# Why AI Shouldn't Calculate Critical Customer Numbers Alone When accuracy matters, deterministic tools must verify AI-generated figures - **Source:** [Entrepreneur – Latest](https://brifing.si/source/1bee40f8-abda-4cd6-b055-05148ce79f53) - **Published:** 2026-09-25 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/117e86cc-c446-4e11-9f0e-e93f8b79a949 ## Widespread AI adoption in SMBs 77% of small and midsize businesses in the U.S. regularly use AI, with customer service being among the top three applications. This rapid adoption creates potential risks when AI systems handle numerical outputs that customers treat as factual. The challenge emerges when large language models (LLMs) predict numbers rather than calculating them deterministically, unlike traditional computing systems designed for precise mathematical operations. ## The BMW chatbot mishap A Toronto BMW dealership faced consequences when its AI chatbot 'Quinn' mistakenly quoted a car's loan balance ($19,000) as its buyback price. The system confused the outstanding loan amount with a purchase offer, forcing the dealership to honor the incorrect figure after media scrutiny. This incident highlights how AI-generated numbers can appear authoritative while being fundamentally wrong, potentially damaging customer trust and creating legal liabilities. ## Creative vs. factual AI outputs While AI excels at creative tasks like drafting emails where multiple valid solutions exist, it struggles with numerically precise outputs requiring single correct answers. The article distinguishes between creative applications (where variability is acceptable) and factual calculations (where reproducibility is essential). This difference becomes critical when customers act on AI-generated numbers as definitive information in financial transactions. ## Omni Calculator's solution Omni Calculator developed a framework where AI describes calculation logic, but deterministic engines perform actual computations. Their Builder tool converts natural language requests into calculator logic that runs on a reliable math engine. This separation ensures numerical outputs remain consistent across multiple runs, addressing LLMs' inherent variability in mathematical precision while maintaining conversational interfaces. ## ORCA Benchmark findings Omni's research tested free-tier AI models on mathematical accuracy, revealing performance ranging from 48.4% (ChatGPT) to 70.4% (Grok). The study identified three failure modes: raw calculation errors, rounding mistakes, and formula misinterpretations. Notably, models frequently changed correct answers to incorrect ones (60-65% of cases) when users challenged responses with 'Are you sure?', demonstrating unreliable numerical confidence. ## Architecture for reliable outputs The article proposes splitting AI workflows: let LLMs interpret queries and select appropriate tools, while deterministic systems (like Wolfram Alpha) handle precise calculations. For mortgage lenders, this means AI would extract relevant inputs but delegate amortization calculations to specialized engines. This hybrid approach maintains conversational interfaces while ensuring numerical accuracy through verified computational backends. ## Implementation blueprint Practical implementation involves connecting LLMs to calculation APIs that execute formulas deterministically. When a query contains numerical inputs, the AI passes them to these tools rather than attempting calculations itself. The model then incorporates the verified result into its natural language response. This maintains user experience while eliminating mathematical errors that could have financial or legal consequences. ## Key distinction for deployment The article emphasizes that sounding right and being right are fundamentally different capabilities in AI systems. While models increasingly provide plausible-sounding answers, businesses must implement verification layers for numerical outputs customers act upon. This requires careful system design that recognizes LLMs' strengths in language processing while mitigating their weaknesses in mathematical precision through architectural safeguards. ## Sources covering this story - https://www.entrepreneur.com/business-news/tech/the-test-every-business-should-run-before-letting-ai-calculate-prices-payments-and-other-numbers-customers-act-on — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._