In this opinion piece, Tony van den Berge, Cloudflare’s VP, EMEA, examines why CFOs must take a more active role in AI investment, governance and value creation as adoption accelerates across the Middle East and Africa.
By: Tony van den Berge, VP, EMEA at Cloudflare
Across the Middle East and Africa, AI is moving rapidly from experimentation into everyday business operations. Organisations are exploring how generative AI can improve productivity, automate processes, enhance customer experiences and unlock new sources of growth. Yet as adoption gathers pace, a critical question is emerging: how can businesses ensure that their growing investment in AI delivers measurable commercial value?
For many organisations, the initial priority was to experiment quickly and identify opportunities. Today, the challenge is more complex. Businesses need to understand which AI tools employees are using, what those tools cost, how they affect performance and whether the benefits justify the investment.
This shift brings CFOs into a more prominent role. AI is no longer solely a technology decision or an innovation initiative. It is becoming a question of financial discipline, resource allocation and long-term business performance.
AI adoption is accelerating. Financial oversight must follow
The scale of AI adoption across the Middle East underlines the urgency of this transition. PwC’s Middle East Workforce Hopes and Fears Survey 2025 found that 75% of regional employees surveyed had used AI in their jobs during the preceding 12 months, while 32% reported using generative AI tools daily. The corresponding global figures were 69% and 28%, respectively. Microsoft’s AI Diffusion Report also placed the UAE at the forefront of global adoption, estimating that 64% of its working-age population was using generative AI by the end of 2025. Across Africa, however, adoption remains uneven, reflecting differences in digital infrastructure, connectivity, skills and access to technology.
These findings point to two complementary challenges for MEA businesses: managing increasingly widespread AI usage in established digital economies, while ensuring investments elsewhere translate into sustainable business capabilities.
As AI becomes embedded in more workflows, its financial implications become harder to treat as incidental. Spending can arise through enterprise subscriptions, API consumption, cloud infrastructure, model usage and automated agents operating continuously in the background.
Unlike a conventional software licence, some AI costs fluctuate with usage. The number of requests, complexity of a task, volume of information processed and model selected can all influence expenditure. As organisations move from employee experimentation to large-scale deployment, these variables can make budgets more difficult to forecast.
CFOs therefore need to help establish a clearer understanding of AI as a business investment, with spending evaluated against defined objectives rather than treated as an undifferentiated technology expense.
The missing link between AI spending and business value
Many organisations want to demonstrate the return on their AI investments, but measuring expenditure alone provides only part of the picture. Finance and technology leaders need to understand what is driving those costs and whether the associated activity is delivering meaningful outcomes.
Which departments consume the most AI resources? Which applications generate measurable productivity improvements? Are employees using advanced models for tasks that could be completed effectively by simpler, less expensive alternatives? And how much additional value does automation deliver once implementation, oversight and operating costs are taken into account?
Without this level of visibility, rising expenditure can be difficult to interpret. Increased usage might indicate successful adoption and stronger business performance, or it might reflect duplicated tools, inefficient workflows and unnecessary consumption.
The answer is not to default to the cheapest available model. Different applications demand different levels of capability. Summarising a routine document is not the same as analysing complex business risks, supporting sophisticated software development or assisting with decisions that require greater accuracy and scrutiny.
The objective is to match the technology to the task, balancing cost against performance, reliability and the consequences of an incorrect result.
Finance teams can help establish this discipline by working with IT and business leaders to allocate expenditure to specific departments, projects, applications and use cases. This creates a stronger foundation for comparing costs with outcomes, identifying inefficiencies and directing investment towards applications that demonstrate genuine value.
Building a governance framework that enables innovation
The expanding role of CFOs should not be interpreted as a mandate to restrict AI adoption. Excessive cost-cutting can undermine the productivity improvements, innovation and competitive differentiation that businesses are seeking.
Instead, finance leaders should work alongside technology, security, legal and business teams to establish a governance framework that allows AI to scale responsibly.
Three priorities are particularly important:
- Visibility: Organisations need a clear picture of where AI is being used, which tools and models are involved, what information they access and how much they cost. This is increasingly important as employees adopt tools independently and AI agents begin performing tasks with limited human intervention.
- Accountability: AI expenditure should be traceable to the teams, projects and business outcomes it supports. Clear ownership makes it easier to evaluate performance, identify duplicated spending and distinguish valuable applications from those that are failing to deliver.
- Control: Budgets, usage thresholds, alerts and access policies should reflect the needs and risk profiles of different business functions. Teams undertaking complex, high-value work may require more capable models, while routine tasks can often be handled through more economical options. Autonomous agents should also operate within clearly defined permissions and spending limits.
For organisations operating across MEA, these measures must accommodate different levels of digital maturity, operational requirements and regulatory obligations. A framework that works for a digitally advanced enterprise in the UAE may need to be adapted for a business operating across multiple African markets with different infrastructure constraints and resource priorities.
Governance should therefore provide consistency without imposing a one-size-fits-all approach. The aim is to enable appropriate experimentation while maintaining visibility, accountability and control as deployments expand.
Making CFOs a partner in AI-driven growth
The next phase of AI adoption will require closer collaboration between finance, IT and business leadership. Technology teams understand infrastructure, model performance and security requirements. Business units understand operational challenges and customer needs. Finance leaders bring the discipline needed to evaluate investment decisions, compare outcomes and allocate resources effectively.
Together, these perspectives can help organisations move beyond measuring AI success through adoption rates or the number of projects launched. More meaningful indicators include time saved, operating costs reduced, service quality improved, revenue opportunities created and risks mitigated.
This approach also helps organisations make better decisions about where to invest next. Successful pilots can be scaled with greater confidence, while applications that fail to deliver expected benefits can be reassessed before they consume further resources.
For CFOs, the priority is to become involved early, helping shape investment criteria and performance measures before AI usage becomes too widespread to manage effectively. This means working with business and technology leaders to establish clear objectives, assess the total cost of deployment and ensure that spending decisions remain connected to strategic priorities.
AI is entering a new stage of enterprise maturity. Across the Middle East and Africa, the opportunity extends beyond adopting new tools to building businesses that can use them effectively, securely and sustainably.
Ultimately, competitive advantage will not come simply from spending more on AI or deploying it faster. It will come from knowing where AI creates value, understanding what that value costs and having the governance to reproduce those results at scale.
That is why the CFO must become an active partner in AI strategy. When financial accountability is built into adoption from the outset, AI can move beyond being an expanding operational expense and become a managed investment in long-term business growth.
Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the official editorial stance or policy of Techitup Middle East.
