Trustworthy AI as a value lever

2026-06-05 · Source: The Information · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Consulting & Professional Services · Depth: Intermediate, medium

Summary

A survey of 154 readers of The Information, conducted with EY Consulting, reveals that while 71% of executives recognize trust in AI as crucial for business outcomes, less than half (44%) of companies have effective structures to support it. Published on June 5, 2026, the report highlights a significant gap between awareness and operationalization, describing current efforts as "chaotic and ineffective." It identifies trust as a "value lever" that enables faster, more confident AI deployment. Key steps to build trustworthy AI include embedding trust directly into decision-making workflows and systems, aligning diverse executive mindsets (e.g., enthusiastic champions vs. cautious evaluators), and assigning shared responsibility across functions by making them co-creators of AI solutions. The survey also indicates a prioritization of foundational strategies like cybersecurity and secure data pipelines.

Key takeaway

For Directors of AI/ML struggling to scale AI initiatives, you must move beyond recognizing trust's importance to actively operationalizing it. Implement responsible-by-design AI frameworks that embed trust levers like transparency and accountability directly into workflows, rather than relying on after-the-fact governance. Align your executive teams by fostering co-creation across functions, ensuring diverse goals are factored in from the start. This approach converts enthusiasm into structured, confident AI adoption, mitigating risks and accelerating business value.

Key insights

Operationalizing trustworthy AI is critical for business value, yet most companies lack effective structures.

Principles

• Trust enables faster AI deployment.
• Embed trust into AI design and workflows.
• Align diverse executive perspectives.

Method

Design responsible-by-design AI frameworks by embedding transparency, explainability, liability, governance, and compliance directly into AI processes.

In practice

• Prioritize cybersecurity and secure data pipelines.
• Make all relevant functions co-creators of AI solutions.

Topics

• Trustworthy AI
• AI Governance
• AI Adoption
• Executive Alignment
• Cybersecurity
• Data Pipelines

Best for: CTO, Executive, AI Product Manager, Consultant, Director of AI/ML, VP of Engineering/Data

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Information.