How Spotify Uses AI to Sound Out New Efficiencies

Artificial intelligence (AI) is transforming the way we work across industries. From back-office finance departments to front-facing customer support, AI's power lies in its ability to discover and introduce efficiencies. In unison with human experts, its potential knows no bounds. This is exactly where Sidney Madison Prescott, global head of intelligent automation at Spotify, and her team of engineers and solution architects come in.

In our TELUS International Studios interview with Prescott, she explains how her team seeks out Spotify's most mundane, repetitive tasks with the express purpose of establishing new efficiencies. Prescott deploys a wide range of technology to get the job done, including robotic process automation (RPA), machine learning and AI. This quest for intelligent automation and simplicity drives greater data quality and can help ensure that Spotify employees — or "Spotify-ers" as Prescott refers to them — are focusing on tasks and projects that add value. Enabling these efficiencies means a quicker time to market and new and improved products and features, according to Prescott. Ultimately, it all comes together to better the customer experience (CX) of Spotify users.

Perhaps the best way to learn how Spotify uses AI to improve real internal processes is with a concrete example from Prescrott:

"One example is when we go into the Spotify application and we look at all of our different audio ads that run within the application, we have to test those ads to ensure that the audio integrity is there. This is a process where we take each ad and we test to make sure […] that everything is up to spec for that particular advertisement. This is a manual process, but we have been able to automate this process and leverage robots to actually check each advertisement to make sure that it's running properly, that it is showing the right or the correct specs in terms of the visuals and that the audio levels are appropriate. This is a really perfect example of something that you would wouldn't think that humans are doing today, but in reality, it's a very integral part of the responsibilities of the team."

We know that AI's effectiveness depends on the sourcing of large amounts of data that is used to train AI algorithms to complete tasks. A significant amount of time and effort must be spent by data experts to ensure they're not only selecting the correct kinds of data, but also labeling it appropriately. When all is said and done, what does "intelligence" in the context of AI actually mean?

"From my perspective, in terms of the team that we have today and the technology that we are leveraging, we really are looking at the distinction between: Is a particular automation cognitive, in that it can facilitate different ways of learning? And is it able to recognize different characters? Does it have facial recognition? Things of that nature.

"Now, there's a separate part of the intelligent automation piece, which is also the robotic process automation [component]. And those robots are not cognitive. Meaning that they cannot execute anything that has not been predefined in their developer build workflows."

Prescott posits that intelligent automation really "comes together" when RPA is combined with AI or machine learning engines. "So it really is the amalgamation of these different tools coming together to be able to facilitate a specific set of business outcomes, which we would typically relegate to, let's say, human intelligence rather than a machine," explains Prescott.