Intelligent automation in the workplace is becoming more relevant in the modern market. As automation technology becomes more refined and smart business models allow business owners to optimize their workflow, more and more are turning to intelligent automation for their internal and client-facing processes alike.
There are several different types of automation in the workplace, including digital automation, such as cloud and app services, as well as physical automation like robotic process automation. Different types of automation will achieve different results, and knowing how much and what kind of automation will be beneficial for your business will come down to having a better understanding of what automation is and the different ways it can be applied.
Here are some of the most important terms to know to better understand what intelligent automation is:
This is the ability of any computer or computerized object to simulate human learning. It is often talked about alongside automation because AI can assist the automation of processes.
This is the operation of automating business processes. BPA can also be a term applied to specific software designed for this purpose.
This is when digital technology is used to automate one or more business processes. This doesn’t necessarily mean that the entire process is automated – for example, your business may digitize and automate your weekly ad disbursement, however, the marketing team is still required to create the actual copy.
When used concerning automation, integration describes the ability of software to communicate efficiently with one another as well as other compatible technology. Integration can also refer to the process of implementing BPA or other new software into your current business’s processes.
This describes a subsection of AI that enables machines to learn new tasks. This can assist automation and integration efforts between other AI software.
Process orchestration describes how automated processes are set up and executed. This can be done manually, or with different BPA algorithms.
Robotic process automation describes the use of robotic technology to automate once-manual work. This may often be used for repetitive tasks that can be easily automated via a rule-based system.
As the name suggests, a rule-based system describes the logic that automated processes, specifically RPAs, have been programmed to follow.
When discussing automation in the workplace, there are generally two types of automation that are referred to: intelligent automation and robotic process automation.
Intelligent automation is the combination of AI, machine learning, and process automation that come together to form smart business models. Each of these facets of intelligent automation is meant to be able to learn, think, and adapt independently, which is a key reason why it is described as “intelligent” or “smart.” An example of intelligent automation is a customer service chatbot, which is designed to answer consumer questions or lead them to the right department.
These bots can have hundreds, if not thousands, of unique interactions a day. Therefore, they use AI, historical and real-time data, and other machine learning processes involved in intelligent automation to operate successfully.
RPA is a simpler version of this. RPA processes are designed to repeat rule-based processes essentially ad infinitum, or until the rules for the process change. For example, bottle-capping machines often utilize RPA, because they repeat the same process. In this way, RPA is less “smart” than intelligent automation, but it can still be incredibly useful for manufacturing and production.
Advantages may include:
Because automated processes can work continually, you may see an increase in your productivity, as well as have the ability to allocate employees to higher-level jobs that may not be or can’t be automated.
You may be able to save on labor costs by instituting intelligent or robotic automation for certain processes. However, it still may be worth employing a supervisor for these processes, to intervene should there be an error or other outlier that requires attention.
Intelligent automation is designed to adapt quickly therefore it can scale your business operations quickly and efficiently.
Some processes that can be automated, such as sorting, may pose physical safety risks to workers operating with or around large machinery. By automating these processes, you can reduce these risks.
Because intelligent automation is always collecting data, it is much easier to access data at each stage. This can help you better address problems, create more comprehensive insights, and make data-driven decisions and forecasts.
Here are some general tips that can help your start integrating intelligent automation into your current business processes:
Before you can start on intelligent automation, you’ll want to consult your analytics service provider to evaluate the efficiency and performance of your current processes. This will help you understand where you may have room to optimize and can help sharpen your priorities for automation, especially if you’re working within a limited budget.
Not every process can or should be automated. Identifying what areas will benefit you and your business’s workflow or productivity with the addition of automation is a crucial step in getting the most out of your investment. You can do this independently, but AI consultations can be invaluable for beginners who may have less experience or knowledge when it comes to these types of assessments.
One of the first steps that you should make in implementing intelligent automation processes is updating your network infrastructure. Working from a legacy system can make it harder to integrate intelligent systems. By updating to a cloud-based service, you’re improving your network’s accessibility to intelligent software.
This is another crucial step when implementing new automated services. You’ll want to test the automation, whether it’s digital or RPA, before you start using it full-time. These tests will help you prevent errors during working hours and allow you to demonstrate to or train employees.
It’s important to note that this process may vary depending on your unique business needs and that it may need to be implemented gradually to ensure that the automation is installed correctly.
Several industries are using intelligent automation, across several different sectors, to improve their business processes. Here are a few examples:
Intelligent automation in healthcare can be used in several different ways to improve patient care and professional diagnostics. The use of electronic patient files and charts is providing healthcare professionals and patients real-time updates concerning tests and bloodwork. Automated microchips can be used to monitor vitals constantly, and smart pills, which contain diagnostic tools like sensors or cameras, can be used to control the release of medicine; both of these have been growing in the market. These tools can help medical professionals improve their insights, make data-driven diagnoses, and even improve patient comfort during certain procedures.
The manufacturing industry may be one of the first that comes to mind when speaking of integrating intelligent automation in the workplace. Repetitive processes, such as labeling, sorting, or packaging, may all benefit from RPA. Using intelligent automation for customer interaction can similarly help improve user experience and response time to queries.
Finance is another sector in which there are big opportunities for intelligent automation. AI and intelligent automation can help improve mobile banking apps, of which Dataprot predicts that more than 7 billion people will use by 2021. Intelligent automation is also helping financial institutions improve their risk modeling, which can make huge impacts on internal decision making.
The life insurance industry uses RPA and intelligent automation to provide faster claims processing, improve customer service, manage risk, reduce human errors, and cross-promote products and services. By utilizing intelligent automation in insurance underwriting, what would typically take insurance underwriters weeks to complete due to manual processing, can be actualized with automation in hours, if not in real-time.
Similar to many tech markets, the intelligent automation market is making rapid growth. By 2023, it’s projected that the industry of intelligent automation will be valued at $13.75 billion as an industry. This comes as no real surprise, as consumers and business owners alike are expecting more accurate and accessible data from the processes they interact with.
Fortunately, intelligent automation is designed to evolve as our expectations and technology does. Implementing intelligent automation into your business can help you stay on the cutting edge of your industry and provide valuable optimizations and insights into your business data going forward.
For decades, Windows served as the workhorse of the business world. In recent years, however, a significant transformation has occurred with the rise of cloud infrastructure platforms. Enterprises now realize that legacy on-premises Windows workloads are impeding their progress. Core challenges include licensing costs, scalability issues, and reluctance to embrace digital transformation.Explore
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore