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Can artificial intelligence and data inspire employees to perform at their very best? If you ask Sean Minter, CEO and founder of the data-driven people enablement platform AmplifAI, the answer is an unequivocal yes. And as of today, the six-year-old startup has a lot more cash on hand to help it make the case for an AI-enhanced human workforce.
“Our view of AI is that it isn’t here to replace people,” Minter said. “We use AI to enable people to become better at what they do, and let AI become their trainer and their coach.”
AmplifAI offers a SaaS self-learning platform that plugs into a variety of company data sources to analyze employee performance and deliver personalized feedback and actionable suggestions to team members and managers at every level of the org chart. Using a company’s highest-performing employees as a benchmark, AmplifAI uses its own proprietary data smarts to generate performance-related recommendations aimed at elevating others in the organization. In short, it’s data-powered professional development.
After expanding its user base tenfold over the last year, AmplifAI just announced a $16.5 million series A funding round led by Greycroft, with LiveOak Venture Partners, Dallas Venture Partners, and Capital Factory chipping in. AmplifAI, which is utilized by teams at brands like The Home Depot and Omnicare365, plans to use these new funds to scale its AI-driven platform and put more sales and support resources in place internally as it continues to grow.
AI for a people-focused problem
Minter, a self-described serial entrepreneur, knew there was a problem that needed solving when he was running an organization with over 15,000 employees around the world. For him, one of the biggest challenges of managing such a sizable workforce was explaining variances in performance among employees and clients, especially when a particular department — say, tech support or customer service — was inexplicably performing well in one locale, but not another.
“It’s an age-old problem,” Minter said. “When you have a big group of people, you’re going to have some people that do really good and some that don’t do so good. Why does that happen? How do I enable everybody else to become a better performer?”
To solve this mystery, Minter started with data. His team built out an engine for ingesting and aggregating data from a variety of company sources, like CRM software, collaboration platforms, and pretty much any employee-facing tool from which useful data could be extracted easily — or at least, somewhat easily.
“The number one complexity of implementing any new client [on AmplifAI] is that there’s no standardization of data,” Minter said. “Every client has different datasets, different systems, different capabilities, different needs.”
To rectify this, AmplifAI built its own proprietary data ingestion system that integrates with dozens of platforms and accounts for the wide range of data formats, schemas, and APIs flowing out of various other enterprise tools. Once ingested and aggregated, this multi-faceted pool of workforce data can then be analyzed and put to good use.
AmplifAI uses unsupervised learning to read this data, understand the organization’s workforce overall, and suss out which employees are high-performing, which are average, and which are performing below average. With that crucial nugget of intel, AmplifAI is then able to create a persona of what a high-performing employee looks like and generate and deliver what it calls “next best actions” for managers, coaches, and other employees who aspire to the reach the level — if not the pay grade — of their high-performing colleagues.
Growing people as technology grows
AmplifAI isn’t the first company to take a software-focused approach to workforce management and development. Offerings from bigger plays like Oracle, SAP, and Workday, to name just a few, have all taken various cracks at this problem over the years. What sets AmplifAI apart for its growing roster of enterprise customers — Minter says the company has yet to lose an RFP — is its own unique, home-baked approach to wrangling complex and varied datasets and using the latest in AI technology to make sense of it all.
“What’s enabling us to do this now, as opposed to 10 years ago, is … a lot of the technology that has been built around this, like the cloud capabilities and the AI models that are now available,” Minter said.
As complex and sophisticated as these things can get, the company’s biggest challenge has nothing to do with databases or machine learning at all.
“The most challenging part is not the technology,” Minter said. “It’s changing human behavior. What we’re trying to do is get people to work differently compared to how they used to work.”
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