How automation reshaped nearly 2,000 banking jobs in Zimbabwe

By Tafadzwa William Mutsika
If you have visited a bank branch in Harare lately, you may have noticed longer queues outside ATMs and shorter ones inside. Fewer tellers. Familiar faces gone.
For nearly a decade, Tendai Mano not her real name worked as a customer service officer at a major bank in Harare. She knew her customers by first name. She helped students open accounts. She calmed angry pensioners. She was good at her job.
Then, in 2023, the letter came.” Due to automation, your position has been declared redundant. Your services will no longer be required. Effective immediately.”
No consultation. No transfer. No training for a digital role. Just a signature and a security guard walking her to the gate.
Tendai is now fighting back. Her case is before the courts, where her lawyers argue the bank did not follow proper process. The bank argues efficiency and progress. While she waits for a judgment, she has agreed to speak but only under a pseudonym.
Tendai Mano is not a statistician. She is a mother. A former banker. A woman who now stands in the same long ATM queues she once served from behind the counter.
Between 2020 and 2025, over 2,000 banking professionals in Zimbabwe lost their jobs exactly like this. Every single year. The reason cited in company statements is almost always the same: automation.
To understand how the crisis unfolded, a look back at two shocks is necessary. The COVID-19 pandemic forced banks to close branches and push customers online almost overnight.
First shock: 2020 – COVID-19: The pandemic forced banks to close branches and push customers online overnight. Fast, messy, but effective.
Second shock: 2022–2023:Banks realized they never had to fully reopen those human counters. Cheap AI tools arrived.
Post-COVID: A temporary measure became a permanent, profitable reality. The rapid rise of cheaper AI tools gave banks the financial justification to make those permanent changes in the banking sector.

Graph by Tafa Mutsika Source: DataMedia- verified against annual reports from nine commercial banks: Additional figures came from the Bank Workers Union of Zimbabwe. The dataset covers 2020–2025.
The chart shows a sharp peak in 2022 at 421 retrenchments, followed by a gradual but persistent decline, still above 300 in 2025.
What the data shows is that automation did not stop after an initial wave of job cuts. It stabilised at a consistently high level. Banks reduced retrenchments not because they reversed course, but because they had already removed the positions most easily replaced. Every year since 2022, more than 300 banking professionals have been removed. This is not a one-off restructure. It is a sustained pattern.

Graph by Tafa Mutsika Source: DataMedia- verified against annual reports from nine commercial banks: Additional figures came from the Bank Workers Union of Zimbabwe. The dataset covers 2020–2025.
Automation alone accounted for 691 retrenchments, the single largest official reason. Restructuring followed with 411, cost cutting with 314, liquidation with 175, branch closures with 130, outsourcing with 117, digital shift with 95, AI implementation with 60, and merger with 30.
When “automation” and “AI implementation” are added together, they account for 751 jobs. However, labour analysts note that categories such as “restructuring,” “cost cutting,” and “digital shift” often involve automation as an underlying driver, meaning the true figure is likely higher.
Engineer Tasimbiswa Makombe, a mechatronic engineer specialising in digitalisation and AI, has expressed his opinion on the matter.
He said banks are firing experienced workers and replacing them with software rented from South Africa or India. He argues that no large-scale new jobs are being created and no systematic retraining is happening.
“I design intelligent systems for a living, so I am not afraid of automation. But what banks are doing – firing their seasoned people for rented code from South Africa or India, with no retraining and no new roles – is not digitalisation. It is dehumanisation,” Makombe said.
“A mechatronic system is only as strong as its feedback loop. Cut out the experienced workers, and you cut out the very feedback that prevents catastrophic failure. This is not progress. This is a race to the bottom, and the bottom always breaks first.”
Economist Mr. Titus Makuvise offered a more measured assessment. He acknowledged that automation has a generational dimension.
“The older generations are the ones that are at risk, but the younger people are actually getting employed,” Makuvise said. “So this assertion that people are losing out jobs because of technology is not empirically proven. It’s just opinions, but not facts.”
On policy, he proposed a concrete mechanism drawn from South African precedent.
“Implementing a mandatory automation adjustment fund – I think it’s critical,” he said. “Banks can actually pay a certain value of electronic transfers into a fund managed by the Minister of Skills or the Minister of Labour, with input from representatives from the banking sector.”
He warned that without such a fund, Zimbabwe is trading stable middle-class careers for cheaper processing.
“Without a reskilling mechanism, you are actually trading stable middle-class jobs for lower cost and faster applications. And the net impact on financial inclusion is unclear because there’s no proper research that has been done to ratify that.”
Beyond these three points, Makuvise elaborated on several structural risks facing Zimbabwean banking. He argued that digital platforms do not align well with an economy where 70% of activity is informal. In his view, rural clients and small businesses still depend on face-to-face relationships. Over-automation, he suggested, risks pushing these customers away from formal banks entirely and towards informal lenders or mobile money operators.
He also pointed to the erosion of institutional memory. When 2,000 experienced staff leave, he explained, banks lose accumulated knowledge of fraud patterns, regulatory requirements, and long-standing client histories. Automation, he noted, cannot yet replace that tacit expertise. The net result, in his assessment, depends on what banks do next. If automation simply means fewer staff delivering the same products, the system becomes more efficient but financially weaker. If it enables better data-driven lending and new services, both efficiency and inclusion could improve. Zimbabwe, he added, is not unique in facing these trade-offs, but its weaker social safety nets and fewer alternative tech jobs make the consequences more severe than in countries like Kenya or South Africa.

Graph by Tafa Mutsika Source: DataMedia- verified against annual reports from nine commercial banks: Additional figures came from the Bank Workers Union of Zimbabwe. The dataset covers 2020–2025.
Faced with these figures, the Institute of Banking Zimbabwe (IOBZ) has defended the sector’s record. The industry body points to existing upskilling programmes and what it describes as successful redeployment outcomes for retrenched workers.
“IOBZ offers upskilling programs in digital banking, data analytics, and fintech through partnerships with international institutions,” the institute said. “We encourage members to upskill in areas like AI, cybersecurity, and digital payments.”
On the question of whether these programmes actually help displaced bankers find new work, the IOBZ cited internal tracking data.
“We track employment outcomes of retrenched bankers who’ve completed our training. Findings show approximately 70% get redeployed within six months, often in digital transformation roles.”
The institute also outlined its policy position on the broader transition. “IOBZ advocates for a balanced approach to tech adoption, emphasizing upskilling and workforce transition strategies. We engage policymakers on supporting displaced workers through training and entrepreneurship initiatives.”
What remains after these 2,000 retrenchments, according to economists and labour analysts, is a banking system stripped of tacit credit expertise and institutional memory assets that rented software, in the view of critics like Makombe, cannot fully replace. Whether Zimbabwe follows South Africa’s path of mandatory retraining funds or continues without a safety net will determine if this efficiency drive becomes a net loss for the broader economy
Zimbabwe is not alone in facing banking automation, but its trajectory differs from regional peers. In Kenya, the shift to mobile banking (M-Pesa) created new agent networks that absorbed some displaced tellers. In South Africa, mandatory industry retraining funds provide formal reskilling pathways. Nigeria’s banking sector added over 15,000 tech-adjacent roles between 2020 and 2024, according to the Chartered Institute of Bankers of Nigeria. Egypt’s Central Bank mandated that banks spend 1% of profits on staff digital upskilling. Zimbabwe has none of these mechanisms. The result is that displaced Zimbabwean bankers face a weaker social safety net and fewer alternative tech jobs than their regional counterparts

Graph by Tafa Mutsika Source: DataMedia- verified against annual reports from nine commercial banks: Additional figures came from the Bank Workers Union of Zimbabwe. The dataset covers 2020–2025.