When Zimbabwean government officials assert that “Zimbabwe is ready for Artificial Intelligence,” the declaration is often received as a broad symbol of technological ambition. The phrase circulates easily in political speeches and media commentary, typically interpreted as evidence of digital progressiveness.
By Brighton Musonza
Yet, when examined through the analytical lens of digital economics and infrastructure development, the claim reveals implications far more structural than technological. AI readiness is not principally about algorithms, robotics, or futuristic consumer applications. Rather, it is fundamentally anchored in infrastructure capacity, data ecosystems, institutional organisation, and the economic architecture that enables computational technologies to generate value.
Recent commentary by Zimbabwean billionaire Econet Telecommunications founder Strive Masiyiwa, writing on social media platform, provides a useful entry point into this discussion. In this statement, Masiyiwa observed:
“We are planning to build an Industrial Park on a 1,000-acre site outside Harare Airport. It will eventually house more than 300 businesses. For me, this is a fast-follow, after seeing a similar project in Nigeria, called Eko Atlantic. We actually located our Nigerian data centre there. If my dream is fulfilled, it will be the largest industrial hub in the country… much bigger than anything there today.”
Although framed as an industrial development initiative, the strategic significance of this announcement extends well beyond land development or conventional industrial policy. It reflects an ongoing shift toward cloud infrastructure expansion, regional data centre ecosystems, and the monetisation frameworks underpinning contemporary AI economies. Industrial parks anchored by data centres increasingly represent the physical manifestations of digital economic strategy rather than traditional manufacturing zones.
AI Readiness as Infrastructure Readiness
Within policy discourse, AI readiness is frequently misinterpreted as a question of software adoption, coding skills, or digital literacy. However, contemporary AI systems are computationally intensive and structurally dependent on large-scale infrastructure. Advanced AI models require immense compute resources, high-performance storage architectures, ultra-low-latency networks, and continuous streams of structured data. Without these foundational layers, AI technologies remain aspirational rather than operational.
In practical terms, readiness for AI increasingly denotes readiness for cloud deployment environments, regional data centre capacity, high-bandwidth fibre connectivity, and edge computing frameworks capable of supporting distributed workloads. Masiyiwa’s emphasis on industrial parks and data centre development signals recognition of this infrastructural reality. AI does not exist as a standalone technology; it is embedded within a layered digital infrastructure economy that determines both scalability and commercial viability.
Global technology leaders such as Nvidia and Google are aggressively advancing AI monetisation strategies. Yet, critically, their commercial models depend not solely on end-user demand but on intermediary infrastructure markets. These include entities capable of provisioning compute, hosting workloads, orchestrating storage systems, and facilitating large-scale data processing. The economics of AI monetisation are therefore inseparable from infrastructure distribution.
This structural dependency elevates the strategic relevance of regional infrastructure players such as Cassava Technologies, Liquid Intelligent Technologies, and Econet InfraCo. These firms do not primarily function as consumer-facing technology vendors. Rather, they operate as the connective architecture of the AI economy, enabling compute distribution, storage orchestration, and network integration at scale.
The logic of staged capital deployment within this sector further illustrates the infrastructural nature of AI readiness. The phased investment strategy adopted by STANLIB in Africa Data Centres exemplifies a broader pattern characteristic of capital-intensive infrastructure markets. Entry capital followed by progressive expansion mirrors how institutional investors typically approach digital infrastructure assets, balancing long execution timelines with prudent risk management. For investors, data centres increasingly resemble fibre backbones and energy grids: predictable, inflation-resilient real assets generating recurring revenue streams rather than speculative technology ventures.
For Cassava Technologies, this structuring permits partial value realisation from a capital-heavy enterprise while retaining strategic positioning within an integrated ecosystem spanning connectivity networks, cloud services, and AI infrastructure. ADC’s continental footprint — supported by Liquid Intelligent Technologies’ independent fibre network and earlier financing arrangements such as the expansion facility from Rand Merchant Bank — reflects how digital infrastructure value is progressively unlocked.
Comparable financing dynamics are visible across Africa’s broader digital infrastructure landscape. The 2Africa subsea cable consortium, involving firms such as Meta Platforms, MTN, and Orange, illustrates patient capital deployment aligned with multi-year construction and activation cycles. Similarly, Teraco Data Environments attracted successive investment rounds from Digital Realty, reinforcing the principle that infrastructure value materialises incrementally as facilities achieve scale.
These examples situate Zimbabwe’s infrastructure repositioning within an established continental trajectory rather than an isolated phenomenon.
The Misreading of Market Repositioning
Much of Zimbabwe’s domestic discourse about Econet’s delisting from the Zimbabwe Stock Exchange (ZSE) and re-listing on the Victoria Falls Stock Exchange (VFEX) has focused narrowly on financial transitions, particularly corporate restructuring and exchange listings, often analysing stock movements without interrogating deeper strategic drivers. This perspective risks conflating surface-level financial mechanics with structural economic repositioning. The ongoing shift is neither cosmetic nor purely financial. It reflects alignment with evolving digital infrastructure economics, where value creation is migrating from consumer-facing applications toward foundational infrastructure layers.
In the global technology landscape, economic value increasingly accrues to compute provisioning, data storage orchestration, network distribution, and AI workload hosting. AI, contrary to popular perception, has not yet reached full commercial maturity. The Covid-19 pandemic temporarily suppressed enterprise cloud investments as organisations prioritised liquidity preservation. However, post-pandemic dynamics have accelerated renewed capital allocation toward cloud ecosystems, data centres, and AI-enabling infrastructure. The commercial race is now intensifying.
AI Monetisation: Still in the Development Phase
Despite widespread enthusiasm, AI remains in a developmental and adoption phase rather than full-scale commercial consolidation. Major technology firms continue to experiment with viable monetisation architectures. Partnerships between AI chipmakers, cloud providers, and infrastructure companies underscore this transitional landscape. Collaborations involving Supermicro and Nvidia reveal how AI solutions are increasingly packaged not merely as software applications but as integrated infrastructure stacks designed for scalable deployment across enterprise environments.
Retail ecosystems offer a particularly illustrative example of this transition.
Retail Is No Longer Just Commerce — It Is Intelligence
Globally, AI is transforming retail from a transactional framework into an intelligence-driven system. Contemporary retail environments increasingly rely on behavioural analytics, predictive demand modelling, personalised engagement mechanisms, real-time inventory optimisation, and advanced loss prevention systems. Consumer experiences now reflect this computational evolution. A seemingly simple interaction, such as browsing eyewear via Edel-Optics — demonstrates how AI-powered visualisation, recommendation engines, and virtual try-on technologies are redefining purchasing behaviour.
Retail is evolving into a data-centric decision architecture. Yet AI-driven retail models depend critically on four interdependent variables: data continuity, operational scale, systemic structure, and persistent customer identity.
Zimbabwe’s Informality Challenge: The Hidden AI Constraint
Zimbabwe’s expanding economic informality introduces a subtle yet profound constraint on AI readiness. Informality does not merely shrink tax revenues or formal retail footprints. It weakens the statistical and operational foundations upon which AI systems depend. AI systems require continuous streams of structured, machine-readable data. Informal economies, characterised by cash transactions and fragmented record-keeping, generate limited digital traces. AI thrives on behavioural exhaust, whereas informality produces data opacity.
AI investments are inherently capital-intensive. Infrastructure, data engineering, model training, and integration systems require scale economies. Fragmented micro-enterprises struggle to amortise such costs, rendering AI economically irrational rather than technologically infeasible. AI optimisation further relies on standardisation, including barcoding systems, inventory controls, logistics frameworks, and vendor integration. Informal systems often lack predictable procurement and structured workflows. AI cannot optimise what lacks systemic order.
Similarly, AI personalisation depends on persistent customer profiles linked to behavioural histories and payment-linked identities. Informality dilutes identity continuity through anonymous transactions, constraining the value potential of AI-driven consumer intelligence.
The True Meaning of “AI Readiness”
Within this analytical framework, declarations of AI readiness may be interpreted less as claims of technological sophistication and more as recognition of an infrastructural transition. AI readiness increasingly signifies entry into the cloud infrastructure phase, investment in data centre ecosystems, and repositioning within digital infrastructure value chains. Masiyiwa’s industrial park vision aligns precisely with this interpretation. Industrial parks anchored by data centres are not simply development projects; they are strategic nodes within emerging AI economies.
Zimbabwe Is Not “Losing AI” — But It Risks Losing Preconditions
Zimbabwe’s structural challenge is not exclusion from AI technologies. Rather, it concerns erosion of enabling conditions, including data continuity, scale aggregation, system predictability, and identity frameworks. These elements constitute the true currencies of AI-driven economies.
Alternative Pathways: AI in High-Informality Economies
Informality does not eliminate AI opportunities; it reshapes them. Digital payment systems can reconstruct data trails even within informal environments, sustaining AI applications in fraud detection, transaction analytics, and credit modelling. Platform-based aggregation can digitally consolidate fragmented retailers, centralise data flows, and create synthetic scale economies. Non-traditional data models may still capture mobility patterns, price dynamics, and geospatial demand clusters, albeit requiring context-specific methodologies rather than imported templates.
Conclusion: AI as Economic Architecture
Ultimately, AI adoption is less about technology and more about economic architecture. Zimbabwe’s trajectory underscores a broader developmental truth: algorithms alone do not create AI economies. Infrastructure, structured data ecosystems, and organisational scale do. Viewed through this lens, industrial parks, data centres, and fibre networks are not peripheral developments. They represent the foundational grammar of AI readiness.
The debate must therefore move beyond rhetorical declarations toward a more substantive inquiry: is Zimbabwe building the structural conditions required to extract long-term value from AI? That question, far more than any slogan, defines genuine readiness.
