INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
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AI-Powered Performance Management: A Case Study in Accra
Samuel Asante
School of Business and Economics, Universiti Putra Malaysia, Serdang, Malaysia
DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000337
Received: 12 November 2025; Accepted: 18 November 2025; Published: 22 November 2025
ABSTRACT
Artificial Intelligence (AI) is revolutionizing the management of organizations around the globe and how
employee performance is measured and improved. The study investigated the use and effect of AI-powered
performance management systems in selected firms in Accra, Ghana. By employing a mixed-methods
methodology, data were gathered from 120 employees and managers across multiple sectors, including
banking, telecommunications, and technology. Results indicate that AI tools facilitate transparency, objectivity,
and efficiency in the performance assessment processes. Nevertheless, challenges to implementation such as
high costs, shortage of technical know-how, and data privacy concerns remain. The study argues that by
augmenting AI with human supervision and ethical frameworks, AI can support strategic human resource
development and organizational excellence. Recommendations include capacity building, regulatory policy
development, and adoption of hybrid appraisal models.
Keywords: Artificial Intelligence, Performance Management, Human Resource Management, Organizational
Efficiency, Ghana
INTRODUCTION
As Artificial Intelligence (AI) uses in organizations continue to rise in prominence, this application of AI in the
workplace has transformed how companies do staff performance management practices. AI-based systems
provide real-time analytics, predictive insights, and automated feedback to mitigate subjective aspects of an
employee evaluation process (Huang & Rust, 2021). In developing countries like Ghana, such systems are
gradually beginning to attract recognition in the context of digital transformation (Asare & Ofori, 2022). Yet,
the nature of AI in performance management practices in Ghanaian businesses has received limited research
attention. This study therefore studies the adoption, impact, and challenges of AI-driven performance
management in Accra.
Objectives:
1. Examine the extent of AI adoption in performance management.
2. Assess its impact on organizational efficiency.
3. Explore employee and managerial perceptions.
4. Identify challenges affecting implementation.
LITERATURE REVIEW
Concept of AI in Performance Management
AI uses smart algorithms to look at employee data, find patterns in performance, and make appraisals based on
facts (Kaplan & Haenlein, 2023). AI systems offer ongoing monitoring, which lets organizations make
decisions more quickly and fairly than traditional evaluations do.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
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Theoretical Framework
This study is anchored on three key theories:
Technology Acceptance Model (Davis, 1989): Explains how perceived usefulness and ease of use
influence the adoption of AI tools.
Resource-Based View (Barney, 1991): Positions AI as a strategic organizational resource that
enhances competitive advantage.
Socio-Technical Systems Theory (Trist & Bamforth, 1951): Emphasizes the interdependence
between technology and human systems in performance management.
Empirical Review
Research conducted by Guenole et al. (2021) and Huang & Rust (2021) indicates that AI improves
transparency and mitigates prejudice in evaluations. Omondi and Were (2022) and Akinola (2022) discovered
that infrastructural deficiencies and ethical concerns impede AI deployment in Africa. In Ghana, there is a lack
of research (Boateng & Agyeman, 2023) that focusses explicitly on AI-driven performance management,
showing that there is a big gap in the context.
METHODOLOGY
Research Design
A descriptive case study strategy was employed, incorporating both quantitative and qualitative methodologies
to yield full insights regarding AI adoption in Accra.
Population and Sample
The research included 120 participants, comprising 100 employees and 20 managers from the banking,
telecommunications, technology, and service industries. We used both purposive and random sampling
methods.
Data Collection and Analysis
Data were gathered through questionnaires and semi-structured interviews. Descriptive statistics were used to
look at quantitative data, while theme analysis was used to look at qualitative replies. Strictly followed ethical
rules, such as getting permission and keeping things private.
RESULTS AND DISCUSSION
AI in Performance Management.
A total of two-thirds (68%) of organizations in Accra have adopted AI-powered management systems in their
performance appraisal in accordance with the findings presented above. They mostly hail from sectors like
banking, telecommunications, tech, and professional services, where digital transformation has been sharper.
Another 22% of organizations said they were moving toward adoption of AI and were in the process of piloting
hybrid systems, which combined manual assessments with digital dashboards. The last 10% relied only on a
traditional, paper-based appraisal system. Top AI-powered tools and technologies used by these organizations
often include automated performance dashboards, HR analytics software, and machine learning–based
evaluation algorithms that track performance trends and provide predictive reports. Among the tools discussed
were Power BI, Oracle HCM Cloud, SAP SuccessFactors, and proprietary homegrown AI-applications.
Managers interviewed stressed that implementation of AI had increased transparency, objectivity and
promptness in appraisals by introducing the use of AI. One HR manager stated:
“With AI-driven dashboards, we no longer rely solely on subjective impressions; performance discussions are
now backed by concrete data.” This finding also supports Guenole et al. (2021) and others that AI integration
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
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for performance management results in improved accuracy and uniformity through the minimization of human
bias. In like manner Boateng and Agyeman (2023) found that Ghanaian companies using AI in their human
resources processes have greater employees’ accountability and trust in the organizations. But adoption rates
differ as organizations grow, as institutions become digitally responsive and as leaders turn inward. Larger
companies with well-established digital infrastructures tend to use AI more quickly than do small and medium-
sized enterprises (SMEs), whose financial and technical constraints frequently prevent adoption. This trend
supports the Resource-Based View (Barney, 1991) which argues that technological adoption is contingent on
the existence of specific organizational capabilities (for example, capital, expertise, human resources).
Impact on Organizational Efficiency
Organizational efficiency and performance results have been positively influenced by the implementation of
AI-driven systems. Data shows that 75% of employees perceived that AI systems have improved their
productivity while 68% of managers observed improved decision making and operational speed. These
perceptions were also evident across verticals revealing increasing confidence to optimize human resource
management processes via AI. Performance systems powered by AI were attributed to:
Automating reporting tasks, reducing the administrative burden on HR departments.
Providing real-time performance tracking, which allows supervisors to identify productivity trends and
intervene proactively.
Supporting predictive analytics, enabling managers to anticipate performance gaps and recommend
targeted training interventions.
Ensuring fairer assessments, as AI tools rely on quantifiable data rather than subjective judgments.
These findings strengthen Kaplan and Haenlein (2023) who highlighted the fact that AI systems can help
organizations become more efficient and precise in their operation. Additionally, the results are consistent with
Brynjolfsson and McAfee (2023) which found that companies implementing Artificial Intelligence gain in
terms of measurable productivity improvement, and strategic agility. However, there is an underwhelming
concern on the part of a small percentage of employees (around 15%), about whether reliance on automation
could undermine interpersonal feedback and mentorship, which are integral aspects of employee development.
Such a concern resonates with Bostrom (2023), who cautioned against reliance on AI that might, as he points
out, depersonalize work relationships without human intermediation. In summary, however, there is evidence
suggesting that AI integration is a useful tool in optimizing performance management, provided that the
synergy between the technology and human judgment survives.
Employee and Managerial Perceptions
Negative attitudes, though nuanced, between employees and managers toward AI-guided systems were mixed.
Around 70% of respondents agreed that AI-powered performance tools were fairer, more transparent, and more
data-driven than regular appraisal systems. Employees liked that AI eliminated favoritism and enabled them to
monitor their performance metrics themselves. Some 10% had negative perceptions of AI, citing concerns
about privacy, fear of job surveillance, and not knowing whether their data would be secure. These patterns
indicate that AI acceptance is increasing, but trust and digital literacy are still crucial dimensions of adoption.
For qualitative interviews, a very big positive is that they ask the staff for human involvement in feedback and
appraisal discussions. Although employees appreciated the efficiency of AI-based systems, they added, the
value should be taken into account as well, emphasizing emotional intelligence and empathy traits which
machines cannot substitute. In the opinion of managers too, AI should be an aid, not substitute, for human
judgment. These results are in accordance with Trist and Bamforth's (1951) Socio-Technical Systems Theory.
They claim that technology should not replace human elements, but should complement them. They also
support the Technology Acceptance Model (Davis, 1989), indicating here that how employees perceive AI
tools to be useful and easy to use is a key factor in their acceptance. Yet the long-term effect of AI on
performance management might be different only if long-term trust and engagement throughout the process of
adoption can now be established.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025
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Challenges Identified
Despite the promising benefits of AI-powered performance management, the study identified several
challenges that hinder full-scale adoption among organizations in Accra:
1. High Cost of Technology (72%): The initial cost of acquiring AI software, maintaining digital
infrastructure, and training staff remains a key barrier, particularly for SMEs.
2. Limited Technical Expertise (65%): A shortage of skilled AI and data analytics professionals
constrains the effective use of these systems.
3. Resistance to Change (58%): Employees and managers accustomed to traditional appraisal systems
often resist technological shifts due to fear of redundancy or lack of familiarity.
4. Data Privacy Concerns (47%): Employees expressed discomfort about how performance data are
collected, stored, and used, reflecting wider concerns about workplace surveillance.
5. Lack of Clear AI Policies (40%): Many organizations operate without well-defined policies or ethical
guidelines governing the use of AI in HR processes.
These findings are consistent with those of Akinola (2022) and Omondi & Were (2022), who found
comparable infrastructural, ethical, and regulatory limitations in other African settings. The lack of national
frameworks on AI ethics and governance further complicates implementation, creating uncertainty for
organizations seeking to digitize HR operations. As a result, tackling these challenges is a multi-level process,
encompassing investment in digital infrastructure, capacity building, and regulatory support to ensure safe and
equitable AI usage in Ghana’s corporate sector.
Discussion
Overall, our findings suggest that AI-powered performance management systems have begun to transform
organizational operations in Accra, with an increasing emphasis on efficiency, fairness, and strategic decision-
making. However, the benefits are moderated by organizational readiness, user trust, and technical capability.
The research findings strengthen the Technology Acceptance Model (Davis, 1989) by indicating that
employees' perceived usefulness and ease of use of AI tools have a substantial impact on their likelihood to
adopt AI. Likewise, to better understand AI-based performance systems, the Resource-Based View (Barney,
1991) is validated in the sense that firms that possess superior financial and technical capacity successfully
implemented it. Furthermore, the study stresses that the Socio-Technical Systems Theory should be considered
when considering AI adoption because AI adoption must strike the right balance between technological
features and human-powered management practices. By balancing technological advancement with support, a
balance can be maintained between AI and employee well-being. AI has the potential to transform performance
management in Ghanaian organizations but sustainable implementation is contingent on leadership buy-in, user
education, ethical governance, and continuous technological adaptation. This is why AI can be a transformative
tool that benefits both organizational performance and trust and engagement among employees, once these
elements are brought together.
CONCLUSION AND RECOMMENDATIONS
Conclusion
AI-powered performance management tools make Accra-based businesses far more fair, efficient, and able to
make decisions based on data. But for automation to work, there needs to be a balance between it and human
monitoring. Companies that put money into being ready for digital technology and ethical governance are
better able to use AI to get long-term performance results.
Recommendations
1. Capacity Building: Train HR professionals and employees in AI literacy and data interpretation.
2. Ethical Frameworks: Develop clear policies on data usage, privacy, and accountability.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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3. Hybrid Models: Combine AI insights with human judgment for balanced appraisals.
4. Regulatory Support: Government should establish national AI standards to guide organizational
adoption.
5. Future Research: Further studies should assess the long-term impacts of AI adoption across sectors in
Ghana.
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