As AI adoption accelerates across the Middle East and Africa, finance leaders must help organisations manage costs, strengthen governance and ensure measurable business returns.
By Tony van den Berge, VP, EMEA, Cloudflare
Artificial intelligence is rapidly moving beyond experimentation and becoming part of everyday business operations across the Middle East and Africa. Organisations are exploring generative AI to improve productivity, automate processes, enhance customer experiences and identify new growth opportunities. However, as investment increases, businesses face an important question: how can they ensure that AI delivers measurable commercial value?
Initially, many organisations focused on experimenting with AI and identifying potential applications. The challenge has now evolved. Businesses must understand which AI tools employees are using, how much these technologies cost, their impact on performance and whether the resulting benefits justify the investment.
This changing environment is bringing the chief financial officer (CFO) into a more central position. AI is no longer simply a technology decision or an innovation project. It is increasingly connected to financial management, investment priorities and long-term business performance.
AI Adoption Is Growing, Increasing the Need for Financial Oversight
The pace of AI adoption across the Middle East highlights the importance of stronger financial oversight. PwC’s Middle East Workforce Hopes and Fears Survey 2025 found that 75% of employees surveyed in the region had used AI in their jobs during the previous 12 months, while 32% reported using generative AI tools daily. The corresponding global figures were 69% and 28%.
Microsoft’s AI Diffusion Report also identified the UAE as a leading market for AI adoption, estimating that 64% of its working-age population was using generative AI by the end of 2025.
Across Africa, adoption remains uneven, reflecting differences in digital infrastructure, connectivity, skills and access to technology.
These findings highlight two related priorities for businesses across MEA: managing widespread AI usage in established digital economies and ensuring that investments in developing markets translate into sustainable business capabilities.
As AI becomes integrated into more business workflows, its financial impact is becoming increasingly difficult to overlook. Costs can emerge from enterprise subscriptions, API consumption, cloud infrastructure, model usage and autonomous agents operating continuously in the background.
Unlike traditional software licences, certain AI expenses fluctuate according to usage. Request volumes, task complexity, the amount of information processed and the selected model can all affect spending. When organisations move from individual employee experimentation to large-scale implementation, forecasting these expenses becomes more challenging.
CFOs therefore need to help organisations treat AI as a structured business investment, with expenditure evaluated against clearly defined objectives rather than grouped into general technology costs.
Connecting AI Expenditure with Measurable Business Value
Many organisations want to understand the return on their AI investments. However, tracking expenditure alone does not provide a complete picture. Finance and technology leaders must also understand what drives those costs and whether the associated activities generate meaningful business outcomes.
Which departments consume the most AI resources? Which applications produce measurable productivity improvements? Are employees using advanced models for tasks that less expensive alternatives could handle effectively? How much additional value does automation generate after accounting for implementation, supervision and operating expenses?
Without this visibility, increasing AI expenditure can be difficult to assess. Higher usage may indicate successful adoption and improved business performance, but it could also point to duplicated tools, inefficient processes or unnecessary consumption.
Selecting the cheapest available model is not necessarily the answer. Different business applications require different levels of capability. Summarising a routine document, analysing complex business risks and supporting sophisticated software development are very different tasks, particularly when accuracy and reliability are critical.
The objective should be to select technology that matches the requirements of each task while balancing cost, performance, reliability and the potential consequences of inaccurate results.
Finance teams can support this approach by collaborating with IT departments and business leaders to allocate AI expenditure to specific teams, projects, applications and use cases. This makes it easier to compare costs with outcomes, identify inefficiencies and prioritise investments that demonstrate genuine value.
Establishing Governance Without Slowing Innovation
The expanding involvement of CFOs should not mean restricting AI adoption. Excessive cost-cutting could undermine the productivity improvements, innovation and competitive advantages organisations expect from these technologies.
Instead, finance leaders should collaborate with technology, security, legal and business teams to establish governance frameworks that allow AI adoption to expand responsibly.
Three priorities are particularly important:
1. Visibility
Organisations need a clear understanding of where AI is being used, which tools and models are involved, what information they access and how much they cost. This becomes increasingly important as employees independently adopt AI applications and autonomous agents begin performing tasks with limited human intervention.
2. Accountability
AI expenditure should be traceable to the teams, projects and business outcomes it supports. Establishing clear ownership helps organisations evaluate performance, identify duplicated spending and distinguish valuable applications from initiatives that are not delivering expected results.
3. Control
Budgets, usage thresholds, alerts and access policies should reflect the requirements and risk profiles of different business functions. Teams handling complex, high-value work may need more capable models, while routine activities can often use more economical alternatives. Autonomous agents should also operate within clearly established permissions and spending limits.
For organisations operating across the Middle East and Africa, these measures must account for different levels of digital maturity, operational requirements and regulatory obligations.
A governance framework suitable for a digitally advanced enterprise in the UAE may require adjustments for an organisation operating across multiple African markets with different infrastructure limitations and resource priorities.
Governance should therefore establish consistency without imposing a uniform approach on every market. Its purpose is to support responsible experimentation while maintaining visibility, accountability and control as AI deployments expand.
Making CFOs Strategic Partners in AI-Driven Growth
The next stage of enterprise AI adoption will require stronger collaboration between finance, IT and business leadership.
Technology teams understand infrastructure, model performance and security requirements. Business units are familiar with operational challenges and customer expectations. Finance leaders contribute the financial discipline needed to assess investments, compare outcomes and allocate resources effectively.
Combining these perspectives can help organisations move beyond measuring AI success through adoption rates or the number of projects launched.
More meaningful performance indicators include time saved, operating expenses reduced, service quality improved, new revenue opportunities generated and risks mitigated.
This approach also enables businesses to make more informed investment decisions. Successful pilot projects can be expanded with greater confidence, while applications that fail to deliver expected benefits can be reassessed before consuming additional resources.
For CFOs, involvement should begin early. They need to work with technology and business leaders to define investment criteria, establish performance measures, evaluate total deployment costs and ensure that expenditure remains aligned with strategic priorities.
AI is entering a new stage of enterprise maturity. Across the Middle East and Africa, the opportunity is no longer limited to adopting new tools. Organisations must develop the capabilities to use AI effectively, securely and sustainably.
Ultimately, competitive advantage will not come simply from investing more in AI or deploying it faster. It will depend on understanding where AI creates value, knowing what that value costs and establishing the governance needed to deliver consistent results at scale.
That is why CFOs must become active participants in enterprise AI strategy. When financial accountability is incorporated from the beginning, AI can move beyond being a growing operational expense and become a managed investment in sustainable business growth.