From Business Challenge to Agentic AI: A 95% Efficiency Gain in Less Than 60 Days

Jun 12, 20254 mins read

Artificial Intelligence is no longer aspirational—it’s fully operational. Across industries, executive teams are moving beyond exploratory pilots and into targeted, outcomes-focused AI deployments. At the core of this shift is a practical question: how can AI be applied quickly, responsibly, and effectively to solve high-impact business challenges?

Argano recently partnered with a leading Fortune 100 media and family entertainment enterprise to answer that question in real time. In less than two months, the team designed and implemented an AI-powered proof of concept (PoC) that delivered a 95%+ reduction in resolution time for a high-volume IT support issue—unlocking the potential for over $100 million in annual productivity savings.

A widespread challenge with a measurable cost

The initial challenge was a common one, familiar to any large organization: employee access issues. Password resets, lockouts, and registration delays accounted for over 90,500 internal IT support tickets within a 12-month span. While each ticket may seem minor in isolation, the aggregate impact was staggering.

Tickets remained open for an average of 10.5 working hours—equivalent to more than a full business day lost per incident. At an average internal hourly rate of $110, each support delay represented roughly $1,155 in lost productivity. This translated to over $104 million in soft cost impact annually—excluding the time, resources, and overhead required to manage the help desk itself.

This was not a technology inconvenience. It was a business problem at scale. Rather than address it with sweeping changes or overly generalized AI models, Argano took a surgical, business-aligned approach to design a targeted proof of concept that could demonstrate results quickly and serve as a foundation for future AI growth.

A business-first approach to AI

The team began by identifying the two most frequent ticket types—password resets and registration issues—as candidates for AI acceleration. These were consistent, repetitive, and relatively low risk, making them ideal for automation. The goal was simple but impactful: reduce average resolution time by at least 10%.

Instead of deploying a chatbot or relying on large datasets to “train” an AI model, Argano used a different approach—Agentic AI. This system architecture is designed around conditional logic and graph theory, enabling the AI to navigate complex workflows using business-defined logic rather than probabilistic outputs. This approach addressed a common barrier in AI initiatives: the lack of usable or structured data.

By avoiding dependency on historical ticket data, Argano dramatically accelerated development. The system’s deterministic nature ensured predictable, explainable behavior—critical for trust, compliance, and user adoption. By modeling the process on actual business workflows, the solution could mirror real-world decision-making paths with speed and accuracy.

An intelligent system that works on day one

The proof of concept was deployed as a web-based application. Users interacted with a smart interface that dynamically adjusted based on responses, guiding them toward resolution in a matter of seconds. Each interaction was logged, analyzed, and fed back into the system to support future optimization. This created a virtuous cycle: more use led to better results, without sacrificing control or oversight.

One of the core differentiators of the Agentic AI approach is that it delivers business value immediately while remaining extensible. As the system matures, additional functionality—such as conversational interfaces, automated resolution recommendations, and proactive agents—can be introduced in phases. The long-term vision is a self-evolving AI ecosystem that supports human agents and, in many cases, resolves issues before they ever become visible to IT support.

In its initial deployment, the system reduced average resolution time from 15–20 minutes per ticket to just 17 seconds to one minute. This 95%+ reduction in time created instant ROI, both in recovered employee productivity and reduced load on the support desk.

From proof of concept to scalable transformation

While the outcome of this PoC was exceptional, the broader implication is even more important: AI can be deployed quickly and responsibly to solve real business problems. The success of this initiative was not due to experimental algorithms or speculative tools—it was the result of applying a clear business lens to AI development.

This is the core of Argano’s approach. Experienced AI consultants work with organizations to identify discrete, high-value opportunities, then builds AI solutions that align with workflows, governance models, and long-term strategy. Rather than relying on abstract experimentation, Argano helps clients operationalize AI at the pace and scale that enterprise demands.

Starting with the right use case

Every enterprise has friction points—repetitive processes, manual tasks, or unresolved inefficiencies—that are ripe for AI intervention. The question is which challenge to tackle first.

Argano offers an Agentic AI Maturity Assessment to help organizations evaluate readiness, prioritize use cases, and define a path from idea to production. Whether the goal is cost reduction, productivity gains, or better service delivery, Agentic AI provides a framework for sustainable, scalable automation.