By Brandon Nussey, CFO at JAGGAER
The modern CFO has a key role to play in helping drive the acceleration of AI. It is critical that this role is understood and acted on now or CFOs risk spending the next decade playing catch-up. Over the last few months, the mandate for finance chiefs has fundamentally shifted; the role is no longer merely about traditional capital allocation. Instead, it has evolved into a strategic necessity to direct investment into embedding AI into the customer offering and utilising it to revolutionise internal operations.
A two-pronged approach: external value vs internal efficiency
Navigating the AI transition requires a rigorous analysis of opportunities versus threats across both the internal and customer-facing tracks. On the customer-facing side, AI-powered features are no longer “nice-to-haves”, or even differentiators, but are essential for market relevance. Companies that fail to invest now will cede important ground to competitors. These innovations critically also allow for premium pricing and improved customer retention, particularly when clients are involved in the pilot stages of product development and are able to steer innovation.
There are, however, some key financial hurdles to realising these benefits. Variable costs remain notoriously difficult to forecast at scale, and a rapidly evolving legislative landscape carries significant penalty risks. Moreover, the “build-versus-buy” equation is complicated by the speed at which foundational models are becoming both more capable and more affordable. The CFO must lead this evaluation, ensuring that pricing models remain flexible enough to keep costs well below the customer’s demonstrated willingness to pay.
Internal sandboxes
Internally, the focus should be on phased AI roll-outs that automate high-volume, low-risk processes. These initiatives have been shown to yield measurable ROI in the short term while simultaneously helping construct and establish the data governance infrastructure required for future external productization. Perhaps most importantly, these internal applications help educate the workforce on the risks and realities of AI before a product hits the market. This internal adoption therefore provides a solid foundation for customer-facing tools, supporting the investment thesis with real-life data collected in internal sandboxed environments.
The internal transition also comes with a unique set of cultural and behavioural challenges. Low adoption rates and the rising use of unauthorised tools (shadow tools) can undermine the success of these initiatives. Automation bias, a tendency to over-rely on decision-support tools, is also a risk that needs to be addressed through company culture and training.
To mitigate this, staff should be encouraged to familiarise with the tools in the relatively safe internal environment, so they can experiment with their impact on processes, and allow the business to measure and tweak performance.
Meaningful gains over marginal shifts
Currently, one survey reported that approximately 19% of company revenue is being funnelled into AI projects, yet much of this spend is failing to move the needle. While enterprise-wide ChatGPT licences might be part of that expenditure, they rarely deliver the transformative shift required. True impact is found in more granular, functional gains. For example, improving a developer’s productivity by 13% is the type of tangible result that makes a real difference to the bottom line.
Crucially, AI is not a tool for headcount reduction. Instead, it serves to eliminate repetitive, low-value work. By automating routine tasks, AI helps remove demotivating elements from the work life for example by shortening development cycles in engineering, slashing ticket volumes in customer support, or automating the rules-based procure-to-pay cycle in procurement. The end goal is to free up human talent for complex, relationship-critical interactions.
Finance departments specifically stand to gain through AI-powered analysis tools. These systems can run variance analysis continuously, flag budget deviations, and remodel forecast scenarios. By narrating these numbers in plain language, AI helps bridge the gap between raw data analysis and commercial insight.
The road to 2026: from experimentation to tangible results
As we move through 2026, the era of unbridled experimentation must give way to strategic discipline. Currently, too many businesses are running experiments that fail to provide credible internal case studies outside of a startup environment. The long-term “stickiness” of far too many exciting internal projects still remains unproven. Double-digit improvements are entirely possible, but only if the selection of projects is made strategically.
The end of 2026 is expected to be a turning point where initiatives finally transform into tangible benefits. The CFO will then have a wealth of customer signals regarding usage, adoption, and risk to weigh up. This data, combined with global macro-economic factors, will place “push” functions like procurement under an even brighter spotlight.
Ultimately, the CFO’s competitive edge in this era is what it has always been: a reliance on facts and intelligence. By reading and interpreting adoption data and risk indicators, finance leaders can prevent a “Wild West” environment of unbridled development from draining critical resources from the business. Intentionality today will consolidate the business’s AI position in the future, ensuring that real innovation becomes tangible by the end of the year. In a fast-moving market, preparing the internal workforce and processes for what is to come is more important than ever.
