Why AI Sounds Confident Even When It Is Wrong: A Risk Every CA Must Understand
Artificial Intelligence tools have become remarkably good at producing answers that *sound* correct.
* The language is polished
* The structure is logical
* The tone is confident
And that is precisely where the risk begins.
Confidence Is Not Accuracy
When a human professional is unsure, the uncertainty usually shows.
They pause. They qualify. They say, “I need to check.”
AI does none of this by default.
AI systems are optimised for language generation, not truth verification. Their core task is to predict the most likely next word, not to validate facts against reality.
As a result:
* A correct answer and an incorrect answer can look equally confident
* There is no built-in signal that something is wrong
* Fluency becomes indistinguishable from correctness
For professionals trained to rely on evidence, this is counter-intuitive—and dangerous.
The Illusion of “Expert Tone”
One of the most misunderstood aspects of AI is this: AI does not know when it does not know.
When reliable information is missing, the model does not stop. It fills the gap with a statistically plausible response.
This produces what feels like an “expert tone”:
* Authoritative wording
* Clear explanations
* No visible uncertainty
In human professionals, such tone usually signals competence.
In AI, it is merely a stylistic output.
Trusting tone instead of substance is where professionals get exposed.
AI does not understand:
* Materiality
* Professional skepticism
* Audit risk
* Regulatory intent
* Client-specific context
It cannot distinguish between:
* “Generally true” and “professionally acceptable”
* “Well-explained” and “defensible under scrutiny”
Those judgments remain the CA’s responsibility—entirely.
The Probability vs. Truth Gap
Large Language Models do not “know” facts. They are probabilistic systems.
When asked to cite a section of the Income-tax Act or summarise a GST notification, the model is not verifying a source. It is predicting what a *correct-sounding* answer should look like, based on patterns in its training data.
Because it has absorbed millions of pages of professional writing, it is exceptionally good at mimicking the language of authority—even when the underlying detail is incomplete, outdated, or entirely fabricated.
This is why errors are rarely obvious. They are plausible.
Why This Is a Professional Liability
For a CA, the real danger is not a trivial mistake.
It is a credible one.
* Legal risk: There are already documented cases where legal filings contained fabricated case citations generated by AI—complete with convincing names, dates, and references.
* Financial risk: An AI summary of a due diligence report may state, with complete linguistic confidence, that “all contingent liabilities have been considered,” while missing a critical footnote because it prioritised statistically common outcomes over outlier risks.
In our profession, “the AI said so” is not a defence.
The signatory bears responsibility—always.
Studies indicate hallucination rates ranging from low single digits to over 20% on complex queries. In audit or tax work, even a small unverified error rate is unacceptable.
How CAs Should Mitigate the Confidence Risk
The shift required is simple but non-negotiable: from users to reviewers.
| Strategy | What It Means in Practice |
| Trust, but Verify | Never rely on AI-generated figures, sections, or interpretations without checking the primary source—Act, notification, standard, or ERP data. |
| Prefer RAG-Based Tools | Use AI systems with Retrieval-Augmented Generation that reference your uploaded documents instead of relying purely on training data. |
| Break the Confidence Bias | Explicitly instruct the AI to state “Information not found” when data is missing, instead of estimating or assuming. |
The Safer Mental Model for AI
For professional work, AI should be treated as:
* A drafting assistant
* A thought organiser
* A starting point for analysis
It should not be treated as:
* A validator
* An authority
* A substitute for professional judgment
If an AI output influences a decision, the CA must still be able to answer:
“How do I know this is correct?”
If the only answer is “because the AI said so,” the risk remains unmanaged.
Conclusion: Efficiency Without Compromising Integrity
The confidence gap in AI does not mean rejecting technology. It means using it with discipline.
As AI becomes embedded in everyday professional workflows, CA firms must formalise safeguards:
* Human-in-the-Loop: No AI-generated output should leave the firm without human sign-off. Think of AI as a high-output intern with zero accountability.
* Source Mapping: Any legal section, ratio, or conclusion suggested by AI must be traceable to a primary source.
* Transparency: Be open internally—and where appropriate, externally—about AI usage. Opacity increases risk; transparency reduces it.
The Bottom Line
In the age of AI, professional skepticism - the cornerstone of the CA curriculum—is no longer just a compliance requirement. It is a competitive advantage.
AI can provide speed and structure. Only a Chartered Accountant can guarantee judgment, context, and truth.
The future belongs to the Augmented CA: one who uses AI for efficiency—but relies on professional judgment for the final word.
