The latest IDC forecasts indicate that European spending on AI will total almost $470 billion by 2030, with the market expected to quadruple over the next five years. As organisations increasingly adopt a hybrid approach to AI, deploying different models and services for different workloads rather than relying on a single provider, costs are rising rapidly and the bill is landing on the desk of the CFO.
In this new environment, the role of the CFO is evolving. Finance leaders are now being asked entirely new questions while navigating an unfamiliar token currency. Ultimately they must determine what specific AI-enabled outcomes actually cost and establish which initiatives are generating measurable value. Navigating this puzzle is becoming increasingly pressing as boards and investors demand stronger governance and clearer returns, while employees continue to pull AI into everyday business processes.
The era of funding AI experimentation on the assumption that adoption alone will create value is ending. In response, CFOs must walk a fine tightrope between financial discipline, governance and accountability to create sustainable business value.
AI has created a new enterprise economic model
Just 5% of AI projects deliver measurable returns, resulting in mounting pressure on CFOs to justify investments that were made in haste during the initial deploy and experiment phase. In doing so, CFOs must understand the economics behind them.
One of the reasons AI presents such a unique challenge is that it introduces a different cost structure from previous generations of enterprise software. Traditionally, investments were relatively easy to forecast through licences, seats and contracts, allowing organisations to build predictable budgets around technology spending.
When it comes to AI, cost is dictated by usage, and what begins as a modest pilot project can quickly evolve into a substantial operational expense. In some workflows, recurring software consumption can even exceed the cost of the human effort AI was intended to augment. CFOs must therefore be measuring cost against business outcomes as adoption moves beyond experimentation and into day to day operations.
The financial and security risks of scaling AI
As usage scales, organisations are facing challenges that go beyond cost management alone. Research shows that 83% of organisations report AI involvement in their security incidents, spanning external attacks, internal exposure, and the growing targeting of AI systems themselves. This issue is compounded by the rise of hybrid AI and the adoption of several models, making it increasingly difficult to track what tools are being used and where risks are emerging.
From an external perspective, AI is also changing the threat landscape, with threat actors capitalising on the speed and accuracy AI provides and deploying it across a growing range of malicious activities. IBM’s latest Cost of a Data Breach report revealed that AI enabled breaches cost an average of $6 million.
As AI evolves beyond a purely technical conversation, finance leaders are becoming increasingly central to how organisations manage both the opportunities and risks associated with adoption.
Building the right governance framework
In order to do this, it is critical that CFOs establish strong financial frameworks around AI adoption. That starts with creating guardrails that allow employees to experiment and explore new use cases while maintaining visibility over token consumption and higher volume deployments that can quickly drive costs. Organisations need to encourage innovation while retaining a clear understanding of where spending is occurring and how AI usage is growing across the business.
It also means resisting the temptation to scale AI investments too quickly and ensuring controls such as ownership and governance are in place before additional spending is approved. Equally important is the creation of cross functional governance structures that bring together leaders from across the business to evaluate opportunities and ensure AI initiatives align with wider priorities. This could include establishing an AI council that unites leaders from engineering, marketing, HR and finance to help guide AI adoption across the organisation and provide oversight as new use cases emerge.
Finally, visibility must remain a priority. Finance leaders should work closely with IT and security teams to develop a comprehensive understanding of where AI tools are being used, what data they are accessing and how they are being governed. Organisations that maintain this level of oversight will be far better positioned to measure value, understand costs and assess risks as AI adoption continues to accelerate.
AI is creating a new economic model for the enterprise and as organisations expand their use of hybrid AI the relationship between cost, value and risk is becoming a business priority.
For CFOs, the challenge is no longer simply approving investment in AI. It is ensuring that innovation is accompanied by the governance and financial discipline needed to generate sustainable business value. Those organisations that develop clear frameworks will be in a far stronger position to realise the benefits of AI while keeping spending and risk under control.
Tony Jarjoura
Tony Jarjoura, CPA, is Chief Financial Officer at Gigamon and leads the global finance team and accounting operations. He brings more than 20 years of financial, technical, and operational experience to the executive team. Prior to Gigamon, Tony served high-tech growth companies and gained valuable experience in the assurance practice of Ernst & Young LLP. Tony holds a BS in Business Administration, magna cum laude, from the University of San Francisco, California, and is a certified public accountant.
