Artificial intelligence (AI) extends robotic process automation (RPA) and increases business efficiency.
Intelligent automation (IA) — an end-to-end intelligent automation solution that combines robotic process automation (RPA) and artificial intelligence (AI) — can provide many benefits that aid in the digital transformation of an organization.
AI is the perfect complement to RPA, together providing more accurate and efficient automation powered by an informed knowledge base. AI is the process behind the effort to simulate human intelligence in machines, while RPA automates processes that use structured data and logic.
What is intelligent automation?
Intelligent automation (IA) is the combination of AI and automation technologies, such as cognitive automation, machine learning, business process automation (BPA) and RPA. IA capabilities simplify processes. This simplification enables the user to think about the outcome or goal rather than the process used to get that result or the boundaries between applications.
The use of intelligent tools, such as virtual assistants and chatbots, equips organizations with key insights that help in automation efficiency and faster response to customers. For example, tools like optical character recognition (OCR) allow paper-intensive industries, such as healthcare or financial services, to automate text analysis and drive better decision-making.
Uses for intelligent automation
There are many uses for IA, all of which ultimately help provide a better customer experience. Some of the uses include the following:
- Intelligent document processing (IDP): Forms of business data like images, emails and files often appear in an unstructured format. IDP uses IA tools like RPA, machine learning and natural language processing (NLP) to extract, validate and process that data.
- Process discovery: IA can help create a complete guide for automating a process using RPA.
- Streamline workflows: IA can use data to automate workflows for faster, more efficient processes
- Production and supply-chain management: IA can be used to predict and adjust production to respond to changes in supply and demand.
Top four ways IA combines AI and RPA to increase business efficiency
Aristotle believed, in reference to human perception, “the whole is greater than the sum of its parts.” The extension of RPA with embedded AI capabilities epitomizes this statement. AI utilizes information gathered from various sources and feeds that information to tools and products to increase the value of their interactions. RPA provides value in automating processes based on structured data, many of which previously required manual intervention. On their own, each provides value. But the combination of the two (i.e., IA) provides tremendous value in creating solutions that use a technological knowledge base to streamline processes and interactions between applications. The subsequent solutions are faster and more accurate, and contribute to gaining the following four efficiencies:
- Increase productivity: Automated applications and processes run faster. Automation of applications and processes, plus the automation of decision-making, forecasting and predictions from multiple sources of structured and unstructured data in real time empowers organizations with greater productivity and accuracy in their planning cycles. For example, Deloitte, an IBM customer in the finance industry, recently used RPA to create bots assigned to automate production of monthly management reports.
- Reduce costs: According to Deloitte, “Executives estimate intelligent automation will provide an average cost reduction of 22%,” although they also found that, “organizations currently scaling intelligent automation say they have already achieved a 27% reduction in costs on average from their implementations to date.”
- Improve accuracy: The use of both structured and unstructured data and the automation of repetitive processes ensures better decision-making, and less human intervention results in more precise results. An IBM customer in the finance industry recently used RPA to create bots assigned to automate production of monthly management reports. This automation eliminated errors introduced into the process through manual data entry, improving the accuracy of these reports and many others. Also, the use of OCR can help speed up data processing and automate data extraction from many sources.
- Enrich the customer experience: Organizations that use technology can better understand customers' needs, communicate more effectively and bring higher-quality products to market. Customers, in turn, are typically more satisfied in their buying experience. GAM, an IBM customer in the asset management industry, used bots to provide first-line customer support and pricing quotations. This optimization dramatically improved the time it took to provide answers to customers questions, improving the customer experience and streamlining the buying process.
Misconceptions about intelligent automation (IA)
Several misconceptions threaten to slow IA adoption, but they are easily dispelled. Some of these misconceptions include the following:
- IA replaces a human workforce: IA actually supplements or augments a human workforce by taking over repetitive tasks so the human, thinking workforce is available to work on more complex or more pressing matters. IA increases the accuracy of the outcome for the tasks that it is responsible for, reducing the need to resolve errors, which can cause latency and require pulling resources off other projects. It creates new opportunities centered around new skills that can be developed through retraining. For the human workforce, this is a great opportunity to refresh your skillset and build a stronger background for future growth.
- IA is nice to have, but not needed: IA is no-longer optional. Automation-infused applications are prevalent in our daily lives, such as speaking to Alexa or using a weather app. Likewise, in business, IA is a necessity to keep up with the market, stay competitive and satisfy customers. Organizations using manual processes just can’t keep up the pace. Not only that, but automation improves the quality of the product and quality of customer service by reducing errors and increasing the speed and efficiency of repetitive processes. Organizations that don’t adopt IA will struggle to succeed.
- IA can make unbiased decisions: IA formulates decisions based on input gathered and received, much of which is situational or provided by individuals and organizations responsible for that input. Therefore, the decisions made are inherently biased.
IA adoption challenges
Adopting IA is not without challenges. However, those challenges can be effectively remedied. Some of the challenges include the following:
- Skillset and knowledge gaps are remedied through retraining staff or partnership with a Process as a Service vendor who sets your IA in motion and manages it for you.
- Process ambiguity is a challenge if processes within the organization are not well understood. Process mining and process discovery remedy this challenge by helping businesses with process mapping — a necessity before embarking on an IA implementation.
- Lack of focus on standardization is faced by anyone implementing IA. No standard approach to automation exists, so each automation product vendor may approach the same process differently. This can be challenging if an organization decides to switch vendors. With so many vendors and organizations discussing this challenge, hopefully standards are forthcoming.
- Difficulty identifying opportunities and developing an automation platform is a challenge that a partner can address. Many types of partners solve this, from SaaS (Software as a Service) vendors to PaaS vendors, to systems integrators — they select the solution and the automation software that works best for your organization.
- Inadequate tools to develop and execute an end-to-end solution can halt the adoption of IA before it begins. If an organization has the skills in-house or can retrain existing team members, they can ensure the proper RPA tools, such as software robots, are put in place. This can also be remedied through partnership.
Use cases: Using IA to solve real-world challenges
Intelligent automation (IA) is pervasive across all industries to streamline processes and create efficiencies that provide more accuracy, faster response time and higher-quality product. Here are a few examples:
In the real estate industry, IA provides the first line of response to interested buyers. Bots use intelligent automation to provide faster, more consistent responses and engage buyers before involving a representative. Bots are also used to value properties by comparing similar homes and create an average of sales to prescribe the optimal selling price.
Bots forecast loan default, using machine learning and data analytics to create models that predict risk. In addition, RPA can automate the loan approval process and help reduce human bias.
In a production environment, RPA streamlines business operations and reduces the risk of error by automating repetitive tasks and processes, including anything from back-office parts inventory management to the assembly line. RPA can also be used to anticipate inventory using data analytics to evaluate existing inventory usage rates and collate that information to generate a recommendation.
A production environment — or any environment that relies on vendor relationships — can benefit from IA to analyze and select vendors. IA employs OCR (Optical Character Recognition) to gather and analyze data from multiple inputs in different formats and uses data analytics to compare vendor capabilities, reliability and compare pricing.
Intelligent automation (IA) trends and future direction
The future of IA is boundless. An example of new technology being developed that uses IA to provide greater value to our daily interactions with technology is cognitive automation. Cognitive automation is a progression of IA that uses large amounts of data, connected tools, diagnostics and predictive analytics to create solutions that mimic human behavior. Using natural language processing (NLP), image recognition, neural networks, deep learning and other tools, cognitive automation attempts to mimic more human behavior, including emotional reactions and other natural human interactions. An example of cognitive automation in use is the adoption of robotics to supplement patient care in nursing homes and hospitals.
Hyperautomation takes IA to the next level, automating as many processes and applications as possible, using tools such as business process management to standardize the approach to automation across the organization and create even greater business value.