How the Financial Industry Can Leverage Agentic AI to Drive Growth

Artificial intelligence (AI) adoption across the financial industry is accelerating, with a strong focus on strategic growth. In 2023, financial services firms spent $35 billion on AI, with projected investments across banking, insurance, capital markets and payments businesses expected to reach $97 billion by 2027 — according to the World Economic Forum’s report on AI in financial services. These numbers prove that AI is already having a strong impact on the financial services industry. And, its presence presents a significant opportunity in the coming years. 

The Current Landscape of Agentic AI

EY conducted a survey to see where financial institutions are currently at with their AI-adoption strategies. It found that 77% of banks have softlaunched GenAI applications, while only 31% have moved forward with implementation of agentic AI solutions.

While GenAI is a stepping stone for financial institutions to drive back-office efficiency, agentic AI is entering the scene to drive impactful growth and revenue. This transition was discussed in a session at the Financial Brand Forum 2026 with MX’s Chief Business Officer, James Dotter, and Primitive’s Founder and CEO, Derek White.

White pointed out a key distinction between the two types of AI. Generative AI injects global intelligence into an organization, while agentic AI is designed to amplify and inject new work, acting on your behalf. He hypothesized that in the very near future, banks will have more AI agents than human employees — increasing their ability to make an impact. 

Here are a few key ways that financial institutions can find success as they step into the world of agentic AI:

Using the Right Data and the Right Models

As financial institutions assess their current AI strategies, they must focus on accuracy. This means accuracy in data and accuracy in models.

As Dotter stated, "Any model, whether it's a basic statistical algorithm or an AI agent, is only as good as the data that it ingests. If it's garbage in, it will be garbage out." Financial institutions that want to maximize their growth potential with powerful agentic AI will have to ensure that the data feeding these models is enhanced, cleansed, and usable. The foundation for any model is quality data. 

Additionally, financial providers should identify when probabilistic or deterministic models should be used — because they each have different strengths. For example, probabilistic models can be powerful tools for predicting theoretical outcomes. Alternatively, when you already know the answers, a logic-based, deterministic AI model prevents you from relying on guessing. 

Ensuring high data quality — while using the right model — will help financial providers unlock greater potential with agentic AI.

Enhancing AI Capabilities to Drive Revenue

Financial institutions that adopt agentic AI are able to enhance human capabilities to drive revenue. Agentic AI can enhance the capabilities of a single employee, putting global intelligence at their fingertips to quickly identify and act on growth opportunities.

Dotter highlighted the depth of engagement that agentic AI brings, saying, "When we start thinking about this evolution into agents, it expands the personalization in an incredible way... all of a sudden it starts to get soulful with these agents." As institutions look to bring agentic AI into their strategic planning, they’ll be able to create even more personalized experiences.

As part of the discussion, Dotter and White illustrated how this dynamic could look when done correctly. Primitive recently launched its AI agent operating system — providing financial providers worldwide with access to AI that will drive revenue. Primitive’s partnership with MX equips financial institutions with the ability to leverage MX’s Growth Agent to reduce the manual work in identifying opportunities for growth — like consumers that are ready for deposit switches. 

Making AI Work from Any Starting Place

Businesses everywhere are at different stages of AI adoption. Even if you have yet to make significant AI moves, it’s not too late to get started. But, there are key building blocks that will help you prepare for the next stage of AI. Upon laying enhanced, structured data as the foundation for successful AI adoption, the discussion followed a "crawl, walk, run" strategy — by building incrementally upon each previous stage.

• Crawl: Using agents internally
• Walk: Putting informational agents in front of customers
• Run: Opening up full transactional capabilities.

When banks and credit unions feel comfortable about building out internal agents, they will be prepared to leverage AI to make customer-facing information available. Once customer-facing AI has built trust across the customer base, institutions will be ready to deploy AI that helps consumers transact. It’s all about assessing where you are on your AI adoption journey and seeing how to get to the next phase.

Moral Responsibility and the Future

All things technology, innovation, and AI can help financial institutions build toward an economic system that benefits consumers. To close out the session, Dotter emphasized that financial institutions have a “moral responsibility” to create changes for consumers that positively impact them. It’s in providers’ hands to change the industry for the better.