The pain points that drive IT and business leaders to implement process automation are clear: broken or slow business processes, automation wins that don’t scale, and data silos. All three create problems that can stop critical digital transformation work from reaching its goals. These problems not only cost time and money but also open companies up to risk and compliance trouble and employee engagement woes.
Businesses today know that operational efficiency, effectiveness, and productivity are keys to competitive advantage. And process automation tools are being adopted more and more because they help IT and business leaders turn those goals into reality. If you’ve spent time researching ways to boost efficiency by taking advantage of automation opportunities, you’ve likely come across the related terms hyperautomation (which is synonymous with process automation) and intelligent automation.
Business process automation (BPA) refers to the use of computer systems and software to automate business processes or tasks. These days, many people simply refer to BPA as process automation: a set of tools that automates entire business processes, end to end. Robotic process automation (RPA), on the other hand, is a single part of a process automation toolset. RPA fits into a process automation strategy by automating repetitive tasks within a process.
Have you seen the efficiency gains you’d hoped for from your investments in task automation tools like robotic process automation (RPA)? Despite prioritizing automation efforts, many IT leaders still lack the tools for broader, end-to-end process automation—or as Gartner calls it, hyperautomation. If you’ve found yourself grappling with numerous disconnected islands of automation, where do you go from here?
Are you confused about the multiple types of business process automation? You’re not alone. Automation tool vendors seem to have created more flavors than a contestant on Top Chef. Let’s get to the bottom of the differences between the various types of process automation and find out what they can and can’t do, in plain terms. Here’s a starting hint: Get ready for synonyms. In some cases, different analyst firms and tool vendors use different terms for the same ideas.
Imagine your business is an ocean. Under the surface, a rich ecosystem of processes, people, and technology needs to work together to create a healthy environment. Above the surface, constant waves can make or break your business, either propelling you to new opportunities or sinking you under pressure.
Data fabric might seem like yet another data management technological innovation you have to learn about—but this one is different from the rest. While technology continues to evolve, the data problems that enterprises face are in many cases getting worse over time: data, data, and more data is permeating the business.
Artificial intelligence (AI) promises outsized gains in business efficiency. And since organizations live or die by their processes, integrating AI into the picture through AI process optimization can help boost ROI with end-to-end process automation and enhancements. This blog will explore five ways AI can be used to improve and optimize process efficiency. But first, let’s explain what AI process optimization means.
I’m a firm believer that banks rarely invest to drive revenue and elevate customer service. But I recently hosted a roundtable discussion in London with Appian, a process automation platform company, where leaders from mainstream and challenger banks in the UK gathered to discuss the challenges facing financial institutions when adopting new technology.
Data management in the modern enterprise requires skill and stamina. With the mountainous rise in data volume and the complexity of managing disparate data sources across a wide variety of legacy systems and hybrid cloud environments, it can seem virtually impossible to successfully climb past enterprise data obstacles to success. However, data fabric technology eliminates some of those obstacles. Is it the answer for your data strategy?
