AI in Finance—Where Are We Really?
Artificial intelligence is no longer an emerging idea sitting on the periphery of finance transformation—it is a strategic capability that’s embedding itself at the core of how finance teams operate, plan, and deliver value. But for many CFOs, one key question remains, how and where to deploy it to maximise outcomes.
While headlines continue to highlight the theoretical potential of AI, finance leaders need a clearer view of what successful, real-world implementation looks like. Which organisations are doing this well? What outcomes are they achieving? And how are they building the right teams, tools, and governance to make it work?
From Proof of Concept to Practical Performance
The finance sector continues to lead AI adoption in the UK. According to research by OneAdvanced, 40% of finance teams have already implemented AI into parts of their operations—more than any other sector surveyed. This isn’t just about automation or cost reduction; it’s about increasing speed, accuracy, and foresight in a function that thrives on control and insight.
For some early adopters, AI is already playing a crucial role in planning and forecasting.
Unilever, for example, developed an AI-powered customer connectivity model to improve planning, forecasting, and replenishment. Piloted with Walmart in Mexico, the system integrated real-time sales data to create a seamless and highly responsive supply chain. The result? Over 98% on-shelf availability and a significant reduction in inventory. Unilever is now rolling this model out across global key customers, including in the UK and US. For CFOs, the implications are clear: real-time data and AI can reshape planning accuracy and supply chain finance assumptions alike.
What Are Leading CFOs Actually Doing with AI?
While many organisations are still in pilot mode, others are already deploying AI at scale to tackle known inefficiencies or risks. A recent report by Deloitte—The FinanceAI™ Dossier—provides a useful lens into how AI is reshaping finance functions through specific, high-impact use cases.
Among the most promising are:
- Autonomous Close: AI can help automate financial close processes, reducing manual reconciliation and significantly improving speed to insight. For finance teams caught in labour-intensive month-end routines, this is not just a productivity gain—it’s a strategic unlock.
- Cash Flow Forecasting: Using historical data, AI models can provide more nuanced and dynamic forecasting, enabling tighter cash and working capital management in volatile markets.
- Finance Insight Engines: These tools ingest and analyse complex financial datasets, helping CFOs and FP&A teams extract actionable insights and generate board-ready commentary far more efficiently.
- Dynamic Risk Monitoring: Real-time, AI-driven assessments of financial risks (from credit to operational exposure) enable finance functions to move from reactive to predictive postures.
These aren’t distant ambitions. They are available, investable use cases for today’s CFO.
AI as a Risk Radar
Beyond performance gains, AI is proving highly effective in risk detection and customer insight. NatWest Group, for instance, recently invested in Serene, an AI platform that identifies early signs of customer financial vulnerability. By leveraging behavioural signals and predictive modelling, NatWest can proactively support customers before issues escalate—a critical capability in a cost-of-living crisis.
While focused on retail banking, the implications translate neatly to finance. AI-enabled monitoring of spend patterns, working capital shifts, and revenue signals could give finance leaders an early warning system for business unit distress or client-side risk. This kind of foresight—once only possible through months of modelling—can now be surfaced in real-time.
Operational Efficiency and Compliance at Scale
Document automation is another quietly transformative area. BT Group, in partnership with Wipro, has used Amazon Textract to automate the processing of large volumes of financial documentation—reducing turnaround time from weeks to minutes and achieving a 25% cost saving. Accuracy and compliance have improved alongside productivity, demonstrating that AI doesn’t just accelerate workflow, it strengthens control.
This is especially relevant for CFOs navigating increasingly complex audit, tax, and ESG reporting landscapes. AI that can extract, classify, and validate financial data at scale may soon become foundational to the finance tech stack.
The Evolving CFO Agenda
AI is not just another tool in the digital transformation toolkit—it is reframing the expectations placed on CFOs.
As Deloitte notes, tomorrow’s CFO will need to adopt three new modes of leadership:
- Technology Leadership – guiding the integration of AI systems that directly affect finance performance and governance.
- Data Stewardship – ensuring quality, security, and explainability in data-driven models, with close attention to regulatory compliance.
- Change Management – leading the culture shift required to build confidence in AI tools across finance teams.
These responsibilities signal a broader evolution in the CFO’s remit—from financial steward to transformation leader. In our piece on The Modern CFO, we explore this shift in greater depth: today’s finance leaders are being called on to balance commercial acumen with strategic vision, digital fluency, and a people-first mindset.
Embracing this expanded agenda is essential for any CFO looking to lead through disruption and set the pace for sustainable value creation in the age of AI.
What This Means for the Finance Team
As AI becomes more embedded in core finance activities, the composition and capabilities of finance teams are evolving too. For many, the shift will not be dramatic—but it will be fundamental.
The traditional hallmarks of a high-performing finance team—technical rigour, controls, discipline, analytical thinking—will remain essential. But the context in which those strengths are applied is changing. Routine tasks like reconciliations, reporting roll-ups, and basic variance analysis are increasingly being automated. The result? A greater share of the team’s time can now be redirected toward forward-looking analysis, scenario planning, and commercial partnering.
That doesn’t mean a reduced need for people—it means a redefinition of their role.
But here lies a critical challenge: the skills to lead, implement, and sustain AI in finance aren’t yet widespread. Research by OneAdvanced found that almost one-third (30%) of organisations cite a lack of internal expertise as the reason they’re not moving forward with AI adoption, while only 28% believe the UK has sufficient skills and experience for effective and safe implementation. Similarly, a Gartner study revealed that 42% of organisations across the UK, US, and Germany see talent and skills shortages as the top barrier to AI implementation.
These findings should be a wake-up call. While the appetite for AI is growing, the readiness of teams to engage with it remains a significant blocker.
Finance teams that succeed in this transition will need to be:
- Digitally fluent – confident in using AI-powered tools to extract insights and test business assumptions.
- Curious and adaptive – open to changing long-established ways of working.
- Storytellers, not just scorekeepers – capable of interpreting AI-generated outputs and delivering strategic narratives to senior stakeholders.
For CFOs, this means investing not only in systems but in people—creating structured upskilling programmes, fostering experimentation, and providing the psychological safety for teams to learn by doing.
There is also a cultural nuance: if AI is perceived as a threat to job security, resistance will follow. But if it’s positioned as a way to elevate the work finance teams do—removing drudgery and expanding influence—it becomes a rallying point.
As Chris Morrison, MD at Cedar Recruitment points out, “we’re not replacing accountants with AI—we’re helping them become better analysts, faster storytellers, and sharper business partners”.
Summary: Confidence Over Hype
The organisations getting ahead with AI aren’t the ones chasing shiny innovations—they’re the ones solving specific business problems through measured, value-led deployment.
What Unilever, NatWest, and BT demonstrate is that AI adoption in finance isn’t a leap of faith. It’s a process of targeted experimentation, strategic investment, and strong leadership. As use cases continue to mature—from autonomous close to risk sensing—CFOs have a unique opportunity to reshape their function as a driver of insight, agility, and trust.
So, where are we really with AI in finance?
We’re in motion. And the CFOs who move with clarity, discipline, and vision will shape not just how finance runs—but how it leads.
At Cedar, we work with finance leaders who are navigating this shift first-hand—building future-ready teams, sourcing AI-literate talent, and securing the leadership needed to drive meaningful transformation.
Whether you’re looking for interim expertise to lead a finance transformation programme or permanent finance professionals who can embed AI capability into your team, our specialist recruitment consultants are here to help.
Get in touch today to discover how Cedar can support your next critical hire—and help you shape the finance function of the future.
