Why AI strategy? The benefits of being strategic with new tech and 7 steps for building a winning AI strategy that delivers ROI for South African companies
Looking to drive your company into a new era with AI?
It’s a remarkable technology that’s being adopted at incredible rates – generative AI use increased from 33% to 71% in 2024, business-function AI use is up to 36%, and data management up to 42%.
Yet, naturally, it needs to be used responsibly: The technology is not flawless, and many companies have gotten into serious trouble being too trusting of the tech. Not to mention the risk of mass layoffs without accounting for the human cost, potentially destabilising our economies.
While there are many great opportunities with AI in South Africa, most recent research shows that 80% of companies fail to benefit from AI, so they mainly attract the negatives to themselves. Success requires a deliberate, integrated AI plan — one that balances innovation with risk management and ensures long-term scalability.
Here’s an AI strategy framework designed for SA companies:
An AI strategy is a comprehensive roadmap for embedding AI across a business’s operations, processes and decision-making frameworks in a way that aligns with overall business goals. Rather than treating AI as a series of isolated experiments, a proper AI strategy defines how AI will help the business achieve measurable outcomes like faster innovation, improved operational efficiency, better customer experience and competitive differentiation.
A well-designed AI strategy considers not just the technologies but also the infrastructure, governance, data ecosystem, talent requirements, and ethical implications necessary for success.
See the full guide to AI in South Africa.
Businesses with a strong AI strategy gain multiple advantages. Research shows that organisations with enterprise-wide AI strategies are more than three times as likely to transform successfully compared to those without.
Key benefits include:
South African corporates face unique challenges — ranging from data availability to regulatory compliance — but the framework for building an effective AI strategy is globally proven. Here’s how to approach it:
Every effective AI strategy must begin with a clear understanding of your business’s core objectives. Instead of selecting technology first, define your strategic ambitions: new markets, efficiency gains, customer growth, or product innovation. AI should serve these goals, not distract from them. Anchoring in business strategy ensures AI initiatives stay focused and valuable.
Facilitate a leadership workshop to identify and prioritise three to five strategic goals. Only once goals are set, map AI’s potential contribution to each area. Also see the guide to building an innovation strategy in SA.
Once strategic priorities are established, the next step is to pinpoint where AI can create measurable value. AI can act as a researcher, thought partner, simulator, and more. Building a focused portfolio of use cases with clear business KPIs ensures alignment, avoids wasted effort, and lays the foundation for broader AI maturity.
Interview department heads to surface 10+ potential AI applications. Shortlist use cases based on feasibility, value potential and alignment with strategic goals. Also, consider points from your efficiency strategy.
AI’s effectiveness depends on the quality of its inputs. Companies that rely on public or commoditised data produce undifferentiated outputs. A proprietary data ecosystem — curating unique internal, partner and customer data — provides a long-term competitive advantage and enables more powerful, personalised AI outcomes.
Audit existing internal data assets, assess gaps, and form partnerships or collect new primary data to fill strategic needs for high-value AI use cases. See the power of unlocking data-driven development.
Building an AI strategy is not a job for IT alone. It demands collaboration between strategy, operations, technology and domain experts. A successful team blends business acumen with technical skills, change management expertise, and strong leadership to drive adoption across business units.
Identify internal champions across departments. Supplement with external AI specialists where needed, and create a cross-functional task force reporting to executive leadership. Also, see how you can augment with tech team services.
AI poses ethical risks — from bias to explainability issues — that must be addressed from the outset. Embedding governance around data privacy, fairness, transparency, and regulatory compliance protects both the business and its customers while strengthening trust in AI initiatives.
Draft AI-specific governance policies covering data ethics, model validation, bias monitoring and incident management. Involve legal, compliance and HR early in the process. See the latest trends in AI in South Africa.
Rather than massive up-front deployments, successful AI strategies focus on launching small, high-impact pilots. Early wins create momentum and surface real-world insights. AI is not static — businesses must commit to refining models, learning from deployment, and evolving continuously to stay competitive.
Select one or two pilot projects tied to strategic goals. Measure results rigorously, iterate based on feedback and prepare a roadmap for broader scaling. See how to run validation experiments and master A/B testing.
Even the best AI strategy will fail without organisational support. CEOs and executives must become visible champions of AI, communicating how it ties to the company’s future success. This top-down alignment inspires trust, prioritises resources, and accelerates cultural change across the enterprise.
Host company-wide sessions explaining the AI vision. Regularly update staff on progress, celebrate early wins, and frame AI as an opportunity, not a threat.
While building an AI strategy, several traps can undermine success:
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