Rewriting processes executed by Humans, Robots and AI Agents

For decades, quality managers, process managers, ISO certification officers, continuous improvement analysts, and other specialized professionals have relied on solid methodologies such as Lean, Six Sigma, and SIPOC to document, analyze, and optimize their organizations’ operational flows. However, these methodologies were created under a fundamentally human premise: every task was assigned exclusively to people.

That reality has radically changed. Today, the arrival of technologies such as Robotic Process Automation (RPA), advanced automations, and Artificial Intelligence (AI) agents has not only revolutionized the practical execution of many activities, but also raises serious questions about the traditional way in which business processes are designed, documented, and improved.

This article seeks to address this new context in a profound, critical, and professional manner, offering managers and leaders of process and quality areas a clear vision of how to adapt to this new operational environment. The key question we pose is: are they truly updating and redefining their processes according to this new landscape, clearly identifying which tasks should be performed by humans, which should be executed by robots, and which by AI agents?

Currently, many quality and process departments limit themselves to documenting procedures using traditional formats—such as printed diagrams, manuals, or static files—ignoring the urgent need to use standard languages such as BPMN (Business Process Model and Notation) and advanced technological platforms like Appian, UIPath, or Interfacing for comprehensive process automation and orchestration.

Additionally, process managers and leaders should position themselves as true agents of change within their organizations, being the first to proactively identify automation opportunities: “This process should be executed by a robot,” or “This task could be handled by artificial intelligence.” However, in many organizations, these professionals remain in reactive roles, far from the strategic leadership that the current context demands.

This article explores in depth the transformative impact that new technologies—AI, RPA, and other automations—have on traditional process and quality management methodologies such as Lean, Six Sigma, and SIPOC. The effective incorporation of robots and intelligent agents not only changes task execution, but also profoundly reshapes how performance is evaluated, risks are managed, traceability is ensured, and overall process quality is guaranteed.

Finally, this analysis aims to empower process and quality leaders by offering them a strategic, technical, and methodological perspective to effectively lead the transition toward hybrid operational models. With advanced tools and a mindset aligned with the digital era, these professionals can transcend their traditional roles and become key drivers of digital and operational transformation in their organizations.

Now is the time to question, rethink, and rewrite processes with clarity and purpose—intelligently distinguishing between what is human, robotic, automated, and intelligent.



>> What is a business process? <<

Introduction and current context of process management

Historically, process management emerged as a response to the need to optimize resources, reduce errors, and improve the quality of products and services offered by organizations. The first methodologies and tools—such as Lean Manufacturing and Six Sigma—were developed in a predominantly industrial context, where processes were linear, repetitive, and entirely executed by humans. This human-centered approach made it possible to control each task, directly identify failures, and continuously improve operations through manual interventions, constant audits, and incremental adjustments.

Over time, organizations began to adopt more structured approaches to documenting processes, highlighting tools like the SIPOC model (Supplier, Input, Process, Output, Customer), which provided clarity and standardization. These methodologies became global benchmarks for ISO certification and quality assurance, positioning process and quality departments as strategic areas within companies.

However, the current technological revolution is profoundly transforming this traditional view. The emergence and evolution of tools such as Robotic Process Automation (RPA), generative artificial intelligence, and intelligent automation platforms have radically changed the operational landscape. Today, many activities previously performed by humans can now be carried out more efficiently, quickly, and accurately by advanced technological agents.

This new reality presents a significant challenge for quality and process managers: it forces them to rethink not only how they document or improve processes but, more fundamentally, how they design and execute them. The introduction of robots and intelligent agents changes not only who performs a task, but also how it is managed, measured, controlled, and optimized.

In this context, the main challenge organizations face today is not merely adopting new technologies, but fully redefining the structure and logic of their processes. It is imperative that quality and process professionals take on a proactive and strategic role—actively identifying where automation should be integrated and how to effectively leverage artificial intelligence. This adaptation is not optional; it is essential for competing in an increasingly digitized and dynamic business environment.

This introductory chapter seeks to clearly establish the context and relevance of this transformation, preparing the reader to deeply understand how to approach process management in the new operational era.

Impact of intelligent automation on traditional methodologies

Traditional methodologies for process management and optimization—such as Lean, Six Sigma, DMAIC, and SIPOC—have been essential in achieving high standards of quality, operational efficiency, and continuous improvement over the past decades. However, the accelerated adoption of emerging technologies, including Robotic Process Automation (RPA), Intelligent Automation, and Artificial Intelligence (AI) Agents, presents a profound and disruptive impact on these traditional models.

Lean Manufacturing has enabled organizations to identify and eliminate activities that do not add value to the customer, creating a more efficient and results-driven operating environment. Similarly, Six Sigma has focused on reducing process variability, controlling defects, and driving continuous improvement through rigorous statistical analysis. While both methodologies have proven effective, they were developed under the fundamental assumption that tasks would be performed by people—making them insufficient in an increasingly automated context.

The arrival of RPA and AI agents introduces the ability to execute repetitive and cognitive tasks with unprecedented levels of precision, consistency, and speed. This shift compels us to rethink traditional concepts within these methodologies. For instance, the notion of "value-added" in Lean must now consider that some human-performed tasks—though seemingly efficient—might be more effectively executed by a robot or intelligent algorithm. Similarly, the statistical precision pursued by Six Sigma gains new dimensions when integrating predictive analytics and machine learning models that drastically reduce human error.

The SIPOC model also undergoes a significant transformation. Traditionally centered on human suppliers, manual inputs, and observable outputs, SIPOC must evolve to include digital suppliers, automated inputs, and algorithm-generated outputs. This new technological dimension radically redefines how processes are documented, managed, and audited.

This chapter explores these impacts in depth and proposes how process managers and analysts can adapt their practices to ensure that traditional methodologies evolve to incorporate the new capabilities offered by intelligent automation. The key is to recognize this transformation not as a break from the past, but as a logical and necessary evolution of best practices in process management.

Classification and selection of executors in modern processes

Proper assignment of executors within Business Process is fundamental to achieving optimal levels of efficiency, quality, and productivity. In today’s digital era, this assignment goes beyond the traditional division of human roles to include clearly differentiated categories: human executors, traditional automations, robots (RPA), and artificial intelligence (AI) agents.

1. Human Executors: Essential for tasks that require intrinsically human skills such as empathy, critical judgment, creativity, complex decision-making, and strategic leadership. Human executors should focus on activities that involve complex or emotionally significant interactions, while repetitive or predictable tasks should be delegated to other types of executors.
   
   

2. Traditional Automations: These are systems configured to perform linear and repetitive tasks with minimal or no human intervention. Their primary use is to integrate existing IT systems via scripts and predefined flows. They are ideal for managing processes with strictly defined rules and little contextual variation.
   
   

3. Software Robots (RPA): Software robots mimic human actions performed on IT systems. They are capable of executing repetitive, structured tasks based on clear rules—such as data entry, transaction processing, data migration between systems, and automatic report generation. Notable platforms include UIPath and Automation Anywhere.
   
   

4. Artificial Intelligence (AI) Agents: These intelligent agents possess advanced capabilities such as machine learning, natural language processing, and autonomous decision-making based on complex contexts. They are ideal for predictive analytics, advanced chatbot customer service, automated validations, and support for complex business decisions.
   
   

Effectively selecting the right type of executor for each process step not only improves operational efficiency but also optimizes costs, increases accuracy, and frees up human talent for more strategic functions. For efficient assignment, it is vital that managers and process analysts use modern tools such as CX Matrix®, advanced BPMN, and robust technological platforms like Appian or Interfacing.

This chapter outlines a practical framework for identifying, evaluating, and properly selecting the most appropriate executor type for each activity within a process. The ultimate goal is to ensure that each task is performed by the executor that maximizes value, efficiency, and quality—preparing organizations to lead in an increasingly automated and intelligent business environment.

Practical implementation of process redesign with intelligent automation

Once the current context and the specific types of executors involved in modern business processes are understood, it becomes critical to address how to effectively implement operational redesign. This chapter provides a practical approach to implementing redesigned processes that integrate humans, robots, automations, and artificial intelligence agents.

The first step is to carry out a detailed diagnosis using advanced tools such as the CX Matrix®. This diagnostic phase helps determine precisely which existing activities should be maintained, which can be automated, and which require specialized human intervention. The matrix not only facilitates visualization of the current state but also enables a clear projection of the desired future state, integrating advanced execution technologies.

Subsequently, it is recommended to use advanced notations such as BPMN (Business Process Model and Notation) to clearly document the new process design. The advantage of BPMN lies in its ability to graphically represent complex workflows that involve multiple types of executors, clearly defining tasks, decisions, events, and specific responsibilities.

The selection of robust technological platforms is fundamental for practical implementation. Tools like Appian, UIPath, Interfacing, Automation Anywhere, or Bizagi offer advanced environments for the execution, automation, and integrated orchestration of hybrid processes. These platforms allow the implementation of workflows that efficiently combine human decision-making, robotic interventions, and AI-powered automated tasks.

It is also essential to manage a structured change management process during implementation, ensuring that those involved clearly understand their new roles and the strategic value that automation brings to the business. Quality and process managers must proactively lead this transition, clearly communicating the expected benefits such as error reduction, improved operational times, increased productivity, and better end-user experience.

Finally, continuous monitoring and iterative improvement are crucial. It is important to integrate specific metrics, clear KPIs, and advanced analytical tools to evaluate the performance of the automated process in real time and make timely adjustments. This dynamic ensures that the process remains aligned with the organization's strategic objectives, generating sustainable long-term value.

This chapter, therefore, offers a comprehensive and practical framework for process and quality leaders to successfully implement the necessary operational transformation, thus ensuring their organizations remain competitive in an increasingly digitalized and automated global economy.

Governance, risk, and quality assurance in automated processes

The effective implementation of automated processes through robots, traditional automations, and artificial intelligence agents requires a clear and robust vision of governance, risk management, and continuous quality assurance. In a highly automated environment, governance takes on new dimensions and demands a proactive approach to identifying and mitigating emerging risks.

Governance in automated processes involves establishing clear policies, defined responsibilities, and strict control and supervision protocols. Organizations must define who is accountable in case of errors or failures in tasks executed by robots or AI, and how to manage exceptions that fall outside the automated logic. This approach demands a clear organizational structure, supported by a corporate culture that prioritizes transparency, accountability, and agility in responding to incidents.

Risk management in automated processes includes systematically identifying and evaluating potential risks associated with technological implementation, such as technical failures, algorithmic errors, AI model biases, or security vulnerabilities. It is essential to have solid contingency plans and rapid recovery strategies in place for any system failure.

Continuous quality assurance must also adapt to this new context. Traditional methods of auditing, manual review, and performance evaluation should be complemented by advanced techniques such as predictive analytics, real-time monitoring, and automated validations. Advanced technology platforms enable the implementation of continuous monitoring systems that provide early alerts, ensuring that any deviation or anomaly is promptly identified and addressed.

Furthermore, ensuring full traceability in automated processes becomes crucial. This includes clearly documenting the decision logic of each robot or AI agent and establishing protocols to validate the accuracy and consistency of their outputs. Modern tools such as Appian and UIPath offer advanced features for automated traceability and continuous process auditing.

Finally, ongoing training and awareness for involved teams is fundamental to maintaining a high quality standard in this new operational paradigm. Quality and process managers must take an active role, providing targeted training and promoting a clear understanding of the strategic and operational impact of automation.

This chapter provides a comprehensive approach to effectively managing governance, risk, and quality in automated processes—ensuring not only operational efficiency but also organizational trust in the transition to highly digitalized and hybrid operating models.

The evolving role of the quality and process manager as a strategic change agent

In today’s evolving business landscape, the role of the quality and process manager must shift fundamentally from its traditional function. It is no longer enough to simply document, audit, and manage existing processes. Today, process professionals must become active and strategic agents of change within their organizations.

The first responsibility of this new role is to actively promote an organizational culture that embraces innovation and technological change. This type of strategic leadership involves proactively challenging the operational status quo, clearly identifying tasks that could benefit from intelligent automation, and effectively communicating these opportunities across the organization.

To achieve this, the quality manager must possess a deep understanding of emerging technologies and their practical applications in process redesign. This includes familiarity with advanced platforms like Appian, UIPath, Interfacing, and Automation Anywhere, as well as agile methodologies and low-code or no-code approaches that enable fast and adaptive changes.

This new role also requires the development of advanced analytical skills to interpret data generated by automated systems and AI agents, using that information to drive continuous process improvements. Additionally, the manager must be capable of leading and facilitating multidisciplinary teams that bring together experts in technology, operations, and customer experience, ensuring that automation decisions reflect real strategic and operational needs.

Moreover, the quality manager must play a critical role in the ongoing education and training of staff, ensuring that all levels of the organization understand the purpose, benefits, and challenges associated with process automation. Effective change management, including clear and consistent communication, is essential to mitigate resistance and foster adoption of new operational practices.

Finally, the modern quality manager must actively contribute to shaping the organization's future operational strategy, working closely with senior leadership to ensure that automated processes align with business objectives and generate sustainable competitive advantages.

This chapter concludes the article by emphasizing the critical importance of this evolved role and offering a clear vision of how quality and process professionals can successfully position themselves as strategic leaders and key agents in their organization’s digital and operational transformation.

Conclusions and future perspectives

Process management has entered a critical stage of evolution, driven by the convergence of advanced technologies such as Robotic Process Automation (RPA), Intelligent Automation, and Artificial Intelligence agents. This transformation not only redefines how organizations execute and manage processes, but also radically reshapes the strategic role of quality and process leaders.

Throughout this article, we have examined in depth the impact of these new technologies on traditional quality methodologies such as Lean, Six Sigma, and SIPOC, demonstrating how these tools must adapt to remain effective in highly automated contexts. A practical guide has also been presented to identify and appropriately assign the ideal executors for each operational task, emphasizing the crucial role this assignment plays in driving efficiency and competitiveness.

Additionally, effective strategies have been outlined for successfully implementing redesigned processes using advanced technological platforms, along with robust approaches for governance, risk management, and quality assurance in hybrid automated environments. Finally, we have underscored the transformation of the quality and process manager’s role into one that is strategic and proactive in guiding both organizational and technological change.

Looking ahead, the most significant challenge will be maintaining a strategic and flexible mindset in the face of rapidly accelerating technological innovation. Process and quality managers will need to be prepared to constantly adapt, anticipate new trends, and continuously master new technological and analytical skills. Only by doing so will they continue to deliver real strategic value to their organizations and ensure sustained competitiveness in increasingly dynamic and demanding markets.

In conclusion, intelligent process automation is not merely a technological opportunity—it is a strategic necessity. Organizations that adopt these practices swiftly, led by empowered and well-prepared quality and process managers, will be the ones that lead the future of business.

Success stories and practical examples of intelligent automation in business processes

The successful implementation of automated and AI-assisted processes is already a tangible reality in various industries around the world. This chapter analyzes key real-world examples that clearly demonstrate the strategic, operational, and financial benefits achieved through the adoption of advanced technologies.

Case 1: Financial Sector – Robotic Process Automation (RPA)

A global banking institution adopted UIPath to fully automate its repetitive processes in credit management and internal audits. The result was a 70% reduction in processing time, a significant decrease in human errors, and an efficient reassignment of human resources to more strategic and analytical tasks.

Case 2: Insurance industry – Artificial Intelligence (AI)

A renowned insurance company implemented intelligent agents for predictive analysis and automated claims management. By leveraging AI, they reduced the average claim processing time by over 60%, increased fraud detection accuracy, and significantly improved the customer experience.

Case 3: Manufacturing – Intelligent automation in production processes

A multinational automotive company implemented intelligent automation platforms such as Appian and Interfacing to optimize its production and logistics processes. This hybrid approach resulted in a 40% reduction in operating costs, a 50% improvement in production times, and ongoing assurance of high-quality standards.

Lessons learned and best practices

From these real-world cases, several key lessons emerge:

• The importance of precise diagnostics using advanced tools such as CX Matrix®
  
  

• The critical need to effectively integrate technological platforms with existing processes
  
  

• The relevance of clear and proactive leadership from process and quality managers
  
  

• The strategic value of continuous change management and effective communication
  
  

These practical examples not only confirm the tangible benefits of adopting advanced technologies in business processes, but also offer a concrete guide for other organizations to follow and successfully adapt these innovations to their specific contexts.