Racing with Machines: Labor Force Innovation and the New Digital Frontier
This post was originally featured on Forbes.com.
In 1975, the fastest supercomputer cost approximately $5 million at the time and processed at the same speed of today’s $400 iPhone 4, according to a recent McKinsey report on digital technologies. Imagine telling someone in 1975, “In 35 years, you will have a device in your pocket that can call anyone on the planet, take a photo or video, and literally search and return any piece of information from human history in less than a second.”
It wasn’t long ago when online travel reservation systems reduced the need for travel agents; scanning systems at grocery stores reduced the need for check-out clerks; and automated gas pumps reduced the need for gas attendants. A fundamental driver for this progress has been computing power. There is no doubt that the 21st century has revealed that technological change, driven by computing power, is cycling ever faster and across every industry.
Various advances are fundamentally reshaping the world of computing and creating a new inflection point in the marketplace, which include advancements in machine learning (“ML”) and artificial intelligence (“AI”). When you combine the sheer power of computing with ML + AI you create even more opportunities for workers to focus on creating additional value and innovation in all aspects of business and our lives. And the scale of change is significant.
By Cisco’s estimate, there will be 50 billion things (mobile devices, parking meters, thermostats, cardiac monitors, tires, supermarket shelves, etc.) connected to the Internet by 2020.
These technological advancements are paving the way for what I call “Labor Force Innovation,” which employs new technology platforms, software algorithms, and robotics to better manage, monitor, and optimize work processes and tasks across many industries. With the rapid advancement of digital technologies, companies can streamline decision-making processes, scale more effectively, add efficiencies across functional areas, and swiftly respond to changes in the market.
WorkFusion’s Software-as-a-Service (SaaS) crowd computing platform exemplifies this capability as it transforms knowledge work by leveraging machine learning.
The company offers a platform to effectively route knowledge work to either internal subject matter expert employees, external crowdsourcing resources or to machines.
The platform monitors each given task and off-loads repetitive tasks to the machines over time. This generates greater efficiency and cost savings while also freeing-up skilled workers, such as analysts, to focus on the more creative, problem solving work that they were trained to do versus mundane data search and entry.
Hointer’s use of robotics is also emblematic of this new shift in that it illustrates how automation helps people more efficiently buy clothes in stores. The company has one of each retail item hanging in its stores, and offers an app for consumers to select and scan their desired items in the appropriate size.
They are then directed to a changing room where a robotic system in the backend delivers the selected items. Consumers try on and select the items they want to keep, scan and pay for them, then go on their merry way. This creates more opportunities for workers to focus on higher level customer service for customers, in addition to the creation of a whole new kind of consumer experience in-store given the space that is freed up by moving the piles of same articles of clothing to the storeroom, out of the way of the consumer experience.
Adam Waytz (Northwestern University’s Kellogg School of Management) and Michael Norton (Harvard Business School) recently coined the term “botsourcing” to refer to work that will likely be taken care of by robots, including robots driving taxis as was recently predicted by Uber’s Travis Kalanick, and serving as real-estate agents or health-care assistants. There is still work to be done on optimizing robot interaction with humans, including a welcoming facial construct, non-creepy voice, mimicry, empathy and some human-like unpredictability, but this kind of research moves us closer towards what they call the “coming robot invasion.”
Additional industries that can benefit from Labor Force Innovation are financial, legal and health related as they automate and reduce repetitive data search and entry or classification types of tasks.
Retail warehouses have also benefited from new automation technologies, computing and robotics, as evidenced by Amazon’s acquisition of Kiva Systems for $775M in 2012, now used in its warehouses.
Wall Street has leveraged immense computing muscle, high speed networks and data analytics to gain competitive advantages across dynamic markets, though the debate about the risks associated with high frequency trading continues.
Google continues to execute on its self-driving vehicle initiative powered by AI.
As explained by Sergey Brin, these cars’ top speed currently maxes out at 25 mph. In the future, they could hit top speeds of up to 100 mph or more and connect together like a train on the highway, enabling their passengers to work, read, or text safely while being driven to their target destination. A Boeing-sponsored project at MIT called “A Robot on the Shoulder” (or Supernumerary Robotic Limbs) aims at assisting factory and loading dock workers with a pair of robotic arms for heavy lifting.
While the explosion of technologies has created enormous economic value by increasing productivity, as Erik Brynjolfsson and Andrew McAfee argue in their book Race Against the Machine, business model innovation, organizational processes, institutions, and skills have not kept pace with these technological changes. We clearly need to ensure that our education systems and tools continue to evolve and keep up with the rapid pace of technical evolution. The objective is that Labor Force Innovation will create more time for workers to think creatively, to innovate, and to be more stimulated and engaged in their work after repetitive tasks are reduced.
Automation is clearly here to stay and will continue to sweep across almost every vertical industry and market sector. No doubt, it leaves in its wake a set of challenges, including quality assurance, privacy and safety. In the 19th and 20th centuries, waves of automation eliminated jobs in key economic sectors like agriculture, but new opportunities were identified in which labor assets could be redeployed when workers learned the necessary skills to succeed. Nobel economist Robert Solow stated that economic growth comes from people working smarter, not harder. The hope is that Labor Force Innovation fulfills this potential while the new wave of technology innovation unfolds around us.
Katherine Barr is a General Partner at Mohr Davidow Ventures, a venture capital firm based in Menlo Park. Her current areas of focus include Retail/eCommerce Innovation, Labor Force Innovation, and LifeTech. Follow her on Twitter.