The Trust–Value Flywheel: Dual-Track Performance as the New Standard for AI Excellence

GAIEM
Sep 12
2 min read

Executive Summary

GCAIE research across 130 enterprises and 40 public-sector agencies in 14 jurisdictions shows a striking pattern:

Organizations that focus only on business value deliver short-term gains but fail to scale, while
Organizations that simultaneously track trust and value metrics achieve 2.7× faster scaling, 55% lower regulatory friction, and 3.4× higher brand equity growth.

We call this the Trust, Value Flywheel, a reinforcing loop where trust accelerates value creation, and visible value reinforces trust. It reframes excellence not just as maximizing economic return, but as delivering high-value AI that is safe, ethical, and societally aligned by design.

Strategic Context

AI systems are becoming more consequential, and scrutiny from regulators, investors, and the public is intensifying. Yet most organisations measure only value outcomes (ROI, cost savings, productivity) while neglecting trust metrics (ethics, explainability, safety, inclusion, ESG impact).

Only 21% of organisations in the GCAIE 2025 survey had defined Responsible AI KPIs embedded in enterprise scorecards.
Over 60% of scaling failures cited “loss of stakeholder trust” as a primary barrier.
Regulators are shifting from permissive to “assurance-based” regimes, requiring evidence of safety, fairness, and accountability before deployment, as codified in EU AI Act risk tiers and the Measure + Manage functions in NIST AI RMF.

By contrast, leading organizations have built dual-track performance systems, tracking both trust and value, as required under the continuous monitoring and stakeholder engagement clauses of ISO/IEC 42001.

GCAIE Insight

The Trust–Value Flywheel describes how embedding trust metrics alongside value metrics creates a self-reinforcing engine for sustainable AI excellence.

GCAIE benchmark data (2025):

Dual-track organisations reached full deployment in 11 months on average, vs 29 months for value-only peers.
They experienced 58% fewer regulatory delays, due to pre-validated safety and fairness controls.
They outperformed peers by 3.4× in brand equity gains, measured via analyst ratings and Net Trust Scores.

Core dimensions of dual-track performance (Global AI Excellence Model (GAIEM)):

Value metrics: ROI, cost efficiency, productivity uplift, revenue growth, operational resilience.
Trust metrics: Bias detection and mitigation, model transparency, human oversight effectiveness, environmental and social impact scores, stakeholder perception indices.
Integration levers: Trust KPIs embedded in executive scorecards, trust gates in lifecycle processes, and public transparency dashboards.

Leadership Implications

For corporate leaders:

Establish a dual performance architecture where trust KPIs carry equal weight to value KPIs.
Tie executive incentives to trust as well as value outcomes.
Implement trust assurance dashboards for boards and regulators.

For public-sector leaders:

Embed trust metrics in public procurement scoring and licensing conditions.
Require AI assurance statements covering fairness, explainability, and societal impact.
Fund sector-wide Responsible AI assurance sandboxes to build trust readiness in high-risk domains.

GCAIE has embedded trust and value as co-equal pillars within the SCALE Assessment Tool, enabling organisations to: