How to combine RPA, AI, and data to build a journey of Intelligent Process Automation?

Have you ever wondered how far your company can go with automation? If the focus was previously on eliminating repetitive tasks with RPA, the market is now moving to a new level: Intelligent Process Automation (IPA). By combining technologies such as artificial intelligence, IPA goes beyond automatic execution — enabling faster, data-driven decisions.

According to McKinsey, companies that adopt IPA in their core processes can achieve up to a 30% increase in operational efficiency and reduce operational costs by up to 25%. IDC estimates that global investments in intelligent automation will exceed $20 billion by 2026.

In this article, you will understand the pillars of IPA, its competitive advantages, and examples of application in different sectors. Read on and discover how to evolve your automation journey — with intelligence.

How to combine RPA and machine learning models in an Intelligent Process Automation architecture?

The combination of RPA and machine learning is what allows automation to evolve from operational to strategic. RPA automates structured tasks, while machine learning brings adaptability, analysis, and decision-making capability — enabling a leap in value for automation.

But in practice, how does this integration happen within an IPA architecture?

Understanding the roles: RPA vs. Machine Learning

• RPA acts as the executor. It simulates human actions in systems, such as filling out forms, moving files, extracting data, and sending emails. Ideal for structured and predictable processes.
• Machine learning acts as the brain. It analyzes large volumes of data, identifies patterns, makes predictions, and recommendations — learning over time and continuously improving results.

Practical example of integration

Imagine the process of analyzing reimbursement requests. RPA can:

1. Access the ticketing system and capture the request data;
2. Extract attached documents (like receipts);
3. Fill in information in the company’s ERP.

But when we add machine learning to the architecture, it is possible to:

• Automatically classify the types of expenses based on the content of the documents;
• Detect anomalies or fraud risks by comparing the employee’s history;
• Automatically approve or forward requests based on compliance predictions.

In this scenario, RPA performs the actions while the ML model provides analytical inputs for smarter decisions — forming a closed loop of cognitive automation.

How does this combination work in the architecture?

1. Data capture: RPA collects structured and unstructured data from different sources (systems, emails, PDFs, etc.).
2. Pre-processing: The data is transformed and organized to feed the machine learning model.
3. Decision-making: The ML model analyzes the data and returns a decision, prediction, or classification.
4. Automated action: Based on the ML response, RPA executes the next step in the process.
5. Feedback loop: Results are stored and used to continuously train and improve the ML model.

By combining the precision of RPA with the adaptive intelligence of machine learning, your company can create more agile, resilient, and scalable workflows. This synergy is what makes Intelligent Process Automation so powerful — it’s not just about doing things faster, but about doing them better.

What bottlenecks signal that your company needs to evolve to an Intelligent Process Automation strategy?

Many companies start their automation journey with isolated RPA initiatives aimed at eliminating simple manual tasks. However, over time, new challenges arise — and the gains begin to stagnate. It’s at this moment that the need to evolve to a more robust, intelligent, and scalable approach emerges: IPA.

But how can you know if your company is ready — or rather, needs — to take that next step?

Below, we list the main operational and strategic bottlenecks that indicate your current automation needs to be reinforced with artificial intelligence:

1. High volume of manual exceptions

If a large portion of automated tasks requires constant human intervention due to exceptions or variations in data, it’s a sign that the current model is unable to handle the process’s complexity.

2. Low scalability of robots

Is your technical team overwhelmed with maintaining scripts or creating new workflows? When traditional RPA starts to hinder the growth of automation due to lack of flexibility, it’s time to incorporate technologies that learn over time — such as machine learning and process mining.

3. Difficulty dealing with unstructured data

Invoices in PDF, emails with free text, images or scanned documents: if these formats are a barrier to automation, IPA can help with resources like OCR, NLP, and AI trained to interpret non-standardized content.

4. Dependence on human decisions in critical processes

If the operation relies on human judgment to classify, prioritize, or validate tasks on a large scale, IPA can automate these decisions based on predictive models, reducing time, errors, and subjectivity.

5. Lack of visibility and insights into processes

Often, RPA automates without generating intelligence. If the company can’t understand bottlenecks, identify causes of rework, or anticipate risks, it’s time to include analytical layers that provide visibility and a basis for strategic decisions.

6. Automation with stagnant ROI

If the return on investment in automation is declining or if initial gains have already been captured, it’s a good time to reevaluate the architecture, include AI, and explore new avenues for efficiency.

Is your data governance prepared to support automated decisions within an Intelligent Process Automation strategy?

At the center of any Intelligent Process Automation initiative is a simple but non-negotiable principle: no automated decision will be better than the data that feeds it.

As companies shift from task automation to decision automation, the role of data changes from operational support to strategic asset. And that requires a new approach to governance. It’s no longer just about keeping databases organized or meeting compliance requirements. It’s about ensuring that the algorithms that automate financial flows, credit grantings, prioritization of services, or ticket triaging — just to name a few examples — are operating based on reliable, updated, contextualized, and ethical information.

Governance as a prerequisite for autonomous decisions

By incorporating machine learning, NLP or statistical models into automation, you give the machine the responsibility to interpret and act. And for that interpretation to be valid, data must meet three fundamental requirements: quality, traceability, and usage control.

An AI model trained on inconsistent or biased data will not only replicate human errors — it will also scale them in speed and volume. Likewise, if the company cannot trace where the data that feeds an automated decision comes from, it loses the ability to explain, audit, or correct that decision. This is not merely a technical issue — it is a business risk.

The absence of governance also undermines internal trust in automation. Business professionals begin to distrust robot decisions, resort to manual workarounds and, ultimately, hinder the adoption of IPA due to lack of data reliability.

What differentiates an organization prepared for IPA?

Companies that excel in adopting intelligent automations do not start with AI — they start with data. Before scaling cognitive bots, they build a governance structure that:

• Defines data ownership, with clear responsibilities for its curation and maintenance in each area;
• Establishes quality standards, creating objective criteria for “usable” data in automated decisions;
• Creates data lineage mechanisms, allowing tracking of an information’s journey — from origin to use in a predictive model;
• Institutes access and consent policies, protecting sensitive data and ensuring compliance with regulations such as LGPD, HIPAA, or GDPR;
• Fosters algorithmic transparency, creating mechanisms to audit, explain, and justify the decisions made by automated systems.

More than that: these companies see data as a product — with a life cycle, usability criteria, responsibilities, and value metrics.

The maturity of automation depends on the maturity of data

An organization that wishes to scale IPA needs to treat data governance as an integral part of the automation architecture — not as a parallel project. The intelligent robot needs to know it can trust the data it receives, just as the business team needs to trust the decisions it makes. And this trust is born from well-defined processes, clear responsibilities, and consistent policies.

What KPIs should be used to measure real gains with Intelligent Process Automation?

Like any investment, implementing IPA is only justified if it generates measurable value for the business. However, one of the most common pitfalls in the initial waves of IPA is measuring only what is easy — such as automated tasks or execution time — and not what really matters: impact on the business, operational efficiency, and continuous evolution of automated decision-making.

Instead of tracking isolated operational metrics, companies need to establish KPIs that reflect the value generated by intelligence applied to automation, focusing on cost reduction, quality increase, risk mitigation, and insight generation.

Below, we highlight the main indicators that should guide this analysis.

1. Cost reduction per automated process

This is one of the most classic KPIs, but it needs to be refined in the context of IPA. With the introduction of AI, it is expected that automation not only executes faster but also makes decisions that avoid rework, fraud, or waste. This requires calculating the average cost before and after intelligent automation, considering not just execution time but also avoided errors, corrected decisions, and eliminated processes.

2. Bots’ autonomy index (touchless rate)

How many decisions can bots make autonomously, without the need for human intervention? IPA differentiates itself precisely by reducing the dependency on human validations in processes with complex variables. This KPI reveals how much AI is truly empowering the robots — and not just following rules.

3. Accuracy of predictive models

When IPA uses machine learning, it’s essential to monitor the accuracy of the models. A ticket classifier, for example, should be evaluated by its success rate in correctly classifying demands. A model that predicts default risk, on the other hand, should be accompanied by metrics such as precision, recall, and F1 score. Accuracy KPIs ensure that automated decisions are reliable and improve over time.

4. Decision cycle and average response time

Another critical KPI for processes involving analysis and automatic response is the decision time. Reducing this cycle, without compromising quality, is one of IPA’s significant differentiators compared to traditional automation. Monitoring the time from data receipt to automated action helps quantify agility gains.

5. Direct impact on business KPIs

IPA should be built with a business-back logic, meaning it connects to the organization’s strategic objectives. This means that success is not only in the efficiency of automation but also in the impact on metrics such as:

• Reduction of default (in automated credit models);
• Increased customer satisfaction (in automated service flows);
• Increased productivity per employee (in internal processes);
• Reduction of losses or frauds (in automated risk analyses).

6. Evolution of adoption rate and scalability

IPA is not a one-time project, but a continuous journey. Therefore, it is also essential to track indicators that show the maturity of the organization in expanding intelligent automation, such as:

• Number of processes automated with AI;
• Number of involved areas;
• Percentage of processes that evolved from traditional RPA to IPA.

Measuring correctly is what separates isolated experiments from scalable strategies. The right KPIs not only allow justifying the initial investment but also adjusting courses, better training models, engaging involved areas, and, most importantly, generating real intelligence for the business.

How does Objective structure automation projects focused on progressive gains, scalability, and governance?

The adoption of Intelligent Process Automation should not be treated as an isolated or purely technical project. To generate real and sustainable value, it is essential that automation aligns with the organization’s strategy, respects data maturity, engages business areas, and aims to generate intelligence — not just efficiency.

At Objective, we structure intelligent automation projects based on three fundamental pillars: progressive gains, scalability with control, and governance from the start.

1. Progressive gains: start small, grow safely

We know that each organization has a different level of maturity in automation. Therefore, our initiatives are built with an iterative approach that prioritizes quick value deliveries — without losing sight of the long term. We work with waves of automation: starting with high-impact, low-complexity processes, and from learnings, expanding to more sophisticated cases with AI support.

This progressive vision allows us to validate hypotheses, engage stakeholders, and demonstrate concrete results from the early stages.

2. Scalability with control: focus on architecture and sustainability

More than just “automating,” our proposal is to design intelligent and scalable automation architectures, that connect RPA, machine learning, NLP, OCR, and other technologies in a modular and integrable way with your legacy or modern systems.

With this model, we avoid the creation of automation silos and ensure that new initiatives can be added safely, reused, and interoperate — without compromising performance or governance.

Additionally, we rely on continuous monitoring practices, flow versioning, and specific CI/CD pipelines for automations, ensuring technical and operational stability even in critical environments.

3. Governance from the start: data, compliance, and traceability

Every Objective project is guided by good governance practices, from curating the data used to the explainability of the AI models. We combine tools and frameworks tailored to each client’s reality, ensuring:

• Clear mapping of process and data ownership;
• Compliance with regulations like LGPD and industry standards;
• Visibility over automated decisions, with an audit trail and algorithmic transparency.

In addition, we empower internal teams so that they not only use automation but also manage it as an active part of operations.

IPA represents a strategic advancement in the automation journey — but to reap its results, it’s necessary to go beyond the basics. Talk to an Objective specialist and discover how to structure a progressive, intelligent, and data-driven journey.