Data: The unsung hero of AI in Tax

Why AI in Tax needs integrated data 

Tax departments manage large volumes of sensitive information across transactions, filings, and jurisdictions. The challenge is usually not whether the data exists, but whether it’s organized in a way AI can use effectively. “You can have the best AI on the planet, but if you are not letting it access the best data—or the data in a manner that provides the best value and insights—it’s not going to be useful for the business,” explains Iain McIntosh, Deloitte Tax LLP’s Tax Transformation Consulting principal.

That’s why integrated tax data matters. When data is fragmented across systems with inconsistent taxonomies and limited access, AI can be harder to scale and less likely to produce reliable outputs. When data is centralized and structured, AI can better support insights, decisions, and automation.

In practice, that typically starts with centralizing priority data, harmonizing key fields across jurisdictions and business units, and establishing common taxonomies for transactions, filings, and controls. At Deloitte, we use technologies like Intela to consolidate tax data into one place, creating a single point of collaboration across the tax environment. With that foundation, AI can help professionals interact with data more directly and generate insights faster than through manual processes.

Organizations don’t necessarily need perfect data before they begin. A practical approach is to start with a small number of high-value use cases while improving the data foundation in parallel. The goal may not be perfection on day one, but a foundation strong enough for AI to reduce manual effort and improve decision-making.

The risks of low-quality tax data

Low-quality tax data can contribute to filing errors, increased audit exposure, and reputational risk. Inconsistent reporting may reduce confidence and limit an organization’s ability to capture the broader benefits of automation and more autonomous AI capabilities. Human-in-the-loop review remains essential, particularly when foundational data is fragmented or unreliable. For that reason, many leaders pair AI with trust mechanisms such as reconciliation, lineage, policy-based access, and exception workflows to help make outputs more defensible.

Strong data can do more than reduce risk; it can also create the structure needed to support classification, standardization, monitoring, and remediation more effectively. To move from experimentation to scale, tax leaders may want to consider focusing on a disciplined set of actions:

• Prioritize a small number of high-value use cases
• Centralize and harmonize the data needed to support them
• Define ownership for data quality and access
• Embed governance and compliance controls early

The payoff is typically not just cleaner data; it’s also the ability to move from slow, manual processes toward faster, more connected workflows that can support AI in Tax at scale.

From data foundation to AI scale

Building a strong data foundation is critical to move from basic automation to more advanced AI capabilities, including agents and agent-enabled workflows. In many cases, the constraint is not the model itself, but whether the underlying tax data is ready to support broader adoption. Scaling also requires an operating model to support what gets built. Where that foundation exists, organizations may be better positioned to move from disconnected tools and one-off pilots toward a more integrated model in which AI can surface answers and insights far faster than traditional systems. 

This need for sustainment is especially important in Tax, where models, workflows, and data environments continue to evolve. Early AI deployments can also help surface data issues on an ongoing basis rather than treating remediation as a one-time effort. Organizations that strengthen that foundation may be better positioned to move more efficiently, respond to regulatory change more effectively, and realize greater value from AI over time. Deloitte’s experience suggests that lasting value comes not just from building AI capabilities, but also from sustaining the platform, processes, and maintenance model behind them.

Where tax data meets possibility 

For tax executives, the path to scalable AI value may not begin with selecting better models alone, but also with building a stronger data foundation. Organizations that centralize, harmonize, and govern their tax data are often better positioned to move beyond isolated pilots and support faster decisions, more dependable outputs, and more advanced AI capabilities over time.

In that sense, data does not simply support the Tax AI journey; it can significantly shape how far and how quickly tax functions can progress. Leaders who invest in that foundation may be better positioned to reduce manual effort, improve collaboration, and translate AI potential into measurable enterprise value.

Deloitte has applied technology to Tax for more than 20 years, and we are putting that experience to work as AI changes the way tax functions operate. Through our own investments in AI and analytics across service delivery, we are seeing how smart tax data management can open new possibilities for more connected and responsive tax operations.

Contact us to explore where those possibilities could take your organization.