Combining Two Technologies Into One Intelligent Automation
By now, many enterprises are familiar with using robotic process automation (RPA) for automating repetitive tasks. But with evolving cognitive automation technologies, RPA is taking leaps and bounds beyond simple task automation, to complex and advanced learning and decision-making done through artificial intelligence (AI) and machine learning (ML).
What’s the Difference Between RPA and Machine Learning?
In its basic form, RPA mimics human actions to perform rules-based tasks with complete accuracy, faster than a human. But RPA bots, or automation ‘software bots’ on their own, don’t learn from their actions or adjust to changes. They simply conduct routine work, such as administrative tasks, exactly as they’re told to do.
Machine learning (ML), on the other hand, involves a learning process. ML mimics human learning behavior to make decisions and adapt when necessary. This is done with machine learning algorithms analyzing and learning from data, and then making predictions for future behaviors – without requiring human interference.
An RPA bot might process an invoice, but let’s say the submitter makes a mistake on the form. ML can catch the mistake, learn from it and, if it occurs again, correct it the next time it happens – ensuring better quality results in the future.
So, while RPA can move the chess pieces, ML can learn how to win the game. Throw in a dash of artificial intelligence (AI), and you’ve got yourself a chess master capable of thinking up new strategies.
How is machine learning different from artificial intelligence?
We often hear AI explained as the ‘thinking’ technology and ML as the ‘learning’ technology – but how exactly does that make ML and AI different?
AI is a bucket containing many technologies, including natural language processing (NLP), deep learning, virtual agents, and – you guessed it – machine learning!
AI mimics human thinking to perform complex tasks, including reasoning and learning. ML is the ‘learning’ subset of AI that uses algorithms trained on data to make informed decisions.
Here’s another way to look at it: AI is the system that enables thinking, and ML is the output of what’s been learned. AI allows a machine to simulate human intelligence and problem-solve, while ML allows a machine to autonomously learn from past data.
- AI = Thinking
- ML = Learning
- RPA = Doing
Where does intelligent automation fit in?
Intelligent automation (IA) combines business process management (BPM), RPA and AI, which includes ML.
IA = BPM + RPA + AI (ML)
An intelligent automation platform enables RPA bots – what we call ‘digital workers’ – to interact with data and applications with more flexibility and adaptability. These digital workers can perform higher-level tasks without a human. Think of this collaboration as evolving software robots with intelligent automation.
Examples of intelligent automation could be chatbots answering customer queries, automating HR time-off requests or processing background checks for new hires. IA has use cases across various industries and can help streamline workflows and end-to-end business processes.
How Do Machine Learning and RPA Work Together?
ML adds to RPA to accelerate its automating capabilities, and this translates to better efficiency and cost savings for your business processes.
You can read more on automation software and how to apply it to your processes.
What are examples of machine learning with RPA?
By adding ML algorithms to RPA, you can expand your digital workforce from administrative tasks to complex processes. Here are some use cases of what this automation genius can do:
Data extraction and processing:
- RPA bots can extract data from structured sources like databases, spreadsheets and web forms.
- ML models can help with processing and structuring data into a machine-readable format.
- RPA bots are rule-based, meaning they follow predefined instructions.
- With the addition of ML, historical data is analyzed and classified so your RPA bots can make decisions within a process based on those ML predictions.
- RPA is built to automate repetitive tasks, but it struggles with tasks involving unstructured data or complex decisions.
- ML assists RPA by handling complex tasks. For instance, ML can classify and prioritize incoming emails, allowing RPA bots to route them to the appropriate department or respond with predefined actions.
- RPA can automate existing processes, but if the process contains bottlenecks or inefficiencies, RPA bots on their own can’t solve that.
- By analyzing historical data and performance metrics, ML models can identify the bottlenecks, suggest process improvements and adjust RPA bot behavior to optimize efficiency. Process mining and task mining can also go a long way in improving process efficiency by analyzing as-is processes and identifying automation opportunities.
- An RPA bot can process customer queries.
- ML adds sentiment analysis and natural language processing (NLP) to help determine if the customer is happy or unhappy and notify the customer service agent where necessary.
- RPA bots can be triggered to take immediate corrective actions or send alerts when anomalies are detected.
- ML can help with risk management by analyzing patterns and identifying anomalies in real-time, then flagging potential fraudulent activities for review.
- RPA bots can automatically produce monthly management reports, eliminating potential human errors caused by manual data entry.
- ML can speed up the data processing and flag any inconsistencies.
It’s clear from these examples that RPA and ML are both useful technologies – and they work better together. Isolated, RPA and ML can perform some functions, but as a team, they hold a lot more potential for your business, whether you’re in finance, healthcare, claims processing, manufacturing, etc.
How Can I Use These Technologies?
RPA has been around for a while, and now AI has allowed it to evolve into an intelligent automation solution you can leverage in your business processes. If you’re looking to optimize your processes, then AI and RPA are a match made in heaven.
How can I use ML in my business?
SS&C | Blue Prism® Decision enables process developers to integrate ML-based decisions into their workforce and build ML models in minutes through autoML. Decision automates human-like decisions with minimal effort and expertise, providing detailed audit logs to explain how a decision was reached.
As an example, Decision can streamline a bank account application process by reducing the number of steps a digital worker goes through. An agent would need to consider where the client is applying, if they’re a resident of the applicable country, if they’re the right age, whether they have an existing account, and so on. But Decision does all of that so you can make informed decisions sooner.
How do I deploy a digital workforce?
SS&C | Blue Prism® Enterprise contains everything you need to build a digital workforce. It’s a secure and scalable platform for developing process automations and running, managing and orchestrating your digital workforce.
Enterprise includes your digital workforce, our Design Studio and a Control Room. Your digital workforce is your autonomous software robots that mimic and learn business processes, augmented with AI and ML capabilities. The Design Studio is your no-code, drag-and-drop automation builder with reusable ‘objects’ that make it easy to reuse and scale process automations across your business. Finally, the Control Room is where you assign processes to your digital workers and scale tasks on demand. It also gives you real-time transparency of your process proficiencies.
The Potential of Automation
As we’ve explored in this blog, automation has a lot of potential across various industries and business processes. It saves time and brings cost savings to your organization, making your work more efficient and productive.
To find out how you can achieve digital transformation for your organization, contact an SS&C Blue Prism expert today.