Artificial intelligence is rapidly becoming a core component of tax due diligence, helping professionals review transaction data, assess tax positions, identify risks and accelerate reporting across complex deals. Yet as AI adoption matures, the competitive advantage may no longer lie in selecting the right technology, but in knowing how to direct it effectively.
Analysis from TAINA Technology suggests firms are beginning to discover that AI performance is heavily influenced by the quality of the instructions it receives. Rather than relying on broad or conversational requests, AI agents consistently produce stronger and more reliable outputs when they are given clear, objective-driven tasks with defined expectations.
This represents a shift from traditional professional communication. Tax advisers are accustomed to asking open-ended questions, encouraging interpretation and leaving room for discussion. However, Richard Kent’s analysis for TAINA Technology argues that AI agents operate differently, responding more effectively to direct, action-oriented instructions than exploratory prompts.
The distinction becomes particularly important during tax due diligence engagements, where accuracy and consistency are essential. Instructions asking an AI agent to broadly review deferred tax calculations or “look for issues” can leave significant room for interpretation. By contrast, directing an agent to identify inconsistencies, highlight errors, assess assumptions and review specific calculations provides a structured objective that improves the quality of the resulting analysis.
The same principle applies across a range of tax due diligence activities, including tax return reviews, historical liability assessments and transaction structuring. In each case, unclear instructions can reduce confidence in AI-generated outputs and create unnecessary inefficiencies within review processes.
TAINA Technology’s analysis also makes clear that effective prompting is about more than simply issuing short commands. Kent argues that context, objectives and operational constraints remain fundamental to producing reliable outputs. Once these parameters are established, however, framing requests as explicit tasks enables AI agents to deliver more consistent and actionable results.
The approach also extends to situations where the methodology is not immediately obvious. Rather than requesting general opinions from an AI agent, professionals can define the desired outcome and allow the technology to determine the most appropriate method. TAINA Technology highlights FATCA compliance testing as one example, where users can instruct an agent to identify the most effective testing methodology for a dataset instead of asking for broad observations.
As AI becomes increasingly integrated into tax due diligence, firms are likely to require new skills centred on managing, directing and validating AI agents. According to TAINA Technology’s analysis, organisations that achieve the greatest return on their AI investments may not necessarily be those deploying the most sophisticated tools, but those that establish disciplined approaches to communicating with them.
Kent concludes that adopting AI successfully requires professionals to define objectives clearly, use precise action-oriented language and provide sufficient context for AI agents to execute tasks effectively. As tax due diligence continues to evolve, the ability to communicate effectively with AI could become just as valuable as the technology itself.
Read the full TAINA Technology analysis.
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