Over half of us now use AI to search the web — yet the stubbornly low data accuracy of common tools is quietly creating new and significant business risks.
While generative AI (GenAI) offers undeniable efficiency gains, a new investigation highlights a stark disparity between user trust and technical accuracy — one that poses specific risks to corporate compliance, legal standing, and financial planning.
"Around a third of users believe AI is already more important to them than standard web searching."
— Survey of 4,189 UK adults, September 2025
For the C-suite, the adoption of these tools represents a classic 'shadow IT' challenge. If employees trust AI for personal queries, they are almost certainly employing them for business research — often without oversight or verification protocols in place.
🔍 The Accuracy Gap: What the Investigation Found
The investigation, conducted by Which?, tested six major AI tools — ChatGPT, Google Gemini (both standard and 'AI Overviews'), Microsoft Copilot, Meta AI, and Perplexity — across 40 common questions spanning finance, law, and consumer rights.
Around half of AI users report trusting the information they receive to a 'reasonable' or 'great' extent. Yet, looking at the granularity of the responses provided by AI models, that trust is often misplaced.
| AI Tool | Accuracy Score | Ranking |
| Perplexity | 71% | 🥇 1st |
| Google Gemini AI Overviews | 70% | 🥈 2nd |
| ChatGPT | 64% | 5th |
| Meta AI | 55% | 🙄 Lowest |
⚠️ Note: ChatGPT, despite its widespread adoption, ranked as the second-lowest performer. This disconnect between market dominance and reliable output underlines the danger of assuming popularity equals performance in the GenAI space.
⚖️ High-Stakes Errors: Finance & Legal Risks
The investigation revealed that all tested AI tools frequently misread information or provided incomplete advice that could pose serious business risks.
💲 Financial Risk Example:
When asked how to invest a £25,000 annual ISA allowance, both ChatGPT and Copilot failed to identify a deliberate error in the prompt regarding the statutory limit. Instead of correcting the figure, they offered advice that potentially risked breaching HMRC rules.
⚖️ Legal Risk Example:
When queried about a dispute with a builder, Gemini advised withholding payment — a tactic that experts noted could place a user in breach of contract and weaken their legal position. AI tools also commonly failed to distinguish between legal statutes in Scotland versus England and Wales.
This "overconfident advice" creates real operational hazards. If an employee relies on AI for preliminary compliance checks or contract review without verifying jurisdiction or legal nuance, the organisation could face regulatory exposure.
The inconsistency across platforms necessitates a rigorous "human-in-the-loop" protocol for any business process involving AI to ensure accuracy.
🔗 Source Transparency & Data Governance Concerns
A primary concern for enterprise data governance is the lineage of information. AI search tools frequently cited sources that were vague, non-existent, or of dubious accuracy — such as outdated forum threads. This opacity can lead directly to financial inefficiency.
In one test regarding tax codes, ChatGPT and Perplexity presented links to premium tax-refund companies rather than directing users to the free official HMRC tool. These third-party services are often characterised by high fees.
In a business procurement context, such algorithmic bias from AI tools could lead to unnecessary vendor spend or engagement with service providers that fail to meet corporate due diligence standards.
The major technology providers acknowledge these limitations, placing the burden of verification firmly on the user — and, by extension, the enterprise.
💬 Microsoft on Copilot:
"Copilot answers questions by distilling information from multiple web sources into a single response. We encourage people to verify the accuracy of content."
💬 OpenAI on Accuracy:
"Improving accuracy is something the whole industry's working on. We're making good progress and our latest default model, GPT-5, is the smartest and most accurate we've built."
🛠️ Mitigating AI Business Risk: A Governance Framework
For business leaders, the path forward is not to ban AI tools — which often drives usage further into the shadows — but to implement robust governance frameworks.
① Enforce Specificity in Prompts
Corporate training should emphasise that vague queries yield risky data. If an employee is researching regulations, they must specify the jurisdiction — e.g., "legal rules for England and Wales" — rather than assuming the tool will infer the correct context.
② Mandate Source Verification
Trusting a single AI output is operationally unsound. Employees must demand to see sources and check them manually. For high-risk topics, findings should be verified across multiple AI tools or "double-sourced." Tools like Google Gemini AI Overviews, which allow users to review presented web links directly, performed better in scoring precisely because they facilitate this verification process.
③ Operationalise the "Second Opinion"
At this stage of technical maturity, GenAI outputs should be viewed as just one opinion among many. For complex issues involving finance, law, or medical data, AI lacks the ability to fully comprehend nuance. Enterprise policy must dictate that professional human advice remains the final arbiter for decisions with real-world consequences.
The AI tools are evolving and their web search accuracy is gradually improving — but as the investigation concludes, relying on them too heavily right now could prove costly. For the enterprise, the difference between a business efficiency gain from AI and a compliance failure risk lies entirely in the verification process.