AI in tax and accounting: Where it can help most — and how to use it responsibly and at scale

A practical roadmap for tax and accounting firm leaders to adopt AI responsibly, deliver real ROI, and scale with confidence

After years of tax and accounting firms treating artificial intelligence as an experiment or isolated use case, the technology has reached an important threshold: the practical application stage.

AI is now becoming embedded in daily operations, as firms use it to ingest and extract data, perform tax research, and integrate it into other workflows.

Still, firms are understandably cautious, citing concerns about data governance, validation, and security, as well as defensibility and explainability in an audit scenario.

As a result, many firms find themselves stuck between curiosity and commitment, struggling to find a path forward that doesn’t introduce undue risk.

So, how do they move to the next stage of AI adoption?

What’s working for tech-forward teams

So far, the firms enjoying the most AI success are the ones that focus on deliberate, responsible progression rather than going full steam ahead without a plan.

While other firms hesitate and debate whether AI is ready for prime time, these firms are cleaning up and restructuring their data in preparation for AI. They are re-examining their entire workflows, considering where AI’s strengths could be most helpful, and identifying critical tasks it isn’t yet ready to manage. They are establishing governance and data security policies and determining success metrics for each stage of AI integration. And they are training team members throughout the process.

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Responsible AI is about structure, not restriction

Responsible AI provides a framework that enables firms to adopt modern technologies without compromising their professional standards or ethical obligations.

To build their own version of responsible AI, firm leaders would do well to keep three core principles top of mind:

1. Source visibility: AI’s work product should be grounded in authoritative, citable guidance just like a human accountant’s would. The humans in charge must be able to pinpoint the source of AI’s conclusions and weigh whether or not they are defensible.
2. Humans in charge: Having an expert in the loop is mandatory. A human must be able to review, correct, and overrule AI at any point in any workflow. AI should be trained to escalate any issue on which it is uncertain to the human expert rather than muddle through questionable results downstream.
3. Domain expertise: To build an effective AI agent for tax and accounting applications, experts with deep, specialized industry knowledge must be involved in its creation. Domain experts bridge the gap between raw data and real-world utility by defining problems, curating the data used to train AI, and validating its output.

Building trust in AI’s work product

By baking confidence indicators, exception flagging, and clear escalation paths into AI workflows, human experts can develop a better sense of which results are reliable and which still require human override.

This approach also provides a real-time progress report on the firm’s AI deployment and a chance to course correct before it is too late.

Client data protection: Addressing a top leadership concern

When deploying AI in an industry as heavily regulated as tax and accounting, few issues cause more apprehension than the implications for client data protection.

Modern, enterprise-grade AI platforms address this by providing tenant-level data isolation, which segregates data, models, and workloads for each client. This system means that AI agents can access only specific data from a specific client, ensuring that data privacy, security, and regulatory compliance standards are met.

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Get crucial pieces in place before scaling AI firm-wide

Both technological and cultural prerequisites can determine whether a firm’s foray into AI is successful.

Technical readiness: Scaling AI requires unified data across tax, audit, and practice management systems. APIs must permit AI not just to recommend actions but to execute them within governed parameters. Robust audit trails, role-based controls, and escalation paths are essential.

Checklist: What CIOs/CTOs need to put in place before AI

Cultural readiness: Executive sponsorship sets the tone for successful AI adoption. Team members are savvy enough to recognize when leadership isn’t fully on board and may adjust their attitudes to mirror the stance of their bosses.

More ways to win

Remind team members what AI is and isn’t: Leaders should alleviate fears that the endgame is headcount reduction and instead focus on selling AI as an engine for growth, quality, and sustainability.

Recruit staff at all levels: Identify internal champions across departments to help communicate governance policies and best practices.

Start training now, not later: To smooth AI’s transition from pilot stage to everyday use, begin training team members early in the process, not after AI has been rolled out.

Wolters Kluwer’s Future Ready Accountant Report surveys have repeatedly found that skills gaps are one of the biggest obstacles to AI adoption. Combat this issue by assessing the firm’s current level of AI literacy and work from there.

Getting a baseline sense of the firm’s AI literacy will inform leadership on next steps and help them prepare staff to work with domain-specific, authoritative AI tools reshaping the industry.

Everyone starts somewhere, so start somewhere

Remember, firm leaders don’t need to have all the answers up front before proceeding.

Start simple by picking one workflow and learning everything about it and how AI impacts each step of the process. Determine and measure ROI metrics, review AI work output, retool the workflow as needed, and review again. Then scale with intention, carefully embedding AI into additional workflows.

The winners of the AI race won’t necessarily be the firms that get their AI systems to market first. It will be the ones who put the most thought into how AI can improve their efficiency, quality, capacity, and client trust.

But all firms must begin by crossing the starting line.