A Global Institute of Credit Professionals (GICP) study, “Credit in the Age of AI,” delivered a finding that cuts against the capital expenditure (CapEx) logic of institutional finance, establishing that more algorithms do not produce more value, especially when organization overlook foundational needs for credit management automation
AI is moving deeper into credit, but the winners will not be those that simply buy more tools. In this case, credit management automation creates stronger results when governance, skilled staff, clear workflows, and human judgment move with the technology, new research suggests.
The GICP found that organizations with similar AI use get different results, with the report pointing to one reason: technology works best when people, processes, and operating models change with it.
The determination of highly impactful AI outcomes in credit operations is not what the software can or cannot do, but it’s all about the governance architecture, workflow redesign, and most importantly, leadership alignment needed to embed the AI software into how institutions actually function. More lenders seek to automate credit management without weakening oversight.
Governance Separates AI Use from AI Impact
AI adoption is growing but remains shallow across credit, as the report illustrated that gap, revealing that 56% of organizations use AI in less than 25% of credit processes, including 14% that do not use it. Use is concentrated in lower risk work such as drafting, analysis, research, and information gathering.
The strongest impact is closer to credit decisions. Risk assessment and credit scoring show value but carry more responsibility. An algorithm for credit score may speed up analysis, yet it still needs validation, clear data controls, and experienced people who can challenge the result.
This is where credit risk AI governance becomes central. GICP found that 67% of organizations with eight or nine governance measures reported high AI enabled impact, compared with 23% with zero or one measure.
Formal frameworks matter too. Half of high-impact organizations had formal governance, compared with 18% among those with minimal or mixed impact. That gives AI governance for financial institutions a role in turning adoption into value.
The same principle applies to AI for private credit, where underwriting and risk assessment depend on reliable data and accountability.
It also matters for AI in private banking, where client relationships and judgment cannot simply be handed to a model.
“AI value does not come from automation alone. It comes from combining technology with governance, skills, data discipline and human judgment,” stated Karaiskos exclusively.
That message reaches automation in investment banking, where speed can help teams, but weak controls can make poor decisions move faster.
For credit management automation, governance is not an extra layer added after deployment. It is part of what allows AI to be trusted and used in everyday credit work.
People and Workflows Decide Whether AI Scales
GICP found that the strongest organizations are changing how employees work, where high-impact organizations introduced 2.58 workforce and workflow changes on average, compared with 1.22 among lower-impact organizations.
Those changes include AI-related roles, technical training, adapted team structures, and more human-AI collaboration. This matters for agentic finance, because more autonomous systems require people who understand when to trust an output and when to question it.
Training supports credit management automation by helping credit professionals evaluate AI outputs instead of accepting them automatically. It’s not about removing judgments, but giving professionals better information, faster analysis, and clearer actions.
“The future is unlikely to be AI replacing credit professionals. It is far more likely to be AI augmenting the work of credit professionals, allowing them to make better-informed and better-supported decisions,” said Karaiskos.
A SparkOptimus benchmark shows the same problem. Only 15% of AI initiatives reach real scale. Organizations with a formal data strategy move five times as many use cases from pilot to scale, while those with a centralized AI technology stack scale twice as many.
That pattern matters for agentic finance because autonomous systems work at scale only when data, governance, and staff capability are ready before deployment. It also explains why AI for private credit cannot be treated as a plug-in tool.
AI for private credit work crosses underwriting, monitoring, pricing, client information, and risk controls, so workflows must connect the technology to each decision point. For automation in investment banking, changing the tool without changing the process can leave much of the value trapped in pilots.
GICP also found an investment gap. While 65% of respondents identified leadership and governance as a major barrier, only 38% plan to prioritize investment there. That gap could slow credit management automation even as AI use grows.
The next stage of agentic finance will depend less on how quickly organizations add tools and more on whether they can build the right operating model around them.
“In short, competitive advantage will come from combining technology with skilled professionals, effective governance and disciplined execution,” summed up in a final GICP response.
For organizations trying to improve credit outcomes, that is the dividing line. Credit management automation can support faster and better decisions, but only when AI is backed by trained people, redesigned workflows, strong controls, and clear accountability.
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