Many companies have already implemented RPA to streamline repetitive tasks, reduce errors, and increase efficiency. However, combining RPA with AI/ML development provides even better results. According to Deloitte estimates, intelligent automation can reduce costs by an average of 22% and increase revenue by 11% over the next three years. Moreover, organizations that are currently scaling intelligent automation have already achieved an average cost reduction of 27%.
While RPA services help automate structured, repetitive processes, AI integration allows for more complex decision making, pattern recognition, and advanced data analysis. Together, these technologies create a powerful synergy, transforming traditional automation into intelligent automation that can adapt, learn, and optimize processes in real-time.
How do RPA and AI augment each other? What new functionalities does AI provide to RPA platforms? What are the capabilities of the top AI-powered RPA platforms? How to implement intelligent automation?
How RPA and AI are combined?
Although RPA excels at automating rules-based tasks, it is limited by its inability to recognize patterns or understand unstructured data. Unlike RPA, AI succeeds in these areas, bringing advanced capabilities like pattern recognition, data analysis, and decision making to the automation process.
Combining both technologies creates intelligent automation, where RPA handles structured processes, and AI manages unstructured data and complex decisions. Here’s how these technologies enhance each other's capabilities.
RPA enhancing AI capabilities
- Data acquisition: RPA bots can quickly gather, clean, normalize, and label data from multiple systems, providing high-quality training data for AI models to accelerate AI development.
- Legacy system integration: Some legacy systems may not have the necessary connectors or APIs to interact with an AI. RPA bridges the gap between these systems, allowing smooth operations across different technology generations.
- Understanding AI reasoning: RPA can track the steps taken by AI models, offering transparency and explaining how specific conclusions are reached. This overview helps to review the AI decision-making processes.
- Human-in-the-loop (HITL): Acting as a safety net, RPA can flag potentially questionable AI outputs for human review. For instance, if AI approves a loan for a customer with a poor credit history, RPA ensures compliance by flagging it for further human assessment.
- AI supervision: RPA continuously monitors AI systems for errors, biases, and performance issues, flagging data anomalies to maintain AI accuracy and reliability over time.
AI enhancing RPA capabilities
- Exception handling: AI can identify and handle exceptions that might halt an RPA process, such as unexpected data formats or errors. This keeps automation running smoothly and reduces downtime.
- Cognitive automation: Through pattern recognition and data analysis, AI mimics human judgment, enabling more accurate and insightful outcomes. In such a way, AI expands RPA's functionality beyond simple rule-based automation.
- Self-optimizing processes: AI can suggest improvements to RPA workflows by analyzing past performance data, leading to continuous optimization.
- Predictive capabilities: Utilizing historical data, AI predicts potential issues and recommends preventative actions, enhancing overall process efficiency and reducing the likelihood of future problems.
- Advanced data processing: AI's ability to analyze and interpret unstructured data, including images, text, and speech, allows RPA to execute more complex tasks, broadening the range of automatable processes.
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What AI functionalities do AI-powered RPA platforms have?
Intelligent data extraction
AI-powered RPA platforms use various advanced technologies to extract and process information from various document types, including structured, semi-structured, and unstructured. AI can parse letters, invoices, receipts, and other structured documents, ensuring comprehensive data extraction across different formats. This capability also includes parsing and understanding semi-structured data from forms and reports, and unstructured data from emails and PDFs.
As for visual data, Computer Vision helps to read and interpret text on images and handwritten notes, converting them into structured data to prepare high-quality input for AI models. The document understanding capabilities of AI-powered RPA platforms significantly reduce manual effort and minimize errors in data extraction, ensuring high-quality output and accelerating AI development.
Task mining
Task mining uses AI to analyze and understand employees' tasks, pinpointing repetitive and rule-based processes that will benefit from automation. Task mining tools monitor user activities and gather data to identify the best candidates for RPA implementation. This approach automates the identification process, saving time and resources. It also ensures that the most suitable tasks are chosen for automation.
Furthermore, task mining helps continuously refine and improve workflows based on real-time data. For instance, in a finance department, task mining can analyze employee interactions with financial software, identifying repetitive tasks such as invoice processing and data entry.
Generative AI
Generative AI models can learn and adapt over time, improving performance based on new data and feedback. This self-learning capability makes RPA bots more efficient and accurate, reducing the need for constant human intervention and updates.
Generative AI also automates content creation, freeing up human resources for strategic tasks and ensuring consistent quality and tone in communications. For example, a customer service department can use generative AI to automatically draft responses to customer inquiries, providing quick and accurate support while maintaining a personalized touch.
AI model integration
Integrating AI models into RPA workflows allows for advanced analytics. These models can analyze large datasets to predict trends, detect patterns, identify anomalies, and suggest optimal actions. AI models enable RPA bots to perform more complex tasks and make informed decisions.
AI model integration also allows workflows to adapt to changing data and conditions in real-time, supporting more strategic and high-value tasks by leveraging AI-driven insights. In supply chain management, for instance, AI model integration can predict demand trends and optimize inventory levels, automating the reordering process and reducing excess stock.
RPA AI capabilities by platform
UiPath
UiPath integrates AI through its AI Fabric and cognitive automation capabilities, allowing users to deploy, manage, and continuously improve ML models within their automation workflows. Cognitive automation leverages AI to allow UiPath to extract and interpret data from unstructured data sources such as text documents, images, audio, and video.
UiPath offers NLP for interpreting text and Computer Vision to enable bots to interact with user interfaces in a human-like manner. UiPath’s autonomous automation features allow robots to manage other robots, optimizing workflow automation with centralized work queues and reducing costs by synchronizing queued work and robot deployments.
Automation Anywhere
Automation Anywhere enhances its RPA capabilities with AI through its IQ Bot and Discovery Bot. IQ Bot uses ML to understand and process semi-structured and unstructured data, improving accuracy over time and with human validation. The Discovery Bot leverages AI to identify automation opportunities by analyzing user actions and suggesting processes for automation.
Automation Anywhere also incorporates NLP and advanced analytics to provide deeper insights and more effective automation solutions. With generative AI features like Autopilot, users can quickly move from discovery to automation, while Automation Co-Pilot boosts productivity by guiding effective prompt generation and fine-tuning automation processes.
Kofax
Kofax combines RPA with AI-driven cognitive document automation. Its advanced OCR (Optical Character Recognition) extracts data from various document types with high accuracy. Machine Learning capabilities enable Kofax to classify documents and extract relevant information, continuously improving with feedback.
The platform also offers generative AI-powered features, such as a developer assistant that turns natural language prompts into actionable workflows and advanced data extraction that intelligently retrieves information from documents.
Power Automate
Microsoft's Power Automate incorporates AI through AI Builder and pre-built AI models. With AI Builder, users can create custom AI models for tasks like form processing, object detection, and text classification. These models can be later integrated into Power Automate workflows.
Power Automate utilizes AI for process automation, including predictive maintenance and anomaly detection, enhancing operational efficiency. Integration with other Microsoft products, such as Power BI and Azure Cognitive Services, provides advanced analytics and insights. Power Automate also supports conversational AI, integrating with the Microsoft Bot Framework and Azure AI to build intelligent chatbots that streamline customer service and support operations.
Read more about RPA tools comparison
How to implement intelligent automation?
At N-iX, we deliver a comprehensive suite of RPA services designed to cater to our clients' diverse needs. Our expertise in RPA and AI allows us to offer two distinct approaches, ensuring that we have a solution that fits whether your company is new to RPA or already experienced in automation strategies.
Option 1: Pilot project for RPA and AI implementation
Our first approach is for businesses looking to explore the potential of RPA with a pilot project, allowing them to test and refine before scaling.
1. Building a business case for automation
At N-iX, we help you select a specific process or task that can benefit from automation. This process is typically repetitive and rule-based but ensures a clear and measurable impact. Additionally, we identify areas where AI can enhance the RPA solution, such as incorporating ML models to handle exceptions or improve data extraction accuracy.
2. Proof of concept
Our experienced engineers will partner with you to integrate an RPA solution that will suit your business case. We conduct a thorough process assessment and create a proof-of-concept to ensure feasibility and effectiveness. Our team selects the most suitable technology stack considering existing infrastructure and integrates the required AI functionality.
3. Solution implementation
N-iX deploys your AI-powered RPA solution in a live environment, ensuring that bots are configured correctly and integrate seamlessly with your existing systems. AI components are trained and validated to deliver accurate and reliable results.
4. Performance evaluation and monitoring
We continuously monitor the RPA bots’ performance, tracking key metrics such as accuracy, efficiency, and cost savings. AI models are also monitored to ensure they function optimally and improve over time through learning and adaptation. Our team gathers feedback to assess the impact on your business operations.
5. Scaling
If the pilot project demonstrates significant benefits, N-iX helps you plan to scale the RPA and AI solution across other processes and departments. AI insights are used to optimize and enhance the automation strategy continuously.
Option 2: Strategic RPA and AI implementation
The second approach is designed for companies with clear automation objectives and KPIs ready to implement a comprehensive RPA strategy from the outset.
1. Business objectives validation
While your company already has defined goals and KPIs, N-iX will work with you to validate and refine these objectives to ensure they are well-aligned with your overall business strategy. This step ensures that your goals, whether reducing operational costs, improving accuracy, increasing productivity, or enhancing customer satisfaction, are precise and measurable.
2. Building automation strategy
We conduct a comprehensive analysis to identify multiple processes that align with your automation objectives. At this stage, we evaluate each process's complexity, volume, and potential ROI. Our engineers use AI tools to discover and prioritize processes based on data patterns and operational insights. N-iX creates a detailed plan outlining the steps, timelines, resources, and technology required for implementing the RPA and AI solution.
3. Deployment
We implement the RPA and AI solution according to the detailed plan. Our process involves developing and testing the bots, training AI models, integrating them with your existing systems, and ensuring they meet your objectives and KPIs. Our data engineers incorporate continuous AI learning to adapt to changes and improve outcomes.
4. Monitoring, optimization, and scaling
N-iX monitors the performance of your solution against the predefined KPIs. We conduct regular reviews and optimizations to improve the efficiency and effectiveness of the automation. Successful implementations are scaled across your organization, leveraging lessons learned and best practices from initial deployments. AI-driven insights are used to fine-tune and expand the automation strategy, ensuring sustained benefits and innovation.
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Why choose N-iX for AI-powered RPA solution implementation?
- As a partner of UiPath and Automation Anywhere and a Kofax and Pega services provider, we offer a broad spectrum of RPA tools to suit your specific needs.
- N-iX has a team of over 200 data engineers with deep expertise in AI/ML, Data Science, and data analytics, ensuring our company delivers cutting-edge, AI-powered RPA solutions.
- We work according to agile methodologies, which suit the iterative nature of RPA implementation and AI/ML model training, enabling us to deliver flexible and responsive solutions.
- With 160 active clients and 21 years in business, N-iX has a proven track record of forming lasting partnerships across varius sectors.
- As an enterprise-focused company, we serve Fortune 500 companies and global industry leaders, including notable clients like Lebara, Gogo, Currencycloud, Fluke Corporation, to name a few. Solutions by N-iX are designed to meet the complex needs of large-scale operations.
- N-iX is dedicated to maintaining the highest industry standards, adhering to PCI DSS, ISO 9001, ISO 27001, and GDPR, making our solutions secure and compliant with global regulations.
