Many organizations lack a clear strategy to measure GenAI reliability and move models into production. Without robust evaluation, governance and alignment with business goals, pilots risk bias, privacy breaches and negative ROI, leaving AI experiments confined to isolated use cases instead of enterprise-wide impact.
64% of enterprises still can’t prove their GenAI pilots are reliable, responsible or even relevant.
Turns out, generating ideas is easy. Scaling them? That’s where most get stuck.
This report lifts the lid on why so many pilots stall and how a small group of leaders are quietly scaling with confidence.
Their secret? Measurement that matters, governance with backbone and trust that travels from boardroom to codebase.
Inside, discover
Why 60% of leaders see reliability as critical, yet 64% still lack a strategy to measure it.
How gaps in evaluation and governance are driving negative ROI and trust concerns.
What needs to change, from bias detection to explainability - to scale GenAI responsibly.
Explore the key insights! Download the infographic for a visually rich summary
Technology strategy can’t live in a slide deck. It only delivers value when it’s embedded across teams, tied to business outcomes and executed with discipline - not as a fixed plan, but as a learning cycle that allows you to adapt as you go.
FAQs
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Effective measurement combines deterministic evaluation methods, automated bias detection, privacy-preserving strategies and explainability tools. By embedding these into operating models, companies can assess model accuracy, compliance and trustworthiness while linking AI initiatives to tangible business outcomes like improved resilience, productivity and market value.
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Key risks include publishing plagiarized content, generating biased or incorrect outputs, disclosing sensitive information and violating data privacy regulations. Implementing audits, monitoring and transparent evaluation practices reduces these risks and ensures AI deployment is secure, fair and compliant with regulatory standards.
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Organizations that implement disciplined evaluation, governance and measurement see measurable gains: faster deployment cycles, lower operational costs, stronger digital resilience, enhanced software development efficiency and higher customer engagement. This approach transforms isolated AI experiments into sustainable, enterprise-wide competitive advantage.