Artificial intelligence is entering a new phase in financial services, moving beyond analysis and automation into execution. Known as agentic AI, this next-generation of technology is emerging, and while earlier tools focused on generating insights, this new class of systems can take action, manage workflows, and support decision-making in real time.
In an all-encompassing guide, dynamic AI decisioning platform Earnix, has outlined the potential for insurers and banks, and how exactly this technology moves beyond providing insights to actively carrying out tasks.
Moving from insight to action
Agentic AI refers to systems that can act independently to achieve defined goals. Unlike traditional AI tools that generate responses or predictions, these systems are designed to take action, make decisions, and manage workflows with limited human input.
Earnix highlights several defining features of agentic AI. These include autonomy, goal-driven behaviour, adaptability, and the ability to interact with both humans and other systems.
In practical terms, this means AI can move from answering questions to actively supporting day-to-day business processes.
Beyond large language models
Large language models, or LLMs, have become widely used across financial services. They are effective at generating content, answering queries, and summarising information. However, their role is largely limited to providing knowledge.
Agentic AI builds on this foundation by applying that knowledge to real tasks.
Earnix compares the difference to that between an encyclopedia and an advisor. While an LLM can explain a product, an AI agent can recommend suitable policies, calculate financial scenarios, send reminders, and prepare client materials.
This distinction highlights a broader shift in how AI is being used across financial services.
Supporting insurers and advisors
For insurers, managing customer portfolios involves constant monitoring of renewals, claims, and coverage needs. Agentic AI can automate many of these processes.
According to Earnix, AI agents can track policy renewals, identify cross-selling opportunities, and flag when a customer may need adjustments to their coverage. In claims scenarios, they can assist by preparing documentation, suggesting next steps, and estimating outcomes based on historical data.
Bank advisors face similar demands. Agentic AI can help by monitoring accounts, analysing market conditions, and identifying risks or opportunities within client portfolios.
This allows advisors to spend less time gathering and processing data, and more time focusing on client relationships and strategic decision making.
Where agentic AI delivers the most value
Earnix notes that agentic AI is particularly effective in complex workflows. These include processes that involve multiple data sources, ongoing monitoring, and iterative decision making.
For simpler tasks, traditional automation may still be sufficient. However, as workflows become more dynamic, AI agents can take on tasks that would otherwise require significant manual effort.
Navigating regulation and compliance
As adoption increases, regulation remains a key consideration. In Europe, financial institutions must align with frameworks such as the EU AI Act, the Digital Operational Resilience Act, and the Insurance Distribution Directive.
Earnix points out that organisations may need to adopt structured AI governance frameworks to meet these requirements and ensure responsible deployment.
A tool to support, not replace
While the idea of autonomous AI raises questions, Earnix positions agentic AI as a tool to support professionals rather than replace them.
By automating routine work and delivering real-time insights, AI agents allow advisors to focus on higher value activities such as client engagement, problem solving, and building trust.
As digital transformation continues across financial services, the adoption of agentic AI appears increasingly likely. The challenge for organisations will be how quickly they can integrate these capabilities to remain competitive.
Read the full guide from Earnix here.
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