Agentic AI is everywhere in today’s technology conversation. The concept is gaining enormous attention, but the real understanding of what it means and what it will realistically do for financial institutions often lags behind the hype.
In this post, our CEO, Slaven Bilac, shares how he thinks about the rise of agentic AI and what financial institutions should realistically expect from it. Before founding Agent IQ, Slaven led Google Cloud’s AI business, where he worked closely with organizations adopting AI at scale. Drawing on that experience, he outlines practical steps financial institutions can take today to prepare for the next stage of AI innovation.
What agentic AI actually means
Agentic AI represents one of the most discussed advancements in artificial intelligence today. More accurately, it reflects a vision of where AI capabilities are heading.
The idea is straightforward. Instead of AI simply following prescriptive instructions, it becomes a goal-oriented reasoning system. In theory, this type of AI can determine what needs to be done, identify the available tools or information, and figure out how to accomplish a task.
If you look at the current technology landscape, it may seem like everyone is already doing this. In reality, most implementations are still far from fully agentic systems.
There are many stages an AI implementation must go through before it becomes truly agentic. In some industries, including financial services, a purely goal-oriented approach is not always desirable.
Take a simple example such as transferring funds between accounts. In this case, you do not want AI to figure out how to complete the task. You want it to follow a defined, compliant procedure that mirrors the standard operating processes your staff already follow.
In many cases, the future of AI in these workflows will look less like autonomous reasoning and more like an evolution of robotic process automation (RPA). The key difference is that instead of requiring complex scripting and technical configuration, AI systems will be able to observe workflows or review operating procedures and learn how to execute them correctly.
Rather than inventing new approaches, the AI follows the institution’s established rules.
How agentic AI may actually work in practice
Today this still sounds somewhat futuristic. However, rapid advances in underlying technologies, particularly large language models (LLMs), are making this vision increasingly plausible.
New capabilities such as computer-use and browser-use tooling allow AI systems to interact with applications and websites much like a human employee would. With the right permissions and roles assigned, an AI system can combine vision, reasoning, and language generation to navigate systems and complete tasks.
In practice, this means AI can be provisioned similarly to an employee. It receives access to specific systems, operates within defined permissions, and carries out tasks across multiple applications.
The difference is that it can do so faster, continuously, and without fatigue.
The real question: how institutions should prepare
More powerful AI capabilities are clearly on the horizon. That part is not new.
The more important question is what financial institutions should be doing today to prepare for this next stage of AI deployment.
1. Create or update your AI policy
Every institution should have a documented framework that outlines how AI can be used responsibly.
This policy should address compliant, ethical, and secure use of AI across the organization. It should also define appropriate human oversight and specify when AI can act independently versus when human approval is required.
Importantly, this document should not be static. As tools evolve and usage expands, the policy should evolve with it. Early versions do not need to be lengthy or complex. They simply need to establish clear principles.
2. Clean and organize your data
AI can still provide meaningful value even when data is incomplete or imperfect. However, the quality of outcomes improves significantly when the data available to AI is reasonably structured and well organized.
Fortunately, modern AI systems require far less preparation than traditional data systems. Tasks like manual spell correction or extensive normalization are often no longer necessary.
Still, there are limits.
A simple rule of thumb applies. If a task would be difficult for a well-trained person to interpret because the data is too messy or inconsistent, it will likely be difficult for AI as well.
Improving data accessibility and organization today will dramatically increase the value AI can deliver tomorrow.
3. Prepare an API layer for AI access
As AI tooling evolves, new standards are emerging that make it easier for AI systems to interact with applications and data.
One important example is Model Context Protocol (MCP), which allows AI systems to access functionality through APIs. By exposing specific APIs and carefully controlling permissions, institutions can determine exactly what data AI can access and what actions it can perform.
For example, some APIs may allow read-only access, while others may permit write operations under defined conditions.
This layer becomes a crucial control point. It enables AI to work with your data while ensuring the institution retains governance over how that access is used.
4. Identify high-value use cases
Not every AI project will produce meaningful results. Identifying the right initial use cases is critical.
Focus on areas where AI can improve efficiency, reduce friction, or enhance decision-making without introducing unnecessary operational risk. Starting with well-defined use cases helps institutions learn how AI performs within their environment before expanding into more complex deployments.
5. Start small and iterate quickly
AI capabilities are evolving rapidly and will continue to do so.
Because of this, long multi-year planning cycles are rarely effective. It is nearly impossible to anticipate every potential capability or use case in advance.
A more practical approach is to begin with small, well-scoped deployments and iterate quickly based on real-world experience.
The institutions that will benefit most from AI are not necessarily the ones with the most elaborate long-term plans. They are the ones that build internal knowledge, test thoughtfully, and continuously adapt as the technology evolves.
Looking ahead
Agentic AI will likely become a powerful new layer of automation across many industries.
For financial institutions, the path forward will not be about replacing processes with autonomous reasoning systems overnight. It will be about combining AI capabilities with the governance, compliance, and operational rigor that financial services require.
Institutions that start preparing today by establishing policies, improving data accessibility, and building AI-ready infrastructure will be in the best position to safely and effectively deploy these technologies as they mature.
The future of AI in financial services will not be defined by hype. It will be defined by thoughtful, responsible implementation.
