AI is less mysterious than before. You’ve tested the waters with automation and artificial intelligence pilots. Now it’s time to eye AI adoption at scale. In a new PwC pulse poll, 84% of businesses say implementing AI “at scale” in their organizations is necessary to be successful over the next three years. What’s more, 70% say these efforts are designed to strengthen competitive positions for the long term.
Moving to scale requires taking pilots out of the lab and into the enterprise. Making this leap means building the right model to monitor, manage, reuse and improve what are likely to be hundreds of automations over time.
Before you scale automation, ask three questions.
Not sure how core enterprise systems interact with today’s flexible and adaptable automations? Today’s intelligent automation toolbox is much more than the simple data-prep rule-based RPA tools you may know. They’re a new class of software designed for business users that can be combined together for powerful uses and work on top of core platforms, like large ERP systems.
In a recent webcast poll, we asked more than 800 people about the levers they pull to improve productivity: only 23% said their automation strategy uses both big ERP and small automation sprints together. That’s a missed opportunity.
Here’s why parallel big and small automation work well together: core platforms enable standardization and control, while smaller solutions inject intelligence and new insights into the workflows. Both can help in targeted situations, especially when small automations are used by people closest to the work.
For example, we recently worked with a client with a paper-based invoice process. Rather than make the leap to a system upgrade, we worked on an optical character recognition (OCR) solution that could be set up in weeks to work with the existing system. The OCR converts physical documents into text that other machines can read. From there, there’s little need for manual data entry.
Solutions like these drive change in pockets, allowing for agile work. Teams spend less time on data tasks and start to change the ways they work to prepare for even bigger automations down the road.
It pays to make the case for scaling up only when the speed of innovation matches people’s willingness to adopt different ways of work. People will use innovations that make their jobs easier or help them get important work done. But if they don’t trust reports from a bot, for example, or don’t want a voice assistant to be their co-worker, these solutions likely aren’t going to be widely used.
When automations are balanced with other steps like streamlining processes or data and upskilling people, big productivity boosts are within reach. With technology and behavioral change together, it’s now common to see 30-40% productivity increases at the functional level.
In our survey, latecomers to using intelligent automation are more focused on cost benefits—automating those tasks that are expensive for people to do.
At a time when many are rightly concerned about mishandling the people side of automating tasks, most first-movers to leverage intelligent automation say employee satisfaction will increase with further use.
That’s because leading companies are adopting new operating models and teaching AI specialists and citizen developers to work together. Sometimes that means empowering employees to experiment with automating their own task-level work—and rewarding them for it. People are given the freedom to opt-in to training and are given automation tools and incentives so that they can figure out automations that work in their own business context. The best of the solutions from these citizen developers are offered up to the rest of the company to adopt.
This is a pragmatic solution—when they’re given permission to automate tasks on their own, the people closest to the work can quickly tackle pain points and achieve all kinds of desired outcomes.
To be sure, a downside of this type of freedom is that IT loses control. To scale automation sustainably, make sure a central group maintains ownership of decisions about how you’ll monitor, manage, reuse and improve automations over time. Employee-led pilots create proof of concepts, but it’s the center that shapes strategy and scales successes throughout the enterprise.