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. The Asset Valuator in Opagio Intangibles is a deterministic calculation, not an AI model: it applies the method you choose to the assumptions you enter, names the method used, restates your assumptions in plain language and, for Relief from Royalty, MPEEM, Replacement Cost and With and Without, adds a sensitivity table, so every result can be explained and challenged.

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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