The Global Institute of Credit Professionals found that almost half of organizations using AI across 25% to 49% of workflows reported high organizational impact. That figure rose to 66% among those using it across 50% to 75%, though the report cautions that only six respondents fell into the latter group and one in the highest adoption tier.
The findings come from the institute's global "Credit in the Age of AI" research, which surveyed credit professionals to understand how adoption patterns, governance, and workforce changes correlate with impact.
Governance maturity and workforce shifts correlate with higher impact
GICP constructed a Governance Maturity Index around accountability, oversight and model-assurance practices. Among organizations with eight or nine governance factors in place, 67% reported high AI impact, compared with 23% of those with zero or one.
Workforce and workflow changes also aligned with impact. Organizations reporting low AI impact had made just over one change per organization on average, while medium-impact organizations made nearly two and high-impact organizations made about 2.6. The changes measured included job responsibilities, workflows, hiring criteria and skill requirements.
For executives building AI strategy, these findings suggest that governance maturity and deliberate workforce adaptation are tied to impact. An AI Learning Path for Vice Presidents of Strategy offers targeted training on aligning AI governance with business outcomes.
Core credit decisions deliver the strongest impact
The strongest reported impact appeared closest to credit decisions. Risk assessment, scoring and early warning received the highest average organizational-impact score in the study, at 3.16 on a five-point scale. GICP found higher uptake in easier-to-check activities such as drafting, analysis and research, while decision-centric uses placed greater demands on validation, monitoring and control.
The investment gap is visible in the data. GICP said 65% of respondents cited at least one leadership, strategy or governance-related barrier, while only 38% were investing in governance or AI strategy.
Risk management remains a scaling constraint
Earlier McKinsey research surveyed senior credit-risk executives at 24 financial institutions in 2024 and found 75% identified risk and governance as significant barriers to scaling generative AI. Only one-third of those institutions had established a center of excellence to manage generative AI use cases.
For U.S. creditors subject to Regulation B, using AI in credit decisions does not change adverse-action explanation requirements. CFPB guidance says creditors using complex algorithms, including AI, must still provide specific and accurate principal reasons for adverse action. The National Institute of Standards and Technology's voluntary AI Risk Management Framework separately sets out governance practices covering defined responsibilities, staff training, human oversight and AI testing.
Why this matters for Executives and Strategy
The research makes clear that neither adoption breadth alone nor technology investment alone predicts impact. Executives should focus on governance maturity, how they change teams and processes to match AI capabilities, and where they deploy AI in the credit workflow. An AI for Executives & Strategy track can help leaders build the governance and workforce practices that correlate with higher reported impact.
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