapatra (2021). The

integration of AI can improve services by automating

repetitive tasks, allowing employees to do other tasks

(Mohapatra, 2021), thereby saving time and

improving labour efficiency and fairness (Li et al.,

2022). Knowledge sharing allows the employees to

interact with the AI system using verbal instructions,

and allows AI to learn and update its feedback loop

based on the data (Li et al., 2022).

AI is integrated into KM systems. Kumar (2025)

explains that AI technologies integrated into KM

systems within the healthcare sector may enhance

clinical performance and patient satisfaction by

improving diagnostic accuracy, supporting informed

decision-making, and enabling healthcare providers

to better understand and tailor services to patients’

needs, thereby strengthening their value proposition.

Lastly, AI can be integrated into the clothing business

system, as mentioned in the article by Liu and Li

(2022), An AI algorithm optimises production

processes, enabling the clothing line to operate more

efficiently and fostering a positive relationship

between customer trust and the system.

Theme 5: Information Security (2 articles): This

theme discusses the articles on information security

in AI systems to prevent knowledge from leakage.

Creating knowledge from AI requires the content

to be protected. Samtani et al. (2023) found the

importance of investing in AI security systems to

protect sensitive knowledge and data from potential

cyber threats. Similarly, Sahay et al. (2021) highlight

the importance of securing KM systems in AI to

protect sensitive information from leaking and

unauthorised access. Securing AI systems is essential

from cyber-attack, and all organisations need to have

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a security strategy to protect the AI systems from

attack to ensure the privacy and integrity of the data;

this strategy will help to create trust between the

organisation and their stakeholders’ confidence in

transparency and explainability.

Limitations: The reviewed studies highlight the

potential of AI to enhance organisational

performance and strengthen security within

knowledge management systems. However, there is

still limited understanding of the risks and challenges

associated with AI implementation. Further research

is needed to explore how organisations are addressing

these concerns, particularly in terms of safeguarding

data and ensuring secure AI integration.

3 CONCLUSIONS

This systematic literature review explores the gaps at

the intersection of AI and KM processes, with a focus

on knowledge creation, sharing (both tacit and

explicit), and utilisation within organisations. A

thematic analysis of 42 academic articles helped

identify key gaps in the literature, leading to the

emergence of five core themes: AI–individual

collaboration, trust and ethics, ingenuity,

organisational performance, and information

security. While AI has the potential to enhance KM

processes (Alavi et al., 2024), it also presents

significant challenges, including algorithmic bias and

ethical concerns (Alavi et al., 2024; Anthony et al.,

2023; Benbya et al., 2024).

The findings present both opportunities and

challenges that AI introduces within KM processes.

Opportunities include improving employees’ daily

tasks (Paschen et al., 2020), and enhancing

knowledge sharing between organisations and

customers by improving the overall customer

experience (Kane, 2017). AI also enables the creation

of new knowledge and opportunities for organisations

(Olan et al., 2022; Shollo et al., 2022), thereby

helping them gain a competitive advantage within

their industries (Liu et al., 2024) and fostering open

innovation (Broekhuizen et al., 2023).

However, AI also raises concerns such as ethical

transparency (Jarrahi et al., 2023), trust (Sanzogni et

al., 2017), and the risk of providing misinformation

to customers (Alavi et al., 2024). Additionally, the

literature remains limited in empirical research and

shows a degree of industry-specific bias, revealing

clear gaps in current knowledge.

This review underscores the importance of

understanding the existing body of literature within

the interdisciplinary field of AI and KM frameworks,

and the need to adopt novel research approaches.

Future research should adopt both quantitative

and qualitative methods to better examine the

complexities of AI–human collaboration across

industries and to address the ethical challenges in AI-

driven knowledge management processes. By doing

so, scholars and practitioners can foster a more

intelligent and ethical knowledge culture that

leverages AI effectively for both short- and long-term

organisational learning and innovation.

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