The Evolution of Automation: From Execution to Decision

For years, talking about automation meant eliminating repetitive tasks: copying data, generating reports, sending emails. Today, technology has taken a step further. With the incorporation of artificial intelligence, automation no longer just executes — it thinks, prioritizes, and decides.

In this article, we explore how companies are evolving from traditional automation to intelligent automation, and what that means for real-time decision-making.

1. From Execution to Decision

In early RPA (robotic process automation) projects, the value lay in repetitive, error-free execution. A bot could do the work of 10 people by copying data between systems. But if something deviated from the script… it stopped.

Today, with generative AI, which already includes built-in decision models, automated flows can understand natural language, evaluate multiple scenarios, and make decisions based on rules and context.

This doesn’t replace humans — on the contrary, it enhances them, freeing up their time and expanding their impact.

2. What Enables This New Automation?

There are three key pillars that make this transition possible:

• Predictive models: Analyze large volumes of historical data to anticipate behaviors (failures, purchases, fraud, etc.).

• Internal copilots: Conversational interfaces integrated into tools like CRM or ERP, enabling process activation through text or voice.

• Intelligent orchestrators: Systems that automatically prioritize and assign tasks based on availability, urgency, and value.

At Meridian, we’ve integrated these three levels for clients in logistics, manufacturing, and financial services, with remarkable results — from operational savings to improved customer experience.

3. Real Example: Autonomous Decisions in Customer Support

One of our clients faced request peaks that overwhelmed their support team. With an intelligent system, we automated not just the initial response, but also the classification, escalation, and resolution of cases based on urgency and customer profile.

Previously, 80% of the time was spent on repetitive tasks. Today, over 60% of cases are resolved without human intervention.

4. What Is Needed to Get Started?

Intelligent automation is not as simple as it seems. It requires:

• Well-defined processes
• Clean, available data
• A repetitive approach that learns and adapts
• A technology partner with business vision

At Meridian, we believe the future of companies doesn’t just depend on doing more, but on deciding better, faster, and with context.