On May 31st, 2018, we had the privilege of hosting our second event in partnership with Mercer: Human Machine Teaming in Tomorrow’s Tech Enterprises. Featuring panelists from IBM Watson, Cisco, and Marsh Digital Labs, the panel discussion and keynote took an in-depth look at the automation revolution and what it means for the workforce of today—and how employers can best prepare for a workforce that is, essentially, part-machine. Check out highlights from the event below. Some comments have been edited for clarity and/or brevity.
Sheela Sukumaran – Partner, Technology Industry Leader @ Mercer
Rajat Mishra – VP CX Strategy @ Cisco Customer Experience
Asha Vellaikal – Head of Marsh Digital Labs @ Marsh
Rishi Vaish – VP of Development @Watson Customer Engagement
The event opened with a keynote from Sheela Sukumaran, Partner, Technology Industry Leader at Mercer. She began with the question at the heart of the event: “How do you build a workforce for tomorrow? And how do you motivate the workforce that you DO have?” On any scale, it’s today’s wise choices that make tomorrow better—and employers should be looking to leverage technology’s current trajectory to stay ready for the workforce of the future.
Despite certain apprehensive projections (that robots will make humanity redundant) the reality is a little more complex—and a little less frightening. Sheela spoke about the concept of jobs unbundling: “Jobs aren’t going away—but pieces of them are.” As more of our daily tasks are automated, the human workforce will be called upon for more complex jobs, roles that require a human element that is, at present, un-programmable into even the most intelligent of machines. Process efficiency doesn’t change the need for empathy, judgment, and critical thinking.
Ultimately, business leaders are tasked with a complicated challenge: syncing up long-term with the human element of their operations. A singular vision for the future of the company—for what you’re trying to accomplish as a company AND as a workforce–means building a business that can scale. Business leaders must think strategically when building toward automation.
The panel then took a deeper dive into the issues and topics addressed in Sheela’s keynote.
Where are we in this journey?
Rishi: “It’s early days. I don’t think you could call [the machines] we produce today something you could team with. But it’s good that we’re having the conversations about it now, because it could impact the way we build our machines in the future.”
Asha: “The tech is further ahead than what is currently being implemented. We’re still trying to get very basic business processes automated—because most are extremely data-poor.”
Rajat: “The markets are really what’s ahead right now. My 3-year-old daughter says ‘good morning’ to Alexa. We also have to think differently about the scale of intelligence: why should it stop with Einstein? Why would a human be the peak of intelligence? We’re ahead at a market level, but if you zoom out and take in the entire scale, it’s obvious we have a long way to go.”
Are these the right paths? Are there other choices organizations should be making?
Rajat: “Remember Moneyball? It still holds true today. Don’t look where everyone else is looking. And don’t think you have to go outside to hire good people—people can be trained to build solutions.” Innovation happens on a grassroots level—you just have to be able to find it.
Rishi: “Automation isn’t necessarily doing employees any favors by itself. When the variables in a data set are extraordinarily large – that’s when you need machine learning.”
Asha: “No matter how large the data set, you will have to involve a human at some point. So how can we use this technology (which is needed) to create new types of jobs? Take StitchFix as an example: their business model would be impossible without machine learning—but a human touch is needed to sort what the machine has collected and create a customized clothing box for each individual user. The algorithm does the heavy lifting, allowing a human to have a job as a personal stylist.”
Women are disproportionately impacted by Machine Learning—a lot of traditionally female roles are succumbing to automation. How should business leaders factor this in?
Rishi: “Machines will inherit the bias of the people creating them. A lot of models are trained by people, who can’t help using themselves as an example—which creates culture and language issues. As leaders, we have to be aware. As practitioners, we have to identify it and solve for it.”
Rajat: “We have to move away from black box practices. Yesterday, knowledge was power. Today, learning is power.”
Rishi: “Technology is at a point where we can scale learning like never before. We have the tools to correct things more quickly, as well. We can educate people on how to educate themselves.”
How do you navigate the change? What are some concrete things organizations can do? We’re on the train, how can we make the journey?
Rajat: “Be clear on where you want the distribution—50% human, 50% machine? Less or more?” The number isn’t as important as having the discussion in the first place. We must embrace wayfinding: pick a direction for your company and move toward it slowly, but with intent. The rate of change today is so fast you can only see so-far ahead.”
Sheela: “It’s like car headlights on a dark night. You can only see a few feet in front of you—but if you move forward carefully, you won’t lose the road.”
Asha: “There’s a lot of demand, and a lot of fear. People are both intrigued [by machine learning] and terrified of losing their jobs. We need to make the workforce more technologically savvy, and create more community-building within industries.”
Rishi: “Encourage proactive education so people can take part, get involved, take ownership. The more we can communicate, the better. Put technology in the context of their world—transparency helps employees more than anything.”
What would you like to see addressed that isn’t part of the conversation? What are we NOT looking at?
Asha: “In most industries, there are full-time jobs dedicated to modular explanations. Tech and AI, in this case, needs to catch up. You’d never accept a ‘just because’ answer to a ‘why’ question from a healthcare professional.”
Rishi: “Further to the ‘black box’ style of thinking—not being able to explain WHY something happened. The more complicated and robust our systems become, the more unclear the process.
Rajat: “I think we don’t have the right emotional response to the inevitable super-intelligence that’s coming. It’ll be the last invention that we’ll make. Change happens at an exponential speed.”
What is the role of HR?
Rishi: “HR helps assess whether you’re doing enough for organizational health. The framework is what will have to evolve. We are constant learners now because of our technology, so HR will need to be approached from that standpoint.”
Rajat: “HR and business are most powerful and function best when they’re operating from the same plan.”
For more quotes and highlights from the evening, check out #SVFMercerHR on Twitter.
This event was hosted in partnership with Mercer.
Head of Operations and Communications