Artificial intelligence has quickly become one of the most debated and considered technologies in banking today. From customer service chatbots to workflow automation, financial institutions have moved beyond asking whether they should implement AI to evaluating how it can improve efficiency, reduce costs, and address growing operational demands.

The conversation has now shifted toward agentic AI. The promise of this technology is compelling. Unlike traditional automation, agentic AI is designed to execute work, make decisions, and coordinate actions across business processes. Yet as interest grows, so has a new concerning trend.

Often referred to as “agent washing,” conventional automation tools, scripted workflows, or AI-powered interfaces are being misleadingly marketed as autonomous agents, despite lacking the capabilities required to operate independently in complex business environments. Industry analysts note that many solutions promoted as agentic AI function more like advanced assistants than truly autonomous systems. For community and regional banks, understanding the difference between the two is becoming increasingly important to achieving operational impact.

Despite mounting market interest, most banks are not investing in AI simply to adopt the latest technology. They are looking for practical solutions to staffing challenges, increasing compliance obligations, growing fraud activity, and rising operational complexity.

With activities like dispute investigations, fraud reviews, or compliance workflows, a chatbot can answer basic questions, while a rules-based platform may route simple tasks. However, the greater opportunity lies in technologies that can manage work across an entire process. A more mature system should be able to evaluate information from multiple sources, respond to changing circumstances, coordinate activities across teams, and help move work toward resolution.

For banks already operating with leaner teams and tight budgets, distinguishing between task automation and true process execution is especially critical. Adopting a solution incapable of delivering its marketed level of autonomy often results in paying higher prices for capabilities that deliver limited value, which can cause initiatives to ultimately fail. Gartner projects that more than 40% of agentic AI initiatives will be abandoned by the end of 2027 as organizations struggle to demonstrate business value, control costs, and manage risk.

 

While being able to spot agent washing is an important first step, banks must also determine where a solution falls on the AI maturity spectrum. Four areas can help frame this evaluation:

Structured Automation represents the foundational level, where systems follow predefined rules to perform repetitive tasks like routing requests or assigning work queues. They operate reliably but cannot move beyond their programmed ruleset or defined parameters.

Hybrid Intelligence combines automation with AI-driven analysis. These systems can review information, identify patterns, and generate recommendations, but employees remain responsible for all key decisions and execution.

Adaptive Systems are more advanced and can respond to changing conditions within a process. Rather than relying solely on predefined responses, they evaluate context and adjust actions when exceptions arise.

Collaborative Networks represent the highest level of maturity. These systems have autonomous orchestration, coordinating activities across multiple workflows, systems, and stakeholders while operating within established governance controls.

Understanding where a solution falls within this framework helps institutions align expectations with realistic capabilities while avoiding higher costs and stunted outcomes. Banks should focus less on how a solution is marketed and more on how it actually performs in production environments. They should ask key questions like whether the system can execute multi-step workflows with limited intervention, adapt to shifting conditions, coordinate work across departments, and maintain transparency and auditability throughout the process. These capabilities reveal far more than product labels or polished demos.

As AI becomes more involved in operational execution, governance becomes increasingly important. Regardless of the level of automation deployed, institutions remain accountable for compliance, risk management, customer protection, and audit readiness. While greater autonomy can help with efficiency, it does not reduce those responsibilities.

One of the biggest risks of agent washing is evaluating technology based on demonstrations rather than outcomes. Many AI solutions perform impressively in controlled environments. However, banking involves operating across complex multiple systems, regulatory requirements, documentation standards, exception handling, and audit expectations. These needs can vary greatly even between similar institutions.

For community banks, trust and accountability remain key competitive differentiators. Technology initiatives should therefore be evaluated not only on efficiency gains but also on their ability to support transparency, oversight, and control. Successful AI deployments combine automation with structured governance, clear policies, and auditable workflows. Organizations that embed this into operational design will be better positioned to responsibly scale AI initiatives.

Agentic AI has the ability to transform so many banking functions, ranging from fraud operations and dispute management to compliance and customer service. Yet realizing that potential requires a clear understanding of how a solution actually works.

The institutions most likely to benefit will be those that evaluate technologies based on operational performance rather than marketing claims. In an industry built on accountability and trust, the most important question for agentic AI is whether the technology can reliably deliver results while maintaining the oversight and control that banking requires.

About Author:
Paresh Ashara, VP - Data Analytics, AI & Automation at Quinte