Insights on Responsible AI from the Global AI Trust Maturity Survey
The rapid growth of generative AI and large language models (LLMs) is driving adoption across business functions, with the aim of boosting productivity, efficiency, and innovation. However, these benefits can only be realized if AI is deployed safely and responsibly. Responsible AI (RAI) practices are key to a broader AI trust strategy, which generates trust among customers, employees, and stakeholders regarding the organization’s use of AI. By addressing critical aspects such as data governance, explainability, fairness, privacy, security, and transparency, RAI helps organizations mitigate risks, build trust, ensure accountability, and maximize the impact of their AI solutions.
Survey Overview
A recent survey of more than 750 leaders across 38 countries provides insights into the current state of RAI in enterprises. Respondents come from various industries, including technology, healthcare, and more, representing roles in legal, data and AI, engineering, risk, and finance. Their responses were assessed using the McKinsey AI Trust Maturity Model, an RAI framework encompassing four dimensions—strategy, risk management, data and technology, and operating model—with 21 subdimensions.
The average RAI maturity score for organizations surveyed was 2.0 on a scale of 0 to 4. About 36 percent of respondents fell under Level 2, indicating that organizations are still integrating responsible AI practices, such as defined key risk indicators and data quality guidelines.
Leading Industries and Geographical Insights
Industries such as technology, media, and telecommunications (TMT) and financial and professional services are leading the way with an average RAI maturity score of 2.1. Geographically, India stands out in RAI maturity, scoring 23 percent above the global average with a score of 2.5, followed by the United States at 2.4, indicating a greater awareness of the risks enterprises face.
Investment Trends in Responsible AI
Most organizations surveyed plan to invest more than $1 million in RAI in the coming year, with larger organizations planning to invest significantly more. These investments include hiring RAI professionals, building or purchasing technical systems, and engaging legal or professional services related to RAI. There is a strong correlation between companies with higher RAI maturity scores and greater levels of investment, suggesting that increased investment may help advance RAI maturity.
Challenges and Barriers
Despite the progress, obstacles to implementing best-in-class RAI practices remain. Respondents identified knowledge and training gaps (51 percent) and regulatory uncertainty (40 percent) as significant challenges. This indicates that organizations still lack clarity about how to implement the correct practices to gain the associated benefits.
However, a lack of clarity should not justify a passive approach. As enterprises continue to adopt AI across business functions, building new risk management and mitigation capabilities alongside bold AI road maps will be crucial in ensuring safe and trustworthy use. Organizations investing in AI trust now will benefit later from faster adoption and greater resilience against risk as they work to capture the full potential of AI.
Conclusion
The insights from the Global AI Trust Maturity Survey highlight the growing importance of responsible AI practices in driving business success and building trust among stakeholders. As the landscape of AI continues to evolve, organizations must prioritize RAI to navigate the complexities and challenges of adopting these transformative technologies.
