RPA journey: Schneider Electric’s global plan taps Blue Prism, UiPath

Schneider Electric’s RPA journey is now part of that transformative vision. The company’s initial pilots paved the way for the 170 bots in production as of July 1. The majority of those RPA instances — a bit more than 100 — are in China.

“They have been more advanced,” Hackenson said of the Chinese RPA adopters, citing a regional CIO who was able to rapidly identify use cases and competing technology priorities in the company’s other geographic regions.

Avoiding duplicative bots

Schneider Electric’s RPA team serves as the company’s global entry point for software bots.

One of the team’s roles is to minimize duplicated effort as requests for bot development roll in from the business side. “So, if we have a call center in Europe put in a request for a use case and another one comes in from the U.S. for something very similar, the global team knows we have already built this,” Hackenson said. Without the global team, RPA activity would exist in isolation within business groups, hampering cross-company visibility.

If bots live in silos, “we don’t know what is going on and we can’t leverage what has been created,” she added.

Schneider Electric currently uses RPA software from Blue Prism and UiPath. Hackenson said the platforms are similar, but the company continues to compare them. Thus far, bots have been deployed in customer care centers, finance, HR and supply chain.

Hackenson said she had hoped to have more than 220 software bots in operation “We just don’t want to automate siloed processes with RPA,” Hackenson said. “We want to cut across a process end to end.”

RPA use cases may also be turned down if the processes they seek to automate are fundamentally broken. And others may fail to offer enough transformative potential.

The RPA team, however, has begun training the business side on appropriate RPA use cases and how best to take advantage of the technology.

“Since we have done the training, we have seen a big increase in use cases now being accepted,” Hackenson said. “We still believe there is a learning curve on RPA.”

Another issue has been setting expectations for software bot performance. In some cases, the approved RPA use cases have fallen short of the envisioned objective, which has caused the RPA team to pause and revisit some initiatives. She cited one example in which a customer call center bot was expected to take five full-time equivalents (FTEs) off inbound ordering tasks, allowing the employees to pursue higher-value activities. The initial implementation of the bot, however, only freed up two FTEs.

“We are learning it is an iterative process,” Hackenson said. The organization had to rethink its labor-reduction expectations and assess whether more tasks needed to be automated to reach the objective, she noted.