How do you build trust in AI-driven products and recommendations?

Full Explanation

Customers distrust AI when it's a black box. Trust-building AI practices: 1) Explainability: explain why the AI made a recommendation ("valuation is 6x ARR based on comparable SaaS companies with similar NRR"). 2) Confidence disclosure: state confidence level ("this valuation has 70% confidence range £5M-£7M"). 3) Training data: disclose what data trained the model and any biases. 4) Human override: allow users to contest or override AI outputs. 5) Fallback: if AI confidence is low, offer human alternative. Healthcare AI that diagnoses without explaining its reasoning triggers distrust. Conversely, AI that explains reasoning and acknowledges uncertainty builds confidence. Opagio's valuator uses AI to analyze comparable companies and validate assumptions, but always explains the methodology and confidence levels. The tool is AI-enhanced, not AI-blind. Data privacy and security are foundational requirements, not optional features. Opagio implements role-based access control, audit logging of all data modifications, and encryption throughout the data lifecycle. Users retain full ownership of their data and can export or delete it at any time. These commitments are documented in publicly available terms of service, privacy policy, and data processing agreements — all of which are versioned and maintained with the same rigour as the platform's codebase.

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