Every company, regardless of its industry or size, operates through processes that define how it creates value. Just think of invoicing, order management, the many micro-activities that make up the order-to-cash and procure-to-pay cycles, and relationships with customers, suppliers, and supply chain partners.
The complexity of these processes can vary significantly, but one element is common to all of them: the more complex a process is, the more likely it is to include repetitive activities, standardizable steps, and points where human intervention adds little real value.
It is precisely from this observation that, several years ago, the concept of process automation (today at the center of the technology debate) emerged as a response to the need to make processes more efficient, faster, and easier to control.
KEY POINTS
- Business process automation is a key driver of efficiency and competitiveness, as it reduces time, costs, and errors while improving control and operational quality.
- Automation initially focused on repetitive, rule-based activities and has gradually expanded to include process phases that require interpretation and decision support.
- Technological evolution has been continuous: from traditional integrations to robotic process automation, up to the most recent models based on Agentic AI.
Process automation: what it means and how it works in 4 steps
Automating a process means analyzing how it works, understanding its phases, dependencies, control points, and interactions between people, systems, and data, and then intervening where standardized activities exist.
Let’s look at how it works, step by step.
Process knowledge (assessment)
Automation starts with process knowledge. Before taking action, companies need to understand how work is actually carried out. To achieve this, there are sophisticated analytical methodologies and tools, including process mapping techniques, application log analysis, and process mining platforms, that enable the reconstruction of process behavior from data.
Analysis of inefficiencies
Understanding how a process works, even when it is complex, makes it possible to identify inefficiencies. A flow can be considered optimizable when it shows certain characteristics summarized through specific KPIs: long cycle times, bottlenecks, repetitive manual steps, frequent errors, or the need for data reprocessing.
Process Reengineering
Once critical points have been identified, the next step is not automation, but process redesign. Many organizations have realized that automating an inefficient flow simply means making an existing problem faster. This is where reengineering comes in: unnecessary steps are eliminated, decision rules are simplified, responsibilities are clarified, and the number of exceptions is reduced.
Process Automation
At this stage, it becomes possible to identify the areas most suitable for automation. As mentioned, these typically include repetitive activities based on clear rules, with structured data and a limited number of variables: data entry and transfer between systems, checks, reconciliations, notifications, updates, document generation, or workflow activation. These are all operations that are essential for the company’s functioning but rarely require creativity, judgment, or complex decision-making capabilities. For those cases, AI (or rather, collaboration between professionals and AI-based systems) comes into play.
The evolution of process automation techniques: from RPA to Agentic AI
Business process automation is the result of a gradual evolution that has closely followed the development of information technologies. Over time, tools, architectures, and implementation methods have changed, but the objectives have largely remained the same:
- Increase efficiency by reducing operational time and costs;
- Improve workforce productivity;
- Reduce errors;
- Make processes more controllable and traceable;
- Ensure compliance with internal policies and regulations;
- Build a stronger and more organized corporate image;
- Achieve smoother relationships with customers, suppliers, and partners.
The potential of automation has always been highly significant. Not surprisingly, it has absorbed a substantial share of IT investments over time and contributed to driving the evolution of the technologies themselves.
Before RPA: complex, expensive, and rigid integrations
In the early stages of process dematerialization and digitalization, automation meant acting directly on information systems by creating custom integrations. ERPs, management systems, document management platforms, and vertical applications were connected through tailored developments, often lengthy and expensive.
The main issue was that these systems were not designed to communicate easily with one another, as they do today through APIs. Differences in data formats, application logic, and interfaces made each integration a project in its own right, with long implementation times and costs that were difficult to sustain—especially for processes that changed frequently.
Robotic Process Automation, the turning point: automating without changing systems
The introduction of Robotic Process Automation represented a decisive shift in how process automation is approached. The underlying idea is simple: instead of integrating systems, software can be used to replicate the actions a person performs on application interfaces.
A software robot can access a management system, copy data, enter it into another system, fill in forms, generate documents, send emails, perform formal checks, and update statuses by following predefined rules. In practice, the system operates as a user would, but faster, continuously, and without errors caused by distraction or fatigue.
This approach has made automation far more accessible. Implementation times have decreased, initial costs have become sustainable, and many repetitive activities previously excluded have entered the automation scope.
Robotic Process Automation, which continues to be a cornerstone of process automation, has proven particularly effective for high-volume, rule-based operations such as data entry, reconciliations, and formal checks. Since, as noted, the first automatable areas of any process are the routine ones, it is clear why RPA quickly found such a wide field of application.
Alongside its undeniable strengths, RPA has traditionally had limitations. Software robots work well in stable contexts with clearly defined rules, but struggle with discretionary situations, unstructured data, or cases requiring interpretation. Moreover, automating individual activities does not equate to optimizing the entire process: in many cases, operational phases are accelerated, but real benefits emerge only when RPA is embedded within a broader vision capable of governing and orchestrating the end-to-end flow.
From RPA to Intelligent Automation: automation learns to decide
In recent years, advances in AI techniques have opened a new phase in process automation, extending it to contexts where it is necessary to interpret information, classify content, identify anomalies, or make data-driven decisions.
In this scenario, the term Intelligent Automation is increasingly used to describe an approach that combines technologies such as RPA, data analytics, machine learning, generative AI, NLP, and many others, in order to act not only on individual operational tasks, but on broader portions of the process.
As a result, not only does the scope of automation change, but so does its objective, shifting toward the ability to orchestrate complex flows, manage exceptions, and support real-time decision-making.
Within this context, the hyperautomation paradigm has emerged. It is not a technology, but a systemic approach to automation: processes are broken down into their different phases, and within the same flow, different technologies and solutions can coexist, each applied where it generates the greatest value. This is what happens, for example, in complex processes such as the order-to-cash (O2C) cycle, where operational activities, controls, document interactions, and decisions must be managed in a coordinated way, while maintaining an overarching view and overall governance of the process.
Agentic AI for process automation
This evolutionary path also includes what is known as Agentic AI, which can be considered part of the broader hyperautomation paradigm. It is not a break from what has come before, but rather a new way of integrating AI into business processes, including core ones.
Intelligent, specialized software agents are emerging, designed to perform very specific tasks within processes. These agents can be integrated into complex workflows to handle activities that, until a few years ago, required the exclusive involvement of experienced professionals: analyzing documents, classifying information, generating structured content, or triggering actions based on context.
This does not mean that processes become fully autonomous or that the human role disappears. While it is impossible to predict how far this transformation will go, what we observe today is a model in which AI, LLMs, and generative systems create the greatest value when embedded in human-in-the-loop approaches—that is, when they remain tools that support those who hold expertise, experience, and responsibility for the process.
The Kirey approach to process automation
Making companies more efficient, increasing control, and improving the ability to manage complex activities: these are the goals that guide Kirey in designing and developing systems and solutions for process automation, integrating technological, methodological, and business expertise.
Our strength lies not only in our technological capabilities, but also in our consulting mindset. As we have seen, automating a process does not simply mean speeding up what already exists; it means analyzing it, understanding it, simplifying it, and, when necessary, redesigning it. This is work that requires experience, method, and a concrete understanding of operational dynamics.
Over the years, we have supported large companies across different industries, tackling complex processes and articulating organizational contexts. This has allowed us — and continues to allow us — to intervene not only on tools, but on the entire process evolution journey, identifying the areas of greatest impact and the technologies best suited to generating value.
Contact us to find out how we can help make your company more efficient, modern, and resilient in the market.
