AI in Zimbabwean Business: What It Has Enabled and What Must Be Digitised First

What Is the Impact of AI on Business in Zimbabwe?

I worked at the Grain Marketing Board (GMB) in 2021 until August and experienced the workflow first hand. A grain delivery was recorded on paper at a collection point (for those that did not go to the depot directly), daily cumulative figures were reported each day, the papers went to the depot at the end of each week and was keyed into SAP, and receipts were printed for farmers. Farmers had no digital way to check their transaction status and had to call or visit the depot or the collection point.

The data captured was rich: grain type, weight, moisture, tonnage, farmer details, collection point, location, and empty sacks issued vs returned, among other details. In our view that makes grain marketing one of the sectors whose institutional data is closest to AI-ready. The open questions are whether the data is usable outside the system that holds it, and whether the farmer who supplied it can see it.

AI is used in Zimbabwe at less than half the global rate for individuals, and its impact on business will be set less by new AI applications than by whether data is captured digitally at source, standardised across institutions and reachable outside the system of record, and by whether the people who use it are confident in digital tools.

What Has AI Enabled for Business So Far?

Where AI Is Already in Use

Visible deployments cluster in customer service, farmer advisory and access to frontier models. Econet AI lists EcoChat AI, a generative assistant; Yamurai, a multilingual customer service assistant; and EcoFarmer AI, a farming advisor with photo-based disease detection among others. Independently measured business impact for these tools is still thin.

What the National AI Strategy Expects AI to Enable

The National AI Strategy 2026 to 2030, launched on 13 March 2026, names five flagship adoption areas: precision agriculture, predictive healthcare, smart mining, public service delivery and anti-corruption analytics. The IMF estimates that about 40 percent of employment in emerging market economies and 26 percent in low-income countries is exposed to AI.

How Does Zimbabwe's AI Adoption Compare With Other Markets?

Microsoft's AI Diffusion report for the first quarter of 2026 measures the share of people aged 15 to 64 who used a generative AI product in a period.

Zimbabwe sits at less than half the global average and about 37 percent of South Africa's rate. Growth is steady, but the gap to the global average widened from 8.2 to 9.3 percentage points across the three periods.

What the Adoption Gap Measures and What It Does Not

The figure counts individuals, not firms. We found no published official measure of AI use by Zimbabwean businesses. The nearest is the 2023/24 POTRAZ and ZIMSTAT business survey, reported by Techzim, which found 94.2 percent of institutions using the internet and 94.8 percent using email. Businesses are online, so the open question is what they record and structure once there.

What Data-Ready Markets Have in Common: A Shared Data Layer

Estonia's X-Road defines common protocols so separate systems exchange data directly, and e-Estonia reported in 2017 that it connected 99 percent of government services digitally. India's AgriStack, per its Agriculture Ministry in 2024, comprises a farmers' registry, geo-referenced village maps and a crop sown registry, with a target of digital identities for 110 million farmers. In both, the data layer is national infrastructure that applications consume.

Why Zimbabwe May Not Realise the Full Impact of AI Soon

Digital Capability Gaps Limit Who Can Use AI Output

DataReportal estimates 6.54 million internet users in Zimbabwe at the end of 2025, or 38.4 percent of the population, with 67.1 percent of people living in rural areas. NDS2 reports internet penetration of 83.5 percent in 2024, up from 59 percent in 2020, a figure that likely counts access rather than the individual users DataReportal counts. Mobile connections equal 95 percent of the population, but a connection is not a skill.

On skills, the USAID assessment found adult literacy of roughly 90 percent, yet stakeholders named digital literacy a key barrier, especially for smallholder farmers, and linked low uptake of some digital tools to it and to slow rural internet. The IMF finds low-income countries "less exposed but underprepared, especially in digital infrastructure and digitally skilled labor force".

AI use also concentrates among the more educated and younger. Eurostat found that in 2025, 49 percent of people aged 16 to 74 in the EU with a high level of formal education used generative AI tools, against 22 percent of those with little or no formal education, and notes that the pattern mirrors digital skills and technology use. Microsoft attributes the gap between the Global North (27.5 percent) and the Global South (15.4 percent) to limited electricity, connectivity and digital skills.

That makes the meaning of digital literacy important, because messaging is not the same skill as productive use. The ITU's original list of nine ICT skills under SDG indicator 4.4.1 includes using basic arithmetic formulas in a spreadsheet, sending e-mails with attached files, and finding, installing and configuring software; social media use is not among them.

Structured Institutional Data Can Still Be Out of Reach

The GMB workflow shows that the constraint is not always data quality. A 2013 news report confirms SAP was being implemented at the board, where poor depot connectivity had "impeded the successful implementation of SAP". The records SAP holds appear to be structured, so the harder question is who can reach them, and how. During my time at the GMB, I worked beside a Cottco collection point that followed the same pattern we used; I, however, had no view of Cottco's internal systems.

Who Owns Data Held in a System of Record?

In law, the institution controls it. The Cyber and Data Protection Act defines a data controller to include public bodies and gives data subjects a right to access their personal information held by a controller. A farmer's right of access therefore exists on paper, even though the only practical route we saw was a call or a visit to the depot.

On the vendor side, SAP's cloud terms state that "Customer retains all rights in and related to the Customer Data" and that the customer can access and download it in a common format. Those terms cover cloud services, and we cannot say what GMB's own licence provides.

What Does It Take to Get Data Out of SAP?

Ownership is not the same as extractability. SAP's 2018 guidance on indirect access treats read-only use through other systems as licensable use, but exempts scheduled exports from predefined queries that do not trigger processing in the ERP system; on-demand retrieval is not covered. In our reading, that helps explain why data tends to leave SAP as reports. SAP Note 3255746, as summarised by Matillion, adds that third-party applications may no longer use the ODP APIs to access SAP ABAP sources, and SAP points customers to SAP Datasphere for replication.

Whether GMB could export row-level records for analysis depends on its SAP version, licence and pipeline, so it is a question to ask, not assume. Some records now sit elsewhere: as of June 2025, GMB had certified ten facilities as warehouse operators on the Zimbabwe Mercantile Exchange (ZMX) platform, where farmers receive digital warehouse receipts by USSD or app, and the coverage does not say how the two systems share data.

Why Building on Frontier Models Does Not Remove the Data Problem

Econet AI's product page states that EcoChat AI is "Powered by OpenAI's ChatGPT". That is sound engineering.

The model supplies language and reasoning, while local data supplies the facts, and the World Bank names "context", meaning data, as one of four foundations alongside connectivity, compute and competency. In our reading of announced products, many Zimbabwean AI tools follow the same pattern, which makes the data underneath, and whether it can be reached, the real differentiator. The strategy itself lists data silos and "lack of standardized national data governance" among its weaknesses, and Gartner predicts that, globally, through 2026 organisations will abandon 60 percent of AI projects unsupported by AI-ready data.

Is the AI for Impact Challenge (AI4I) Starting Too Early?

AI4I is not premature as a talent and prototype pipeline, and a third of its shortlist is already working on data. It is early as a route to farmers and firms, because the four tracks, as reported, do not include the digital capability, or the access to institutional records, that the winners' users would need.

What AI4I Is and Who Runs It

TechnoMag reports that POTRAZ leads AI4I with the ITU, the ICT Ministry and the Zimbabwe Centre for High Performance Computing, as the AI Grand Challenge's implementation framework. It has four tracks: Data, Design, Development and Deployment. Thirty teams were shortlisted (10 Data, 5 Design, 10 Development and 5 Deployment).

Where AI4I Already Addresses the Data Problem

The Data track is "focused on the ethical collection, curation, and governance of sovereign data assets". Ten of thirty shortlisted teams sit there. A team can build a dataset, but access to an institution's records needs the institution at the table, which the coverage we reviewed does not clearly describe.

The Sequencing Risk for Farmers and Firms

Africa AI News reports a Grand Challenge goal that the winning solution reach 25 percent of its targeted farmers. Reach depends on connectivity, devices and skills, and the evidence above suggests those are uneven. Pairing each Deployment team with digital-skills and data-access work in its sector before launch would protect that reach target.

Does Nzwisiso.ai Need Digital Literacy First?

The strategy describes Nzwisiso.ai as a campaign to make AI "understandable, relevant and trusted", targeting "a verified 60% of the adult population". On digital literacy, the strategy includes a "Digital Mhuri/Umdeni" curriculum reform and a measure of the adult workforce with at least intermediate digital skills. The question is weighting: the flagship with a public reach target is the AI awareness campaign, while digital skills sit inside the talent pillar.

MISA Zimbabwe expects the campaign to be "most effective among already-connected populations". A Techzim contributor who builds farming tools for WhatsApp and USSD argues that "awareness alone does not translate into adoption". In our view, the campaign gains most if its first lesson is productive digital capability rather than messaging alone: recording and finding information, spreadsheets and mobile money, with AI introduced as a use of those skills.

What Will Decide Whether Project Pangolin Works at Sector Level?

Why Project Pangolin Is Conceptually Sound

The strategy describes Pangolin as "a secure, sovereign platform providing national datasets and computing power for AI development", with a federated model in which sensitive data stays within ministries and is reached through privacy-preserving APIs. Its stated data milestone is that the National Agricultural Dataset and the National Health Records Dataset are 50 percent digitised and curated.

Four Implementation Tests at Sector Level

The open questions concern delivery, not concept. Techzim noted that the strategy includes no budget, and MISA Zimbabwe warns that a national strategy has no direct legal force and that centralising data is not occurring within a robust regulatory environment. Four tests would show whether a sector is Pangolin-ready:

1. A sector data standard exists, written by the organisations that produce the data, so that a delivery, a grade or a patient visit means the same thing in every institution. Minister Tatenda Mavetera said in October 2026 that "common standards and systems are needed to exchange information lawfully and securely".
2. Capture is digital at source, so the record carries the time and place of the event and not the date it was later keyed in.
3. The institution can export row-level records with stable identifiers and documented definitions, not only reports, through a pipeline its software licence permits.
4. Each dataset has a named owner and a published quality measure, such as the strategy's planned "Data Availability Score".

How Does This Align With the National AI Strategy and NDS2?

The AI Strategy states that it is aligned with Vision 2030, NDS1 and NDS2, and describes its adoption pillar as aligned with NDS2 priorities. NDS2, which runs from 2026 to 2030, sets an objective to "build a knowledge-driven and digitally enabled economy" and says it "focuses on digitalisation, expansion of ICT infrastructure and digital literacy". It also expects investment in "research, artificial intelligence and innovation hubs" to boost productivity.

Sector data shapes are the operational link between the two documents. NDS2 commits to "dedicated data units" in all ministries, departments and agencies, and to ZimStats integrating "big data, geo-spatial analytics and AI-enabled platforms", while its agriculture priority rests on productivity and food security outcomes that only standard, reachable records can measure.

What Businesses Can Do Before Buying AI

Digitisation, education and data discipline can run in parallel, and a business need not wait for a national platform.

1. Capture each transaction once, at the moment it happens, in fixed fields rather than free text.
2. Write a one-page data dictionary that names each field and its allowed values, and make every branch and clerk use it.
3. Ask every software vendor in writing how you can export your row-level data, in what format and under what licence terms, because "reports only" is a finding, not an answer.
4. Connect the places your data already lives, such as WhatsApp threads, spreadsheets and the accounting system, so that one customer is one identity, as set out in our analysis of business data fragmentation.
5. Train the people who enter and use the data before training anyone on AI.
6. Choose the AI use case last and test it on your own records before paying, as our real estate AI readiness analysis does for estate agencies.

WhatsApp is not the problem here: it is where much of the data is born, as our piece on running operations through WhatsApp explains. The task is to capture that data in a structure, not replace the channel.

Digitise, Standardise, Open Up, Then Apply AI: The Sequence the Evidence Supports

The pieces of national work do not compete. Digitisation, education and data standardisation run in parallel, and Pangolin-ready datasets are what they produce together, provided institutions can get their own records out of the systems that hold them. Progress should be measured less by AI applications launched than by the share of a sector's records that a machine can read and an institution can reach.

At Sparkline Labs, the work that comes before AI is solution architecture that defines the data shape a business needs, and integration and automation that connects the systems it already runs, including getting data out of the ones it already owns.

Sources

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