Are Large Language Models the Holy Grail of Valuation?

Are Large Language Models the Holy Grail of Valuation?

The rapid advancement of artificial intelligence (AI) is reshaping how valuation professionals work. From extracting data across disparate sources to assisting with the drafting of valuation reports, AI tools are increasingly embedded in day-to-day valuation workflows. When used appropriately, AI can save hundreds of hours, accelerate analysis, and reduce certain forms of human error by processing vast volumes of market data and identifying patterns that may not be immediately apparent to analysts.

That said, the risks associated with AI use in valuation should not be underestimated. High-profile incidents have emerged in which professional reports prepared by reputable firms cited legal precedents or case law that did not exist. In July 2025 alone, more than 50 publicly reported cases across multiple jurisdictions involved fabricated legal citations generated by AI tools—many of them produced or relied upon by experienced professionals at established firms. While improvements in model training have reduced hallucination rates in many large language models (LLMs), the implications for professional work remain material. Research shows that out-of-the-box LLMs such as GPT-4 achieve only around 56% to 81.5% accuracy on financial factual questions, a level that falls well short of the near-perfect reliability expected in professional valuation engagements.

A Reality Check on AI Accuracy

The technical limitations of LLMs in valuation extend beyond obvious hallucinations. General-purpose models frequently misinterpret financial statements, confuse closely related accounting concepts, and produce incorrect ratio calculations—particularly when numerical reasoning or contextual judgment is required. These weaknesses can materially undermine risk assessments and result in conclusions that would not withstand internal review or regulatory scrutiny.

Empirical research reinforces these concerns. Studies indicate that even advanced retrieval-augmented generation (RAG) systems achieve only around 56% accuracy on realistic finance-focused question-and-answer tasks. This reinforces an important point: current AI tools are best viewed as analytical aids rather than substitutes for professional judgment.

Regulatory and compliance considerations add further complexity. AI-generated outputs often appear confident and polished, yet may contain subtle errors or compliance breaches—a phenomenon sometimes described as “false confidence.” This is particularly problematic in valuation, where adherence to professional standards such as USPAP, IVS, and industry-specific guidelines requires not only numerical accuracy but also transparent, well-reasoned, and defensible methodologies.

Positioning Valuation Professionals in the AI Era

So how can valuation professionals position themselves in the current wave of AI transformation? The answer lies neither in blind enthusiasm nor outright rejection, but in adopting a balanced human-AI collaboration model that plays to the strengths of both.

Build AI Literacy and Maintain Critical Oversight

Valuation professionals must understand what AI tools can and cannot do. This includes recognizing hallucinations, understanding how LLMs generate outputs, and knowing when independent verification is essential. Any AI-assisted output, whether related to comparable company selection, cash flow analysis, or market research, must be corroborated using trusted data sources and professional judgment. Ultimately, the valuation analyst remains the most critical element of any engagement, applying contextual understanding of markets, financial theory, and client-specific circumstances that no algorithm can replicate.

Adopt Hybrid Workflows

Leading practitioners are increasingly adopting hybrid approaches that combine AI-driven efficiency with human expertise. AI can be highly effective for initial data collection, pattern recognition, and scenario analysis, while complex judgments—such as discount rate selection, management assessment, or evaluation of qualitative risk factors—remain firmly within the domain of experienced professionals. Evidence suggests that organizations using AI appropriately in financial forecasting have achieved meaningful reductions in forecasting error, demonstrating that AI can enhance outcomes when deployed with discipline.

Focus on Irreplaceable Human Value

Rather than diminishing the role of valuation professionals, AI should enable them to focus on higher-value activities. Relationship management, ethical judgment, professional skepticism, and the ability to explain complex conclusions clearly to stakeholders remain uniquely human skills. Valuation professionals are ultimately accountable for their conclusions and serve as the public face of their firms—responsibilities that cannot be delegated to software.

Commit to Continuous Learning

The AI landscape is evolving rapidly, and professionals must remain engaged in ongoing learning. This includes staying informed about emerging domain-specific financial AI tools, understanding their limitations, and keeping abreast of professional and regulatory guidance on acceptable AI use in valuation practice.

The Path Forward

Large language models are not the holy grail of valuation. They are powerful tools that, when deployed thoughtfully and supported by rigorous human oversight, can materially enhance efficiency and analytical depth. The future of the profession belongs to practitioners who can integrate AI into their workflows while preserving the professional judgment, skepticism, and accountability that define high-quality valuation work.

The more relevant question is no longer whether AI will replace valuation professionals, but how professionals can harness AI to deliver more accurate, insightful, and timely valuations—without compromising professional standards. The answer to that question will shape competitive advantage in the valuation profession for years to come.