From ‘black box’ to clarity: the future of explainable AI in accountancy

In accountancy, trust is everything. Clients no longer just want results; they demand to see the reasoning, judgment, and compliance behind them. The adoption of artificial intelligence is testing this foundation – unlike earlier technologies, which only sped up day-to-day admin tasks. AI has the power to change the way decisions are made and how work is delivered, yet the challenge is that many AI tools function as black boxes, meaning they generate outputs without transparency. For firms, this raises important questions: where does the AI source its data, why is it producing particular answers, how can it be kept within guardrails, and how can its contribution be measured across the firm?

What firms are grappling with

Accountancy firms are experimenting with AI, but once tools move beyond pilots into daily use, a harder challenge emerges. Managers need confidence that AI-generated communication reflects the right tone and firm standards, leaders want to see whether it truly improves accuracy and efficiency, and teams must be able to follow the reasoning behind outputs in order to judge quality and build trust. These concerns go to the heart of whether AI strengthens firms or leaves them more exposed.

When AI black boxes are left in systems, producing answers without showing how they got there, firms are unable to explain or verify the process behind a result.

The black box challenge is not limited to AI models themselves. Many firms already struggle with visibility across emails, client conversations, and fragmented data flows. Without transparency, AI risks making this opacity worse. Yet it also offers the chance to reverse it. Notetakers on client calls, automated data summaries, and reasoning chains can give firms a clear view of activity for the first time. In this way, explainability is not a barrier but a lever for better understanding how a firm works.

For accountancy firms, the urgency is being driven by their own clients. AI is already being used by finance teams and business owners in their daily lives; the problem is that they now need the same level of accessibility, speed, and clarity from their advisors. Additionally, they seek assurance that AI is used morally, under human supervision, and in ways that improve rather than deteriorate the relationship. Accountants cannot compromise on compliance or trust, but they also cannot remain motionless as competitors use AI rapidly.

Turning accountability into a design principle

Accountability has always been non-negotiable: every calculation and every piece of advice must be justified. Now, that same standard applies to technology like AI, and explainability is what will set firms apart. With knowledge now widely available through AI, the differentiator is how professionals combine their judgment with transparent systems that can show their workings.

For leaders, explainability means being able to see where data comes from, how reasoning steps were applied, and whether outputs follow firm standards. For teams, it turns AI into a tool that can be reviewed, improved, and trusted. And for juniors, it creates a new way of learning: by seeing the logic and trade-offs behind a decision, they build the same judgment that would once only have come from years of mentorship.

By making AI decisions transparent, firms not only uphold professional standards, but they also strengthen their own people, creating trust at the top, quality in delivery, and development in everyday workflows.

What good practice looks like

The challenge now is how firms can put these principles into practice. That starts with combining explainability with governance and oversight. AI should not just provide answers, but also reveal the reasoning and sources behind them, so that professionals can follow the logic and judge its quality. Guardrails are just as important, ensuring that the firm’s tone of voice, templates, and standards are consistently applied across every output. Leaders also need visibility into how and where AI is being used, the contribution it is making, and the value it brings to the business.

When firms achieve this balance, adoption becomes safer and more effective. It shows where human expertise remains essential, while also allowing services to scale in a way that does not erode trust. It also turns AI into a learning tool: by showing the reasoning behind decisions, junior staff gain daily exposure to the judgment and trade-offs that underpin professional work.

This level of transparency cannot be delivered by generic, off-the-shelf tools. It demands careful design, close collaboration between technologists and practitioners, and continuous refinement as both regulations and client expectations evolve.

The road to trusted AI in accountancy

There is real pressure on firms to adopt AI quickly, but moving too fast with systems that lack transparency could cause more harm than progress. In professional services, trust has always rested on clarity. Explainable AI allows professionals to stand behind their work, enables juniors to learn, and reassures clients that AI is applied responsibly.

Accountancy is not moving toward a future where advisers are replaced by machines. Instead, it is already entering a period where human judgment works hand in hand with transparent systems that strengthen delivery, relationships, and trust across the profession.

By Joris Van Der Gucht, Co-founder and CEO at Ravical