Process Orchestration: What It Is and Why It Matters

By Allexey Bernacchi, CEO & Founder of Smarthis

Process orchestration: what it is and why isolated automation is no longer enough

In recent years, companies of all sizes have invested heavily in automation: RPA for repetitive tasks, chatbots for customer service, and AI tools for data analysis. Each of these initiatives, on its own, delivered real efficiency gains. The problem emerged later: with dozens of automations running in parallel, without communicating with one another, companies began facing a new type of bottleneck — not a lack of automation, but a lack of coordination between them.

This is the problem that process orchestration solves. In this article, we explain what orchestration is, how it differs from traditional automation and RPA, why the market is moving toward this model, and how it translates into tangible results through a real-world case involving 400,000 hours recovered per year.

What is process orchestration

Process orchestration is the coordination layer that connects different automations, systems, and AI agents into a single workflow, enabling them to interpret context, make decisions, and act in a coordinated manner rather than operating as isolated initiatives.

Unlike traditional automation, which executes a specific task repetitively based on predefined rules, orchestration adds three layers:

• Context interpretation: the system understands the situation before taking action rather than simply following a fixed script.
• Coordinated decision-making: multiple agents/automations exchange information before executing an action.
• Chained action: the output of one automation automatically feeds the next step in the process, without manual intervention to "reconnect" systems.

In practice, an isolated RPA automation can, for example, extract data from an invoice. Orchestration goes further: it decides what to do with that data, triggers the appropriate financial system, notifies the responsible person if there is an exception, and records the decision history, all within the same workflow.

Automation, RPA, hyperautomation, and orchestration: what is the difference

These terms are often used interchangeably, but they describe different stages of maturity:

RPA (Robotic Process Automation) is the use of software robots that replicate repetitive human actions within systems, such as copying, pasting, and filling out forms. Its typical limitation: it executes isolated tasks but does not make decisions or interpret context.

Process automation is the broader set of tools, including RPA, workflows, and integrations, applied to an end-to-end process. Even so, each automation typically operates independently from the others.

Hyperautomation is the combined use of RPA, AI, machine learning, and process management tools to automate as much of a business process as possible. The focus is on automating more tasks, but this does not always solve the challenge of coordinating them.

Process orchestration is the layer that connects automations, systems, and AI agents so they can make decisions and act in a coordinated, context-aware manner. It requires data and architectural maturity to work effectively.

In short, RPA and hyperautomation answer the question "how can we automate more tasks?". Orchestration answers a different question: "how can we make these automations work together?".

Why isolated automation is no longer enough

A recurring pattern among companies that have scaled automation in recent years is that each area (finance, HR, operations, customer service) implemented its own solutions, using different tools and vendors, without a common integration model. The result is efficiency gained within each silo, but friction in the transitions between them — precisely the points where errors, rework, and delays tend to occur.

Data on AI agent adoption reinforces this shift: 35% of companies have already adopted AI agents, while another 44% are moving in that direction, according to a study by MIT Sloan Management Review. More significantly, 76% of executives already view these agents more as coworkers than as tools, which means these "tool-coworkers" need to be able to communicate with one another rather than simply execute isolated tasks.

This is the turning point: when AI stops being a standalone resource and begins operating as part of the workforce, orchestrating how these agents collaborate with one another and with legacy systems becomes just as important as automation itself.

How orchestration works in practice: the BRAX case (Vibra Energia)

A real-world example of orchestration applied at scale is the BRAX project, developed for Vibra Energia. Instead of running isolated automations by business area, the project established an orchestration layer with 160 autonomous agents working in coordination across different operational processes.

The quantified result: 400,000 hours recovered per year — time that was no longer spent on manual tasks and transitions between disconnected systems and could instead be redirected toward higher-value activities.

What makes this case relevant for understanding orchestration (rather than automation alone) is the scale of coordination: 160 agents do not operate as 160 isolated automations — they operate as a network that communicates, prioritizes, and makes decisions collectively.

What changes in practice for companies that have already automated processes

Companies that have already invested in RPA and automation do not need to "start from scratch" to adopt orchestration. The typical steps in this evolution are:

1. Map existing automations and identify where manual dependencies remain between them (transition points that still require a person to "reconnect" one system to another).
2. Standardize the data layer across automations, since orchestration depends on consistent information flowing between systems.
3. Introduce a decision layer: rules or AI agents capable of interpreting context and deciding the next step, rather than simply executing tasks.
4. Measure the end-to-end process, rather than each automation in isolation; the gains from orchestration become visible in the total process time, not in the speed of a specific task.

Frequently asked questions about process orchestration

What is process orchestration? It is the coordination of different automations, systems, and AI agents within a single workflow, enabling them to interpret context, make decisions, and act together rather than operating independently.

What is the difference between RPA and process orchestration? RPA executes specific repetitive tasks within a system. Orchestration connects multiple automations (including RPA) so they can operate in a coordinated manner throughout an entire process, incorporating decision-making and context.

Are hyperautomation and orchestration the same thing? No. Hyperautomation focuses on automating as many tasks as possible using RPA, AI, and other tools. Orchestration focuses on making those automations — whether existing or new — work together in a coordinated manner.

Why do companies with mature automation still face operational bottlenecks? Because isolated automation solves tasks within a silo but does not solve the transitions between silos. These transition points — when a process moves from one system/business area to another — are where errors and delays tend to be concentrated.

Does process orchestration require replacing existing automations? Not necessarily. In most cases, orchestration builds on existing automations by adding a coordination and decision-making layer on top of them, without requiring companies to discard what already works.

Conclusion

Isolated automation has not lost its value; it remains the foundation on which orchestration is built. What has changed is the problem that companies with more mature automation capabilities face today: it is no longer "how can we automate one more task?" but "how can we make everything we have already automated work together?". Orchestrating automations, data, and AI agents within a single coordinated workflow is the natural next step for companies that have already captured the initial benefits of automation and are seeking end-to-end efficiency.