Questions Every Tax Leader Should Ask Before Onboarding GenAI Tools
- sparsh gupta
- Sep 5
- 3 min read
Generative AI promises big productivity gains for tax teams- but it also introduces real risk. From fabricated case citations to hidden data exposure, the wrong tool can cost reputational damage, regulatory penalties, and wasted budget. Below are four simple questions every tax leader should ask vendors before onboarding a GenAI-based research or automation tool. Their answers will tell you more than a sales demo ever will.
1) How does the LLM pipeline work?
Ask for a clear, non-marketing description of the pipeline: retrieval, agent orchestration, grounding/verification, and post-processing. Beware vendors that rely heavily on agentic chains-of-agents: multiple agent hops increase unpredictability and the chance of hallucination. Benchmarks show legal models can hallucinate frequently- a recent public study found domain legal models hallucinated in roughly 1 in 6 benchmark queries or more, underscoring that hallucination is not a theoretical risk but an empirical one. Stanford HAI
2) Which models do you use, and where are they deployed?
Don’t accept vague answers like “we use the best model.” Ask which base LLM (and size) they use, whether it’s fine-tuned, and where inference runs (vendor cloud, public API, or on-prem). Deployment matters for both data security and auditability: many hosted model APIs log queries and may reuse them for training unless contractually prohibited. Public trust in AI companies protecting personal data has dropped in recent years- regulatory scrutiny and client expectations mean you must confirm data handling practices before sharing sensitive tax queries. Stanford HAI, KPMG.
3) How do you demonstrate ROI- exactly?
Generic claims like “saves hours” are meaningless without numbers and references. Ask for concrete KPIs (e.g., reduction in researcher hours per matter, citation verification time saved, error rate reduction), and request references from other tax or compliance teams using the product in production. Vendors who can’t provide measurable customer outcomes or references are asking you to be their guinea pig.
4) What guarantees, SLAs, and insurance back your product?
Tax and legal outcomes have consequences. You need contractual remedies: SLAs for availability and response latency, defined error/accuracy thresholds for research outputs, and indemnities or insurance coverage for damages caused by faulty outputs. If a vendor treats AI as “best-effort” without contractual accountability, you’re shouldering all the risk.
Context & Why This Matters (quick facts)
Real incidents are increasing: tribunals and courts have publicly flagged AI-generated fake citations; for example, the ITAT Bangalore recalled an order after non-existent cases- reportedly produced by ChatGPT - were cited in a tax ruling. These are not isolated anecdotes. kanaksoftware.inCounselvise
GenAI adoption is widespread- around 70%+ of organizations report using generative AI in at least one business function- but adoption outpaces governance, increasing exposure to accuracy and compliance risks. McKinsey & Company+1
Practical Checklist for Vendor Calls
Walk me through your full LLM flow, step-by-step (retrieval → reasoning → citation verification → QA).
Show me the exact models and deployment locations; share your data-retention and training-use policies.
Share ROI case studies and two customer references in tax/compliance.
Provide a sample contract with SLAs, error thresholds, and indemnity terms.
Most vendors will struggle to answer all four. If they do, proceed cautiously- validate with a short, pay-as-you-go pilot and require testable acceptance criteria. If they don’t, walk away.
Final thought
GenAI can transform tax work- but only when it’s engineered and contracted with respect for legal risk, data security, and auditability. Ask the hard questions up front. If the vendor can’t give clear, verifiable answers, you’ll likely back off- and rightly so.
At EonLex, we welcome scrutiny: our product is built to expose reasoning steps, surface verifiable citations, and operate under enterprise-grade data controls. If you’d like a checklist tailored to your procurement process, we can help you evaluate vendors side-by-side.
Comments