82% of UK and Irish Finance Leaders Feel Rushed to Adopt AI, AccountsIQ Survey Finds

October 22, 2026 • FinTech News

Finance leaders across the UK and Ireland are being pushed to roll out artificial intelligence faster than they feel ready for. New research from accounting software provider AccountsIQ found that 82% of finance leaders feel pressured to adopt AI faster than they would prefer, and 41% describe that pressure as significant.

The research paints a more complicated picture than hesitant executives dragging their feet. Most finance leaders actually trust what AI produces. The tension lies somewhere else.

Some 81% of respondents said they fully or mostly trust AI-generated outputs in finance processes. At the same time, 76% worry that their organizations could become too dependent on AI when making financial decisions. Leaders like the tool. They are less sure about handing them the keys.

The research, published as AccountsIQ’s CFO Paradox report, was conducted by Censuswide between July and August 2026. It surveyed 1,036 senior finance decision-makers at businesses with 20 to 500 employees, including 836 in the UK and 200 in Ireland. Ireland showed the sharpest pressure, at 91.5%, compared with 80.3% in the UK.

While the survey did not focus on bank executives specifically, it offers a useful snapshot of what finance functions across the broader economy are experiencing as AI adoption shifts from early experiments to everyday operational use.

Why the Pressure to Adopt AI Keeps Climbing

AI has become a boardroom priority at most companies, and finance teams are feeling the knock on effects. Leadership expects improved productivity, less manual work, and measurable returns on whatever technology budget gets approved.

Finance departments carry a particular burden because their output touches budgets, financial reporting, forecasting, internal controls, and the decisions that follow. Speed is one thing. Getting the numbers right is another, and finance teams cannot sacrifice the second for the first.

The discomfort is widespread, though it does not necessarily translate into resistance. Most finance leaders are not opposed to AI. Their worry centers on implementation: understanding how a system works, what data feeds into it, where it might fail, and who answers for its mistakes.

That groundwork takes time most companies would rather not spend. A business can buy an AI tool in an afternoon, but weaving it properly into financial workflows is a different undertaking altogether.

Trust in AI Outputs Does Not Mean Trust in AI Decisions

The gap between trusting an output and trusting an entire decision-making process is where this survey gets interesting. Finance professionals may find AI genuinely useful for spotting patterns or drafting reports. That usefulness does not mean every recommendation should sail through without a second look.

Consider an AI system that flags an unexpected expense. Someone still needs to check whether that result reflects accurate data and reasonable assumptions, since the model cannot always tell a genuine anomaly from a data entry quirk.

The stakes rise once AI moves from offering suggestions to taking action on its own. A more autonomous system could post entries, trigger workflows, or shape decisions touching customers and company finances. Confidence in the technology should never replace verification, and organizations need explicit rules on which tasks AI can handle unsupervised and which require a human signature.

Many finance leaders already understand this distinction. They see real potential in the technology. They just want more say over how fast it enters their operations and how much authority it gets along the way.

Other Research Points to the Same Underlying Pressure

The AccountsIQ findings are not an outlier. Several other studies published in 2026 describe a similar dynamic: finance leaders under pressure to prove AI is paying off, even as governance and implementation practices continue playing catch up.

KPMG: adoption is surging, but results are mixed. KPMG’s 2026 Global AI in Finance report surveyed 1,013 senior finance leaders across 20 countries and 13 sectors in March 2026, covering organizations with annual revenue of at least 250 million dollars. Active AI use across finance functions more than doubled in two years, climbing from 30% in 2024 to 75% in 2026.

Yet only 23% of organizations said AI was exceeding their return on investment expectations, even though 71% said it was meeting or exceeding them. Broad adoption and decent returns are not the same as outstanding results.

Governance made a measurable difference. Firms that could produce AI audit evidence efficiently reported far stronger outcomes: 33% saw significant improvement in error reduction, compared with just 6% of less prepared organizations. Confidence in scaling AI followed the same pattern, at 42% versus 14%, echoing AccountsIQ’s finding that success depends on the systems built around the technology, not the technology alone.

Avalara: career pressure is intense. A separate study from tax and compliance technology company Avalara looked at AI agent adoption in financial processes. Conducted by Censuswide in June 2026, it surveyed 1,505 CFOs and senior finance leaders across the UK, US, Australia, and India, all at companies with revenue of at least 10 million dollars that had deployed, piloted, or actively evaluated AI agents in the previous year.

Avalara found that 92% felt moderate or significant career pressure to demonstrate returns on AI agent investments, with half calling that pressure significant. Speed dominated the conversation too: 71% said the push to deploy AI agents prioritised speed above all else, while only 7% said their organisations put governance first.

Accountability gaps showed up clearly. Some 23% said responsibility for a serious AI agent error would be unclear or would fall to no one in particular, and 76% admitted they lacked dedicated in-house expertise to understand how their AI agents work. These findings echo the same tension found in the AccountsIQ research: organizations want fast results while still building the expertise needed to manage increasingly capable systems.

UK government: faster adoption paired with stronger safeguards. The UK government’s Financial Services AI Adoption Plan, published on July 14, 2026, encourages financial institutions to scale AI beyond isolated pilots and identifies opportunities in fraud detection, operational efficiency, risk management, and customer service.

The plan also acknowledges challenges around regulatory clarity, workforce skills, resilience, and growing reliance on external AI and cloud providers, recommending tighter coordination between regulators, clearer guidance for firms, and further work on assuring third-party AI systems. This is not a government trying to slow AI down. It is an attempt to help the industry expand adoption while managing risks that could become harder to contain once they scale up.

Why Governance Cannot Be an Afterthought

AI already supports a wide range of financial activities, from transaction monitoring and fraud detection to forecasting, customer support, and document processing. But financial decisions rarely stay contained to a single workflow. Incorrect figures can ripple outward into management reports, lending decisions, regulatory filings, and broader business planning.

That is exactly why governance matters so much. A responsible AI framework needs to spell out who owns each system, what information it can access, what actions it can take on its own, and how staff should respond when something looks wrong. It also needs a clear record of decisions and changes, helping teams investigate errors and explain results to auditors, boards, and regulators.

Human oversight becomes especially important wherever AI touches material financial decisions, with clear rules on where approval is mandatory and which lower risk tasks can run with lighter supervision.

Data quality deserves equal attention. AI cannot compensate for incomplete records, inconsistent definitions, or poorly governed information. A polished, confident-looking output built on flawed data will still mislead the people relying on it. KPMG’s research flags data quality, integration, and interoperability as persistent weak points limiting AI’s value even when the underlying technology works as intended.

The practical takeaway is simple: governance belongs in the implementation plan from day one, not bolted on afterward once something has already gone wrong.

The Real Cost of Moving Too Fast

Pressure to adopt AI creates its own problems when organizations chase launch dates instead of operational readiness. A finance department might roll out a tool before employees understand its limitations, automate a workflow without assigning responsibility for errors, or measure success purely by time saved.

That approach generates plenty of activity without delivering much lasting value. The number of AI tools a company has deployed says little about whether those tools are improving reporting, reducing errors, or supporting better decisions.

Finance leaders should define success before implementation begins, using measures such as processing time, error rates, reconciliation accuracy, and employee workload. Comparing results against a clear baseline matters too, since without it, there is no way to say whether AI genuinely improved performance.

The Avalara research raises one more concern: accountability can become murky once AI agents start taking or recommending actions on their own. Businesses need clarity on who is responsible for their decisions and how to step in when something goes wrong.

A Practical Path Forward for Finance Teams

The combined research points toward an approach that balances genuine innovation with real oversight, rather than treating the two as opposing forces.

Start with specific use cases instead of broad ambitions, identifying tasks where AI addresses a clear, well-defined problem, such as document processing or routine reconciliations.

Set clear approval rules from the outset, defining which tasks AI can complete independently and which require human review, with high impact decisions receiving stronger oversight.

Make every output traceable, so teams can connect important AI generated figures back to their source data, assumptions and relevant approvals.

Test thoroughly before expanding further. Pilot programmes should measure accuracy, error rates and operational benefits, examining failure cases closely, not just successful demonstrations.

Assign clear ownership. Every important AI workflow needs an identified business owner, and the organisation needs someone internally accountable for how it gets used.

Train the people actually using it. Employees need to understand when to trust AI, when to challenge its output, and how to report problems when they spot them.

Taken together, these steps let finance departments move forward without treating governance as a roadblock.

What This Means for Banks and Fintech Companies

The AccountsIQ survey focused on finance decision makers at mid sized businesses rather than bank executives specifically, but its findings carry real weight across the wider financial services industry. Banks and fintech companies face similar questions as they bring AI into fraud monitoring, customer operations, compliance, financial planning and internal reporting.

They also have to weigh data protection, cybersecurity, model risk, third party dependencies and operational resilience, with the right level of control varying by use case, organisation and the potential consequences of getting something wrong.

For fintech providers, this challenge is especially visible when AI features move into customer facing products. Speed can be a competitive edge, but unreliable outputs can undo customer trust fast.

Banks face added complexity, since they typically need to coordinate AI adoption across legacy systems, multiple business units and long established risk frameworks, making implementation more involved than switching on a standalone tool.

The opportunity remains substantial. AI can help financial organisations process information faster, spot unusual patterns earlier and cut down on repetitive work. But lasting benefits depend on clear ownership and measurable performance, not just enthusiasm at launch. For financial technology leaders, the real competitive advantage will likely come from pairing AI capability with reliable data, strong controls and a workforce that knows how to use the technology well.

The Takeaway

The AccountsIQ research captures a genuine tension running through financial technology. Leaders face mounting pressure to adopt AI quickly, yet remain understandably concerned about dependence, accountability and the controls needed to use it responsibly.

Other 2026 studies reinforce the same message from different angles. AI use is expanding fast, expectations for returns keep rising, and governance remains the deciding factor in whether adoption translates into measurable value or just more activity.

The real challenge is not deploying more AI for its own sake. It is figuring out where the technology genuinely adds value, how its results can be checked, and who stays accountable for the decisions it supports.

For banks, fintech firms and finance teams alike, the most effective strategy is unlikely to be the fastest rollout at any cost. It will be a disciplined approach built on clear objectives, reliable data, human oversight and solid evidence of performance. In finance, the real measure of AI progress has never been how quickly a company adopts it. It is whether that company can explain, verify and stand behind the results.