Africa's CEOs Bet Big on AI for 2026 as Data and Connectivity Gaps Slow Progress

AI Is Now the Top Strategic Priority for African CEOs - Here's Your 2026 Plan

Artificial intelligence has moved to the front of the agenda. According to KPMG's 2025 Africa CEO Outlook, 71 percent of CEOs across Southern, East and West Africa are investing in AI to drive efficiency, sharpen competitiveness and build resilience. More than a quarter plan to allocate over 20 percent of annual budgets to AI - nearly double the global average of 14 percent.

The shift is clear: executives are treating AI as a present-day operations lever, not a distant growth bet. The strongest sentiment sits in West Africa at 65 percent, followed by East Africa at 40 percent and Southern Africa at 38 percent.

Why this matters for strategy

• Budget reality: The capital is moving. If your allocation lags peers now, catching up later will cost more and deliver less.
• Operating edge: AI is being deployed to reduce cycle times, raise decision quality and stabilize margins under cost pressure.
• Resilience: Firms are using AI to build adaptive supply chains, improve risk sensing and harden security postures.

What's slowing progress

The blockers are stubborn and well-known: unreliable electricity, patchy broadband and legacy IT that can't handle data-intensive workloads. Ninety-six percent of CEOs cite data readiness as the core barrier - storage, curation and processing capacity aren't where they need to be.

To counter this, leaders are prioritizing cybersecurity and digital resilience (45 percent), integrating AI into operations (40 percent) and adopting scalable tech stacks (34 percent). Generative AI's rise since 2022 is accelerating demand, with estimates that AI could add trillions to Africa's GDP in the coming years.

Executive priorities for the next 12-18 months

• Stabilize infrastructure: Secure reliable power, resilient connectivity and a cloud/hybrid backbone that supports data pipelines and model serving.
• Make data deployment-ready: Establish governance, labeling standards, quality thresholds and retention policies aligned to business needs.
• De-risk security: Tighten identity and access, harden endpoints, implement data loss controls and test incident response regularly.
• Industrialize use cases: Move beyond pilots. Select a few high-ROI workflows (e.g., forecasting, service automation, credit risk) and scale them.
• Formalize model risk management: Set guardrails for bias, drift, privacy and auditability - and track them.
• Build talent pipelines: Upskill and redeploy teams into AI-enabled roles; recruit for missing skills where it's faster than training.

Build, buy or partner? A quick decision lens

Joelene Pierce, CEO Designate of KPMG South Africa, urges companies to be deliberate here. The choice depends on your skills base, risk tolerance and target outcomes.

• Build when the use case is core IP, you have data advantage and can attract/retain engineering talent.
• Buy for standardized workflows (e.g., service desks, marketing ops) where time-to-value beats customization.
• Partner when speed, integration complexity and capability transfer matter - structure for knowledge handover, not vendor lock-in.

Regional signals to factor into your plan

• West Africa: Highest belief in AI's immediate value (65 percent). Leading in role redesign (65 percent) and shifting staff into AI-enabled roles (70 percent).
• East Africa: Hiring for AI skills is the priority (62 percent). Expect faster headcount shifts as new builds go live.
• Southern Africa: Balancing hiring and reskilling. Emphasis on operational integration and governance.

Data readiness playbook (start here)

• Inventory and classify data: Map systems, sensitivity levels and ownership; retire redundant sources.
• Create a clean, governed layer: Stand up a lakehouse or warehouse with standardized schemas, lineage and access controls.
• Enable privacy-by-design: Pseudonymization, minimization and clear retention windows by use case.
• Operational pipelines: Build reliable ETL/ELT, feature stores and monitoring for data freshness and drift.

Security and quantum risk

Security investment is rising, but awareness of quantum threats is still low - only 14 percent of leaders in West Africa and 35 percent in East Africa express concern about current encryption exposure. Treat this as a timing issue, not a theoretical one.

• Now: Inventory cryptography, enforce strong key management, and protect high-value data against "harvest now, decrypt later."
• Next: Build a post-quantum roadmap and run pilots with PQC-ready components aligned to emerging standards.

People: the decisive lever

Talent is the throughline. Eighty-one percent of CEOs believe AI-focused upskilling will directly influence performance. Sixty-seven percent have begun redeploying employees into AI-enabled roles, and 88 percent expect to increase hiring as digital programs scale.

• Redesign roles for human-AI collaboration: Shift routine tasks to machines; elevate judgment, exception handling and relationship work.
• Reward adoption: Tie incentives to AI usage and outcomes, not just completion of pilots.
• Create internal academies: Pair structured learning with live use cases to convert training into output quickly.

A pragmatic budget split for AI

• 50% Foundations: Data platforms, security, reliability, compliance.
• 30% Use cases: Build/scale a small portfolio with clear ROI and owners.
• 20% People and governance: Upskilling, operating model, model risk management.

90-day action plan

• Weeks 1-3: Confirm 3-5 priority workflows; define baseline KPIs (cycle time, cost-to-serve, forecast accuracy, first-contact resolution).
• Weeks 4-6: Stand up data pipelines and access controls; select build/buy/partner path per use case.
• Weeks 7-10: Pilot with real users; implement guardrails; measure productivity and quality shifts.
• Weeks 11-13: Decide scale-up, iterate or stop; publish a one-page scorecard to the exec team.

Questions for your next board meeting

• Where are we allocating >20 percent of budget to AI, and what outcomes are tied to each tranche?
• Which critical workflows will hit production in the next two quarters?
• What is our data readiness score by business unit, and how fast is it improving?
• How are we managing model risk, auditability and customer trust?
• What's our post-quantum plan for high-value data?
• How many roles have been redesigned or redeployed into AI-enabled work this quarter?

Where to upskill your teams

If you need a fast, practical way to train by role and build internal capability, explore focused programs at Complete AI Training - Courses by Job. For a snapshot of current options across providers, see the latest AI courses.

The takeaway is simple: AI is a 2025-2026 operating agenda, not a side project. Set the budget, fortify data and security, and move two or three high-value workflows into production - then scale what works.

Tola Adeyemi, CEO of KPMG West Africa, points to the continent's young workforce as a structural advantage. With fewer generational gaps in AI literacy, companies that invest in skills now will compound the benefits fastest.