Junaid Kleinschmidt | Lead | Intelligence | Accenture in Africa | mail me |
For many organisations, Artificial Intelligence (AI) adoption started small, through proof of concepts and small-scale trials, rather than full-scale rollout.
Teams tested chatbots, employees began using generative AI tools, and technology leaders explored where the technology could make a difference. AI is now finding its way into more everyday business processes. As usage grows, so does the bill.
The problem is that the bill is usually much easier to see than the value. A business can measure what it spends on infrastructure, inference and licences. However, it is much harder to put a number on the customer who stayed because service improved. It is equally difficult to quantify the employee who gained time to focus on more valuable work or the better forecast that helped release working capital. This creates a gap between the cost of AI and the business value it produces.
Understanding the growing cost of AI
That gap is becoming harder for business leaders to ignore. The source material notes that AI-related spending is tracking to more than $800 billion in 2026. Yet, only 23% of surveyed C-suite leaders report widespread and sustained business value from AI across their organisations. Some companies have also reportedly exhausted a year’s AI budget in a single quarter. Clearly, AI is becoming a bigger business cost, making it increasingly important for leaders to understand what they spend and what they receive in return.
This is why the economics of AI deserve more attention. Tokenomics essentially involves understanding what AI consumes and what the business gets back. Every prompt, response, retrieval step and agent interaction that leverages a Large Language Model (LLM) uses tokens. Consequently, consumption is becoming an increasingly important part of the cost of running AI at scale.
For businesses operating in South Africa, investment decisions face close scrutiny. Technology budgets must also demonstrate clear value. Therefore, this shift requires a different way of thinking. The question should not simply be whether an AI tool is useful. Instead, businesses should ask whether the amount they spend on that tool is appropriate for the job it performs and the value it creates. In this context, AI is becoming a bigger business cost that organisations must manage with greater financial discipline.
Choosing the right level of AI capability
One of the biggest mistakes businesses can make is assuming that every task needs the most capable model available. The source material estimates that only 10% to 20% of enterprise tasks are complex enough to justify frontier or near-frontier capabilities. Many routine, well-defined tasks can be handled by lighter models. Meanwhile, more sophisticated models are better suited to complex reasoning, long-context analysis or work where errors carry significant consequences.
That distinction matters because AI costs do not behave like traditional software costs. With conventional software, organisations generally know how many licences they have and what those licences cost. However, AI consumption can change according to what people ask systems to do, how much reasoning they require and how much work automated agents handle. As AI becomes embedded in workflows, usage can continue around the clock. A traditional employee or licence count no longer provides an obvious ceiling on expenditure.
There is also a behavioural element. Making AI more efficient does not necessarily mean businesses will spend less. As the cost of using AI falls, organisations may simply find more things to do with it. The source material describes this as the Jevons paradox. It notes that, in its simulation, only 15% of organisations would bank the savings from a 25% reduction in token prices. The majority would reinvest those savings in new use cases, larger workloads or higher-quality models.
Elsewhere, some organisations are taking a different route entirely. They are moving to open-source models and smaller, more specialised language models. This approach does not eliminate costs, but relocates them. Instead of paying per token, businesses take on the responsibility of hosting, running and maintaining the infrastructure themselves.
Aligning AI spending with business value
For executives, this changes the conversation. The objective is not to tell employees to use less AI. Rather, leaders must ensure that the organisation uses the right level of AI capability for each task. A routine task that a lighter model can complete effectively should not automatically go to a more expensive model. At the same time, businesses should be prepared to invest in greater capability where the potential value or risk justifies it.
This requires closer collaboration between technology and business leaders. AI costs often sit within technology budgets, while the benefits appear in customer service, engineering, marketing, finance or other parts of the organisation. Without shared measures, the CFO sees the cost, the CIO sees usage, and the business sees the outcome. The source material identifies weak business-technology alignment, insufficient data and analytics capabilities, and difficulty isolating AI’s contribution as significant barriers to linking spending with value.
The practical response is relatively straightforward. Leaders need a common view of AI costs, usage and returns. They must establish governance before deployments become difficult to manage. They should also route work to the appropriate level of model capability. Furthermore, they need to review consumption and outcomes regularly as prices, models and use cases change. These measures become essential as AI is becoming a bigger business cost and organisations seek to maintain control over their investments.
Turning AI expenditure into measurable value
For South African businesses, the opportunity is to move beyond treating AI as another technology expense. As adoption grows, understanding where organisations use AI, what it costs and what it delivers will become just as important as choosing the technology itself.
The organisations that get this right will not necessarily be those that spend the most on AI. Instead, they will be the ones that understand where their spending creates genuine business value.
