Problem
ChatGPT, Claude, and Gemini can't solve 3 things enterprises need
The first wave of LLM adoption is over. "Plausible-sounding answers" don't drive real decisions — enterprises are waking up.
Problem 1
Data Sovereignty — internal data can't go to LLMs
Samsung, Hyundai, and LG have banned internal use of ChatGPT. Healthcare, finance, and the public sector treat LLM use itself as a regulatory violation. OpenAI sells on-prem, but not at a price or complexity SMEs can absorb.
"No decision-maker is going to send Samsung's wafer-defect data to OpenAI's servers."
85%
of global enterprises ban public-LLM input of their own data
Problem 2
Numeric Limits — LLMs describe statistics, they don't compute them
The Transformer is a next-token predictor. It does not mathematically guarantee regression coefficients, SHAP values, or Reverse What-If optimizations. It outputs "plausible numbers".
"When an LLM produces an answer, it's a guess, not a calculation. You don't stop a factory line on a guess."
75%
Gartner 2025: enterprise Generative AI rollouts that fall short of expectations
Problem 3
Regulatory Duty — EU AI Act and Korea's AI Framework Act codify explainable AI
Medical diagnosis, credit scoring, and public-policy decisions now legally require XAI. LLM black boxes can't pass. SHAP-based mathematical justification is mandatory.
"EU AI Act takes effect in 2026 — high-risk AI must mathematically justify each decision."
Global
EU AI Act, Korea AI Framework, US AI Bill of Rights — all in force together