How big is your AI opportunity?
SEE YOUR NUMBERHow big is your AI opportunity?
SEE YOUR NUMBERErik Brynjolfsson and Andy McAfee spent years studying how AI changes work. Research that shaped how the world thinks about AI and the workforce.
But none of it answered what every executive is asking now: can we quantify the ROI of our AI?
James Milin saw that gap and brought them together to build the platform that closes it.
That answer became Workhelix. Every dollar companies spend on AI now comes with a real answer behind it, today and tomorrow.
Erik Brynjolfsson
Co-Founder & Co-Chairman of the Board
Erik is the Director of the Stanford Digital Economy Lab, a Professor at the Stanford Institute for Human-Centered AI, and a Research Associate at the National Bureau of Economic Research. Brynjolfsson is among the most cited researchers in information and economics and his work has been recognized with Ten Best Paper awards and five patents.
Andrew McAfee
Co-Founder & Co-Chairman of the Board
Andrew is the Co-Director of the MIT Initiative on the Digital Economy and also the Principal Research Scientist at the MIT Sloan School of Management. Throughout his career at MIT and Harvard, he has studied how digital technologies are changing the business world. He has advised many companies, governments, and international organizations.
James Milin
Co-Founder and CEO
James brings experience and DNA from top technology firms including Google and Amazon. He previously co-founded a venture-funded AI platform business in the Bay Area and was a founding member of the AWS Private Equity sales team. He has spearheaded digital transformations and cloud migrations for hundreds of mid-market and enterprise customers.
Backed by top investors, founders, scientists, economists, and C-suite leaders who share our mission: enterprises need to know the ROI of their AI so they can grow it.
A guide to realizing its benefits while limiting its risks
Andrew McAfee, Daniel Rock, and Erik Brynjolfsson
New AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job.
Erik Brynjolfsson and colleagues
We propose a framework for evaluating the potential impacts of large-language models (LLMs) and associated technologies on work
Daniel Rock and colleagues
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