Artificial intelligence has quickly moved from boardroom discussion to business priority, with organisations across South Africa experimenting with AI-powered tools to improve productivity, automate repetitive tasks and unlock new opportunities. But as AI adoption accelerates, so too does a less visible challenge: understanding, governing and optimising the cost of these rapidly expanding technology investments.
For many organisations, cloud expenditure was once the primary focus of technology cost management. Today, however, businesses are juggling a far more complex technology estate that includes cloud infrastructure, software-as-a-service (SaaS) applications, software licensing, hybrid environments and increasing AI workloads. Managing each of these in isolation is no longer sustainable.
“AI has made innovation more accessible than ever before, but it has also lowered the barriers to technology adoption,” says Byron Coetzee of SoftwareOne. “Business users can access powerful AI tools in minutes, often without engaging IT or finance. While that speed is exciting, it can also create fragmented technology environments where organisations lose visibility into what is being used, what value it delivers and how quickly costs are accumulating. Successful AI adoption isn’t about limiting innovation; it’s about ensuring innovation happens within a framework that provides transparency, accountability and measurable business outcomes.”
This is where FinOps is evolving from a cloud cost-management discipline into a broader business capability, and perhaps the biggest shift in thinking is that optimisation should no longer be viewed purely as a finance initiative.
A source of capacity for innovation
Leading organisations increasingly see optimisation as a way to create capacity for future innovation. Savings generated through improved governance, better utilisation and the elimination of unnecessary technology spend can be reinvested into new digital initiatives, cybersecurity, customer experience improvements or responsible AI adoption. In this way, optimisation and innovation become mutually reinforcing rather than competing priorities.
The financial opportunity is significant. Industry research from IDC indicates that mature FinOps programmes can achieve waste reductions of between 25% and 30%. SoftwareOne’s own experience shows a similar opportunity, with businesses achieving average savings of 23% by better aligning usage and rate strategies, while improving visibility, governance and accountability around technology consumption.
From cost control to strategic discipline
This is where Financial Operations, or FinOps, is evolving into a strategic business discipline. Originally developed to help organisations optimise cloud expenditure, FinOps has matured into a collaborative operating model that brings together finance, technology and business teams to make informed decisions about technology investments. Rather than asking simply, “How do we spend less?”, the more important question has become, “How do we maximise the value of every technology investment?”
Today’s technology landscape is far more fluid than in the past. AI tools can be deployed in minutes, cloud resources scale on demand, and business units often introduce new software independently of IT. While this agility supports innovation, it can also create fragmented technology estates and make spending increasingly difficult to manage.
This is also bringing FinOps and Software Asset Management (SAM) closer together. As cloud, SaaS, software licensing and AI increasingly overlap, organisations need greater visibility across their technology spend. Gartner has highlighted the convergence of SAM and FinOps as an important step towards better cost optimisation and greater technology value.
Recent developments within the FinOps Foundation reflect this changing landscape, expanding FinOps beyond cloud management to include AI, SaaS, software licensing and hybrid infrastructure under a single governance framework. The shift acknowledges that technology investment decisions can no longer be managed in silos if organisations are to balance innovation with financial accountability.
The rise of “Shadow AI”
One area receiving particular attention is the rise of “Shadow AI” and the growing use of AI tools by employees without formal governance or organisational oversight. While often driven by good intentions and a desire to improve productivity, these unofficial implementations can introduce unexpected costs, duplicate capabilities, compliance risks and inconsistent data governance.
Visibility as a strategic asset
Visibility has become one of the most valuable assets organisations can develop. Without a clear understanding of technology consumption across departments, businesses risk paying for overlapping platforms, underutilised licences or AI services that deliver little return on investment. This is where FinOps provides value far beyond cost optimisation.
Effective FinOps encourages organisations to establish shared accountability across finance, procurement, IT and business leaders. Rather than technology decisions being made independently, investment choices become linked to business priorities, expected outcomes and measurable performance. The result is better forecasting, more predictable technology expenditure and greater confidence that resources are being directed towards initiatives that deliver genuine value.
“FinOps is not about restricting innovation or saying ‘no’ to technology investment. It’s about giving organisations the insight to invest more confidently,” says Sarina Byl of SoftwareOne, addressing one of the biggest misconceptions about the discipline. “When businesses understand where their technology spend is creating value, and where it isn’t, they can redirect resources towards initiatives that drive growth, improve customer experiences or accelerate digital transformation.”
“The organisations seeing the greatest success are treating FinOps as an ongoing business function rather than a once-off optimisation project,” Byl adds. “Technology environments evolve constantly, and governance needs to evolve with them.”
Managing the cost of AI
This continuous approach is becoming increasingly important as AI adoption accelerates. AI workloads differ significantly from traditional cloud environments, with costs influenced by factors such as usage, model selection, training, routing and token consumption. Managing AI spend therefore requires FinOps to move beyond traditional infrastructure tracking and closer to the architectural and operational decisions that determine how AI is consumed. Gartner has highlighted the importance of approaches such as model routing and token budgeting in managing AI costs, while also noting that the increasing use of AI within FinOps tools brings new operational and governance considerations.
As South African organisations continue to invest in AI and digital transformation, the focus is shifting from adoption to measurable value. CIOs, CFOs and business leaders need a shared view of what technology is costing, what it is delivering and where investment should be directed.
