Managing Organizational Change in the Age of Artificial Intelligence: Leadership Challenges and Opportunities the Case of Ethio Telecom
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Date
2026-06-01
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Addis Ababa University
Abstract
Artificial intelligence is reshaping how large organizations work and Ethio Telecom which is
Ethiopia's main telecom operator is moving through this change at an early stage and in an
emerging economy environment . By the first half of the 2025/2026 fiscal year it served about 87.1
million subscribers and 49 million mobile data users and had introduced AI-based and digital
services such as Telebirr, Ardi chatbot and biometric registration. Because adopting AI is as much
a leadership and human task as a technical one, this study explores how that change is led and
how employees and customers experience and accept it. It used an exploratory mixed methods and
qualitative dominant design with 70 participants which is made up of 25 employees 15 managers
and 30 customers. Data was from semi structured interviews and open-ended questions, Likert-
scale questionnaires and six non-participant observations and secondary resources. The
questionnaire answers were analyzed in SPSS using descriptive statistics while the open-ended
answers, the observation and the secondary sources were analyzed thematically and brought
together with the numbers through a joint display.
The central finding is that the technical side of the change largely succeeded while the human side
was undermanaged. communication weakened as it moved down the organizational hierarchy and
the weakest link was between senior management and middle managers. Employee fear was real
as a psychological experience, but not based on actual local job displacement. Formal training
was the weakest support so staff gained competence primarily through peer learning and self
learning. Employees were left out of decisions but accepted the change because the tools were
useful. Customer experience split in two ways: routine tasks like payments worked well while
problem resolution was the lowest rated part of the study and connected urban users were served
well while regional and older customers were left behind by a clear digital divide. The one clear
human-side gain was that leading this imperfect change built real leadership confidence in
managers. The study recommends briefing managers properly before each roll out, communicating
honestly about AI and jobs, involving employees in decisions, redesigning training around real job
tasks with follow-up support and keeping the branch and USSD channels open while improving the
chatbot so complex problems can be solved without a call or a branch visit. Its contribution is a
locally grounded account of the human process behind AI-driven change in a large Ethiopian
organization complementing earlier work that measured performance gains but did not explain
how they were achieved.
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Keywords
Artificial intelligence, organizational change, change management, leadership, digital transformation, Ethio Telecom, Ethiopia