By: Salus GRC Technology Team
Salus GRC is witnessing a sea change in the use of artificial intelligence (AI) across the financial services industry. Increasingly, firms are moving from an AI experimentation phase into the active scaling of internal AI systems and use cases. Organizations that previously restricted access to basic tools such as ChatGPT to a small group of employees are now implementing tailored AI processes, or “agents,” across their entire organizations.
Quite simply, companies are no longer just experimenting with AI—they are beginning to scale it.
What Is Agentic AI?
The industry has quickly moved beyond single-problem solutions powered by large language models (LLMs), such as ChatGPT or Claude. While these tools still serve important purposes, many organizations have encountered their limitations and begun asking a broader question: How can we connect more systems, automate more decisions, and accomplish more work?
This shift has given rise to agentic AI. At a basic level, agentic AI systems combine the reasoning capabilities of large language models with access to multiple tools and data sources. By “understanding” how to use these tools, specialized AI agents can develop a plan of action and execute a series of steps to complete a task—often with little to no human intervention.
These autonomous systems represent the next major wave in AI adoption and have the potential to help organizations scale faster while realizing greater returns on their AI investments.
McKinsey recently found that many organizations are already experimenting with AI agents.¹
McKinsey noted that 23% of respondents reported that their organizations are already scaling an agentic AI system somewhere within the enterprise—that is, expanding deployment and adoption within at least one business function. An additional 39% indicated that they have begun experimenting with AI agents.
The implications of this shift are significant. For years, AI adoption largely focused on employee-level productivity tools—providing limited groups with access to GPT models, embedding lightweight automation into workflows, or running isolated proofs of concept. Today, organizations are evaluating AI through a broader strategic lens. Rather than asking whether AI can accelerate a single task, leaders are increasingly focused on how AI can transform enterprise-wide operations.
Agentic AI should not be viewed as a force that is adverse to employees. Instead, it acts as a force multiplier—allowing individuals with deep institutional knowledge to parallelize their work, scale their impact, and operate more effectively.
This shift, highlighted by McKinsey, is meaningful. It signals that AI scaling is no longer confined to early adopters or technology-forward organizations. It is occurring across real industries, within real companies, and with real operational consequences. The window for delaying adoption is closing rapidly.
Salus GRC recommends that as firms expand their AI use-case development, they think in terms of intelligent workflows while closely monitoring the rapid evolution of agentic AI. Organizations that have not yet begun their AI journey, or prepared for agentic AI, risk falling behind industry peers—leading to higher long-term costs or delays in bringing products and services to market.
Sources
¹ McKinsey & Company, The State of AI 2025, November 2025