As Agentic AI Gains Traction, 86% of Enterprises Anticipate Heightened Risks, Yet Only 2% of Companies Meet Responsible AI Gold Standards
With 95% of enterprises facing incidents,
The report analyzed the effects of risks from poorly implemented AI, such as privacy violations, ethical violations, bias or discrimination, regulatory non-compliance, inaccurate or harmful predictions, among others. It found that 77% of organizations reported financial loss, and 53% of organizations have suffered reputational impact from such AI-related incidents.
Key findings include:
AI risks are widespread and can be severe
- 95% of C-suite and director-level executives report AI-related incidents in the past two years.
- 39% characterize the damage experienced from such AI issues as "severe" or "extremely severe."
- 86% of executives aware of agentic AI believe it will introduce new risks and compliance issues.
Responsible
- Only 2% of companies (termed "RAI leaders") met the full standards set in the Infosys RAI capability benchmark — termed "RAISE BAR" with 15% (RAI followers) meeting three-quarters of the standards.
- The "RAI leader" cohort experienced 39% lower financial losses and 18% lower severity from AI incidents.
- Leaders do several things better to achieve these results, including developing improved AI explainability, proactively evaluating and mitigating against bias, rigorously testing and validating AI initiatives and having a clear incident response plan.
Executives view RAI as a growth driver
- 78% of senior leaders see RAI as aiding their revenue growth and 83% say that future AI regulations would boost, rather than inhibit, the number of future AI initiatives.
- However, on average, companies believe they are underinvesting in RAI by 30%.
With the scale of enterprise AI adoption far outpacing readiness, companies must urgently shift from treating RAI as a reactive compliance obligation to embracing it proactively as a strategic advantage. To help organizations build scalable, trusted AI systems that fuel growth while mitigating risk,
- Learn from the leaders: Study the practices of high-maturity RAI organizations who have already faced diverse incident types and developed robust governance.
- Blend product agility with platform governance: Combine decentralized product innovation with centralized RAI guardrails and oversight.
- Embed RAI guardrails into secure AI platforms: Use platform-based environments that enable AI agents to operate within preapproved data and systems.
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Establish a proactive RAI office: Create a centralized function to monitor risk, set policy, and scale governance with tools like
Infosys' AI3S (Scan, Shield, Steer).
Balakrishna D.R., EVP – Global Services Head, AI and Industry Verticals,
To read the full report, please visit here.
Methodology
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