eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
The COVID-19 global pandemic forced plans for investing in digital transformation projects to shift toward maintaining business continuity, setting up a remote workforce and ensuring customers’ immediate needs were met.
Now that the dust has settled, CIOs are re-evaluating their IT objectives and discerning which projects to pick back up, axe or initiate after learning where they experienced major pitfalls during the early months of the pandemic. Prioritizing the right automation projects is important to ensure your organization rebounds and prospers in 2021.
Industry information for this edition of eWEEK Data Points comes from Anthony Macciola, chief innovation officer at ABBYY, a digital intelligence company. Macciola holds more than 45 patents for technologies in mobility, text analytics, image processing and process automation, and is leading AI initiatives at ABBYY.
Here are his best practices about choosing the right initiatives worthy of investment.
Data Point 1: Prioritizing Projects
There once was a time when cost reduction was the primary driver for digital transformation. However, a Forrester Research survey found that cost reduction is now behind investing in technology that will “accelerate digital business transformation, improve customer experiences and improve worker productivity.” The pandemic has actually led to many organizations discovering they have been doing things wrong all along. The Forrester’s “Global Digital Process Automation Survey” showed two-thirds of businesses stated they encountered broken processes when they shifted their workforce to remote due to COVID-19, and now 60% are reconsidering their process strategy.
Using process mining and discovery (PDM) tools is a growing trend for prioritizing projects. Analyst firm NelsonHall estimates the current global PDM market size is at $566.5 million and is projected to increase to $5.4 billion by 2024. Process mining empowers you to use the information contained within your systems to create a visual model of processes, analyze them to identify opportunities for process improvement, as well as real-time operational monitoring and the ability to predict and take pre-emptive action of future outcomes to facilitate decision-making.
Data Point 2: Taking a Data-Driven Approach
To prioritize projects–whether it’s customer onboarding, accounts payable/invoice processing, contracts review, claims processing or call center inquiries–it takes a data-driven approach. Otherwise, you risk succumbing to stakeholders’ opinions, bias, internal politics or low-hanging fruit that doesn’t return significant value.
To identify which processes would be the best fit for automation opportunities, and how the staff interacts with them, it’s crucial to have a bird’s-eye view of your entire operations across the whole organization to ensure full visibility.
For example, if you’re leading an insurance organization and notice an abnormally lower client retention rate over the past six months, you’d want to know exactly where it’s stemming from. So rather than guess that investment in customer renewal workflow is the solution, you may discover that delays in claims processing is the culprit–even down to the adjuster that is having the most challenges and how much you can save by making changes. Using your data to determine which processes to best focus digitization efforts, and knowing the expected return on investment, is a smart way to prioritize.
Data Point 3: Leveraging AI in Automation
The knee-jerk reaction for most companies in the wake of COVID-19 was to halt funding for new technologies, such as automation and artificial intelligence (AI), according to KPMG International. Executives are now optimistic and plan to increase spending in the next 12 months to accelerate digital transformation.
The most common automation projects that stall are robotic process automation related. In another survey, 38% of executives said their RPA projects missed the mark from not truly understanding the intended process being automated or the RPA bots not being able to work with unstructured content, such as invoices, bills of lading or claim forms, into usable content and initiate downstream processes.
This challenge was especially notable when banks had to ramp up their loan processing to accommodate the volume increase due to the Small Business Administration Paycheck Protection Program. Banks experienced 10 years’ worth of loan volume in a two-month period, and those that were in the midst of RPA projects stumbled when bots could not efficiently read various loan applications and supporting documentation. Banks turned to AI enabling technologies such as machine learning, OCR and natural-language processing to deliver cognitive skills to RPA bots to enable reasoning, decisioning and understanding. This allowed bots to transform unstructured data from documents into actionable information ready for upstream processes.
Data Point 4: Adopting Easier to Consume Solutions
The role of citizen developers is growing within organizations. Citizen developers are business analysts and knowledge workers who are not trained as developers or have deep domain expertise in coding or specific technologies such as analytics, capture, RPA or process mining, but are able to use these technologies made available in low-code or no-code applications.
RPA was a catalyst for these easy-to-consume solutions by way of their digital marketplaces. Their marketplaces enabled partners and technology vendors to design low-code/no-code applications that plug right into their platforms with user-friendly, guided interfaces. Now, low-code solutions are available for most systems and intelligent automation and business process management platforms whether on-premises or in the cloud.
In fact, PwC says cloud spending rose 37% to $29 billion during the first quarter of 2020 and expects more spending will be allocated towards cloud solutions in the aftermath of COVID-19.
Whether it’s for replacing stalled projects or starting new initiatives, integrating low-code solutions are a cost-effective way to democratize AI throughout the organization and accelerate digital business transformation.
Data Point 5: Thriving in the Now Normal
All leaders probably related to Microsoft’s CEO Satya Nadella when he said, “We’ve seen two years’ worth of digital transformation in two months.” We can feel more confident after weathering these months of the pandemic and know that early investments in digital transformation helped minimize significant loss. We can also justify kickstarting automation investments leveraging the latest technologies in process mining, AI and no-code/low-code applications to ensure business resiliency and thriving in the now normal.
If you have a suggestion for an eWEEK Data Points article, email cpreimesberger@eweek.com.