Tax Transformation: Build Momentum, Not Just a Road Map

The pace and promise of AI can make it tempting for tax leaders to jump straight into pilots or tools. But lasting transformation rarely happens in a single leap. Instead, tax leaders should start with foundational work and AI experimentation, which can progress together, as each strengthens the other.

Start with outcomes

Anchoring transformation in clear, measurable outcomes remains the essential first step. Align leaders on a small set of goals tied to reporting accuracy, close and filing timeline improvements, audit readiness, and the quality of insights delivered to the business. Then, translate these into key performance indicators (KPIs) with baselines and quarterly targets so progress is visible and decisions stay grounded in evidence.

These steps often are framed as prerequisites for AI, but they should not be viewed as barriers to innovation. As priorities sharpen, opportunities for targeted AI pilots will emerge organically; for example, classification (such as trial balance mapping, fixed-asset lives and methods, meals and entertainment, and research and development expenses), document interrogation (such as purchase agreements, tax notices, and exemption certificates), or narrative drafting (such as controversies, workpaper documentation, and tax footnotes), which can deliver quick wins while broader process and data work continues. These early successes build buy-in and help identify direction.

Build a phased plan without treating transformation as linear

Having a road map still matters. Organize the work into evolving phases – such as build, stabilize, scale, and optimize – and clearly articulate the goals and progress indicators for each, but avoid the trap of strict sequencing. Real transformation is nonlinear: data improves while processes standardize, governance matures while early AI patterns are tested, and operating models evolve alongside technology decisions. Each advance reinforces the next, helping a tax department’s transformation journey progress and its systems strengthen.

Use governance to create clarity, not friction

Governance in transformation does not need to be heavy; it needs to be clear. Tax teams benefit most when they understand who makes which decisions, how risks are evaluated, and what standards guide design and deployment.

A light but deliberate structure, including a steering group for priorities, a technology working team to maintain architectural alignment, and an AI review process focused on responsible use, creates predictability. When decision paths are transparent, innovation accelerates and is meaningful.

Prepare people for a more data-driven, insight-focused operating model

Transformation is not only about technology. As automation and AI take on routine work, tax professionals can spend more time on analysis, review, and advising on tax implications of business developments. The shift is not about teams simply becoming smarter; it’s about tax teams becoming more analytically enabled, more data driven, and more focused on higher-value judgment work.

New responsibilities, including data stewardship, automation oversight, and model validation, support this evolution. Targeted training and upskilling will help teams adopt new workflows confidently. The transformation is more likely to be successful when people understand how their roles are evolving and feel equipped to succeed.

Crowe observation

AI does not need to wait for perfection. Organizations should start where they can, learn quickly, and build scalable patterns.

Looking ahead

AI should support a tax department’s transformation, not define it. Though early pilots can reduce manual effort and prove value quickly, more advanced capabilities, such as embedded decisioning, orchestrated workflows, and predictive analytics, will require stronger data foundations, standardized processes, and mature governance.

Transformation leaders should select KPIs that tax and finance leaders intuitively recognize, like improvements in close and filing timelines, reduction in manual touchpoints, audit adjustments avoided, forecast accuracy, and time shifted from preparation to analysis. They also should share progress transparently and reinvest capacity gains into additional automation, data improvements, and skill development. That ongoing cycle is what turns transformation into sustained, compounding progress.