Is Your RPA Still Working for Your Business?

How to Optimize Your Existing Automation Environment Before Expanding Investments in Automation and AI

In recent years, many companies have invested in robotic process automation (RPA) to automate repetitive tasks, reduce rework, and accelerate operational processes. In many cases, this journey began with a clear promise: deploy bots, save hours of manual work, and free employees to focus on higher-value activities.

However, as these initiatives have matured, new challenges have emerged. The question is no longer simply, “How many bots do we have in production?” It has become, “How much value are these bots still delivering to the business?”

This shift in perspective is important because the broader context has also changed. RPA remains highly relevant. According to Gartner, the global RPA software market grew by 14.5% in 2024, reaching $3.6 billion. The firm also identifies RPA as a core market for improving operational efficiency through tactical automation.

Today, companies with an established automation environment must address challenges across three dimensions simultaneously.

The first is cost optimization. Licenses, infrastructure, support, and maintenance continue to consume budget, even when some bots operate below capacity, support processes that have changed, or require more assistance than they should.

The second is the pursuit of greater efficiency. Business teams expect more reliable automations, fewer incidents, reduced operational dependency, and greater clarity regarding the return generated by each automated process.

The third is the pressure to adopt AI. AI agents, cognitive automation, and new intelligent capabilities are now competing for priority on corporate roadmaps. However, before expanding their investments, many companies still lack a clear understanding of how effectively their current RPA environment is being used. This is the key point: the next stage of automation does not need to start from scratch.

In many cases, the greatest opportunity lies in examining what is already in place, identifying bottlenecks, measuring performance, reducing waste, and transforming the existing automation environment into a more efficient platform for the next cycle of automation.

The objective is not to abandon RPA. It is to make RPA work better.

How DB Can Support the Optimization Journey

DB supports companies throughout the entire automation and AI journey, from initial assessment to continuous improvement.

For companies that already have an established RPA environment, this work can begin with a highly practical objective: understanding the current health of their automations, identifying opportunities for improvement, and supporting operations with greater control, governance, and predictability.

Two Offerings Addressing the Same Challenge

DB helps companies assess, support, and improve their automations with greater clarity, efficiency, and control.

Through DB Analyzer and DB Sustain, we help organizations extract more value from their existing automation infrastructure, enabling their current environment to deliver stronger business results.

DB Analyzer provides an ongoing diagnostic and performance layer for automations. Its purpose is to evaluate existing RPA solutions in a structured manner by combining technical analysis, operational impact, and improvement prioritization.

In practice, DB works alongside the client’s team to determine which bots should be assessed based on their usage and business impact. We then collect technical information and contextual data about the automated processes, review the code according to established best practices, and measure the automation’s impact on day-to-day operations.

At the end of the assessment, each automation receives a score, accompanied by clear insights into where improvements can be made to increase efficiency, stability, scalability, and long-term sustainability.

This score helps transform subjective perceptions—such as “this bot causes too many problems” or “this automation does not seem very efficient”—into a more objective framework for prioritization.

DB Analyzer also identifies issues that often remain hidden within routine support activities, including limited code modularity, insufficient logging, unused variables, fragile selectors, outdated exception handling, unnecessary complexity, and other factors that make maintenance more difficult or increase the risk of failure.

As a result, the company gains a clearer understanding of which automations should be maintained, refactored, replaced, enhanced, or prioritized for the addition of new intelligent capabilities.

DB Sustain, in turn, focuses on the ongoing support and continuous improvement of automations. It is a service package designed to ensure the efficiency and operational continuity of automated systems, built around areas such as active monitoring, code optimization, infrastructure management, incident management, and performance indicators.

This support model enables companies to manage operations more proactively and less reactively. Instead of addressing failures only after they affect the business, the model includes performance analysis, failure detection, proactive adjustments, alerts, notifications, and activity reports.

Infrastructure and license management are also essential components. In more mature RPA environments, efficiency depends not only on the bots themselves, but also on how executions are distributed, how resources are provisioned, how licenses are used, and how the orchestration platform is maintained.

DB Sustain also tracks tactical indicators that are relevant to automation management, including automation effectiveness, success rates, mean time to resolution, number of incidents, service-level agreement performance by category, Net Promoter Score, and license utilization.

These indicators help move RPA beyond a purely operational model and into a performance management model.

In Practice, DB Analyzer and DB Sustain Complement Each Other

While DB Analyzer helps answer, “What is the actual condition of my automations, and where should I prioritize improvements?”, DB Sustain helps answer, “How can I keep this environment operating with stability, governance, and continuous improvement?”

Together, these offerings create a stronger foundation for the next stage of automation.

Before investing in new bots, AI agents, or cognitive solutions, the company develops a clearer understanding of what it already has, how much value it is currently capturing, and where opportunities exist to unlock additional efficiency.

DB’s experience in hyperautomation further supports this approach. Across a range of projects, automations applied to credit processes, human resources, supply chain operations, inventory management, retail, and intelligent document processing have generated significant results.

These results include more than 1,000 hours saved per month in onboarding processes, more than 1,100 hours saved per month in credit analysis, automated reporting for more than 4,000 suppliers, and a significant reduction in payment-slip processing time within a large-scale retail operation.

These outcomes demonstrate that the value of automation does not come solely from putting bots into production. It comes from selecting the right processes, measuring their impact, ensuring stability, and continuously improving the automation environment.

Companies that have already invested in RPA have an advantage: they are not starting from scratch. They already have mapped processes, accumulated knowledge, bots in operation, and an established technology foundation.