Digital transformation has become a buzzword that can be an overwhelming concept. Most businesses know they have to, but they’re not sure where to start. With so many different acronyms and terms in the world of smart automation, it can be hard to know what all of this means.
Understanding the Tools That Transform Your Business – Digital Transformation
According to Gartner, 91% of organizations are engaged in digital initiatives and 87% of senior business leaders say digitization is a priority.
Whether it is a front-end digital platform that enables insurance companies to deliver a better customer experience or major healthcare providers trying to reduce underpayments, initiatives like this are all part of the digital transformation.
The future of work will be highly automated, and for executives responsible for digital transformation, success will depend on getting the right mix of tools. They must also come up with innovations that allow them to disrupt their industry without disrupting its day-to-day operations.
So how do you navigate this ever-evolving intelligent automation journey?
Understanding intelligent automation
Imagine that every morning you pick up your phone just before you leave for work. It’s so routine that it has become automatic, and you do it almost without thinking. But one day you walk through the door and feel like something is missing – you realize you’ve misplaced your phone, so you go back and find it.
You have just imitated intelligent automation.
Smart automation is about taking a machine learned to do simple, repetitive tasks (aka automation). They teach the machine to adapt or correct its performance based on changing variables with incredible speed and precision.
Some of the main benefits of intelligent automation include greater accuracy, lower costs, and improved customer experience.
In finance and accounting, intelligent automation solutions can interface with existing ERP systems and invoice and purchase order data just like humans, but complete them in minutes instead of several days.
In healthcare, for example, finance managers can use intelligent automation in accounts payable to consolidate insurance and patient payment data. The data comes from a variety of sources and performs a comprehensive patient and payor risk analysis to reduce debt and the total number of days overdue.
A similar risk analysis is common in M&A departments, but requires access to data blocked in unstructured documents. Thanks to intelligent automation, this data is accessible, enabling data-driven decision making at scale and on demand.
Using AI in Automation
Intelligent automation uses several AI-based technologies to go beyond basic process digitization and truly digitally transform the way work is done. The goal is to automate more end-to-end processes and decisions while keeping humans in the know.
The solutions used to enable intelligent automation are not mutually exclusive and are most often complementary.
But what do all these tools mean and what are their real benefits?
Robotic Process Automation (RPA)
RPA uses software bots, also known as digital workers, to automate repetitive, error-prone, rule-based manual processes.
RPA is a popular tactical tool for automating mundane tasks to initiate business processes, such as entering data between software applications. Yet many users have been dissatisfied with how RPA providers make too many promises and under-deliver on the ability to truly transform operations digitally.
According to a Deloitte investigation, 58% of executives said they have started their intelligent automation journey, showing organizations are using RPA but going beyond to accelerate the rollout of smarter automation.
AI has become an umbrella term that describes several types of technologies, such as machine learning (ML), natural language processing (NLP), and optical character recognition (OCR).
They perform tasks that previously required human intelligence, such as extracting meaning from images, text or speech, detecting patterns and anomalies, and making recommendations, predictions or decisions. Together, they form the basis of the most widely used AI application in the enterprise: content intelligence.
Content intelligence helps software bots understand and create meaning from business content. It provides cognitive skills that the digital workforce can harness to transform unstructured content into structured, actionable information to make processes more efficient.
NLP is a way for computers to understand human languages. It does this by processing language data and breaking it down by context and syntax to identify which words are used and how they are used.
OCR is the process of mechanically or electronically taking scanned images of handwritten or printed text and digitally converting them to machine-coded text.
OCR uses character recognition to identify text and numbers in order to extract or analyze information from documents and forms. One example is using it in the banking industry to verify checks delivered through an app when a customer submits a photo of it.
ML is defined as a tool that enables a computer to learn from data by examining similar models. It can help the automation process by being able to predict a decision a human would make from repeated patterns.
For example, in the manufacturing industry, with enough data, ML could be used to identify errors and irregularities in manufacturing processes to ensure product quality.
Intelligent Document Processing (IDP)
IDP relies on OCR, machine learning and natural language processing technologies to scan and understand the most troublesome shapes. Then, it adds AI skills to the RPA bots so that they can learn, reason and understand the content of various documents, and categorize them. Finally, it extracts the relevant data for further processing.
According to Everest Group, the IDP market grew 25-50% in 2020, with financial and accounting processes and banking-specific use cases having the most penetration. IDP solutions help businesses save money while improving the productivity of their workforce and the experience of employees and customers.
They are typically integrated with internal automation applications, systems, and other platforms.
Intelligent automation requires monitoring
With so many tools available for your organization to automate, initiate and advance processes, it is imperative to monitor their performance. This should include the ability to identify and rectify bottlenecks and gain insight into the impact of digitally transformed processes on overall operations and the client experience.
Process intelligence, business intelligence, and data science and analytics tools can be used alone or together to help C suite managers and leaders know how their departments are working.
Extracting processes and tasks
The most common and costly mistakes companies make during implementation smart automation initiatives fail to fully understand how their processes work, and then choose the wrong processes to automate.
Harnessing real data from business processes is critical to the long-term success of any automation project. Powered by AI and ML technologies, process intelligence enables organizations to discover, assess, visualize, analyze and monitor process flows.
Linked to process exploration, task exploration can also monitor how individual employees interact with systems to determine whether you need to add more training, reassess steps, or define a new best practice procedure.
The increase in intelligent automation with analytics solves the problems of most organizations which are rich in data and poor in information and knowledge. Analysis software can leverage process data to find time savings, increased productivity, and innovation opportunities.
More advanced analytics automation platforms combine analytics, data science, and business process automation into a single end-to-end platform. This results in efficiency gains, revenue growth, net return, reduced risk and improved skills of your workforce.
Digital intelligence is being able to see, analyze and fully understand the processes and content that drive your organization forward. It allows leaders to identify gaps, bottlenecks and cost drivers to identify the most efficient way to automate processes.
It also allows you to troubleshoot issues causing stagnation and take your automation initiatives to the next level.
Although the term “sweet spot” is often used in sports, it also has a place in automation. In the world of automation, this can be the right combination of tools that strike a good balance between costs and benefits and automation and intelligence.
Smart automation is needed to transform your workplace to empower employees, improve the customer experience, increase return on investment, and gain competitive advantage. It should be part of your organization’s overall strategic digital transformation initiative.
Image credit; fanki chamaki; unsplash; Thank you!