WHEN government officials state that Zimbabwe is not just selling rocks anymore, they are referring to initiatives such as the newly-established lithium sulphate plant in Arcadia, Goromonzi.
This means the country is starting to add more value to its resources.
Instead of simply exporting raw lithium ore, Zimbabwe will soon send out a processed chemical used in making batteries.
That is progress. But in 2026, value is not just created in factories and machines, it is increasingly created by software and technology. The key question is whether Zimbabwe’s lithium plants will use smart, AI-powered systems or just be bigger versions of traditional mines.
The beneficiation promise and limits
The Arcadia lithium sulphate plant will convert locally-mined spodumene into lithium sulphate, a battery precursor. This leads to higher export revenue per tonne and creates more skilled jobs.
Beneficiation, which refers to the process of increasing the value of raw minerals by refining or processing them into more advanced products, in 2026 is not only about where material is processed, it is about who owns the intelligence inside the process.
A plant may sit in Goromonzi, but if the engineering, software and optimisation expertise all reside abroad, Zimbabwe may capture only a thin slice of value.
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The industry has already moved on
Globally, lithium operations are becoming AI testbeds. Modern plants use sensors feeding computers that constantly adjust reagent dosing, grinding pressure, and temperature in real time.
Even small improvements in recovery or energy use add millions over a mine’s life. Some companies build twins, virtual plants running on live data where engineers test settings before applying them. Predictive maintenance systems detect equipment problems before failures occur. These algorithmic advantages often determine, which operations survive tight margins and volatile prices.
What we know about Zim’s approach
Public information about the Arcadia plant’s digital strategy remains scarce. Announcements focus on capacity and jobs, not automation or process control systems.
Zimbabwe’s existing lithium mines operate with mixed technology, some modern equipment, but heavy reliance on manual monitoring rather than automated control.
Chinese firms dominate Zimbabwe’s lithium sector and will likely design the sulphate plant. Standard turnkey contracts leave all proprietary software and optimisation expertise with vendors.
The client gets functioning equipment but limited ability to modify or understand the intelligence running it. Unless Zimbabwe negotiated differently, and there is no public evidence it has, the country may operate sophisticated equipment without controlling the embedded knowledge.
The capability gap
Zimbabwe has mining experience and capable chemical engineers but is only beginning to build skills in industrial data science and process AI. The majority of advanced controller systems are proprietary and developed internationally.
Operating these systems without transparency, while describing the process as beneficiation, continues to delegate the strategic value component externally.
Infrastructure poses another constraint. AI-enabled plants need reliable electricity, stable connectivity, and secure data systems. Zimbabwe’s erratic power, weak industrial internet outside Harare, and limited cybersecurity directly affect automated control performance.
A predictive algorithm is useless if it cannot access real-time data because networks or power have failed.
Skills represent the deepest constraint. Training engineers who understand both chemistry and machine learning takes years. Few Zimbabwean graduates have exposure to this intersection.
Without deliberate partnerships between the plant, universities, and technical colleges, this gap will not close. Meanwhile, the plant will be commissioned and operating patterns entrenched long before a new generation graduates. The predictable result: expensive digital systems get installed to satisfy contracts but get bypassed without skills and infrastructure to use them. The plant functions at 1990s efficiency with 2020s costs.
The affordability question
Could Zimbabwe even afford a fully AI-enabled plant? Advanced process control, digital twins, and predictive maintenance require significant capital and ongoing costs for licenses, cloud computing, and specialist maintenance.
For a country with limited fiscal space and competing infrastructure needs, simpler automation, reliable sensors, basic control loops, operator decision tools, might deliver more value per dollar.
These systems are proven, easier to maintain, and accessible to local teams with modest training. There is also a sequencing question. Should Zimbabwe stabilise power, improve technical education, and build baseline industrial data capacity across sectors before attempting a showcase AI plant?
A flagship project outpacing absorptive capacity may become an expensive monument rather than a capability platform.
Realistic strategy
If Zimbabwe is serious about capturing digital value, the sulphate plant should anchor a long-term industrial learning project.
Start with deep instrumentation and negotiate data ownership upfront. Build reliable basic automation first, with operator training so that the workforce understands what is measured and why.
Add optimisation layers gradually as local engineers gain competence. Advanced analytics come later, once there is institutional capacity to use them.
The human layer is most important. Contracts must mandate joint training, local research partnerships, and gradual handover of process optimisation responsibilities.
Universities must be involved early, with curriculum developmentand placements built into project timelines. For young Zimbabwean engineers, the difference is profound, shifting from operators to innovators. But this will not happen by accident. It must be structured into contracts and enforced through policy.
The power question
These points rely on Zimbabwe having real influence over plant design and operation, which may not be the case. Foreign investors provide crucial resources such as capital, technology, and market access, leaving Zimbabwe with limited bargaining power for local data ownership or technology transfer. Responsibility is also fragmented across ministries with limited technical capacity. Without state capacity, well-intentioned policy becomes unenforceable.
Companies agree to vague commitments on training but deliver token programmes. The plant gets built, but the strategic layer, algorithms, optimisation expertise and ability to innovate, remains offshore.
Zimbabwe’s leverage is time limited. Global demand is strong now, but new deposits are developing elsewhere. If Zimbabwe does not use current investor interest to secure meaningful technology partnerships, the opportunity will pass. Once the plant is built and contracts signed, power shifts decisively toward investors.
The likely outcome
The most probable scenario is neither full success nor complete failure, but muddle. The plant will have some automation, some local engineers, some data systems. It will function and export sulphate. But it will not showcase Zimbabwean technological capability, because no one with power and resources set out to make it one.
That is the real risk, not choosing the wrong strategy, but never articulating one. Beneficiation becomes a slogan rather than a plan. Digital transformation becomes a speech buzzword rather than enforceable commitments.
Ten years from now, the plant will be running and lithium flowing, but Zimbabwe will still wonder why it remains a price-taker in a value chain it thought it had climbed.
Zimbabwe’s lithium story is no longer just about geology or battery demand. It is about data, algorithms, skills, bargaining power, and state capacity. Those invisible assets may prove more decisive than the lithium itself. The question is whether anyone is paying attention.
Bangure is a filmmaker. He has extensive ex-perience in both print and electronic media production and management. He is a past chair-person of the National Employment Council of the Printing, Packaging and Newspaper Industry. He has considerable exposure to IT networks and Cloud technologies and is an enthusiastic scholar of artificial intelligence. — naison.bangure@hub- edutech.com.
