From Exploration to Scale: A Road Map for AI in Tax

For tax teams, AI adoption isn’t a single leap into the future – it’s a staged progression. The journey begins with small, accessible applications that deliver quick wins, build confidence, and create a foundation for more advanced adoption. From there, organizations can gradually embrace more advanced tools and workflows.

Adopting AI not only enhances quality and speed but also drives cost efficiency by reducing time spent on repetitive, manual tasks. This efficiency creates capacity for teams to focus on higher-value activities, allowing tax functions to operate more effectively and at a lower overall cost.

The guiding principle is clear: AI should accelerate professional work, not replace it. Human expertise, judgment, and accountability remain central. AI becomes the so-called “digital assistant,” while tax professionals serve as reviewers and decision-makers.

Here is a five-step approach that most tax teams can use to implement and ramp up AI.

Step 1: Tax research

Tax research is an ideal entry point for AI adoption. Most tax professionals don’t spend six to eight hours a day immersed in regulations. Instead, research is an as-needed task – essential but often time-consuming. That makes it a natural place to test how AI can reduce friction and accelerate insights.

The role of retrieval-augmented generation (RAG)

Modern tax research platforms use RAG. Unlike general-purpose tools, these platforms are restricted to authoritative tax sources. If you ask a nontax question, they won’t attempt to answer because the source data is intentionally scoped. This design reduces the risk of hallucinations and ensures that citations are always tied back to a reliable authority.

For example, Crowe uses AI tools that allow users to pose plain-language questions and receive concise responses with supporting citations. Instead of sifting through pages of regulations, they get a clear starting point, with the ability to drill deeper.

Benefits across experience levels

Experienced professionals that use AI gain efficiency. Since they already know where the answer is likely headed, AI helps them validate or expand their conclusions faster.

Additionally, younger professionals benefit from structure and direction. AI helps point them toward relevant authority while still requiring them to build foundational research and citation skills.

A key reminder: AI is the digital assistant. Just as a senior-level tax professional wouldn’t submit an intern’s work without review, professionals must validate AI outputs before elevating them.

Step 2: Private chatbots

General chatbots – like Microsoft™ Copilot or OpenAI’s ChatGPT – are useful for rewording emails or looking up background information. But tax departments can take a more tailored approach by building chatbots trained only on their internal, approved data.

Examples

• Policy and procedure bots: A chatbot trained on onboarding manuals and compliance policies allows new hires to ask questions conversationally, reducing the need for managers to answer repetitive queries.
• Intelligent document search: Instead of combing through hundreds of invoices or contracts with inconsistent naming conventions, AI-enabled bots can recognize intent and surface the right information quickly.
• Knowledge continuity: Institutional knowledge often walks out the door with retiring staff. Capturing and embedding their expertise into a private chatbot creates a sustainable knowledge base.

One key risk to keep in mind is that if chatbots pull from unvetted sources, outputs might be incomplete or misleading. Carefully scoping them to authoritative data is essential.

Step 3: Content generation

Many professionals struggle not with ideas, but with the burden of structuring them into formal documents. AI alleviates this challenge by turning notes, transcripts, or rough outlines into a structured draft.

Use cases in tax

• Drafting policies from transcripts: An onboarding call can be transcribed and instantly converted into a polished policy document.
• Supporting controversy responses: Responding to IRS information document requests (IDRs) often requires custom responses. AI can help organize facts and generate draft language, giving practitioners more time to refine arguments.
• Enhancing research and development (R&D) credit documentation: Instead of two-page summaries, teams now can generate more comprehensive, 10-page supporting files with the same effort, thus improving audit readiness.

Efficiency comes from producing robust drafts quickly, but professional review ensures quality and defensibility.

Step 4: Smarter document reading and comparison

Optical character recognition (OCR) has long been used to digitize documents, but newer tools go further by interpreting context. AI now can understand what’s on the page, not just where text sits.

Applications

• Tax forms: When line numbers shift from one year to the next, AI recognizes the concept – like charitable contributions – rather than being bound to a static location.
• Contracts: AI can highlight clauses of interest, compare agreements, or flag differences against prior drafts.
• Bulk triage: Teams dealing with thousands of invoices or exemption certificates can use AI to surface key data points instead of reading every page.

The value is in speed – finding the needle in the haystack. By surfacing anomalies and key clauses faster, AI reduces the chance that critical details are overlooked, supporting stronger compliance and contract management.

Step 5: Classification at scale

Classification is one of the most promising yet complex applications of AI in tax. It involves assigning tax treatments at scale, such as coding fixed asset additions or categorizing inventory under last-in, first-out (LIFO) rules.

Examples

• Fixed assets: Crowe has built tools that read descriptions, apply industry-specific rules, and assign lives and methods for hundreds of assets in under a minute. 
• Inventory: LIFO and inflation coding benefit from similar large-scale classification.
• R&D costs: Parsing descriptions and accounts to determine which costs qualify is another natural extension. 

Accuracy and oversight

Anecdotally, high levels of accuracy in AI outputs from RAG-enabled research platforms have been shown, and limitations often are caused by vague descriptions rather than the AI tool itself. Human review remains essential, but instead of manual data entry, staff can focus on supervision and exceptions.

The result? Higher productivity and a more strategic focus for tax professionals.

A practical road map for overall adoption of AI in tax

AI adoption should follow a phased, deliberate path.

• Start small: Choose one use case. Tax research, content drafting, or document reading are strong entry points.
• Partner with IT: Understand your organization’s current technology and future road map.
• Gain a seat at the AI table: Identify your organization’s AI steering committee or leadership team and get involved.
• Keep people central: Require review and validation before outputs are used.
• Measure results: Go beyond accuracy alone – tax teams also can benchmark cost savings from automation, such as hours saved on manual reconciliation or research.
• Develop staff skills: As AI handles more mechanics, staff members must strengthen their authority, citation, and judgment capabilities.

This structured approach allows tax teams to balance innovation with responsibility.

A journey, not a destination

One thing is certain: AI will reshape tax roles. Starting with accessible use cases and scaling to more advanced applications, tax teams can free themselves from labor-intensive tasks and focus on higher-value analysis.

For tax executives, AI adoption is more than a productivity play – it’s a strategic lever to reduce costs, strengthen compliance, and elevate the role of tax within the business. The path forward is clear: Begin with accessible use cases, scale deliberately, and keep professionals at the center.

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