Competing for Talent in the Platform Economy

Don’t take platform workers for granted: Platforms are all the rage these days. Companies are being urged to create their own — à la Uber and Airbnb. But platform advocates often take one thing for granted: a seemingly infinite supply of workers who will happily do the platform operator’s bidding in return for, well, whatever the operator is willing to give them.

That may not be a sound assumption, especially as the competition heats up in platform markets that prove viable. Witness Sheelah Kolhatkar’s article on Uber’s fast-growing rival, Juno, in The New Yorker. “Juno’s business model is to take what Uber has created and appropriate it,” writes Kolhatkar. “Most of what Juno does is predicated on the fact that many drivers feel mistreated by Uber. … If Uber seems cold and impersonal, Juno will smother its drivers with attention. If Uber has raised its commission — the part of each fare that the company keeps — Juno will set a much lower one.”

As the folks at Uber think about how to frame a response to the wooing away of its drivers, they might want to read the new report on platform workers from the Institute for the Future. The IFTF did an ethnographic study of a select group of platform workers. It found the workers fit into seven distinct archetypes and that there are seven qualities that define the platform working experience.

The study also found out what platform workers care about. Their top three concerns: income potential; control over choosing which jobs to take; and work frequency, immediacy of payment, and convenience.

Uber isn’t the only company that should be reading the IFTF report. Its battle for drivers suggests that eventually all successful platform companies will have to compete for contract workers. So they better get to know them.

How to get your company some AI: Gartner held its annual symposium and ITxpo last week and announced its top 10 list of strategic IT initiatives for 2017. Number one: artificial intelligence and advanced machine learning. “Artificial intelligence is going to be the next battleground through 2020,” said Gartner analyst David Cearley, according to the report in TechRepublic.

Just in case your boss hears about this and tells you to get some of that AI, go directly to Thomas Davenport’s new article at HBR.org. “There are so many different types of AI, each requiring some technical knowledge to fully grasp, that newcomers to the field often have difficulty figuring out how to jump in,” says the Babson College management and information technology prof and MIT Initiative on the Digital Economy fellow. “In the simplest case, cognitive technologies can be just more autonomous extensions of traditional analytics — automatically running every possible combination of predictive variables in a regression analysis, for example. More complex types of cognitive technology — neural or deep-learning networks, natural language processing, and algorithms — can seem like black boxes even to the data scientists who create them.”

To simplify matters, Davenport boils down the “cognitive entry points” to three categories: Mostly Buy; Some Buy, Some Build; and Mostly Build. In Mostly Buy, you use AI software from a vendor, such as Salesforce.com or Oracle. “Rather than going all in,” says Davenport, “some companies begin by picking a small project that could benefit from cognitive technology, and use a smaller, less transformative toolset to attack it.” In Some Build, Some Buy, you bolt AI services — like chatbots or even IBM’s Watson — onto your existing analytics. And, in Mostly Build, you use modular, component-based architectures and open-source cognitive software to create AI applications.

Coping with the open office: One of the many things that struck me about MIT prof Catherine Turco’s ethnographic study of a 600-employee social media firm was her description of the company’s open office. Almost everyone loved the setup for the energy and collaboration it engendered, according to Turco. But it is so loud that people across the organization routinely played white-noise apps through their headphones.

Turco’s TechCo eventually built “quiet spaces” along the outside walls of its bullpens, but Robin Camarote writes in Inc. about another company that has found a simpler solution. When the noise and visual distractions inherent in the open-office design became a significant concern among employees at Segment, the startup data management and analytics company’s CEO Peter Reinhardt created an app to collect data on noise levels and installed it on tablets around the office.

“After gathering sufficient data, the leadership team got together to analyze the results,” writes Camarote. “What surprised them was that there were areas of the office that were less noisy than others. This observation was not immediately obvious while working in the space, where the hum seemed to be spread evenly around.”

Then, instead of moving walls, Segment simply moved its people. With the noise data as a guide, the company encouraged staffers to relocate “according to their role and preference for a noisy versus quiet work environment.”