The XAI market sits at $6.55B in 2023 and is on track to exceed $29B by 2032, growing at over 15% CAGR. That growth reflects a trust gap that's difficult to ignore: 80% of enterprises report having a clear AI strategy, yet only 20% of executives trust the outputs their systems produce. When those outputs determine credit approvals, medical decisions, or hiring outcomes, the gap between confidence in strategy and confidence in results creates real exposure.
Most production AI models predict without explaining. In finance, that leaves loan denials no regulator can verify. In healthcare, diagnostic tools clinicians hesitate to act on. In manufacturing, maintenance alerts land without telling engineers which sensor triggered them or why. Regulatory frameworks including GDPR, SR 11-7, Basel III, and the EU AI Act are formalizing accountability requirements, and "the model said so" is no longer a defensible answer.
In this guide from N-iX, Yaroslav Mota, Head of AI and Engineering Excellence, breaks down the XAI toolkit and where it matters most. He maps these against five industries (finance, healthcare, manufacturing, retail, and HR), showing how explainability supports AML compliance, FDA submissions, predictive maintenance traceability, fair hiring decisions, and dynamic pricing audits. The guide closes with five concrete XAI limitations and what N-iX recommends to address each in production.
Discover XAI methods, where explainability applies across five industries, and how N-iX addresses the key deployment limitations: full analysis in this guide!
Only 20% of executives trust their AI outputs. Find out how explainability can change it!
The XAI market exceeds $29B by 2032, growing at over 15% CAGR. Discover the core methods, five industry use cases, and key deployment limitations in this guide!
