Navigating the shift: The AI edge in indirect SALT—accuracy, speed, strategic insight

What if indirect state and local tax (SALT) teams could stop chasing information and start anticipating risks before they happen? AI is turning that “what if” into reality—elevating indirect tax toward smarter, more strategic outcomes.

As rules change across jurisdictions and transaction volumes grow, teams are rethinking how they manage accuracy, scale, and control. The next leap forward isn’t about working harder or faster—it’s about shifting how the work gets done. AI doesn’t just speed up tasks; it reshapes data processing, classification, decision-making, and audit readiness, redefining SALT compliance in real time.

Traditionally, SALT teams review only a fraction of transactions, track exemption certificates on spreadsheets, and rely on institutional knowledge—limiting scale and heightening audit risk.   

AI changes the game. Bespoke AI agents coordinated through an orchestration layer deliver end-to-end visibility and scalable automation across workflows: ingest and classify data, validate exemptions, enrich invoices, and detect anomalies, with a parallel reconciliation stream for compliance checks. The orchestration layer coordinates AI agents to automate steps and support compliance. Governance and data lineage tie outputs to reconciliation controls, tracing every tax-impacted dollar to the exact line on the return. 

Review is easier than ever: a centralized dashboard surfaces progress and AI results, facilitating human-in-the-loop (HITL) feedback and delivering an audit-ready file tied to the ledger. HITL is critical throughout the process to help assess accuracy, specialist review, and quality before moving on. 

The payoff is measurable precision, speed, and compliance. Here’s how it shows up across indirect SALT—from execution to audit defense:

• Sales and use tax: AI can interpret item descriptions, SKUs, and supplier data to classify transactions and determine taxability, mapping exemptions to the correct customers or documents even when the data is incomplete. 
• Property tax: AI can help analyze market trends, recent sales data, and satellite imagery to sharpen valuations, while automating payment tracking and general ledger reconciliation to strengthen compliance and audit readiness. Tracking property tax bills and other information from taxing authorities is streamlined and centralized, so critical information isn't missed or overlooked. 
• Excise or transaction-based taxes: pattern-recognition models highlight anomalies in shipping data, refund claims, or filings to uncover compliance gaps before audits and enable proactive remediation.  
• Audit support: AI-generated audit packages with traceable sources and documentation linked to the general ledger and tax return help support efficient tax audits and maintain controls.   
• Tax notices: AI handles high volumes of notices in varying formats using automated intake and bulk actions, generating responses and tracking status and due dates for timely follow ups and tax-notice management. 

Each use case builds precision into the process—reducing manual reconciliation and enabling proactive correction rather than retrospective clean-up.   

AI changes the nature of SALT work. Teams can move away from production-heavy tasks toward higher-value activities such as issue screening, policy interpretation, strategic advisory, and stewardship. This shift requires teams to retool and learn new capabilities, because AI augments work and cannot replace human judgment, which remains essential for interpretation and governance. Upskilling is central: building fluency, governance literacy, and practical AI skills needed to experiment, iterate, and deploy responsibly at scale. 

In practice, AI helps free SALT professionals from categorizing transactions to focus on interpreting legislative impacts, modeling tax impact of business expansion, and advising on credits and incentives. The shift is from data entry to data strategy.   

A HITL approach helps guide decisions—applying judgment and oversight at defined checkpoints to refine AI outputs and promote dependable outcomes. Self-service, real-time analytics can deliver data-driven insights for greater transparency and a strategic edge.

Unlike traditional rule-based automation, AI uses machine learning (ML) and natural language processing (NLP) to understand complex, unstructured data and learn from it. These intelligent systems enable continuous monitoring and advanced analytics across jurisdictions.  

Examples: 

• Regulatory intelligence: NLP models scan state and local tax bulletins to identify rate changes, nexus thresholds, and incentive updates, alerting teams before changes take effect. 
• Anomaly detection: ML flags mismatched invoice data or duplicate transactions, reducing exposure to adjustments and penalties, tying back to the ledger for completeness. 
• Predictive analytics: Forecast the potential cash-flow impact of rate shifts, audit trends, and credit expirations or limitations, giving tax leaders a forward view of risk.   

Combined with traditional automation, these layers move SALT management from reactive compliance to intelligent anticipation. When integrated with existing systems, they help improve overall efficiency and effectiveness, while enabling greater collaboration, freeing SALT teams to serve as strategists.

AI integration isn’t about bolting tech onto existing processes—it requires reimagining them. End-to-end SALT workflows evolve from periodic, manual checks to continuous, AI-powered monitoring and proactive management leading to more connected workflows and greater transparency. 

Static batch reviews become continuous data validation loops with near real-time monitoring and direct issue flagging. Property and sales tax workflows, historically reviewed quarterly or annually, become live processes that adapt instantly to changes to data, rules, or business conditions. 

Teams gain the ability to dynamically adjust to new tax laws, simulate compliance scenarios, and offer real-time insights to finance and business leaders. In this future, indirect SALT transitions from a compliance center to a strategic enabler of business confidence and control. 

Make your next challenge and opportunity AI-focused, guided by forward-looking design that balances people, technology, and processes. Some moves to consider: 

• Build a strong data foundation through cross-functional collaboration and governance to align data, rules, and responsibilities. 
• Start with pilots, defining success metrics and proving scalability before broader rollout.  
• Embed trust by design with transparency and HITL checkpoints to manage risk.  
• Decide your approach (build, buy, hybrid) using a speed-to-value framework. 

AI’s promise for indirect SALT isn’t just efficiency—it's strategic control. Teams that embrace data orchestration, HITL governance, and responsible AI design can not only keep pace with change but anticipate it—transforming SALT from a compliance center into a value engine.