Navigating the shift: How AI redefines the tax function

The tax function has always been a navigator of complexity. When regulation shifts, business models transform, capital flows rewire how markets operate—tax is one of the first functions to assess and mitigate the associated risks. Today, AI is driving a broad and deep shift, signaling a potential change in not just the tools we use but the structure of the function itself. It’s time to rethink the skills, processes and tech that power a tax department.

There is evidence that AI has the potential to materially change current processes and the underlying technology enablement that is needed to deliver on tax reporting and analytical needs. Concurrently, and related to this change in the processes and outputs, AI has the potential to offset the traditional entry-level tasks once tied to human labor. The result is a change in the productivity paradigm where output no longer scales linearly with headcount. Technical capabilities and human judgment remain critical, but the work itself is being reorganized. Every organization now faces a strategic decision: how to design around the AI capability and skillsets that will anchor tomorrow’s tax function.  

A threshold question being raised today: should a tax function build proprietary AI platforms or access external solutions? It is a question of capability formation and whether the function competes through ownership of proprietary systems or through leverage of external economies of scale. The answer will differ by company size, industry, and strategic ambition, but every company must make the choice deliberately.

• Build: Developing an in-house platform integrates proprietary knowledge directly into the system. AI can absorb the nuances of company data, workflows, and risk profile. Upfront costs and upkeep are substantial—including for talent, infrastructure, governance controls—but the returns lie in differentiation and defensibility. Proprietary platforms create intellectual capital with potential for high value creation, especially if designed and integrated with other enablement functions and financial systems.
• Access: Leveraging external technology and resources provides immediate access to proven technology, with requisite controls and guardrails designed for tax needs, continuous upgrades, and the scale of investments that few stand-alone organizations could match. Professional services firms often embed AI breakthroughs months or years ahead of internal cycles. For many organizations, such access accelerates modernization, reduces technology risk, and allows scarce resources to focus on interpretation and strategy rather than infrastructure.

In practice, most organizations are expected to adopt hybrid models: buying core technology that supports enterprise infrastructure while layering company-specific data, governance, and workflow on top. Importantly, once cross-enterprise capabilities are met, tax-specific capabilities may be best fulfilled through a mix of in-house development and support from professional services firms. 
 
By embedding engineers, data scientists, and tax technologists into client teams, such firms can help configure platforms to reflect the company’s specific context. Firms bring curated skills, cross-industry perspective, and scale of investment to accelerate adoption—while organizations retain elements of control that differentiate: proprietary data models, governance frameworks, and contextual judgment connected to their unique tax strategy. Where a company lands on the build–access–hybrid continuum will depend on its data footprint, enterprise reporting platforms, and AI and agentic roadmap across tax and adjacent functions like finance, controllership, and accounting.

AI is also rewriting the talent model. The tax profession cannot assume that yesterday’s career pathways will prepare tomorrow’s professionals. 

Universities are racing to catch up, updating curricula with data science, analytics, and AI fluency. However, higher education is often not able to move at the pace needed around AI development, nor does it have frequent exposure to the particular talent needs that are surfacing from the process and workflow changes discussed above. That lag creates a capability/skills gap at the entry level—where graduates arrive with strong theoretical training but without the applied skills to operate in an AI-first tax function. 

Historically, associates bridged that gap by cutting their teeth on reconciliations, data analysis, and compliance prep. These are precisely the roles AI is absorbing. The historical bridge is disappearing despite the continued importance of the learning that was provided through those tasks. 

This forces a fundamental rethinking of who to hire and how to train – both reskilling and upskilling. The tax professional of the future is not just technically adept in the code; they are data literate, systems-minded, commercially fluent, and skilled in critical thinking. They bring judgment to complex questions, translating data into business insight and advisory impact. They work side by side with engineers, technologists, and analysts embedded directly in tax teams. The result is not a hierarchy of specialists but a multidisciplinary unit that has foundational tax knowledge, can orchestrate workflows, interpret AI outputs, and advise business leaders in real time. 

Here professional services firms hold an advantage. They can flex across talent pools, bring engineers into tax functions, and rotate professionals across industries and geographies. That ability to combine diverse skillsets and deploy them at scale is a differentiator. For companies, the challenge is to design roles that attract talent who expect more than repetitive work. For professional services firms, the opportunity is to lead—upending the traditional model and building the tax professional of tomorrow.

The tax function has its next challenge and opportunity to navigate. As AI redefines technical execution and process design, it’s also guiding choices around technology investment and talent strategy. Three moves to consider:

1. Choose deliberately between building or accessing AI capabilities, understanding the long-term quality, scaling, and economic implications.
2. Redesign the workforce model, hiring and training professionals for roles focused on interpretation, modeling, and advisory impact.
3. Embed trust by design. Efficiency without transparency or defensibility is fragility, not progress.

Tax has a front seat in the future of organizational transformation. It is where the tension between efficiency, quality, value, and talent will be resolved first. The lesson is clear: transformation will not wait. The organizations that lead will be those that make the right investment tradeoff and make bold choices on talent skill development to create the tax function of tomorrow.