Some organisations are progressing with confidence whilst others are still wrestling with the fundamentals. The State of Play: AI Becomes a Core Competency
By 2025, most finance functions have moved beyond pilots and proofs of concept. AI is no longer a novelty; it is an operational expectation. CFOs are increasingly benchmarking progress, scrutinising outcomes, and building long-term roadmaps for functional transformation.
Several themes define the current landscape:
AI-powered automation in reporting, close processes, compliance, and shared service operations is now commonplace.
Generative AI tools are being used to interpret complex regulatory requirements and draft documentation.
Predictive analytics is enabling more proactive decision-making around spend, market shifts, risk exposure, and liquidity.
AI-assisted customer service and digital support have become standard across major banks and fintechs.
Yet adoption remains patchy. The dominant question has shifted from “what can AI do?” to “how do we embed it responsibly, securely, and in a way that scales without creating new risks?” Many organisations have made progress on surface-level use cases but have yet to unlock deeper operational value.
Where AI Is Creating Real Value
1. Fraud Detection and Risk Management
Machine learning models now monitor transactions in real time, detecting anomalies with far greater accuracy than rule-based methods. The same models are being used to refine credit risk, identify vulnerable customers, and anticipate potential compliance breaches.
2. Smart Automation
The combination of RPA and AI is removing large volumes of manual work across AP, AR, reconciliations, data entry, and control activities. The shift is not simply cost-driven; automation is helping teams redirect capacity toward analysis and business partnering.
3. Personalised Banking and Customer Insight
AI-driven models analyse behavioural and transactional data to tailor financial products, provide spending insights, and deliver targeted credit or investment recommendations.
4. Investment and Portfolio Optimisation
Algorithmic trading platforms, risk models, and machine learning forecasting tools are driving more informed investment decisions. Portfolio managers now use AI to rebalance positions, run simulations, and assess long-term performance scenarios with greater precision.
5. Regulatory and Compliance Support
Generative AI is increasingly used to interpret regulatory changes, summarise obligations, and assist in drafting compliance documentation. For highly regulated institutions, this reduces time spent on manual interpretation and lowers the risk of oversight.
The Barriers Still Holding Teams Back
AI’s momentum is clear, but so are the roadblocks. Finance leaders repeatedly point to several persistent challenges:
Algorithmic Bias
Models trained on historical datasets risk amplifying existing inequalities in lending, fraud scoring, and investment decisions. Regulators are watching this area closely, and organisations cannot afford missteps.
Explainability
Many AI systems still operate as opaque “black boxes,” creating complications when auditors, regulators, or senior stakeholders need line-of-sight into how a decision was produced.
Regulatory Ambiguity
The pace of regulatory development has not caught up with the speed of innovation. Organisations are forced to navigate uncertainty while maintaining compliance with evolving standards.
Data Privacy and Security
AI relies on vast volumes of sensitive data, increasing exposure to cyber threats and the risk of misuse. GDPR obligations add a further layer of complexity to how models are trained and deployed.
Talent Shortages
The most pressing issue is the lack of finance professionals who understand AI. The industry needs hybrid talent with financial literacy, data literacy, and strategic understanding. That blend remains scarce.
Legacy Infrastructure
Many institutions are still constrained by legacy systems that cannot integrate with modern AI tools without significant re-architecting. This slows adoption and limits the impact of more advanced models.
What Comes Next
The global market for AI in finance is forecast to exceed £150 billion by 2030, with the potential to generate more than £800 billion in combined savings and revenue uplift. Realising that value will require more than investment; it will require discipline.
Organisations that move fastest will be those that:
Build ethical frameworks that ensure responsible model development.
Invest meaningfully in data foundations, infrastructure, and governance.
Develop internal talent through structured upskilling rather than relying solely on external hires.
Shift AI from isolated projects to integrated capabilities across FP&A, risk, audit, and commercial finance.
AI is no longer simply a tool for efficiency; it is a catalyst for rethinking what the finance function can be. The challenge for leaders is not whether to adopt AI, but how to build an environment where AI becomes a sustainable, strategic asset rather than an experimental add-on.
The organisations that succeed will be those that blend ambition with rigour, pairing technological capability with clear principles, strong governance, and the right talent. The future of finance will not be defined by AI alone, but by the institutions that learn to wield it well.
