The Agentic AI Execution Gap: Why Banks and Insurers Will Win or Lose on Integration

Por Maneeza Malik, Product Marketing Director

Banks and insurers are entering a new phase of AI adoption. After years of experimenting with predictive models, chatbots and generative AI, many financial institutions are now evaluating agentic AI. These systems are capable of reasoning, making decisions and executing tasks across business processes with limited human intervention.

The potential is significant: AI agents can help automate complex workflows, reduce manual effort, improve operational efficiency and accelerate customer-facing processes. Applications span customer service, fraud investigations, lending, underwriting, claims management, finance, HR and compliance.

The technology itself is advancing rapidly. According to Gartner, by 2028, 33% of enterprise software applications will incorporate agentic AI, up from less than 1% in 2024 — while 15% of day-to-day work decisions will be made autonomously by AI agents.

Yet as organizations move from experimentation to production, many are discovering that intelligence alone is not enough. AI agents only create value when they can access data, interact with enterprise systems and execute actions across end-to-end business processes.

In financial services and insurance, where workflows often span hundreds of applications and regulatory controls, this “last mile” of execution is frequently the biggest barrier to scale.

The question is no longer whether institutions will adopt agentic AI. It is which organizations will successfully operationalize it across the enterprise. And increasingly, integration is emerging as the critical link between AI capability and business outcomes.

The Agentic AI Execution Gap in Financial Services

The promise of agentic AI isn’t just better insights. It is the ability to execute work.

Most financial institutions and insurers have already demonstrated that AI can analyze documents, summarize information, identify patterns and recommend actions. These capabilities are increasingly becoming table stakes. The challenge begins when organizations attempt to move from isolated use cases to production-scale deployment.

Unlike traditional software, AI agents rarely operate within a single application. To complete meaningful work, they must interact with data sources, business applications, workflows and decision-making systems across the enterprise.

To do this, AI agents need access to data, systems, workflows, policies, and business context. Without it, even the most advanced models operate with an incomplete understanding of the environment and limited ability to drive outcomes.

This is where many initiatives stall.

A lending workflow, for example, may involve customer onboarding systems, document repositories, credit bureaus, underwriting platforms, core banking applications, compliance controls and approval processes. An AI agent may be capable of evaluating an application and identifying risk factors, but completing the process requires coordinated access to every system involved.

The same challenge exists across claims processing, fraud investigations, customer service and financial operations. Intelligence may exist, but execution becomes difficult when the underlying systems are fragmented.

Common obstacles include:

• Data distributed across legacy and cloud environments
• Business processes spanning multiple applications
• Inconsistent security and permission models
• Limited visibility into agent actions and outcomes
• Manual handoffs between systems and teams
• Regulatory requirements that demand oversight and traceability

As a result, many financial institutions find themselves with AI agents that can recommend actions but cannot reliably complete end-to-end business processes.

This gap between intelligence and execution is becoming one of the defining challenges of enterprise AI adoption.

Why Agentic AI Needs Integration to Deliver Value in Banking

For decades, integration has been viewed primarily as an IT function. In the era of agentic AI, it has become a strategic business capability.

Consider a fraud investigation. Detecting suspicious activity is only the first step. Resolving the issue may require retrieving transaction histories, reviewing customer records, checking sanctions screening systems, creating case files, notifying investigators and documenting actions for regulatory reporting.

The value is not generated by identifying a potential issue. It is generated by moving the investigation forward. That requires connectivity across systems.

The same principle applies to customer service. An agent assisting an insurance policyholder may need to retrieve policy information, access claims records, verify payment status, update account details and coordinate follow-up communications. The customer experience depends not only on the quality of this interaction, but also on the ability to execute actions in real time.

This is why integration has become the last mile of agentic AI.

Organizations have invested heavily in data platforms, analytics and AI models. However, business value is ultimately realized when AI can operate within existing processes and translate decisions into actions. Integration provides the connective tissue that enables this transition from intelligence to execution.

Without it, AI remains confined to isolated use cases. With it, organizations can begin to automate complete business processes rather than individual tasks.

Governance Cannot Be an Afterthought

Execution alone is not enough. Financial institutions and insurers operate in some of the world’s most heavily regulated environments. Any technology that influences customer outcomes, financial decisions or operational processes must meet strict requirements for transparency, accountability and control.

This becomes even more important as AI systems gain greater autonomy.

Organizations need confidence that agent actions align with internal policies, regulatory requirements and risk management frameworks. They must understand how decisions were made, what information was used and whether appropriate controls were followed throughout the process.

Questions such as these become increasingly important

• Why was a particular action taken?
• What data informed the decision?
• Were policies applied consistently?
• When was human review required?
• Can the process be audited after the fact?

These questions are more than just compliance concerns. They are trust requirements.

Without clear governance mechanisms, organizations may hesitate to deploy AI agents in high-value processes. Conversely, when policy enforcement, monitoring, observability and auditability are built into operational workflows, institutions can scale automation with greater confidence.

The organizations that succeed with agentic AI will not be those that move the fastest. It will be those that establish the controls necessary to move responsibly.

The Future of Autonomous Finance

The conversation around agentic AI often focuses on what the technology can do. Increasingly, the more important question is what organizations can operationalize.

Most financial institutions and insurers will have access to increasingly capable AI models. Competitive advantage will come from how effectively those capabilities are integrated into business operations.

Organizations that can connect AI agents to enterprise systems, workflows and governance frameworks will be better positioned to accelerate decision-making, improve customer experiences, increase operational efficiency and reduce manual effort across the organization.

Those that fail to do so will continue to face a familiar challenge: promising AI initiatives that deliver limited business impact beyond the pilot stage.

Agentic AI represents a significant opportunity for financial services and insurance, but realizing the potential benefits requires more than intelligent models. It requires the infrastructure that enables those models to work within the complexity of enterprise environments.

Jitterbit Harmony: Connecting AI to Enterprise Execution

As adoption accelerates, integration is emerging as the critical bridge between AI potential and business outcomes. Banks and insurers that build that bridge successfully will be the ones that translate AI innovation into measurable results at scale.

This requires a foundation that enables systems, data, workflows and governance processes to operate together.

Jitterbit Harmony provides that foundation through a unified, AI-infused low-code platform that brings together:

• iPaaS
• Data and system integrations
• Full lifecycle Gestión de API
• Application development
• AI assistants and agents
• And most of all, complex workflow and AI agent orchestration at scale — all in a single environment

Rather than relying on fragmented point-to-point connections, organizations can establish a modern integration architecture that supports real-time communication across enterprise systems. This includes core banking platforms, insurance systems, CRM applications, ERP environments, data repositories and more. By connecting these environments, Harmony enables AI-driven processes to operate across the enterprise rather than within isolated applications.

For banks and insurers looking to move beyond experimentation and into production, Harmony provides a comprehensive environment to build, deploy, manage and orchestrate AI agents across the enterprise. Its low-code capabilities accelerate AI agent development by simplifying workflow design, system integration and agent lifecycle management, reducing the need for extensive custom coding.

Financial institutions and insurers can rapidly create AI agents that interact with core banking systems, policy administration platforms, claims processing applications, customer service tools and enterprise data sources. These agents can be embedded within business processes, collaborate with other agents and human users, and execute actions across connected systems in real time. This helps ensure organizations can automate complex workflows while maintaining operational visibility and control.

Equally important, comprehensive AI governance, compliance and security controls are embedded throughout the platform. Monitoring, audit trails, and policy enforcement help organizations scale automation initiatives while maintaining regulatory compliance.. And centralized management capabilities enable organizations to govern agent behavior, track performance, and ensure AI-driven processes remain aligned with business, risk and regulatory requirements

Jitterbit Harmony has achieved Certificación ISO 42001, along with several other security certifications critical for regulated industries. Our industry-leading focus on security ensures that agents can be given the access they need to be successful without additional risk.

The result is a more secure and reliable operating environment for agentic AI — one that supports both innovation and enterprise requirements.

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