Global tax functions today face relentless pressure to deliver accuracy, transparency, and speed. Regulators expect granular reporting, while boards demand predictable outcomes and reduced exposure. As a result, tax risk management has shifted from periodic reviews to always-on controls.
According to the EY January 2026 CEO Outlook Pulse survey, nearly 90% of CEOs expect AI to reshape business models within the next two years. Around 20% already report returns beyond expectations on their AI investments. For tax leaders, this shift directly accelerates AI tax compliance, redefining how enterprises identify, monitor, and manage risk at scale.
Seven ways AI is transforming tax risk management and compliance
AI now plays a decisive role in how modern tax teams manage exposure, compliance, and reporting accuracy. These seven shifts show how tax risk management has evolved as intelligence replaces manual control points.
Strengthening data foundations for risk visibility
AI consolidates fragmented tax, finance, and operational data into a unified, tax-ready view. By cleansing inputs, validating sources, and identifying anomalies across periods, AI improves confidence in reported positions. This data integrity forms the backbone of reliable AI tax compliance and reduces downstream remediation.
Enabling continuous regulatory awareness
AI tools help tax teams continuously track legal updates across jurisdictions and map them to existing tax positions. This capability allows enterprises to respond to regulatory change as it happens, rather than during filing cycles. As authorities adopt AI themselves, this responsiveness becomes essential for effective tax risk management.
Shifting from reactive to proactive compliance
AI tools replace periodic reviews with real-time governance and risk monitoring. Predictive models surface potential compliance gaps early, enabling corrective action before filings or audits. This proactive posture reduces exposure and strengthens the overall AI tax compliance framework.
Automating classification, determination, and provisioning
With AI capabilities, enterprises can automate complex tax classifications, indirect tax determination, and provisioning calculations at scale. Consistent rule application reduces human error and improves forecast accuracy. Automation also frees tax professionals to focus on judgement-driven decisions central to tax risk management.
Improving audit readiness and traceability
Integrating AI in tax compliance creates structured, end-to-end audit trails across tax processes. Documentation stays consistent, searchable, and defensible throughout the reporting lifecycle. This continuous audit readiness reduces disruption and reinforces trust in compliance outputs.
Reshaping operating models across finance
Shared intelligence and workflow automation connect tax, accounting, and finance functions. Query bots, exception handling, and cross-functional visibility improve decision speed without sacrificing control. This integration elevates tax risk management from a siloed activity to an enterprise capability.
Upskilling teams for AI-enabled compliance
AI adoption shifts the tax role from data preparation to analysis and interpretation. Teams equipped to work with GenAI deliver higher-value insights and faster responses. Human oversight ensures AI tax compliance remains explainable, accountable, and aligned with regulatory expectations.
Effective AI tax compliance depends on robust data, scalable platforms, and disciplined governance. Infosys BPM supports finance leaders with integrated finance and accounting outsourcing services that embed AI across tax workflows. This approach enables consistent tax risk management while aligning technology, processes, and people within a unified operating ecosystem.
AI tax compliance best practices
Integrating AI into tax functions requires disciplined execution. These best practices ensure AI strengthens tax risk management while maintaining control, transparency, and trust.
- Define an AI vision and prioritise use cases: Establish clear objectives linked to compliance risk, regulatory exposure, and reporting complexity. Focus first on areas with the highest impact.
- Ensure data readiness and governance: Standardise, validate, and govern tax-relevant data. Strong data foundations directly affect the accuracy and efficiency of AI tax compliance.
- Build transparent and explainable models: Understand how AI solutions reach decisions and document the logic. Explainability is critical for audits, regulators, and internal governance.
- Integrate AI capabilities into existing systems and workflows: Embed AI into ERP and finance platforms to avoid fragmented processes. Seamless integration supports scalable tax risk management.
- Maintain human oversight and accountability: Retain human review for material decisions and exceptions. AI-powered tools can support judgement but cannot replace professional responsibility.
- Invest in secure, future-ready platforms: Protect sensitive tax data with encryption, access controls, and audits. Choose platforms that evolve with regulatory change.
Together, these practices help enterprises operationalise AI tax compliance with confidence. They reduce implementation risk, improve adoption, and ensure long-term value across the finance function.
Conclusion
Rapid advancements in AI capabilities have fundamentally reshaped how organisations approach tax functions and tax risk management. Continuous monitoring, automation, and predictive insight replace fragmented, manual controls. With the right governance and skills, AI tax compliance delivers stronger transparency, faster decisions, and reduced exposure. Enterprises that embed AI thoughtfully position their tax functions as resilient, future-ready contributors to sustainable financial performance.
Frequently asked questions
- How is AI transforming tax risk management from reactive to proactive?
- What role does data readiness play in effective AI tax compliance?
- Which tax processes benefit most from AI automation and why?
- Why is model governance and explainability critical for AI in tax functions?
- What best practices help organisations implement AI tax compliance successfully?
AI shifts tax teams from periodic reviews to real-time monitoring of regulatory changes, predictive gap detection, and automated classification across jurisdictions. This proactive approach surfaces issues early, reduces exposure, and aligns compliance with dynamic global standards.
AI requires unified, cleansed tax, finance, and operational data to generate reliable insights and automate provisioning or determination accurately. Without strong data governance and validation, models produce flawed outputs that undermine compliance and risk decisions.
High-impact areas include tax classification, indirect tax determination, provisioning calculations, and audit trail generation, where AI applies rules consistently at scale. Automation minimises errors, speeds reporting, and frees professionals for strategic judgement in ambiguous areas.
Tax AI must be transparent and auditable to satisfy regulators, withstand scrutiny, and maintain stakeholder trust in automated decisions. Continuous monitoring, retraining, and human oversight prevent drift and ensure outputs remain defensible during audits or disputes.
Best practices include prioritising high-impact use cases, embedding AI into ERP workflows, enforcing human accountability for material decisions, and investing in secure platforms with strong data governance. Cross-functional upskilling ensures teams leverage AI as an enhancer of judgement, not a replacement.
