As enterprises across South Africa wrestle with flat or shrinking IT budgets, AI is introducing a fundamentally different cost dynamic that is volatile, non-linear, and difficult to forecast using traditional financial models. MagicOrange’s head of sales for Africa, Middle East and APAC Stephen Coull explores how CIOs can modernise financial governance and link it to business value.
For most enterprises, technology budgets were already under strain before AI arrived. Vendor prices continue to rise with inflation. Labour costs remain stubbornly high. Executive teams are holding budgets flat – or actively pushing for reductions. At the same time, expectations of IT have not eased. CIOs are still expected to deliver more capability, more resilience and more innovation with fewer dollars.
AI enters this environment as something fundamentally new. Not just another workload. Not just another SaaS line item. AI introduces a cost model that behaves very differently from the cloud adoption waves CIOs have learned to manage over the past decade.
Cloud overruns were painful, but they were at least familiar. Virtual machines left running. Storage sprawl. Overprovisioned capacity. These were governance and hygiene problems. With time, organisations learnt to control them through tagging, chargeback, reserved instances and FinOps practices.
AI costs behave differently.
First, AI consumption is non-linear. Costs scale with usage patterns that are hard to forecast and even harder to simulate. Token-based pricing, model inference costs, training cycles, data movement and GPU consumption do not rise smoothly with business activity. A single successful use case can spike costs overnight and a minor architectural change can double inference spend. Traditional forecasting models struggle to keep up.
Seeing the bigger picture
Second, AI spend is often decoupled from existing ownership models. AI workloads cut across teams, products and functions. A data science experiment becomes a production dependency, a pilot embedded in a customer workflow turns into a permanent cost driver. In many cases, no single team “owns” the spend end-to-end. Finance sees volatility while IT sees utilisation. The business sees outcomes. No one sees the full picture.
Third, AI accelerates the black-box perception of IT. For years, business leaders have struggled to understand what they are paying for in technology. AI intensifies this challenge. When costs rise and explanations involve tokens, embeddings, vector databases and GPUs, confidence erodes quickly. If business leaders cannot understand the cost drivers, they default to a familiar conclusion: the spend must be excessive.
This is not primarily a shadow AI problem. While unsanctioned usage exists and raises legitimate security and data governance concerns for CISOs, the more material issue for CIOs and budget leaders is sanctioned AI that scales faster than financial controls. The biggest costs are usually coming from approved platforms, approved workloads, and approved initiatives that succeed.
The financial impact is real. AI-driven cloud consumption is already distorting run-rate budgets. It introduces volatility into monthly forecasts. It undermines confidence in annual planning. For finance teams accustomed to modeling infrastructure as a relatively stable operating expense, AI breaks assumptions about predictability.
This puts CIOs in a difficult position. They are expected to enable AI adoption to remain competitive. They are also expected to maintain financial discipline. Without better visibility and cost attribution, those goals conflict. CIOs are asked to justify spend they cannot easily explain in business terms. Budget owners are asked to approve investments they cannot confidently evaluate.
Finding a unified approach
The core issue is not AI itself. It is the lack of an integrated financial view of technology.
AI exposes a weakness that has existed for decades. Technology spend has often been managed in silos – cloud here, on-premises there, applications somewhere else. Financial conversations lag technical decisions. Cost is reviewed after the fact, not as part of continuous decision-making. AI simply makes this gap impossible to ignore.
What CIOs and finance leaders need is not another point solution focused on a single cost domain. They need a unified way to understand how technology spend – across cloud, on-premises, and hybrid environments – maps to real business outcomes. They need to see which AI initiatives drive value, which drive experimentation, and which quietly drive cost without proportional return.
When technology spend is clearly connected to business value, the conversation changes. AI investments stop being viewed as risky or opaque. They become strategic choices that can be evaluated, adjusted and optimised. Volatility becomes manageable because drivers are visible. Forecasting improves because consumption patterns are understood in context, not isolation.
AI is not breaking budgets because it is reckless. It is breaking budgets because it exposes outdated financial management models for modern IT.
Organisations that address this gap will move faster with confidence. Those that do not will find themselves slowing innovation to regain financial control – or worse, defending spend they cannot explain.
That is the real inflection point AI introduces for CIOs and budget leaders alike.
