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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026

The Impact of Artificial Intelligence on Knowledge Management and

Organizational Competitiveness: Evidence from Access Bank Nigeria

Plc

Comfort Kasevhemba Aande

Department of Business Administration, MBM (MSc) with specialization in Human Resources,

Wittenborg University of Applied Sciences, Apeldoorn, Netherlands.

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.1501000100

Received: 29 January 2025; Accepted: 03 February 2026; Published: 18 February 2026

ABSTRACT

With the increased awareness of Industry 4.0 and big data, this study provides an opportunity to examine how

AI can address the limitations in traditional knowledge management. This study aims to determine the

relationship between artificial intelligence (AI), knowledge management (KM), and organizational

competitiveness (OC), in the banking sector using a case study of Access Bank Nigeria PLC.

A quantitative method has been employed to analytically explore the research aims. Seven hypotheses were

created; three hypotheses were analyzed based on correlational analysis between AI and KM infrastructure

capabilities (KMI), KM process capabilities (KMP) and KM relational capabilities (KMR); four was analyzed

using the regression analysis (using SPSS) at a significance level of 0.05 to test the relationship between AI

improved KMI, KMP, and KMR on organizational competitiveness (OC). Data was collected through a survey

of 133 employees of Access Bank Nigeria PLC.

This study shows that there is a positive relationship between AI and KMI (β = 0.397, p < 0.001), KMP (β =

0.392, p < 0.001), and KMR (β = 0.312, p < 0.001) from the correlation analysis; and that AI-improved

knowledge management capabilities have more impact on Access Bank Nigeria’s competitiveness than the

traditional KM strategies. This indicates that AI helps improve the KM capabilities which further strengthens

that various KM strategies. Lastly it was found that AI has a positive impact on Access Bank competitiveness

(R² = 0.461, F = 35.445, p < 0.001) and that organizations should further strengthen their integration of AI into

their knowledge management strategies.

Keywords: Knowledge management, Artificial Intelligence, Organizational Competitiveness, Knowledge

Management Capabilities

INTRODUCTION

As the fourth industrial revolution (4IR) approaches, sophisticated technologies like cloud-based computing, big

data analytics, artificial intelligence (AI), and others are changing how many types of businesses operate

(Farishy, 2023). Artificial Intelligence (AI) has significantly transformed how companies run and provide

services to their clients. AI has been incorporated into a number of processes, particularly for making decisions

based on data.

In particular, the banking industry is becoming more customer-focused and technologically competitive due to

the integration of AI into banking applications and services (Singh, 2023). AI-powered technologies are

becoming increasingly useful in helping banks save expenses due to their increased efficacy and ability to make

judgments based on data that is beyond human comprehension.

The creation, sharing, utilization, and organization of information and knowledge inside an organization are all

part of knowledge management (KM), which is a crucial component to business performance (Obaro et al., 2022;

Taherdoost & Madanchian, 2023). However, traditional approaches have shown to be stressful, particularly when

handling large amounts of data and records (Obaro et al., 2022; Taherdoost & Madanchian, 2023). Moreover,

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despite the significance of knowledge management adoption in enterprises, many knowledge management

implementation failures were primarily caused by a lack of analysis and a sufficient grasp of the essential

components of an efficient execution (Adelowo & Titilope, 2020).

Problem statement

According to a report by Access Bank Nigeria PLC CEO, Herbert Wigwe, leveraging technologies like AI and

data analytics will help improve competitiveness in banking and fintech (Vanguard Nigeria, 2019). For the

banking sector, loads of documentation are being processed daily from credit/debit card monitoring, daily

transactions, customer services, and so on and all of these are required for proper knowledge development to

further understand customer needs and requirements.

There are 21 commercial banks, 860 microfinance banks, 5 discount houses, 64 finance companies, and 5

development finance banks in Nigeria and for Access Bank to remain competitive, there needs to be proper

knowledge management and analysis.

Conventional knowledge management has been shown to be stressful (Obaro et al., 2022; Taherdoost &

Madanchian, 2023); it has also failed because the organization was unable to generate, store, and process the

volume of knowledge it possessed. However, thanks to artificial intelligence (AI) systems, this is now possible

(Jarrahi et al., 2022).

Although Okonji et al. (2023) discovered a strong correlation between organizational performance and AI

competency, they were unable to address important implications of AI, did not address the deployment of AI

technologies, and did not address the effects on knowledge management.

In order to determine the effects of AI on knowledge management, several systematic reviews have also been

conducted (Alqahtani et al., 2022; Taherdoost & Madanchian, 2023). However, due to methodological flaws,

such as the articles' perceived credibility (Uttleya et al., 2023) and the possibility of bias in the inclusion and

exclusion criteria (Owens, 2021), these studies have not been able to fully correlate AI and knowledge

management. A cross-sectional investigation will thus be successful.

In support of this, in 2019, Access Bank stated that the organization is revolutionizing how banking will be

carried out through the use of cutting-edge technologies for their various operations. However, the impacts of

these technologies on knowledge management as pertain to competitiveness were not stated.

Although AI is capable of processing and analyzing enormous volumes of data, it is therefore important to

consider how this technological development will impact the organization's knowledge management procedures

and, ultimately, its ability to compete in the banking sector.

Research Objectives

To effectively and quantitatively answer these questions with the adoption of the 2 frameworks identified,

this study will be conducted:

i. To assess and understand the current level of AI and knowledge management in Access Bank.

ii. To ascertain the benefits of adoption of AI technologies in banking industry

iii. Evaluate the relationship between AI, knowledge management, and organizational competitiveness.

LITERATURE REVIEW

Knowledge management (KM) is defined as the following tasks, according to Mutula and Mooko (2008): (i)

compiling information that staff members and customers need into a centralized archive; (ii) determining the

knowledge classifications needed to support the organization's overall strategy; (iii) gathering, organizing, and

sharing information within the organization; (iv) utilizing technology to assist in maintaining and retrieving

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information; and (v) offering accessibility tools (Igbinovia & Ikenwe, 2018) as shown in Figure 1 below.

Figure 1. Triangle of Knowledge management (Igbinovia & Ikenwe, 2018)

Obaro et al. (2022) grouped knowledge management into three; first, KM Infrastructural capabilities, defined as

a corporate architecture for continuously and intentionally producing information is called knowledge

management infrastructure (Obaro, et al., 2022). Tsetim et al. (2020) claim that there are three main ways to

understand the KM infrastructure: Culture (the norms, values, assumptions, and beliefs of employees and

employers within an organization and how they affect decision-making processes); Technology (all of the

company's computerized frameworks, such as transaction processing systems, accounting systems, data centers,

and enterprise resource planning (ERP) systems); and Structure (refers to organization functional departments

and leadership systems to management and monitor organizational goals).

Second, KM process capabilities is defined as the processes involved in efficient knowledge management. These

are entangled or interwoven groups of actions, including production, distribution, storing and extraction, and

utilization. While knowledge facilitators supply the framework needed by the company to improve the efficacy

of information processes, knowledge processes themselves reflect the fundamental activities of knowledge

(Obaro, et al., 2022; Tsetim, et al., 2020). Kaur & Mehta (2016) found that KN process capabilities Knowledge

Management Process Capabilities are crucial for establishing an advantage over rivals in today's contemporary

marketplace.

Lastly, KM relational capabilities defined as the external connections a company has with its suppliers and

customers, which allow it to successfully and efficiently buy and sell goods and services (Obaro, et al., 2022).

Developing relationships may stimulate more creativity and the growth of values. By lowering expenses, raising

revenue, and creating new skills, client interactions may benefit the company and improve the quality of services

provided (Sugiyarti & Mardiyono, 2022; Brous, 2020).

Artificial Intelligence in Banking Sector

The banking industry in Nigeria has started utilizing AI technologies, such as chatbots, which employ AI on

more advanced platforms to provide developing financial solutions and human-like interactions through chat

dialogues, and computerized robots for automating and simplifying procedures. These advances will

significantly reduce the cost of servicing each customer, allowing banks to increase revenue, access more

unbanked individuals, and provide exceptional customer care at a continuously decreasing cost (Agidi, 2019).

In order to ensure that the advantages of financial AI for people ultimately benefit everyone, Ukpong (2022)

advised banks to adopt artificial intelligence methodically, not just as a means of competing, but as a

comprehensive business approach.

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Hypothesis: AI techs have a positive relationship with Access Bank Nig. Plc competitiveness

Knowledge Management & Artificial Intelligence

By tying together elements in unexpected ways, artificial intelligence has the potential to provide fascinating

questions and new sets of infrastructure knowledge on a variety of subjects. Enterprises may utilize enormous

data in novel ways only with self-learning analytical skills and pattern detecting features (Jarrahi et al., 2022).

In a similar vein, AI capabilities can help companies find previously unidentified relationships and information

from information collections that include call transcriptions, emails, the organization chat systems, internet

discussions, and customer relationship management (CRM) databases, among other records of their interactions

with clients (O’Dell & Davenport, 2019; Jarrahi, et al., 2022).

Hypothesis: There is a positive correlation between AI tech and KM Infrastructure capabilities

Taherdoost & Madanchian (2023) discovered that with the simplicity of knowledge processing and analytics,

artificial intelligence enables people and groups to enhance inventive thinking and efficiency at all organizational

levels. It also makes it easy to track outcomes. Businesses gain from higher profitability and ease of achieving

many objectives. This makes them more competitive, lowers costs, bolsters security, and continuously provides

data. These adjustments improve the techniques that may be used at any stage of the KMP. The application of

AI technology in knowledge management offers significant benefits when done correctly. Banks that employ AI

technology derive significant benefits from it for their customers, employees, and enterprises. According to Smit

(2024), banks might use AI to reduce risk, boost profitability and economy, boost staff output, and enhance

knowledge processing across acquisition and analytics (Smit, 2024).

Hypothesis: There is a positive correlation between AI tech and KM Process capabilities

Ullah (2023) pointed out that AI may have a significant influence on managing a company's relationships both

inside and outside the company, particularly when it comes to client management. The author came to the

conclusion that artificial intelligence might be very important in business plans meant to enhance the customer

experience, provided that the costs associated with it are kept to a minimal. AI's constant modifications to

communication are not only redefining the commercial function of the organization, but they are also affecting

marketing managers' decision-making and data-interpreting procedures. More and more managers are realizing

how critical it is to broaden their skill sets, acquire strong technical proficiency, and possess a thorough

understanding of marketing concepts. (Iqbal & Khan, 2021)

Hypothesis: There is a positive correlation between AI tech and KM Relational capabilities in the banking

industry.

Theoretical Framework

This research adopted a dual‑framework approach to explain the theoretical relationships among Artificial

Intelligence (AI), Knowledge Management (KM), and Organizational Competitiveness (OC) within the Nigerian

banking sector. The first framework considered was developed by Bag et al. (2020) to facilitate the critical

understanding of the relationship between Big-data, an important component of Artificial Intelligence

technologies, knowledge management, and organizational performance. This framework connects the role of AI

as a major enabler of knowledge management on organizational performance and emphasizes that when AI is

strategically integrated into an organization’s knowledge management system, the benefits on performance will

be achieved.

The second framework was proposed by Obaro, et al. (2022). This framework directly considers the relationship

between the three knowledge management capabilities and organizational competitiveness, but does not consider

any enabler. The model consists of 3 knowledge management capabilities: KM infrastructural, KM process, and

KM relational which will be adopted in this study. This model has been selected as it analyses the concept of

knowledge management, and organizational competitiveness in the banking industry in Nigeria. Based on the

theoretical frameworks, the current study research framework was developed as shown in Figure 2 below.

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Figure 2. Conceptual Research Framework

• H1: There is a positive correlation between AI tech and KM Infrastructure capabilities

• H2: There is a positive correlation between AI tech and KM Process capabilities

• H3: There is a positive correlation between AI tech and KM Relational capabilities

• H4: AI Improved KM Infrastructure capabilities positively impact on Access Bank Nig. Plc competitiveness

• H5: AI Improved KM Process capabilities positively impact on Access Bank Nig. Plc competitiveness

• H6: AI Improved KM Relational capabilities positively impact on Access Bank Nig. Plc competitiveness •

H7: AI techs have a positive relationship with Access Bank Nig. Plc competitiveness

RESEARCH METHODOLOGY

This research employed a cross-sectional study design in order to gather the required data. Also, the primary data

came from the information gathered from the participants using a well-structured survey questionnaire. A letter

requesting participation in the survey as well as direct communication and referral with recognized organization

staff will be used to get in touch with the participants.

Upon completion of data gathering, a complete data analysis will be carried out using the IBM SPSS 21.

Additionally, regression analysis and the correlation coefficient will be employed. The Cronbach's Alpha

coefficient testing will be utilized to verify the internal uniformity of the research variables and assess the

reliability of the measurement parameters (Asaolu, 2018; Adelowo, 2020).

The target population for this study is the employees of Access Bank Nigeria PLC. The total employees of

Access Bank in Nigeria are 28,000 with 566 branches (Access Bank Plc, 2021). The target location is employees

from various Access Bank branches in Lagos Nigeria (49 branches) and the target population size is taken as

300; using the Taro Yamane formula for sample size calculation at a sampling error of 5% (Asaolu, 2018), the

sample size is estimated at 170.

Knowledge

Management

Capabilities

Independent

variable

Dependent

variables

Technologie

•

Natural Language Processing (NLP)

•

Chatbots and Virtual Assistants

•

Big data analytics for predictive analysis for marketing

•

Big data analytics for customer analytics

•

Knowledge Graphs

KM Infrastructure

Capabilities

Access Bank Nig. Plc

Competitiveness

KM Process

Capabilities

KM Relational

Capabilities

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The reliability of the study instrument was tested using the Cronbach’s Alpha test. Hair, et al. (2016) states that

the value needs to be controlled by more than 0.7 and the validity was tested using the Kaiser-Meyer-Olkin

(KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity.

RESULTS AND DISCUSSION

133 respondents completed the survey for this study out of the expected 170 (78% of the sample was achieved).

According to Ali et al. (2021), for an online survey, the expected response rate should be at least 54% of the total

sample. From this survey, 52% of the respondents are male, 47.06% are female and 0.74% selected others. From

this, it can be inferred that the majority of the respondents are male. Also, age-wise, 48.87% are between the age

of 21-34, 41.35% are between the age of 35-44, 9.02% are between the age of 45-54 and 1.50% are between the

age of 55-65. This shows that the majority of the respondents are between age of 21-44 and they are young.

In terms of qualification, 47.76% have a bachelor’s degree, 20.15% (Higher National Diploma), 19.4% (master’s

degree), 5.97% (National Diploma), 4.48% (post-graduate diploma) and 2.24% (PhD). Additionally, 47.01% are

operational staff, 18.66% are middle management, 14.18% are executive leadership, 10.45% are IT staffs, while

10.45% are in other positions. Lastly, in terms of years of experience of respondents, 47.37% have 1-5 years of

experience, 30.83% have 6-10 years of experience, 15.04% have more than 10 years of experience and 7.52%

have less than 1 year of experience.

For this study, the variables have been abbreviated as: Organizational Competitiveness (OC), AI (Artificial

Intelligence technologies), KMR (Knowledge Management Relational), KMI (Knowledge management

infrastructural), KMP (knowledge management processes), AIKMR (AI improved Knowledge Management

Relational), AIKMI (AI improved Knowledge Management Infrastructural), and AIKMP (AI improved

Knowledge Management Processes). The Likert scale of “Agreedness” (SA-1, A=2, N=3, D=4, and SD=5) have

been used in the development of the questions and the grand mean from the calculation is 1.7279. Based on this,

all mean value between 1-2.5 shows a high level of agreement, a mean value of 2.6-3.5 shows neutrality to

responses and a mean between 3.6 to 5 shows a high level of disagreement.

Table 1. Descriptive statistics of Variables

Variables

Total

Observed

Mean

Median

Std. Deviation

Skewness

133

1.7519

1.5

0.62651

0.824

133

1.6291

1.6667

0.52649

0.435

KMR

133

1.75

1.875

0.48216

0.605

KMP

133

1.7581

1.8333

0.5862

2.03

KMI

133

1.708

1.8333

0.48566

0.474

AIKMR

133

1.7504

1.8

0.44118

0.289

AIKMP

133

1.7566

1.75

0.5214

1.443

AIKMI

133

1.719

1.75

0.45001

0.387

Table 1 above shows the descriptive measures of the study variables. It can be seen that for all study variables,

the mean and median values fall between 1.5 to 1.875 which means that majority of the respondents agree with

the majority of the questions in the survey. The skewness value shows the spread and height of the distribution

around the mean values. George & Mallery (2019) and Hair et al. (2022) noted that a skewness value of zero

means the distribution is perfectly normal, a value of skewness between -1 and +1 means the distribution is

excellent for analysis, while a value of skewness between -2 and +2 is acceptable. Based on Table 4.1 above, it

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can be seen that 75% of the study variables (AI, OC, KMR, KMI, AIKMR, and AIKMI) have a skewness value

between -1 and 1 while 25% of the study variables (KMP, and AIKMP) have skewness value between -2 and +2.

Therefore, based on this, the distribution of data can be concluded to be fairly symmetrical along the mean and

the majority of respondents agreed to the questionnaire meaning their responses fall between 1 to 2.5.

Reliability and Validity

Table 2. Reliability Test

Variable

No. of Measurement items

Cronbach's Alpha

0.898

KMR

0.893

KMP

0.889

KMI

0.891

AIKMR

0.881

AIKMP

0.875

AIKMI

0.879

The Cronbach’s Alpha as calculated from SPSS has been put together and it can be seen that the Cronbach’s

Alpha value for all variables is well above 0.85 as shown in in Table 2 above. This tells that the results from this

study are highly reliable and can be used for making inferential studies.

Table 3. Validity Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.829

Bartlett's Test of Sphericity

Approx. Chi-Square

1418.808

300

Sig.

.000

As shown in Table 3 above, the KMO sampling adequacy was measured at 0.829 which relates to a good measure

and Barlett’s test was significant at p<0.001 which means that the variables are well correlated for analysis.

These tests therefore confirms that the measurement items and the results of this research are valid.

Correlational study

The following correlational hypothesis are tested:

• Hypothesis 1: There is a positive correlation between AI tech and KM Infrastructure capabilities.

• Hypothesis 2: There is a positive correlation between AI tech and KM Process capabilities

• Hypothesis 3: There is a positive correlation between AI tech and KM Relational capabilities

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The table 4 below shows the various correlation coefficients of each variable plotted against each other.

Table 4. Correlation Matrix between AI, KMI, KMP, and KMR

Variables

KMR

KMP

KMI

0.312

0.392

0.397

KMR

0.312

0.525

0.439

KMP

0.392

0.525

0.418

KMI

0.397

0.439

0.418

Sig. (2-tailed)

<0.001

**.Correlation Is Significant At The 0.05 Level (2-Tailed)

From the Table 4 above, it can be seen that there is a positive relationship between AI and KMR, KMP, and KMI

with a correlation coefficient of 0.312, 0.392, and 0.397 respectively at p<0.001. Based on this, it can be inferred

that with AI, the knowledge management practices in Access Bank Nigeria can be highly improved. Therefore,

Hypotheses 1,2, and 3 are accepted.

Regression Analysis - AI Improved KMI

• H

: AI Improved KM Infrastructure capabilities positively impact on Access Bank Nig. Plc competitiveness.

The null and alternative hypotheses for this case are:

• H

4.0

: AI Improved KM Infrastructure capabilities does not have any significant impact on Access Bank Nig.

Plc competitiveness.

• H

4.A

: AI Improved KM Infrastructure capabilities have a significant impact on Access Bank Nig. Plc

competitiveness.

Table 5. Regression Model – AI Improved KMI

Model Summary

Model

R Square

Adjusted R

Square

Std. Error of

the Estimate

F Change

0.585

0.343

0.337

0.42854

68.243

ANOVA

Model

Sum of Squares

Mean Square

Sig.

Regression

12.532

68.243

<.001

Residual

24.057

0.184

Total

36.59

Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Beta

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(Constant)

0.452

3.07

0.003

AIKMI

0.685

0.585

8.261

<.001

Trad. KMI

0.569

0.525

7.053

From Table 5 above, the model has an R-value of 0.585 (58.5%) which shows that there is a 58.5% correlation

between AIKMI and OC. Based on these findings, it can be inferred that the null hypothesis is rejected, and the

alternative hypothesis is accepted that there is a significant and positive relationship between AI-improved

knowledge management infrastructural capabilities and organizational competitiveness of Access Bank Nigeria

Ltd.

In comparison with regression coefficients of KMI on OC, it can be seen that KMI only has 52.5% impact on

Access Bank Nigeria's competitiveness while with AI-improved KMI, a 58.5% impact is recorded on Access

Bank Nigeria's competitiveness. Therefore, the introduction of AI into knowledge management infrastructural

capabilities has more impact on organizational competitiveness than just the traditional knowledge management

infrastructural capabilities.

Regression Analysis - AI Improved KMP

• H

: AI Improved KM Process capabilities positively impact on Access Bank Nig. Plc competitiveness. The

null and alternative hypotheses for this case are:

• H

5.0

: AI Improved KM Process capabilities does not have any significant impact on Access Bank Nig. Plc

competitiveness.

• H

5.A

: AI Improved KM Process capabilities have a significant impact on Access Bank Nig. Plc

competitiveness.

Table 6: Regression Model – AI Improved KMP

Model Summary

Model

R Square

Adjusted R

Square

Std.

Error of

the

Estimate

Change

0.479

0.230

0.224

0.46385

39.061

ANOVA

Model

Sum of Squares

Mean

Square

Sig.

Regression

8.404

39.061

.000

Residual

28.186

0.215

Total

36.590

Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Beta

(Constant)

0.779

5.492

<.000

AIKMP

0.484

0.479

6.250

<.000

Trad. KMP

0.363

0.404

5.054

<.000

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From Table 6 above, the model has an R-value of 0.479 which shows that there is a 47.9% correlation between

AIKMP and OC. Based on these findings, it can be inferred that the null hypothesis is rejected, and the alternative

hypothesis is accepted that there is a significant and positive relationship between AI-improved knowledge

management process capabilities and organizational competitiveness of Access Bank Nigeria Ltd.

In comparison with regression coefficients of KMP on OC, it can be seen that KMP only has 40.4% impact on

Access Bank Nigeria's competitiveness while with AI-improved KMP, a 47.9% impact is recorded on Access

Bank Nigeria's competitiveness. Therefore, the introduction of AI into knowledge management infrastructural

capabilities has more impact on organizational competitiveness than just the traditional knowledge management

infrastructural capabilities.

Regression Analysis - AI Improved KMR

• H

: AI Improved KM Relational capabilities positively impact on Access Bank Nig. Plc competitiveness. The

null and alternative hypotheses for this case are:

• H

6.0

: AI Improved KM Relational capabilities does not have any significant impact on Access Bank Nig. Plc

competitiveness.

• H

6.A

: AI Improved KM Relational capabilities have a significant impact on Access Bank Nig. Plc

competitiveness.

Table 7: Regression Model – AI Improved KMP

Model Summary

Model

R Square

Adjusted R

Square

Std. Error

of the

Estimate

F Change

0.438

0.192

0.185

0.47520

31.035

ANOVA

Model

Sum of Squares

Mean

Square

Sig.

Regression

7.008

31.035

.000

Residual

29.582

0.226

Total

36.590

Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Beta

(Constant)

0.715

4.225

<.000

AIKMR

0.522

0.438

5.571

<.000

Trad. KMR

0.383

0.351

4.286

<.000

From Table 7 above, the model has an R-value of 0.434 which shows that there is a 43.4% correlation between

AIKMR and OC. Based on these findings, it can be inferred that the null hypothesis is rejected, and the alternative

hypothesis is accepted that there is a significant and positive relationship between AI-improved knowledge

management relational capabilities and the organizational competitiveness of Access Bank Nigeria Ltd.

In comparison with regression coefficients of KMP on OC, it can be seen that KMR only has a 35.1% impact on

Access Bank Nigeria's competitiveness while with AI-improved KMP, a 43.8% impact is recorded on Access

Bank Nigeria's competitiveness. Therefore, the introduction of AI into knowledge management relational

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026

capabilities has more impact on organizational competitiveness than just the traditional knowledge management

infrastructural capabilities.

Regression Analysis - AI impact on Access Bank

H7: AI techs have a positive relationship with Access Bank Nig. Plc competitiveness.

Table 7: Regression model of AI impacts on OC

Model

R Square

Adjusted R Square

F Change

Sig. F Change

0.461

0.213

0.207

35.445

<.001

In overall, the impact of AI on Access Bank Nigeria competitiveness was studied based on regression and the

table 7 below gives the regression model.

Based on this model, it can be seen that the model has an R-value of 0.461which shows that there is a 46.1%

correlation between AI and OC. Also, the R square value was 0.213 which means that 21.3% of the changes in

OC is well explained by the model. This therefore concludes that AI techs have a positive relationship with

Access Bank Nig. Plc competitiveness.

DISCUSSION OF FINDINGS

Figure 3. Construct Analysis (Author’s own)

=0.343

=0.479

=0.230

=0.392

, p

<0.05

Knowledge

Management

Capabilities

Independent

variable

Dependent

variables

Technologi

=0.397

, p

<0.05

•

Natural Language Processing (NLP)

•

Chatbots and Virtual Assistants

•

Big data analytics for predictive analysis for marketing

•

Big data analytics for customer analytics

•

Knowledge Graphs

KM Infrastructure

Capabilities

Access Bank Nig. Plc

Competitiveness

KM Process

Capabilities

KM Relational

Capabilities

=0.312

, p

<0.05

=0.585

=0.438

, p

<0.05

=0.192

=0.461

, p

<0.05

=0.213

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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This study presents the empirical study concerning the application of Artificial Intelligence (AI) to Knowledge

Management (KM) and Organizational Competitiveness (OC) by the Nigerian banking industry based on the

case study of the Access Bank Nigeria Plc. The results showed that the implementation of AI contributes greatly

to the improvement of the infrastructure of knowledge management, knowledge management processes, and

relational capabilities, which jointly lead to the creation of a sustained competitive advantage as presented in the

Figure 3 above. These findings support the increasingly common finding of the literature that AI is a strategic

organizational tool and not just a technological one.

Furthermore, the research ascertains that AI significantly improves knowledge management processes, such as

knowledge creation, sharing, storage, and utilization. Predictive analytics-based AI and decision-support systems

will enable the use of data to generate knowledge and enhance learning in organizations. According to the

regression findings, the process of AI-enhanced KM has a statistically significant impact on the competitiveness

of organizations, which implies that companies that use AI in KM processes are in a better position to innovate

and respond to the changing market dynamics.

Besides, the findings also point to the significance of AI in knowledge management relational capabilities.

Customer analytics, chatbots, and personalization tools powered by AI enhance the capabilities of the

organization to gather and use customer knowledge, which in turn enhances the level of relationships with the

customers and other stakeholders in the organization. Greater relational abilities will add to better service quality,

customer satisfaction and customer loyalty which is crucial to competitiveness in the very competitive banking

industry.

The findings on the whole suggest that AI-led knowledge management features can describe a significant amount

of organizational competitiveness variation, highlighting the strategic competence of AI implementation. The

paper shows that AI-based KM strategies are more effective than conventional knowledge management practices

since they will improve efficiency, innovation, and market responsiveness. These results indicate that in order to

remain competitive in a digitalized environment, banks need to consider the strategic incorporation of AI into

their knowledge management systems.

CONCLUSION

The key outcome of this study shows that traditional knowledge management systems (KMI, KMR and KMP)

have positive impacts on Access Bank Nigeria competitiveness, however, when traditional knowledge

management systems are improved or combined with AI techs (as in AIKMR, AIKMP, and AIKMI), there is a

higher performance in terms of competitiveness. Also, this study found that with AI, various processes in the

banking sector can be automated and thereby increasing the productivity of employees and the overall

organizational competitiveness.

It was also found that AI tools like chatbots/virtual assistant and predictive analytics are highly used to support

the traditional knowledge management systems.

In conclusion, banking institutions that want to be competitive in an ever-data-driven and digitalized market

have to focus more on strategically integrating Artificial Intelligence into their knowledge management systems.

The investments in AI technologies must be packaged with the organizational knowledge goals in order to make

sure that the technological capabilities are converted into quantitative performance results.

This study has been conducted using majorly the quantitative research approach, the first recommendation to

researchers from this study will therefore be to carry out cross-sectional research involving both qualitative and

quantitative analysis.

The qualitative aspect will allow for selected few participants express their thoughts on the case study. This will

further give more insight to the level of knowledge management within the industry. Secondly, this study has

been conducted focusing on just a single industry in Nigeria, it will be a great addition to the research world if a

cross-industry analysis can be carried by maybe considering industries like banking, insurance, and information

technology.

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