Mapping Human-System Interactions to Fuel Agentic AI Automation

At the Banking Transformation Summit event, understanding the granular mechanics of internal workflows emerged as a primary requirement for successful digital overhauls. Guy Mettrick, Director of Business Development – Financial Services at Skan AI, outlined how the company anchors its technology at the intersection of human capital and core technology. Specifically, Skan AI supports the banking transformation journey by analyzing the precise interaction of people with the various software systems they use across different business processes.

Mettrick emphasized that establishing a transparent, data-driven understanding of how work is actually done today is the critical first step for any optimization initiative. Once a bank maps these workflows, it unlocks a massive array of transformation opportunities, including the deployment of autonomous agentic AI to systematically automate manual, repetitive tasks. Skan AI provides both the foundational discovery information and the digital agents required to safely navigate this automation journey.

Unpacking the Complexity of Desktop Workflows

Modern banking environments are characterized by intense operational complexity, with employees frequently managing multiple manual handoffs throughout their day-to-day roles. Internal workflows rarely take place within a single application; instead, staff members routinely hop across disparate technology layers, including:

• Legacy mainframe infrastructure

• Modern web applications

• Standard desktop productivity tools like Microsoft products

Skan AI addresses this multi-layered environment by tracking work seamlessly across all of these different systems, team functions, and end-to-end banking processes. Over the last eight years, the company has perfected the technical capability to capture this continuous interaction data.

By creating a highly accurate digital map of human operational behavior, this captured information is transformed into a powerful training asset for agentic AI models. Rather than relying on theoretical process maps, financial institutions can feed their agentic models real-world operational data, allowing organizations to take a confident step forward and deploy these models far more effectively within their active business environments.

Eradicating Exceptions and Operational Failure Rates

Enabling banks to achieve transformation today requires moving past basic process monitoring and delivering measurable operational resilience. When banks rely on manual engineering or abstract rule sets to program automation tools, they frequently suffer from high exception rates and broken workflows because the software fails to account for real-world nuances.

Skan AI’s training approach solves this issue at the data level. Because the resulting AI agents are trained on years of precise human-system interaction data, they inherently comprehend exactly how work is successfully achieved within the context of that specific organization.

When deployed into live banking workflows, these well-trained agents execute automated tasks with a deep awareness of operational dependencies. This comprehensive understanding yields immediate, tangible business benefits, resulting in far fewer failure rates and a drastic reduction in operational exceptions across asset-servicing, retail, and commercial banking pipelines.

Key Highlights from Guy Mettrick:

• Human-to-System Mapping: Mettrick explains that Skan AI focuses heavily on analyzing how bank employees interact with software tools across end-to-end corporate processes.

• The Automation Catalyst: Understanding current operational habits serves as the critical baseline required to identify and execute agentic AI automation opportunities.

• Navigating System Complexity: The technical capacity to track employee workflows across diverse software layers, running from legacy mainframes to web apps and Microsoft products.

• AI Training Agents: Transforming tracked desktop behaviors into rich, contextual data sets used to train highly specialized agentic AI models.

• Eight Years of Optimization: Skan AI has spent nearly a decade perfecting its process-mining and workflow-capture technology specifically for enterprise deployment.

• Dramatically Lower Failure Rates: Training autonomous models on real-world human behavior ensures the resulting digital agents execute workflows with minimal exceptions and errors.