Every day, organizations are being forced to transform by implementing innovative AI models that themselves go through an evolution — from their development to their execution.
Some of these AI models evolve through four distinct phases: Robotic Process Automation (RPA), Cognitive Automation, Digital or Intelligent Assistants, and AI Agents powered by Generative AI (GenAI).
Each phase represents a significant leap in automation, artificial intelligence, and data utilization, enabling companies to operate more efficiently and accelerate their results.
In Phase 1, Robotic Process Automation (RPA) involves using bots to automate repetitive, rule-based tasks that were traditionally performed by humans. These tasks usually include data entry, transaction processing, and simple data manipulations.
RPA is characterized by performing repetitive actions, reducing human error, cutting costs, increasing operational efficiency, and — above all — improving process times so users can focus on higher-value tasks.
In Phase 2, Cognitive Automation takes the lead. This stage represents a major step forward by leveraging artificial intelligence and machine learning to enhance adaptability and decision-making.
Its implementation goes beyond rule-based automation by interpreting unstructured data — such as emails, documents, and images — and continuously learning from both historical and new data to adapt through feedback.
It is especially effective for predictive tasks, such as demand forecasting, where it analyzes past patterns, seasonality, promotions, and economic conditions to guide business decisions related to production planning, inventory management, and resource allocation.
By continuously training on new data, these systems improve over time, offering more accurate forecasts and supporting more complex processes with greater autonomy.
This approach enables more informed business strategies and greater flexibility to adapt to market changes, boosting efficiency and precision across multiple business operations.
In Phase 3, Digital or Intelligent Assistants emerge, resulting from the combination of RPA and Cognitive Automation — integrated into workflows with an additional AI layer.
These intelligent assistants can handle more complex tasks, make decisions based on defined rules, and provide support across various business processes.
They are commonly used as:
-
Smart chatbots offering 24/7 support and resolving common inquiries,
-
Automated virtual agents for employee onboarding (document verification, induction scheduling),
-
Tools for automating routine tasks such as password resets and system diagnostics,
and much more.
In Phase 4, AI Agents powered by GenAI emerge (the most widely used today). These agents not only automate and optimize processes but also generate new content, ideas, and solutions, making independent decisions with minimal human supervision.
By leveraging advanced automation, workflows, and low-code/no-code platforms, organizations can develop AI agents that drive accelerated outcomes across multiple business functions — from contact centers to marketing and HR.
Their enhanced autonomy allows them to adapt to changing environments and provide intelligent support, transforming operations and redefining human–machine collaboration.
Although we know that AI within organizations is constantly growing and evolving, the progression of current models — from RPA to digital assistants and now to GenAI-powered agents — represents a remarkable journey of technological advancement.
Each phase has brought significant improvements in efficiency, decision-making, and business performance. As we continue to adopt and innovate with AI, the future promises to radically transform how businesses operate and achieve their goals.
Looking ahead, the knowledge and efficiencies gained at each stage are expected to drive even greater innovation and operational excellence, ensuring that systems remain at the forefront of technological progress and business value generation.
By understanding and harnessing the capabilities of each phase, companies can better navigate the complex landscape of automation and AI, staying competitive and future-oriented.
