I Have Seen The Future Of Work, And Its Name Is…

Gigster

I recently had a chance to chat with Gigster CEO Roger Dickey. He is a serial entrepreneur who among a variety of start-ups, founded Mafia Wars, which became a $1 billion game with 100 million users for Zynga. In November of 2014, he co-founded Gigster with Debo Olaosebikan, though the spark of an idea occurred to Dickey a decade ago. The first insight was based upon the notion that the economies of scale for the engineering workforce were not accessed effectively. As a result, remarkably similar things were being developed over and over, each time as though it were the reinvention of the wheel. Identifying how future software development might be similar to past efforts could lead to breakthroughs in coding quality and efficiency.

The second notion that occurred to the Gigster founders more recently was that what Uber has done for blue collar work they could do for white collar work. The fact that anyone with a valid driver’s license can open the Uber app and start driving and earning money moments later should be replicatable for people with more complex skills. There was not a way to clock in and make money in the same way.

For Dickey’s co-founder Olaosebikan, it was more personal. He was living south of San Francisco making $500 per month, and could not make ends meet. He needed a way to earn more money.

Gigster CEO Roger Dickey

On the demand side, Dickey and Olaosebikan noted that the need for software is only increasing as every business is a technology business in this day-and-age. That said, with a paucity of truly great coders out there coupled with the ever-changing technology landscape, the need for great software developers with the latest skills is increasing, but their numbers are not increasing at anywhere near the same rate. Moreover, they tend to be clustered in relatively few metropolitan areas with the San Francisco Bay area being the biggest of all. If you are a chief information officer or chief technology officer at a many decades old company in a small city in the middle of the United States, the chances that a top-flight engineer from the Bay Area will move to your company will be difficult. The thought of hiring an entire team of people with this profile will be nearly impossible.

As Dickey notes that “bad talent is more expensive than good talent.” By this he means not only that you get what you pay for, but that the corrections necessary to clean up the messes made by bad talent mean the inefficiencies are compounded. “What if we could put together a great priced software development team into your pocket? It would increase the pace of the evolution of technology.”

Gigster is made up of roughly 30 employees and an army of people working part-time. Those part-time workers often have full-time positions with leading firms like Google and Facebook and moonlight with Gigster projects. These are not the equivalent of $10 rides on Uber. Gigster projects can be many tens of thousands of dollars in cost. How can the company be confident that the group of strangers that come together to deliver the projects with a high quality result? The key to the success of the model is creative use of data and artificial intelligence.

Dickey says, “We believe that work is measurable by data. The key is to mine the data to detect patterns.” These patterns may lead to insights about both why a project is successful and also why it is not. For example, the teams likely use some combination of productivity, project management, and collaboration tools like Asana, Slack, Trello, CircleCI, or the like. Gigster can track which tools which teams are using when. They can identify, for example, the cadence of check-ins on Slack for successful teams versus less successful teams. They may also determine that there is an optimal number of cards created in Trello, monitoring the number of cards completed by when. This is all done with a tool called The Supervisor. This is meant to be a tool that works alongside the team to help guide the team to let it know where issues might arise before they actually do. As the number of projects increases, the historical data becomes predictive data.

The AI engine also helps identify how new requests for work compare with past requests for work. Dickey uses the example of a marketplace for dog-walkers. If that marketplace is structurally similar to one or multiple other marketplaces designed and developed in Gigster previously, the AI engine helps answer questions like

  • Which of the prior projects had high customer satisfaction?
  • Which were on time?
  • Which of the members of past successful teams are currently available?
  • Which project managers are likely to thrive with one client versus another?
  • What time zone is the project in, and can a team be built in the same or nearly the same time zone?

Dickey indicates that success is defined by delivering consistently good client outcomes at scale. This also means doing so faster and at a lower cost.

There are no people involved in developing quotes for customers, either. The artificial intelligence handles that, as well. When one logs onto the site, there is a checklist of items that one can select to help define the work. This is based on 350 micro-features for software development. Dickey says, “We have mapped the software genome.” What attributes do you need on the landing page? Is there a need for photo uploads? Where and how will e-commerce be built in? The checklist helps determine the amount of people needed to do the work which, in turn, defines the level of spend necessary.

The obvious customer base for Gigster is fellow start-up enterprises who cannot afford to build out vast teams of software engineers, but increasingly large enterprises are engaging the company as well. “The enterprise market is like the ocean compared to the pond that is the start-up market where we began swimming,” says Dickey. “We need to take this market very seriously because of the size of the opportunities.” Even he has been surprised by how rapidly enterprises have engaged Gigster. “We have experienced nine-X growth over the past two quarters.” The company’s customer list includes the World Bank, IBM, MasterCard, Square, Airbus, and OpenTable, among many others.

Gigster has an invite only band of over 400 developers, over 200 product managers, and over 100 designers. To identify people, Gigster scours sites like LinkedIn and AngelHub, having identified characteristics of people who are likely to be successful through a pattern match. Part of what makes the job compelling is that it does not require leaving one’s current job. Many of the company’s contractors are moonlighting from world-beating technology companies like Google, Facebook, or Amazon. There are two phone screens, and that process provides the initial grade for contractors called karma. That score goes up or down based upon the work one does. A contractor is staffed on small assignments to start, and only after proving themselves do they advance to larger initiatives. People also shadow one another in order to learn new skills and to evaluate how well teams work together. This has the added benefit of ensuring that if someone leaves a project midstream, which is rare according to Dickey, there is someone who can take over their work.

The combination of artificial intelligence and the contractor base means that Gigster can facilitate all that it does with only about 30 full-time staff. There is a core team that works on the platform, including the AI. There is a sales team, a network team, which helps in the evaluation of people and escalations, as well people who are involved in Finance and internal-facing Human Resources.

Dickey believes that this model will eventually roll out to other industries, as well. He believes that many service industries will likely be targets in the future.

Ultimately, this does feel like a new take on the future of work and the gig economy. In this case, Gigster fosters connecting demand for ever increasingly complex software development needs of businesses with top talent.

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Peter High is President of Metis Strategy, a business and IT advisory firm. His latest book is Implementing World Class IT Strategy. He is also the author of World Class IT: Why Businesses Succeed When IT Triumphs. Peter moderates the Forum on World Class IT podcast series. He speaks at conferences around the world. Follow him on Twitter @PeterAHigh.

Originally published at www.forbes.com on October 31, 2016.

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