Automating the office: Is the smart workplace finally within reach?
As futuristic as it sounds, robotic process automation (RPA) has been around for decades, in different forms. And the overall goal has always been the same: to give businesses an easy way to automate expensive tasks.
The reason few people outside of IT have heard of RPA has to do with the types tasks he was able to automate; so-called software bots (an evolution of old-school macros) are generally well equipped to follow simple rules, but not to handle exceptions.
However, recent advances in artificial intelligence (AI) promise to expand the reach of RPA, which is increasingly capable of performing complex tasks as well. In industry jargon, this is called “hyperautomation”.
One of the companies at the forefront of this movement is Blue Prism, which was acquired earlier this year for $1.65 billion by SS&C Technologies following a period of rapid growth during the pandemic.
The combined entity, SS&C Blue Prism, aims to deliver every piece of the automation puzzle – from process mining, to RPA, to document processing and chatbots – which is supported by AI.
In conversation with Tech Radar ProEric Tyree, who leads AI and automation at the company, explained that the development of large-scale models has turned AI into a readily available utility, opening various doors in the computing space. automating.
“Introducing AI into an organization in a meaningful way is now easier because its ability to automate one type of task is built into an RPA bot,” he told us.
“From a practical perspective, this has made AI almost commoditized, giving life to a stumbling revolution. [Previously]the success rate of enterprise AI projects was so low.
EPS on the rise
The case for more automation is compelling on paper. According to vendors, RPA reduces the amount of resources spent on repetitive administration, eliminates human error, and frees employees to focus on tasks that require abstract thinking.
In a accounting context, for example, automation could allow professionals to spend less time on accounting and data collection, and more time on analysis and business strategy.
The customer service sector is also well prepared to take advantage of this. An agent’s primary goal is to interact with customers, but too much time is wasted moving data between RCMP and database systems, something RPA can handle easily.
Over the past few years, the growth of the global RPA market has been steady, if unspectacular. But the analysis of Grand View Search suggests the industry will take off over the next decade, reaching a compound annual growth rate (CAGR) of over 38% by 2030.
The growth trajectory may have been accentuated by the need to increase efficiency during the pandemic, but Blue Prism maintains that the market has been heading in that direction for a long time.
“Covid has made people realize that you can change the way businesses work quickly and in some cases has forced them to do so. But what really drives automation is economics,” Tyree explained. “The old way of thinking about digitization was to tear down legacy systems and replace them, but RPA represents a much leaner and cheaper way to transform.”
He cited the battle between traditional banks and challenger banks as an example: “Retail banks have traditionally been slow, but they are eaten up by fintech. And because they don’t have time to retool their old domains in response, they have to automate additionally.
In this scenario, adopting automation is not about eliminating marginal productivity gains that improve the bottom line, but rather a strategic necessity.
Bringing intelligence to the party
Until recently, the vision of high automation had remained a bit fanciful; only the largest companies in a small minority of sectors were able to benefit. But there are signs that vendors may soon be able to achieve the original goal of bringing automation to the masses.
Much of the growth in RPA adoption is expected to come from improving the ability to perform difficult processes and decreasing the complexity associated with deployment and management.
“It used to be that if you brought AI into an organization, you bought a platform and brought processes there, which created all kinds of hurdles,” Tyree explained.
“The RPA overthrew it; you attach AI to RPA and bring it to the processes and business units you want to automate. Suddenly, adding a chatbot or facial recognition technology to a software robot became very easy. »
With advancements in the areas of computation and model architecture, the sophistication of these AI-powered RPA bots is also expected to increase significantly in the coming years. We are on an “exponential trendline,” says Tyree.
The result is twofold: the deployment of hyperautomation solutions is greatly simplified and the number of RPA use cases is expanded to include functions such as analysis, design and monitoring.
And as larger enterprises scale up their automation deployments, the lessons should also trickle down to smaller and smaller enterprises in a succession of waves, making the benefits of RPA ever more accessible.
“Major Fortune 1000 organizations have now adopted these technologies on a large scale, driving down costs. A second wave of companies that couldn’t even think of this kind of technology will now be able to afford it,” said Gautam Moorjani, GM Intelligent Automation Solutions at SS&C Blue Prism.
“Over the next three to ten years, we’re going to see those waves come together and create more opportunities.”
not so fast
Although Tyree and Moorjani are optimistic about the potential of hyperautomation, they both admitted that there are still a number of hurdles the industry will have to overcome.
The first is technical. Although developments in computing, storage, and networking have paved the way for AI models with trillions of parameters (the higher the number, the better the model generally performs), other advances will be needed to achieve pervasive automation. And there is no guarantee that the way forward will be smooth.
“We’re not going to see easy, incremental growth in AI capabilities. It comes in spurts,” Tyree admitted. “The big leap in AI right now, for example, has come from someone who looked at neural structures and replicated that architecture.”
“There is also a stronger correlation between the cost of computation and AI than people realize. AI is always ahead of what is economically feasible, and algorithms are always way ahead of what is. possible from a computational point of view.
The second factor has to do with culture and perception. Any conversation about workplace automation is bound to lead, eventually, to a discussion about layoffs in the human workforce.
Although it is widely accepted that the rise of automation will change the configuration of the workforce, there is some debate about the magnitude of this change. The picture painted by vendors is that automation technologies will play a supporting role, but mistrust prevails.
Tyree gestured toward the field of radiology. Although AI models have been shown to be able to matching the performance of specialists in the diagnosis of malignant tumours, experts still express a low level of confidence.
There’s no RPA involved here, but the same general rule applies to all areas of automation. The bar for expected AI performance is much higher; people are unwilling to accept even a small amount of error when a decision has been made by an automated system.
Others worry less about performance and more about the prospect of widespread layoffs, which will disproportionately affect workers in the lowest pay brackets due to the type of tasks RPA is capable of performing.
According to Tyree, it will be possible to aim for zero job losses, but careful management of the transition will be essential. An approach already taken by some companies is to trace the “shortest possible path between the skills that are no longer needed and those that are”, and to try to nudge those displaced by automation into these paths.
“There’s always a natural human anxiety, it’s always something that has to be overcome,” Moorjani added. “But the real use cases are getting more and more compelling.”
“It’s about further distinguishing between what is truly a judgment-based decision and what isn’t, allowing people to focus on what’s right for their organization. All of this is still evolving. »