How Machine Learning Changes BPO
The drill has been the same for decades: companies have shipped low-value, repetitive, or labor-intensive work to cheaper locations around the globe to save costs.
But machine learning is changing that cost equation and causing some companies to rethink their sourcing rationales. Indeed, machine learning could remake the business process outsourcing (BPO) industry. Rote tasks once ripe for offshore centers are being taken over by machines, and outsourcing providers are being forced to come up with new value propositions for customers.
In our study Making the Most of Machine Learning: 5 Lessons from Fast Learners, conducted with the Economist Intelligence Unit, we asked companies not only about their adoption of machine learning but also how they were sourcing their business processes. We found a connection between companies’ use of machine learning and where they sourced their business functions (whether performed in-house or by a third party) Among companies that were realizing benefits from their machine learning investments, 58% said they were spending more than half their budget for business processes locally rather than in distant geographies, compared to 39% of companies that had yet to see real value from machine learning.
This finding suggests that companies that have embraced machine learning as part of their larger digital transformation strategy can increasingly make their sourcing choices based on factors other than cost alone and thus are more likely to keep their most strategic business processes close to home. They may, for example, prioritize the value to customers. In the aftermath of the coronavirus pandemic, companies may also look differently at the risk from business disruption. Companies that build their own machine learning capabilities will have less need for long-term outsourcing agreements or offshoring arrangements to help the business grow; artificial intelligence (AI) capabilities will become a force multiplier for their existing workforces.
Beyond cost reduction
Enterprises at the leading edge of machine learning adoption are “not necessarily looking for near-term cost reductions,” says Stanton Jones, director and principal analyst with business transformation and sourcing consultancy ISG. “They’re looking for things like improving productivity, compliance, or customer satisfaction.”
Consider Intel, an early adopter of machine learning to increase efficiency and quality in its factories. The company moved on to apply those capabilities to its customer-facing business processes. With more than 100,000 reseller-customers, Intel’s own sales force could focus only on its largest clients. However, a sales-enablement system, powered by machine learning algorithms, can identify those resellers that offer the highest probability for sales while keeping the sales process in-house. The system has delivered more than US$100 million in additional revenue, according to Intel’s Chief Data Officer and Vice President of enterprise data and platforms, Aziz Safa.
For outsourcing service providers, meanwhile, machine learning tools to support intelligent business processes can become a competitive differentiator. “Organizations are going to be evaluating the efficacy of the technology that the provider is bringing to the table,” says Jones. “Providers can create a strategic advantage by, for example, having a machine learning algorithm that performs and learns more rapidly than a competitor’s.”
Will machine learning result in the mass repatriation of all the work currently done in offshore locales? Perhaps not. “But when it comes to newer transactional processes, decisions are more likely to lean toward keeping them onshore,” says Arjun Sethi, a partner at the consultancy A.T. Kearney. Companies that do bring processes back home will be able to hire a much smaller number of higher-skilled employees – 25–30% of the number used offshore, according to Jones – and match them up with intelligent automation.
In the short term, meanwhile, not all low-value, repetitive work can be automated by machine learning. Tasks requiring even the slightest degree of intuition or inference still baffle an algorithm. Machine learning can only tell the difference between a cat and a house, for example, if it is fed millions of images of each.
And who’s doing that feeding? Outsourcers. “Most of the software that’s being developed to power autonomous cars is heavily using machine learning, and these machine learning algorithms are having to take in very, very large amounts of unstructured data [in the form of] videos and photos,” Jones says. “There are large teams that are identifying what a stop sign looks like over and over and over again. In many cases, that training is taking place in lower-cost offshore delivery centers.”