Turning transparent, ethical AI into a competitive advantage

With the EU AI Act now taking effect, Europe has entered a new era of AI accountability. Although the UK is no longer part of the EU, the legislation still applies to any business that operates in or trades within the EU. For finance leaders, it’s becoming increasingly essential to understand that transparent, explainable, and well governed AI is no longer optional — it’s a strategic priority. 

The first transparency and documentation obligations came into force in August 2025, with stricter rules for high-risk AI systems to follow in August 2026. By then, every EU country will also have introduced at least one national “AI sandbox”, giving businesses a secure environment to test systems alongside regulators, strengthen oversight and embed responsible practices before full rollout. 

This shift marks a move from experimentation to explainability. Over the next year, finance leaders should ensure that every AI system is transparent and guided by strong ethical standards. By embedding strong governance now, organisations can not only meet regulatory expectations but also strengthen long-term trust with customers, employees, and stakeholders alike. 

When innovation outpaces oversight 

Transparency is becoming a critical concern as businesses rely on AI more than ever before, but too many still treat it as a “black box”, where automated workflows are not always visible or understood. IT teams often use AI to speed up decision-making but often lack insight into how those decisions are made or justified. 

As customers, regulators, and employees all demand greater clarity, this “black box” mindset towards AI needs to change. When an AI tool streamlines a workflow or flags a transaction, its reasoning must also be visible and explainable. Without that transparency, trust is undermined. 

At the same time, innovation continues to move faster than oversight. A recent McKinsey study found that only 1% of organisations consider themselves truly “AI-mature”, highlighting a widespread lack of AI literacy. The same research revealed that only 28% of companies have their CEO directly overseeing AI governance, suggesting that accountability often sits too low within the business, meaning key risks can go unnoticed. 

The challenge for leaders is to balance innovation with clear transparency, ethics, and human judgement. Governance needs be a C-suite priority, ensuring that every major AI system can be clearly explained to both regulators and stakeholders, and that accountability for decision-making remains visible and consistent. 

Building trust through explainability 

Once governance is in place, the next step is fostering trust by being upfront about how AI decisions are made and communicated. 

Explainability enables organisations to identify risks early, improve decision accuracy, and meet growing expectations for audibility. To achieve this, systems must record and trace decision pathways, providing a clear audit trail from data input to final output. 

For example, if an AI tool flags an invoice as high-risk, teams should be able to see why — missing data, unusual spending patterns, or anomalies that triggered the alert. This visibility allow firms to challenge outputs, detect biases, and validate decisions confidently. 

Certain finance automation solutions already embed these principles, offering built-in audit trails and real-time visibility over AI-driven outputs. By adopting platforms that make decision pathways visible, organisations can combine automation with meaningful human oversight.  

As the UK shapes its own AI strategy — and as EU enforcement ramps up in 2026 — businesses that prepare now will find compliance smoother, faster and far less disruptive later on. 

People make the difference 

While explainability builds trust in AI systems, people ultimately maintain it. The biggest gap in AI adoption is the human culture that drives it. Many firms underestimate how much AI success depends on developing the right skills and culture. 

As AI becomes more deeply embedded in financial workflows, businesses will need hybrid roles that combine technical expertise with regulatory understanding and ethical awareness. Finance, compliance, and communications teams must be trained in AI literacy so they can question automated outputs confidently, rather than simply accepting them. 

Human oversight remains essential as people still provide the context and judgement that AI cannot. By reviewing and refining AI-generated outputs, employees can ensure that automation strengthens rather than replaces human intelligence. 

Responsible AI as a competitive advantage 

For forward-looking firms, transparency and ethics should not be barriers to innovation. Those that can demonstrate that their AI is well-managed, explainable, and responsibly governed will earn faster regulatory approval and stronger customer trust. 

As the EU AI Act continues to roll out and the UK continues to shape its own approach, responsible AI will increasingly define competitive advantage. Organisations that act early, document their decision-making, assign clear accountability and embed explainability across every system will be best positioned to adapt to future requirements. 

Ultimately, responsible AI shouldn’t be treated as a compliance task, but as a core part of a firm’s reputation strategy. By prioritising transparency and accountability today, businesses can future-proof their operations and lead with confidence to build lasting trust with customers.