ARTICLE | March 23, 2023 | 4 min read
Using hyperautomation to weather uncertainty
By Spencer Beemiller, field innovation officer, ServiceNow
Almost nobody was talking about ChatGPT a year ago. Now, hardly a week goes by without a think piece on how the robots are taking over. The business world is buzzing with talk of generative AI in our brave new world.
Meanwhile, we are in a period of profound global uncertainty. The International Monetary Fund (IMF) has reported unprecedented leaps in uncertainty since the pandemic. New threats, like financial sector instability, are emerging while the effects of old ones, like Brexit and the war in Ukraine, continue to reverberate. Executives have been responding by slashing budgets and laying off employees en masse.
Against this tumultuous backdrop, innovation continues, as it always does —just look at AI. So when executives wonder what they can do to stay agile in the face of uncertainty, the answer is to become more efficient.
For most organizations, that innovation isn’t necessarily generative AI but hyperautomation: a symphony of technologies including AI, machine learning (ML), and robotic process automation (RPA). In fact, hyperautomation has become a must-have for any organization looking to weather uncertainty.
During periods of uncertainty, companies must do more with less. While their goals remain the same—such as winning new customers while keeping existing ones happy—they lack the resources to throw money and personnel at projects. When budgets are tight, businesses have to get creative.
Doing more with less requires organizations to rethink the processes by which they get work done. That starts with data collection. By gathering the relevant data, organizations can determine which processes are working and where employees and customers are encountering roadblocks. But most companies are not currently equipped to do that. Although teams collect data all the time, it’s often not easy for them to share that data with each other—or parse useful data from noise.
The ancient Greek poet Archilochus wrote, “We don’t rise to the level of our expectations; we fall to the level of our training.” Without visibility into its own infrastructure, a company can’t create holistic strategies for change. It falls to the level of its current patterns, aka its training.
Hyperautomation isn’t simply a buzzword for a host of new technologies. Rather, it’s a system for connecting processes across the organization, boosting efficiency, and empowering leaders to make data-driven decisions. During periods of macroeconomic uncertainty, hyperautomation is crucial for raising a company’s level of training by defining what the service consists of between departments, connecting the platforms, and automating actions to ensure repeatability.
But hyperautomation does not always start with automation. Teams can’t automate if they don’t know what needs automating. Rather, it often starts with discovery—the prelude to training, the warmup, if you will. Think of a marathon runner. They don’t attempt a 26-mile run immediately; they begin by ascertaining their baseline level of fitness.
For most companies, discovery means laying a foundation of their performance with the help of Application Portfolio Management (APM). APM enables teams to see their organization more clearly. What apps are available? What technologies are employees using? Which ones are they not using? And where might technology prove helpful?
Teams can then decide which apps are no longer needed and focus on the apps that are. Eliminating redundancies is crucial for addressing app sprawl, a common problem. Most organizations use upwards of 200 apps, some of which employees aren’t even aware of. Reducing app sprawl is crucial during periods of uncertainty, when money is tight and efficiency is everything.
Think back to the marathon runner. Once they know what they’re currently capable of, they can start training for the race. Similarly, once an organization gets the lay of the land via APM, it can make changes to its architecture. That’s where hyperautomation can really flex its muscles.
The simplest way to think about hyperautomation is as a method to tie things together. For example, imagine a B2C organization that uses three apps. One hosts a chatbot, one connects customers to a human on call, and one serves as a customer complaint platform for case management. During discovery, the organization might find that the complaint platform is overwhelmed, human agents are swamped, and the chatbot is being underutilized.
Using hyperautomation to field customer complaints involves connecting the complaint platform with the chatbot via an integrated workflow. AI can learn the common questions that customers ask and the problems they typically encounter. RPA can enable the platform to filter some of those questions for the chatbot to address while simultaneously serving customers documentation that explains how to resolve others. This frees up human agents to tackle thornier problems.
Executives are often afraid to make bold changes when the future is uncertain. That’s understandable. Nobody wants to be the person who took a big risk and sank the company.
But changing an organization’s processes by investing in new technology is actually a lot less risky than stagnation. Think about some of the great innovative leaps of our time: cloud computing, smart devices, and now ChatGPT.
Organizations can use hyperautomation to weather this storm and emerge stronger than before. Archilochus, who understood the value of training, also had this piece of advice: Be bold!