There’s been a huge appetite ‘s plenty of enthusiasm for artificial intelligence across the finance sector in recent months right now. Barely a week passes without a vendor announcing a new AI-powered capability or integration, a consultancy publishing its latest AI-backed transformation playbook, or a CFO sharing how automation has saved days of work across traditionally manual tasks.
Right now, momentum is with AI, and finance organisations are embracing the rewards it brings from productivity to problem-solving. But in the rush to adopt AI in finance, the question of whether it is being implemented responsibly continues to be asked.
New research by AccountsIQ in March 2026, conducted among senior finance professionals across the UK and Ireland, has brought this issue into the spotlight. An overwhelming 98% of finance leaders say AI regulatory compliance is now an important factor when selecting a finance platform. This near-unanimous figure highlights not only the growing importance of AI in scalable software decisions, but also a clear shift in expectations- AI must be implemented within a robust, regulated framework.
From back-office automation to strategic infrastructure
The case for implementing AI in finance is a well-established one. Intelligent automation is eliminating the most time-intensive manual processes: reconciliations, monthly and quarterlyreporting, variance analysis that have long consumed finance team capacity. It allows teams to get more granular with their data and faster. Tasks that once took qualified professionals the better part of a week can now be completed in a fraction of the time, with greater accuracy and fewer errors introduced by fatigue or data re-entry.
This time saving represents a genuine reorganisation of human expertise: away from low-value data entry and toward the strategic interpretation and advisory work that financeprofessionals have spent years training to do.
AI also transforms the quality of decision-making. Real-time financial insights, predictive cash flow modelling, and anomaly detection give leadership teams a clearer, faster view of their organisation’s position that was ever possible through traditional reporting cycles.
Speed without explainability is a liability
As AI-generated outputs increasingly inform critical financial decisions, the ability to explain and audit those outputs becomes a key requirement. If a system flags a financial anomaly or produces a revenue forecast, finance leaders need to understand why. They need to be able to track the logic, interrogate the data inputs, and stand behind the conclusion in front of a board, an auditor, or a regulator. Black-box AI, where the output arrives without a clear chain of reasoning, is not fit for purpose in a regulated environment such as finance.
The EU AI Act, which was unveiled in 2025 now entering implementation, imposes specific obligations around transparency, auditability, and risk management for AI systems deployed in high-stakes contexts. Financial services sit within that scope.
While the UK is leading its own pathway on AI regulation, the direction of travel is no different: AI systems must be explainable, controllable, and documented. Choosing a finance platform that has not been built with these requirements in mind is more than a compliance risk, it’s a strategic liability as global regulations begin to take shape.
The factors reshaping the finance software landscape
The latest findings highlight the key pressures facing finance leaders when evaluating the AI capabilities of their technology stack. The first is productivity: failure to implement automated, high-volume processes will result in finance teams falling behind teams which have done so previously.
The second is insight: real-time insights delivered by AI raise expectations across a business. Finance teams are now being asked to deliver analysis and insight that is both faster and more forward-looking. The third pressure is regulatory: the landscape continues to shift and evolve.
Falling foul of AI-related regulation will, in the coming years, prove highly consequential for businesses across reputational, financial and operational levels. Finance leaders are right to treat this as a selection standard, rather than an afterthought.
The fourth, and perhaps most underappreciated, is trust. AI capabilities alone are no longer sufficient. Finance teams want platforms built by vendors who take governance seriously, with transparency, auditability, and data integrity at the core, not just headline feature counts.
Trust as the competitive advantage
There’s room for consideration which goes beyond compliance obligations. Organisations that deploy AI responsibly, with clear governance, explainable outputs, and robust audit trails, are better positioned to maintain stakeholder confidence when their decisions are scrutinised. That confidence matters in investor conversations and the everyday trust that boards place in their finance functions.
Finance leaders are increasingly recognising that the right AI platform is not simply the most capable one. It’s the one where innovation and control coexist, where the technology is built to evolve alongside the regulatory environment, rather than scramble to catch up with it.
The AI era in finance has firmly arrived. The organisations that will benefit most are those investing now in platforms that treat compliance and governance not as constraints oninnovation, but as the foundation of it.
