Digital transformation has become a buzzword that can be an overwhelming concept. Most companies know they need to do it, but they are not sure where to start. With so many different acronyms and terms in the world of intelligent automation, it can be challenging to know what it all 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 digitalization is a priority.
Whether it is a digital front-end platform that allows insurance companies to deliver a better customer experience or major healthcare providers trying to reduce underpayments, initiatives like this are all under the digital transformation umbrella.
The future of work will be highly automated, and for executives responsible for driving digital transformation, success will depend on getting the right combination of tools. They must also deliver innovation that enables 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 grab your phone right before you leave for work. It’s so routine that it’s become automatic, and you almost do it without thinking. But one day, you walk out the door, and it feels like something is missing – you realize you’ve misplaced your phone, so you go back and find it.
You just imitated intelligent automation.
Intelligent automation involves taking a machine taught to do simple, repetitive tasks (aka automation). They teach the machine to adapt or correct its performance based on changing variables at unbelievable speed and accuracy.
Some of the top benefits of intelligent automation include greater accuracy, cost reduction, and improved customer experience.
In finance and accounting, intelligent automation solutions can interface with existing ERP systems and data from invoices and purchase orders just like humans but complete them in minutes instead of days.
In healthcare, for example, finance leaders can use intelligent automation within accounts payables to consolidate insurance and patient payment data. The data comes from various sources and conducts a comprehensive risk analysis of patients and payers to reduce debt and total days outstanding.
Similar risk analysis is common in F&A departments but requires access to data stuck within unstructured documents. With intelligent automation, that data is accessible, enabling data-driven decision-making at scale and on-demand.
AI usage in automation
Intelligent automation uses several AI-enabled technologies to move beyond the basic digitization of processes and truly digitally transform the way work is completed. The objective is to automate more end-to-end processes and decisions while keeping humans in the loop.
Solutions used to enable intelligent automation are not mutually exclusive and are more often than not complementary.
But what do all of these tools mean and what are their real-world benefits?
Robotic Process Automation (RPA)
RPA uses software robots, also referred to as digital workers, to automate manual processes that are repetitive, prone to error, and rules-based.
RPA has been a popular, tactical tool to automate mundane tasks to initiate business processes, such as data entry between software applications. Still, many users have been dissatisfied with how RPA vendors overpromise and under-deliver on being able to truly digitally transform operations.
According to a Deloitte survey, 58% of executives reported that they had started their intelligent automation journey, showing that organizations are using RPA but are moving beyond it to ramp up deployment of more intelligent 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. Combined, they form the foundation for the most widely used application of AI within the enterprise – content intelligence.
Content intelligence helps software bots understand and create meaning from enterprise content. It delivers cognitive skills that the digital workforce can harness to turn unstructured content into structured, actionable information to make processes run more efficiently.
NLP is a way for computers to understand human languages. It does this by processing the language data and breaking it down by context and syntax to identify what words are being used and how they’re being used.
OCR is the process of mechanically or electronically taking scanned images of handwritten or printed text and converting them digitally into machine-encoded text.
OCR works by using character recognition to identify text and numbers to extract or analyze information from documents and forms. One example is using it within the banking industry to verify checks delivered over an app when a client submits a photo of it.
ML is defined as a tool where a computer can learn from data by looking at similar patterns. 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 within the manufacturing processes to ensure product quality.
Intelligent Document Processing (IDP)
IDP leverages OCR, machine learning, and natural language processing technologies to digitize and understand the most inconvenient forms. Then it and adds AI skills to RPA bots so they can learn, reason, and understand the content within various documents, and categorize these. Lastly, it extracts relevant data for further processing.
According to Everest Group, the IDP market grew 25-50% in 2020, with finance and accounting processes and banking industry-specific use cases having the most penetration. IDP solutions help enterprises achieve cost savings while improving their workforce productivity and employee and customer experience.
They are typically integrated with internal applications, systems, and other automation platforms.
Intelligent automation requires monitoring
With so many tools available for your organization to automate, initiate, and drive processes forward, it’s imperative to monitor their performance. This should include the ability to identify and rectify bottlenecks and have insights into how digitally transformed processes are impacting overall operations and the customer experience.
Process intelligence, business intelligence, and data science and analytics tools can be used alone or together to help managers and C-suite leaders know how their departments are working.
Process and task mining
The most common and expensive mistakes businesses make when implementing intelligent automation initiatives fail to properly understand how their processes are performing and then choose the wrong processes to automate.
Leveraging actual business process data 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.
Related to process mining, task mining can also monitor how individual employees interact with systems to determine if you need to add more training, re-evaluate any steps, or set a new best practice procedure.
Augmenting intelligent automation with analytics solves most organizations’ problems with being data-rich and information and insight poor. Analytics software can leverage data from processes to find time savings, added productivity, and opportunities for innovation.
More advanced analytics automation platforms blend analytics, data science, and business process automation into a single end-to-end platform. This helps obtain efficiency gains, topline growth, bottom-line return, risk reduction, and upskilling for your workforce.
Digital intelligence is being able to fully see, analyze, and understand the processes and content that keep your organization moving. It enables leaders to identify shortcomings, bottlenecks, and cost drivers to pinpoint the most impactful way to automate processes.
It also allows you to resolve issues causing stagnation and elevate your automation initiatives to the next level.
While the term “sweet spot” is often used in sports, it has its place in automation, too. In the world of automation, it can be the right combination of tools that deliver a good balance of cost and benefits and automation and intelligence.
Intelligent automation is necessary to transform your workplace to empower employees, enhance customer experiences, increase ROI, and gain a competitive edge. It must be part of your organization’s overall strategic digital transformation initiative.
Image Credit; fanki chamaki; unsplash; thank you!
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