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Automation beyond the obvious: how AI and RPA are rethinking the way we work
When we talk about productivity, the real competition between companies is not about who works the most, but about who works the best.
According to the McKinsey Global Institute (2017), approximately 60% of occupations worldwide have at least 30% of their activities automatable using available technologies. While less than 5% of occupations can be completely automated, a large portion of occupations contain repetitive and structured components that can be automated without completely altering the job.
We are not talking about replacing people, but about unlocking human potential. Repetitive, manual, and operational tasks still consume valuable time in areas such as Finance, Customer Service, and Back Office . Meanwhile, technologies like RPA and artificial intelligence have evolved beyond basic automation: today, they are capable of learning, interpreting contexts, and acting autonomously.
What used to be about automating steps is now about automating decisions . And in this new scenario, understanding strategically integrate these technologies
In this article, we will explore this new level of process automation and how Skyone Studio positions itself as a catalyst for this transformation, uniting data, AI, and execution on a single platform.
Enjoy your reading!
When we talk about automation, many people still imagine robotic arms in factories or scripts that mimic computer clicks. But in today's corporate environment , automation has gone far beyond that and is increasingly connected to how data, decisions, and people interact .
More than replacing tasks, the focus today is on rethinking how they happen . How do you integrate systems that don't communicate with each other? How do you ensure the right data arrives at the right time? How do you make workflows smarter and more adaptable? The answer lies in the strategic combination of different technologies —and that's where we begin to build what we call "intelligent automation."
Robotic Process Automation ( RPA ) is usually the first step. It automates repetitive digital tasks and generating reports, with speed, accuracy, and reliability. But its reach depends on fixed rules and predictable paths.
Artificial intelligence ( AI) broadens this horizon. It interprets contexts, learns from patterns, and adjusts decisions based on data. With AI, we move beyond automating only "how to do" and begin automating "what to do," machine learning models come in , capable of identifying patterns in large volumes of data (including data from sensors and IoT devices ) and making predictions that feed into automated decisions in real time.
And in recent years, we've entered a new chapter with the arrival of generative AI . It not only analyzes but creates responses, documents, content, instructions—all based on natural language and user intent . It's what allows, for example, a chatbot to draft personalized messages or a sales system to suggest proposals based on previous interactions. And this is just the beginning.
The most important thing is to understand that automation, RPA, AI, and generative AI are not isolated or competing technologies, but rather form a complementary ecosystem : RPA executes, AI analyzes, and generative AI creates. Together, they allow for the automation not only of steps, but of entire business decisions, with context, intelligence, and scale .
This potential, however, only materializes when there is a supporting structure : well-organized data, fluid integrations, and clearly orchestrated processes. This is what we will explore next: the behind-the-scenes aspects that make automation truly intelligent and viable in the daily operations of companies.
Before we talk about bots that learn or systems that make decisions on their own, we need to look at what underpins all of this "behind the scenes ." After all, no automation works properly if the systems don't communicate with each other, if the data is disorganized, or if the process flow doesn't make sense.
For automation to be truly intelligent, three pillars need to be well-structured : integration, data, and orchestration. And each of these pillars depends on specific technologies that enable the smooth and reliable operation of automated processes.
Integration: Connecting systems that don't speak the same language
In the corporate world, we deal with a veritable "technological patchwork": ERPs, CRMs, legacy systems, spreadsheets, APIs, customer service platforms... Without integration, each of these tools becomes an island , and processes become fragmented, full of rework and risk of error.
In this scenario, iPaaS ( Integration Platform as a Service ) , a technology that allows connecting different systems without reinventing the wheel . It functions as an integration layer between applications, allowing data to flow securely and automated commands to move seamlessly between areas.
Furthermore, modern iPaaS solutions already support IoT devices and machine learning , enabling real-time integrations with sensors, predictive models, and data sources .
In the context of Skyone , this pillar gains even more strength with native connectors and support for legacy systems, facilitating automation even in complex environments , without the need for development from scratch.
Data: Organizing what fuels automation
Automating without structured data is like building an engine without fuel. That's why, in addition to integration, it's essential to ensure the quality, availability, and governance of the information that feeds the workflows .
This is where architectures like lakehouses , combining the flexibility of data lakes with the analytical performance of data warehouses , and automated pipelines , which extract, transform, and deliver the right data, at the right time, to the right processes.
This structure can include everything from pipelines ETL and event streaming flows , ensuring that data movement occurs with monitoring, logic, and minimal latency.
But simply moving data isn't enough: it needs to be controlled. That's where robust version control, event tracking, tokenization, and governance come in, ensuring that this data is used securely, contextually, and with traceability. This is essential when we're talking about reliable automated decision-making.
With this solid foundation , we stopped relying on manual spreadsheets and started operating with live, actionable data, ready to drive faster and more strategic decisions.
Orchestration: giving logic, rhythm, and control to the processes
With integration and data in order, one piece is missing to transform isolated actions into a coordinated operation : orchestration.
This layer defines what happens, when, in what order, and under what conditions . It allows for mapping exceptions, predicting failures, triggering alerts, and adapting the process in real time. With the support of low-code platforms , business areas gain autonomy to model flows, always with centralized governance by IT.
Another distinguishing feature of this layer is its ability to native monitoring and logging , ensuring traceability at every stage and creating a solid foundation for continuous process improvement.
With real-time monitoring capabilities and role -based ), it's possible to ensure that each workflow is executed securely, transparently, and in accordance with corporate policies, even in regulated or highly critical environments .
With these three pillars well established, automation gains scale, reliability, and, above all, intelligence . This is what transforms a set of automated tasks into an adaptive and strategic operation. And that's what we'll discuss in the next section, exploring the role of generative AI as the new brain of processes, capable of interpreting, creating, and acting based on context and intent.
For a long time, automation was synonymous with execution : repetitive tasks, predictable routines, fixed rules. But everything started to change when technology stopped simply following instructions and began generating responses, interpreting contexts, and creating alternatives.
This is the role of generative AI in the business environment. It transforms automation from something programmable into something adaptable . We are no longer talking about a passive assistant, but about AI agents that propose solutions, learn from data, and adjust based on real-world use.
In practice, this means that a chatbot not only answers frequently asked questions, but now it also drafts personalized messages , understands intents, and suggests actions based on history. Another example: a sales co-pilot can generate follow -up emails based on the customer's previous behavior. Or, an automated financial workflow can adapt its rules when it detects anomalies, without relying on new manual instructions from humans .
All of this is possible thanks to LLMs ( Large Language Models ) , trained with massive volumes of data and capable of interpreting natural language, recognizing patterns, and generating coherent content in real time. More than just language, these generative models are already used to create reports, summaries, technical instructions, service scripts, data visualizations, and even code, always based on contextualized input .
But the potential of generative AI is only realized when combined with a prepared ecosystem . Organized data, seamless integrations, efficient orchestration, and access control are prerequisites. The model only works well when there is governance, traceability, and adherence to business rules.
This is why platforms like Skyone Studio are gaining prominence. They offer the necessary environment for AI agents to truly operate with intelligence, autonomy, and security , transforming intentions into real actions with measurable business impact.
And this impact is already happening. In the next section, we'll show how RPA, AI, and generative AI are fitting into the day-to-day operations of businesses, with real gains in efficiency, scale, and quality.
Automating for the sake of automation doesn't get you far. True value emerges when technology organically integrates into daily workflows, resolving bottlenecks, reducing friction, and freeing up time for what truly matters.
Below, we'll explore three areas where intelligent automation is no longer just a trend, but a well-established practice, always supported by robust integrations, structured data, and intelligent models, demonstrating that the results go far beyond productivity .
Back office and Finance
Few sectors accumulate as many critical and repetitive tasks as Finance. Bank reconciliations, tax validations, invoice issuance, cash flow analysis: all these routines demand surgical precision and, at the same time, take time away from strategic activities .
With RPA , these tasks gain speed and reliability. With AI , workflows become adaptable: it's possible to cross-reference data from different sources, identify unusual patterns, and anticipate risks. Machine learning , for example, detect anomalous variations in real time, increasing control and predictability. And with generative AI , management reports and performance analyses begin to emerge automatically, with insights ready for decision-making.
According to McKinsey 2016–17 ), companies that adopt RPA can achieve ROI between 30% and 200% in the first year , with cases reporting up to 200% in initial implementations . The result? Less time spent closing the month, more focus on predictive analytics, planning, and decisions that drive growth.
Customer Service and Support
Everyone has experienced the frustration of repeating the same information in three different customer service interactions. And it's precisely this type of noise that well-implemented automation can eliminate, without sacrificing human empathy .
With RPA , tasks such as opening tickets, updating protocols, and sending confirmations are performed automatically and in a standardized AI then comes in to understand the context of the request, analyze the customer's history, and direct the service with greater precision. And with generative AI , bots go beyond simply responding; they begin to write personalized messages, suggest solutions, and learn from each new interaction, always fed by organized and integrated data via automated
pipelines According to Gartner (March 2025), autonomous AI agents should resolve up to 80% of service cases by 2029 , resulting in a reduction of approximately 30% in operational costs. In practice, this translates into smoother journeys, greater customer satisfaction, and a team focused on what truly requires active listening and human reasoning.
Marketing , Sales and Operations
In these areas, pace is everything. And automation comes in to synchronize movements between teams, data, and channels , without wasting time on what can be automated.
With RPA , it's possible to automate tasks such as lead registration, sending communications, and updating systems. AI helps predict behaviors, identify opportunities, and recommend next steps based on real data. And generative AI closes this cycle with personalized deliverables at scale: proposals, emails , presentations, and even sales
scripts According to McKinsey , companies that automate processes in these areas increase revenue by up to 10% and reduce operational costs by up to 20% . The effect of this investment is direct: a sales cycle that flows with more strategy, less friction, and with teams focused on what truly generates value.
Other areas undergoing transformation
Intelligent automation is also gaining ground in sectors such as Human Resources , with automated resume screening, sending of onboarding communications, and data integration between payroll and talent management systems.
In Logistics and Purchasing , workflows such as order validation, inventory updates, and supplier negotiation can be optimized with RPA and AI. And in IT , automation facilitates everything from access provisioning to incident response.
These examples make it clear: automation is already part of everyday life in critical and strategic areas, bringing concrete gains in productivity, quality, and scale , always with data flowing between systems through integrated architectures and well-orchestrated pipelines.
But that doesn't mean the path is simple or free of obstacles. Implementing truly intelligent automation involves technical decisions, cultural changes, and often overcoming silos and internal resistance.
Therefore, below we will address the main challenges faced by companies on this journey and how to overcome them with strategy, structure, and the right partner by their side.
Like any journey, intelligent automation also brings its challenges. But with a strategic approach and the right technology, every challenge can be transformed into an opportunity for growth .
Below, we highlight some of the most common challenges faced by companies at different stages of automation, along with clear paths to move forward with confidence:
- Disconnected environments : it's common to find companies with systems that don't communicate with each other, making it difficult to create integrated automated workflows. Solutions like iPaaS platforms help connect these points without relying on complex development.
- Starting without a clear direction : beginning to automate can generate doubts: where to start? Ideally, focus on processes with high volume, direct impact on the business, and predictable structure, and from there, expand based on the lessons learned;
- Unstructured data and a weak data culture : data is the foundation of automation. When it is scattered or inconsistent, the process loses efficiency. Structures such as lakehouses and pipelines help ensure that the right information arrives at the right time, reliably. Furthermore, it is essential to promote data literacy within teams, building trust in live, accessible, and traceable data.
- Low team involvement : automation isn't just about technology, it's about people. Including teams from the start, showing practical day-to-day gains, and creating space for co-creation strengthens the culture of innovation. And here, again, data culture plays a central role: teams prepared to interpret and trust information begin to operate with more autonomy and precision.
- Data governance and : Automated workflows require strict access control, traceability, and compliance with internal and regulatory policies. Platforms with features such as role-based access control (RBAC), logging , and continuous monitoring help ensure that these requirements are met from the automation design phase.
- Technology without business alignment : automation needs to make sense within the company's context and objectives. Uniting technical and business areas from the outset, and adopting platforms that allow autonomy without losing governance, ensures that the solution is functional and scalable.
The good news is that these points aren't insurmountable barriers. In fact, they're milestones in a healthy transition towards smarter operations . And each one can be overcome more smoothly when you have a platform designed to simplify and enhance this journey, like Skyone Studio . Keep reading to find out!
As we've seen so far, true automation isn't just about automating tasks. It's about transforming decisions into actions , with security, intelligence, and context. It was with this vision that we created Skyone Studio : a platform that unites data, AI, and orchestration in a single environment, ready to put artificial intelligence into action.
More than just integrating technologies, Skyone Studio connects the dots between intent and execution , offering everything a smart operation needs to scale with control, fluidity, and real impact.
How Skyone Studio Enables Real AI Agents
Many still see AI as an assistant that answers questions. At Skyone Studio , AI goes further : it acts as an agent capable of executing tasks, interacting with systems, making decisions based on live data, and following business rules with complete traceability.
This is possible because our platform combines, in a unified architecture :
- Ready-to-use connectors for ERPs, CRMs, legacy systems, APIs , and IoT devices, eliminating barriers between applications and ensuring fluidity between events, data, and processes;
- Execution of pipelines that transform data into actions with tracking, tokenization, and reliability;
- Low-code visual orchestration , with customizable business logic, role-based access control (RBAC), and real-time monitoring for each executed flow;
- Security and governance are fundamental, not optional. Skyone Studio provides logging token management , and adherence to corporate policies from the start.
In practice, this means that an AI agent can, for example, identify an anomaly in cash flow, consult multiple systems, draft a recommendation, and execute an action—all based on up-to-date data and business context.
In other words, with our platform, AI goes beyond simply suggesting and starts doing , with traceability, control, and governance at every stage.
If you're looking for more than just one-off automation, and want a truly intelligent operation, here's what makes Skyone Studio a unique platform :
- AI agents ready to execute, not just suggest;
- Native connectors and support for legacy systems, for seamless integration;
- Low-code visual orchestration with built-in IT governance;
- Supervised and traceable data pipelines
- End-to-end enterprise security, with logging , RBAC, and compliance from the start;
- A unified environment for data, AI, and automation, with true scalability;
- Generative applications integrated into processes, with a direct impact on areas such as Finance, Customer Service, and Sales.
Want to understand how to apply this in practice? Talk to one of our specialists and see how Skyone Studio can accelerate intelligent automation in your business!
Automation has ceased to be a differentiator and has become a new language of business . And only those who master the inner workings of this "language" speak it fluently: reliable data, well-orchestrated workflows, and artificial intelligence connected to the reality of the operation.
In this article, we show that RPA and AI don't work in isolation . And that when combined with an intelligent and governed architecture, they transform into a new way of working, much more strategic, more fluid, and more efficient .
Here at Skyone, we translate this vision into Skyone Studio, a platform that not only automates tasks but makes intelligence truly happen . From data to decisions, from rules to results, Skyone Studio organizes everything in a single environment, with agents ready to act and create real value.
If you enjoyed this content, how about delving even deeper into the power of AI in business? Read our article "What are LLMs and how to apply them to your business with your own data ," because the future is already happening, and understanding LLMs is understanding the next productivity revolution!
Even with so much information available, many questions remain when it comes to process automation. This is because the topic has evolved: it has gone from being a simple replacement of tasks to becoming a business strategy based on data, AI, and integration.
Below, we answer the main questions of those who are starting out or looking to go further in a direct and practical way.
What is process automation?
Business process automation is the use of technology to perform repetitive tasks, operational routines, and business decisions in an automated way, with minimal or no human intervention.
But in practice, it goes far beyond "doing things faster." Intelligent automation involves connecting systems, organizing data, applying artificial intelligence, and orchestrating workflows with logic and traceability. The goal is to gain scale, free up time for strategic tasks, and improve the experience for teams and customers.
How to automate processes?
Automating a process begins with mapping: understanding where the bottlenecks, manual tasks, and high-volume or high-impact workflows are.
Next, three key elements need to be ensured:
- Integration between systems and data;
- Structured, available, and governed data;
- Orchestration to provide logic and control to the flows.
This makes it possible to apply technologies such as RPA ( Robotic Process Automation ) to perform repetitive tasks; artificial intelligence (AI) to interpret and make decisions based on data; and generative AI to create content, responses, and decisions in real time. Platforms like Skyone Studio facilitate this path by unifying all these steps in a single environment.
What is programmable automation?
Programmable automation is based on fixed rules, predefined paths, and clear instructions—which is common in traditional RPA ( Robotic Process Automation ) tools. It is effective for repetitive and predictable tasks, such as issuing invoices or updating spreadsheets.
But to deal with more complex and variable scenarios, companies are migrating to intelligent automation, which combines artificial intelligence (AI), contextual data, and adaptive business logic. This leap allows them to automate not only the "how to do it," but also the "what to do," with greater agility, scale, and intelligence.
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