Technology has always been a vital part of business operations, but in recent years, the pace of change has accelerated significantly. Businesses must now evaluate and adopt new technologies at an ever-increasing rate in order to stay competitive.
This can be a daunting task, particularly when it comes to choosing between related technologies like robotic process automation (RPA) and artificial intelligence, or more specifically, machine learning. Both offer significant benefits in terms of efficiency and insights, but they also have distinct differences that must be considered.
RPA is typically used for repetitive, rule-based tasks that can be automated with little human intervention. This includes tasks like data entry, file management, and basic customer service inquiries. AI, on the other hand, is more geared towards complex tasks that require human-like intelligence and reasoning. This could include tasks like fraud detection, lead scoring, and predictive maintenance.
In this article, we will explore the different benefits of RPA and AI, and how to use both technologies to get the most out of your business data.
RPA is a process automation technology that uses software bots (or “robotic agents”) to automate repetitive tasks in an organization without requiring any programming skills or knowledge.
RPA is part of the digital transformation movement, which includes technologies like natural language processing (NLP) and computer vision. It also involves the use of new tools and processes such as process mining, cognitive automation, and end-to-end automation.
There are many benefits of digital transformation, including the ability to automate manual tasks, improve decision-making, and forecasting, and to reduce human error.
Gartner coins the term “hyperautomation” to describe the use of multiple automation technologies, including RPA, in order to automate as much of the work as possible. There are many different RPA tools available, including Automation Anywhere and UiPath. Each of these providers offers different capabilities and features.
RPA can be used across all industries, from healthcare to manufacturing, finance, and more. It can be used for automating back-office operations like HR, accounting, and data entry. It can also be used for automating customer-facing processes like chatbots and self-service kiosks.
RPA is different from traditional AI in that it relies on pre-configured rules and scripts to automate tasks. This makes RPA less complex and easier to implement than traditional AI solutions.
The modern enterprise faces a highly competitive landscape. To stay ahead, businesses must find ways to improve efficiency and optimize their operations. RPA can help businesses achieve these goals in several ways.
RPA can automate repetitive tasks that would otherwise need to be done manually. For example, accounting teams often have to manually reconcile hundreds or even thousands of invoices. Without standardized processes in place, this task can take days or even weeks to complete.
And since different contractors and agencies use different formats for their invoices, this task can be error-prone. With RPA, businesses can automate the invoice reconciliation process, freeing up human workers for other tasks.
Beyond invoice reconciliation, RPA can be used to automate other time-consuming tasks such as data entry, customer service, and financial analysis. For instance, when migrating to a new CRM system, businesses often need to manually transfer customer data from the old system to the new one.
With RPA, businesses can automate this task. Or when it comes to customer service, businesses can use chatbots powered by RPA to handle simple customer queries. This frees up human customer service agents to handle more complex issues.
Any time a business is using man-hours to complete a repetitive task, there’s an opportunity to save money by automating that task with RPA.
For example, banks and insurance companies use RPA to automate the claims processing workflow. This reduces the need for human workers, which in turn saves the company money.
Sales and marketing teams can also use RPA to automate tasks such as lead generation and data entry. This allows businesses to invest their resources in more strategic tasks, such as developing new marketing campaigns or improving their sales process.
Not only does RPA have the potential to save businesses money, but it can also help businesses make money. For instance, RPA can be used to monitor pricing patterns and recommend changes that will improve profitability.
In addition to saving time and money, RPA can also help businesses improve their overall efficiency. In many cases, RPA can automate tasks that are not only time-consuming but also error-prone.
For example, when processing customer orders, businesses often need to check for errors such as out-of-stock items or incorrect pricing. This task can be repetitive and tedious for human workers. With RPA, businesses can automate the error-checking process, ensuring that orders are processed correctly and efficiently.
RPA can also be used to automate tasks that require complex decision making. For instance, when processing loan applications, businesses need to consider a variety of factors such as the applicant’s credit score, employment history, and income. This task can be difficult for human workers to do consistently.
With RPA, businesses can create software robots that are capable of making complex decisions. These software robots can be programmed to follow the same decision-making process each time, ensuring that loan applications are processed consistently and efficiently.
In 1950, Alan Turing published “Computing Machinery and Intelligence,” in which he proposed the Turing test as a way to determine if a machine is capable of intelligent behavior. The Turing test has been used as a benchmark for AI ever since.
Unlike AI, RPA does not aim to make machines that can "think" like humans. Instead, its goal is to automate repetitive tasks. However, RPA can be used in conjunction with AI capabilities to create more intelligent software robots.
For instance, when processing loan applications, an RPA-powered software robot can use AI algorithms to predict the likelihood of default. The AI itself would not have the functionality to contact the applicant or collect additional information. However, it could provide valuable insights that would help the software robot make better decisions.
Human-centered AI may also be part of this, with human intelligence in the loop to verify an AI’s decision. In this way, businesses can use RPA and AI together to create more intelligent and efficient software robots.
There's no one-size-fits-all answer to the question of whether to implement RPA or AI in your business. The decision must be made on a case-by-case basis, taking into account factors such as required functionality, budget, time constraints, and the resources available.
Let's consider a few examples to illustrate the difference between RPA and AI. Consider a finance team that wants to detect fraudulent transactions. AI can be used to automate the task of reviewing transactions and flagging those that meet certain criteria (e.g., large amounts, unusual patterns). When it comes to contacting the customer to confirm the transaction, however, RPA would be needed. This is because the task of making a phone call and inputting the customer's response into the system is best suited for a machine that can follow specific instructions.
Another example is a company that wants to develop targeted marketing campaigns. In this case, AI can be used to analyze customer data and predict which leads will be most responsive to a given offer. RPA can then be used to automate the process of sending out the targeted emails or making the phone calls.
Similarly, a sales team may use AI to identify upsell and cross-sell opportunities, and RPA to automate the process of reaching out to customers.
RPA and AI can also be combined in other ways. For example, RPA can be used to gather data from multiple sources, which can then be fed into an AI system for analysis. Or, an AI system could be used to identify opportunities for RPA, such as tasks that are suitable for intelligent automation and would provide the greatest benefit.
There are differences in the resources required to implement RPA and AI. AI largely requires clean, structured data (or semi-structured data), which can be costly and time-consuming to acquire. Working with unstructured data takes even longer.
In contrast, RPA is more about creating tasks that can be automated, such as sending emails, and does not require as much data. AI used to require more expertise and financial resources to implement, but that is changing as the technology matures and more AI-as-a-service offerings become available.
Years ago, businesses would need to create teams of data scientists, who would then spend months or years building AI models. The process was difficult to set up, involved a lot of trial and error, and required constant retraining as data sources and business processes changed.
Thankfully, that's no longer the case. Akkio makes it easy for anyone to get started with AI, regardless of coding or data science knowledge. And because Akkio is cloud-based, it can be set up in minutes, without any IT infrastructure required.
Akkio works by consuming data from any source (for example, from CRMs, ERPs, marketing automation, web analytics, or social media) and making it available for analysis in a centralized location. This data is then used to train an AI model, which can be used to make predictions about future events, such as which leads are most likely to convert or which customers are at risk of churning.
Let's look at the example of fraud detection. Akkio can be used to pull data from multiple sources, such as bank transaction records, credit card statements, and loyalty program activity. This data is then cleansed and normalized, eliminating human and technical errors. Next, Akkio's AI engine will identify anomalies in patterns, such as a sudden increase in the number of transactions or the use of high-risk keywords.
Once the data has been analyzed, Akkio can be used to set up triggers and alerts through integrations, so that suspicious activity can be flagged in real-time. This way, businesses can take action quickly to prevent fraud before it happens.
Akkio can also be used for targeted marketing campaigns. By analyzing customer data, Akkio can predict which leads will be most responsive to a given offer. The system can then be used to automate the process of sending out the targeted emails or making the phone calls.
Similarly, Akkio can be used by sales teams to identify upsell and cross-sell opportunities, and automate the process of reaching out to customers.
As you can see, there are many ways in which RPA and AI can be combined to improve business insights. The decision of which technology to use should be made on a case-by-case basis, taking into account the specific needs of the business.
In recent years, businesses have faced an increasingly competitive landscape. To stay ahead, businesses must automate repetitive tasks and focus on strategic projects that enable competitive advantages.
These technologies can be used in combination, such as with an AI model that predicts which leads are most likely to convert, and an RPA system that automates the process of sending out targeted marketing emails.
Akkio is a cloud-based AI platform that makes it easy for businesses to get started with AI. Sign up for a free trial to see how Akkio can help your business grow.