AI tops Africa's CEO agenda for 2026 as skills and budgets surge

AI: The strategic priority for African CEOs heading into 2026

AI has moved from experiment to executive priority. According to the KPMG 2025 Africa CEO Outlook Survey of 130 leaders across Southern, East, and West Africa, CEOs are optimistic and aligned: AI is the top strategic focus heading into 2026, even with economic and geopolitical pressure in the background.

Confidence is high. 78% report strong business confidence (up more than 12% year-on-year), 98% expect to expand in the short term, and most see AI as the catalyst for efficiency and resilience.

What the data says

• Top challenges: integrating AI into core operations (32%), regulatory pressure (25%), and cybersecurity (24%).
• 71% are investing in AI to drive operational efficiency and long-term resilience.
• 26% plan to allocate more than 20% of their annual budget to AI-nearly double the global average of 14%.
• Talent is central: 81% say AI upskilling directly impacts success; 67% are redeploying staff into AI-enabled roles; 88% expect to increase headcount.

The execution gap: infrastructure and data

Two constraints stand out: infrastructure and data readiness. Unreliable power, limited broadband, and legacy systems limit data-intensive AI. Many firms lack the curated, high-quality local data that production AI needs.

That's the difference between pilots and scale. Fixing it requires a clear plan for power resilience, connectivity, cloud or hybrid compute, and disciplined data governance.

Build, buy, or partner? Decide with intent

"To deploy and scale AI, African organisations are faced with three options: build, buy or partner... There is no one-size-fits-all-approach. The right strategy depends on the organisations' existing capabilities, risk appetite, and strategic objectives... A sustainable approach should be shaped by the business context, the desired outcomes, and the ability to scale and govern AI effectively," said Joelene Pierce, CEO Designate of KPMG South Africa.

• Build: Use when AI is core to differentiation, you have (or will hire) strong data/ML talent, and IP control matters.
• Buy: Use for proven, horizontal needs (e.g., forecasting, customer service) where time-to-value beats uniqueness.
• Partner: Use to bridge infrastructure gaps, de-risk transformation, and accelerate skill transfer with clear SLAs and exit plans.

A 12-month AI plan that actually ships

• Q1: Choose the money-makers
  • Prioritise 3-5 use cases tied to P&L (cost-to-serve, working capital, churn, fraud, field productivity).
  • Define owners, success metrics, data sources, and compliance constraints.
  • Set a stage-gated funding model tied to milestones.
• Q2: Prove value and unblock data
  • Stand up pilots with real users and shadow KPIs in production-like environments.
  • Kick off a data readiness sprint: catalog, clean, secure, and label the top datasets.
  • Run a security review for model and data risks.
• Q3: Scale what works
  • Industrialise 2-3 winning use cases with MLOps, monitoring, and retraining pipelines.
  • Integrate into core apps and workflows; measure adoption, not just outputs.
  • Expand training to adjacent teams and set new operating procedures.
• Q4: Institutionalise
  • Stand up an AI governance board (Legal, Risk, Data, Security, HR, BU leads) with decision rights.
  • Publish policy on data use, model risk, and vendor standards; audit quarterly.
  • Lock in budget for next year's portfolio, tied to realised savings and revenue.

Budget and ROI guardrails

• Fund by portfolio, not project. Expect a few outsized wins to pay for the rest.
• Track hard metrics: cost reduction, cycle-time compression, forecast accuracy, revenue per employee, and risk losses avoided.
• Use TCO thinking: model, infra, data, integration, compliance, change management.
• Ring-fence funds for data cleanup and MLOps-these are the levers that keep ROI compounding.

Talent and org design

• Stand up a small central AI team (product, data science, engineering, MLOps, governance) that partners with business units.
• Redeploy domain experts into AI-enabled roles; pair them with data teams to build usable solutions.
• Incentivise adoption: KPIs for leaders should include AI-driven outcomes, not just delivery.
• Launch targeted upskilling for execs, product owners, and frontline ops. Curate short, hands-on programs that map to roles. For structured options, see AI courses by job.

Data readiness checklist

• Source of truth defined for each critical dataset; owners named.
• Quality baselines in place (completeness, timeliness, consistency) with alerts.
• Clear policies for sensitive data, residency, and retention.
• Data contracts between systems; lineage tracked to support audits.
• Access managed via roles; logs monitored for anomalies.

Security and compliance from day one

• Run threat modeling for every AI use case: data leakage, prompt/response abuse, model drift, and supply chain risk.
• Apply least-privilege access, encrypt data at rest/in transit, and isolate training environments.
• Red-team critical models; monitor outputs for bias and unsafe content; keep human-in-the-loop where risk is material.
• Maintain a vendor risk register and third-party audit schedule.

Infrastructure that won't stall at scale

• Power resilience: dual feeds or generators at key sites; assess cost-benefit of colocation vs. cloud for high-availability workloads.
• Connectivity: prioritise SD-WAN and bandwidth upgrades for data-heavy locations.
• Compute: hybrid approach-cloud for bursty training/inference, edge for latency and data sovereignty needs.
• Costs: negotiate reserved capacity and use smaller, fine-tuned models when accuracy allows.

Vendor selection criteria that protect outcomes

• Proven ROI in your industry and region; referenceable clients.
• Transparent model cards, security posture, data usage terms, and exit options.
• Interoperability with your stack; clear APIs and monitoring hooks.
• Shared accountability: performance SLAs tied to business metrics, not vanity scores.

What good looks like by Q4 2026

• Three to five AI use cases delivering measurable P&L impact.
• Data foundation in place for the next wave of use cases.
• An operating model that combines central standards with business-led delivery.
• Board-level reporting on AI risk, value, and adoption-no surprises.

The signal is clear: African CEOs are backing AI with budget, headcount, and conviction. Turn that intent into outcomes by narrowing scope, shipping fast, and scaling what proves value. The firms that treat AI as an operating system-not a side project-will win the next cycle.