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A Schedule of Success

Making Workforce Engagement More Efficient

Sanish Mondkar, Founder and CEO of Legion.co

Wouldn’t it be great if we could make predictions and what we estimate actually be true? No one can be sure of anything for 100% certainty, but thanks to Legion.co, we can get pretty close when it comes to the workforce. Founder and CEO, Sanish Mondkar believes that in the world of scheduling for hourly employees, things should be fairly simple. Sanish and his team utilize web and mobile platforms to allow employees to share their work preferences, assist employers with filling company needs, and ultimately cut out the hassle of weekly scheduling. Thanks to Legion.co, it looks like you can predict success.

Tamara: Can you share a story that inspired you to get involved in AI?

Sanish: I don’t know that there’s one single eureka moment, but I’ve observed the power of AI for some time. Before I started Legion, I was in executive roles at SAP and Ariba. Enterprise software products have gone through a massive transformation in the past 10 years as they’ve moved to the cloud. The vision for the next decade, certainly, is in creating enterprise applications that are smart enough to make truly autonomous business decisions—but it takes the right market, and a nimble enough company to lead the charge on this change. I realized that the workforce management space was a perfect fit for AI and machine learning because it really demands automation to do right, and there are so many data points that seem to contradict one another. So Legion’s platform basically has a “brain” powered by deep machine learning and AI capabilities to find harmony between employers’ and employees’ needs.

Tamara: Describe your company and the AI/predictive analytics/data analytics products/services you offer.

Sanish: Legion is a next-generation workforce management platform that accurately predicts labor demand, improves employee retention, and reduces compliance headaches. This lets employers focus on delivering great customer and employee experiences while staying within labor budgets and driving topline growth. We use machine learning to automatically schedule the right employees at the right time based on forecasted demand, employee skill sets and preferences, and labor compliance rules.

Legion uses AI to automatically match labor forecasts to the best workers based on a wide variety of factors like employee preferences, skills, productivity, and labor compliance policies. Then these rules can be easily customized to the unique policies and best practices of every customer (and every store, down to the item). Legion’s AI engine continuously learns and improves the matching between company and employee needs, resulting in improved optimization and better engagement.

Tamara: How do you see the AI/data analytics/predictive analysis industry evolving in the future?

Sanish: I think there’s a lot of fluff out there today, where a lot of companies want to be on the leading edge, but are using AI as a buzzy catch-all for data science work that isn’t actually autonomous or rooted in deep artificial intelligence. On the other side, we’re seeing more and more debates about how AI is going to replace human workers. I think we’ll see both of those dynamics change. AI will be foundational to more and more businesses, and as the approach to AI becomes more sophisticated and more common, it will become clear that the big story is not about replacing workers, but it is about changing work and making it more efficient.

Tamara: What is the biggest challenge facing the industry today in your opinion?

Sanish: The biggest challenge facing the retail, food, and hospitality industries is no doubt related to labor. This impacts everyone, but especially the 78 million hourly employees in the U.S. That’s really what motivates our team every day: we believe it’s possible to fundamentally upgrade the jobs for the majority of the U.S. workforce and allow employers to be efficient and innovative at the same time. Machine learning and AI are the tools that make this possible, so that we can provide flexible, efficient ways for employers and employees to engage with each other and enable the future of (much improved) hourly work.

Tamara: How do you see your products/services evolving going forward?

Sanish: Fundamentally, Legion matches labor demand to supply optimally and efficiently. This is big problem that extends to many industries—far beyond retail, hospitality, and food, where we’re focused today. For example, distribution centers, call centers, banks as well as unionized/regulated labor markets all have challenges with matching labor demand and supply. These markets represent a great path for expansion.

Tamara: What is your favorite AI movie and why?

Sanish: The first Matrix movie was amazing for a couple reasons—it really started the conversation around “simulation” that continues today. It’s fun to think of that possibility, especially from a theoretical standpoint. Secondly, the notion of AI gone rogue was nicely dramatized (though of course The Matrix wasn’t the first movie to do that, it did do it really well).

Tamara: What type of advice would you give my readers about AI?

Sanish: AI will (eventually) be transformational in almost every aspect of life, but ignore the fluff and hype that you see everywhere. We are far from that point. There are some key questions to ask while evaluating the next AI-based service. The first is, is the AI-software making truly autonomous decisions and fully replacing the need for humans to make those decisions? Because AI is about more than just providing insights or analytics, but actually making decision. Second, is the software learning and making better decisions with every interaction without needing additional coding? This is equivalent to improvement of human judgment, in many ways. At the end of the day, as with any technology, it’s important to take a critical view of AI—and separate what’s real from what’s frankly not.

Tamara: How does AI, particularly your product/service, bring goodness to the world? Can you explain how you help people?

Sanish: I’ve met with many hourly employees who told me that they needed multiple jobs to support themselves and balance work and life. Yet I also saw data on the employers’ sides that appeared to contradict that dynamic, such as the high attrition rate in the hourly workforce, which often exceeds 70 to 80 percent. It was clear to me that workforce management is fundamentally broken—employers are struggling to hire and retain, while employees are often leaving or have to take multiple jobs because they aren’t getting the flexibility (or consistency) in schedules that they need. Legion is addressing that gap with machine learning and AI.

Tamara: What would be the funniest or most interesting story that occurred to you during your company’s evolution?

Sanish: One interesting observation is that Legion’s users have started to refer to Legion as a person. As in, “Hey Legion gave me these shifts today” or “Go tell Legion you don’t like these hours for work.” Even though we do not have an chatbot or avatar-based user experience, users were intuitively tuning in to the fact that the software (or service) has a brain, and someone behind the scenes is actively making decisions. Some were not sure whether its an actual human behind the scenes or how that’s working.

Tamara: What are the 3-5 things that most excite you about AI? Why? (industry specific)

Sanish: The ability to autonomously learn and make decisions has the potential to transform everything. Furthermore, if you take into account that the “learning” in context of AI (compared to the human brain) is exponential and only bounded by the amount of data you can feed, the ability to evolve the intelligence at a rapid pace holds amazing potential.

In particular, the potential of “networked intelligence” is huge. For example, every autonomous car learns not only from its own directly ingested data points, but also from the aggregated and combined intelligence of every single autonomous car on the road can achieve unparalleled rate of progress. I think we’ve only scratched the surface here, and that potential is a really exciting opportunity.

Tamara: What are the 3-5 things worry you about AI? Why? (industry specific)

Sanish: The hype around AI either creates false expectations or unnecessary fears. Like any other technology, there is theory and there is practical reality—but oftentimes it’s hard to separate the two. There is also a serious shortage of big thinking, skills, and tools necessary to build truly transformational applications of AI. Finally, I expect the early days to be bumpy and full of debate over readiness of AI, some social, ethical, and technological debates.

Tamara: Over the next three years, name at least one thing that we can expect in the future related to AI?

Sanish: These are early days of AI-based applications doing important things. There are a lot of things to learn and improve (in some ways, like the early days of the internet). In the next three years, we will see mistakes or failures attributed to the underlying AI. This will no doubt fuel debates on whether AI is “ready,” but this will ultimately be part of the evolution to get through the first generation of AI-based applications and beyond.

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