{"id":32353,"date":"2018-11-13T14:48:19","date_gmt":"2018-11-13T19:48:19","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/can-ml-make-work-more-human\/"},"modified":"2018-11-13T15:12:49","modified_gmt":"2018-11-13T20:12:49","slug":"can-ml-replace-human-resources","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-ml-replace-human-resources\/","title":{"rendered":"Can ML replace Human Resources?"},"content":{"rendered":"

An opportunity gap<\/b><\/p>\n

Machine learning is infiltrating almost every enterprise. Studies have found that 51% of enterprises consider themselves to be sophisticated or early adopters of machine learning, with the remaining 49% exploring machine learning opportunities <\/span>[1]. Companies have gained immense benefits from machine learning because the system adapts and refines its accuracy over time. For example, Salesforce built a customer relationship management (CRM) system that features \u201cpredictive lead scoring,\u201d enabling a salesperson to identify customers that have higher potential with less effort than previously required [2]. This application is unique in that it can be used within Salesforce to gain more customers, but also sold externally to companies needing a CRM system.<\/p>\n

This duality, of not only building a system for yourself, but also selling it to others was a foundation the foundation for Humu, an enterprise relationship management (ERM) system that focuses on leveraging the power of machine learning and behavioral economics to create more meaningful relationships within a company [3]. This product is coming at a excellent time. According to senior partners at the Boston Consulting Group, \u201cHR\u2019s traditional role and identity as a function and cost center should be transformed\u201d [4]. CHROs are redefining their departments by bringing in technology to help serve the overall company.<\/p>\n

\"\"<\/a><\/p>\n

Humu<\/b><\/p>\n

Humu differentiates themselves from a traditional enterprise system in their approach and outcomes. While traditional enterprises run yearly satisfaction surveys to their employees, Humu takes a holistic approa<\/span>ch that teases out causal relationships and treats them with recommended actions. First, they assess a variety of factors that influence an employee’s life — from commute time to time spent in meetings, no data is too small to care about. Second, they run proprietary statistical models to determine what unique actions will influence happiness, productivity, and prevent attrition. Third, they recommend unique actions to both individual contributors and their managers through nudges — small personalized steps that empower every member of an organization to be a change agent. Lastly, the model improves upon itself by understanding if the recommended action was used and if it were successful.<\/span><\/p>\n

The four steps above outline the simplified process for the \u201cNudge Engine,\u201d the company’s flagship product [5]. Machine learning is the crux of this product, it allows the company to improve recommended actions in meaningful and measured ways.<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

High praise<\/b><\/p>\n

The concept has received high praise from both industry executives and academics. Jonathan Neman, founder and CEO of sweetgreen remarked, \u201cHumu\u2019s science and analytics enable us to measure and improve each and every team member\u2019s experience at sweetgreen\u2026[nudges] create a more engaged and performance driven workplace\u201c <\/span>[6]. Adam Grant, esteemed professor at the Wharton School at University of Pennsylvania, has said \u201cthe team [has] a rare combination of expertise in technology, organizational psychology, and applied experimentation around making work better\u201d [7].<\/p>\n

 <\/p>\n

What\u2019s next<\/b><\/p>\n

Now that Humu has successfully proved their concept, secured an $40 million in Series A and B funding, and fielded inquiries from over 20 of the Fortune 100 list, Humu is focusing on meaningful growth over the next two years <\/span>[8]. Their next step includes nearly doubling their workforce, enabling them to acquire customers from a variety of industries. By retaining a diverse customers, Humu can customize or expand the Nudge Engine into sub-products based on industry or working environment. This is a critical step in ensuring their long-term success — the working context for an accountant and a barista are quite different.<\/p>\n

An organization\u2019s ability to survive in the next 10 years will be based on their ability to respond quickly to change. As technology evolves extremely quickly, organizations and people need to transform at the same pace, if not faster. Humu\u2019s technology, if developed correctly, will make these transitions easier for companies. While Humu remains silent on their medium- and long-term plans, one can surmise they will move beyond an ERM system and position themselves as an enterprise change management (ECM) system. They will have the data and methods to influence a corporation quickly.<\/span><\/p>\n

 <\/p>\n

For Humu\u2019s consideration<\/b><\/p>\n

As Humu eventually expands beyond an ERM to an ECM system, I recommend they focus on developing strong enterprise partnerships. They should partner with leading Cloud solution providers (Amazon Web Services, Google Cloud Platform, and Microsoft). By developing these partnerships Humu can develop an \u201cout of the box\u201d solution that is easily configurable for new enterprises. Additionally, Humu should continue to partner with organizations that will serve as brand champions and extol the virtues of their product.<\/span><\/p>\n

 <\/p>\n

A final thought<\/b><\/p>\n

There is one last consideration to make: the employees who know their organization is using the Nudge Engine. Will they think their peers and managers are being authentic? Or will they assume their peers are just proxies for the Nudge Machine?<\/span><\/p>\n

 <\/p>\n

(787 words)<\/span><\/p>\n

Image Credits:<\/strong><\/p>\n

humu.com<\/p>\n

Citations:<\/b><\/p>\n

[1] Bean, R. (2018).\u00a0The State of Machine Learning in Business Today<\/i>. [online] Forbes. Available at: https:\/\/www.forbes.com\/sites\/ciocentral\/2018\/09\/17\/the-state-of-machine-learning-in-business-today\/#6b4fa3053b1d [Accessed 12 Nov. 2018].<\/span><\/p>\n

[2]\u00a0Davis, B. (2018).\u00a0Ben Davis: Welcome to B2B AI – boring, but effective – Marketing Week<\/i>. [online] Marketing Week. Available at: https:\/\/www.marketingweek.com\/2018\/01\/24\/ben-davis-b2b-ai-boring-but-effective\/ [Accessed 12 Nov. 2018].<\/span><\/p>\n

[3] Bock, L. (2018).\u00a0The Humu Nudge Engine is Making Work Better\u2014Here\u2019s How – Humu<\/i>. [online] Humu. Available at: https:\/\/humu.com\/2018\/10\/08\/humu-nudge-engine-make-work-better\/ [Accessed 12 Nov. 2018].<\/span><\/p>\n

[4]\u00a0Bailey, A. and Welch, D. (2018).\u00a0Embracing HR Disruption: A Leadership Discussion<\/i>. [online] https:\/\/www.bcg.com. Available at: https:\/\/www.bcg.com\/en-us\/publications\/2017\/human-resources-digital-organization-hr-disruption-leadership-discussion.aspx [Accessed 12 Nov. 2018].<\/span><\/p>\n

[5] See note 3.<\/span><\/p>\n

[6] See note 3.<\/span><\/p>\n

[7] See note 3.<\/span><\/p>\n

[8] Valet, V. (2018).\u00a0After A Year Of Secrecy, Laszlo Bock And Wayne Crosby Break Their Silence On Humu<\/i>. [online] Forbes. Available at: https:\/\/www.forbes.com\/sites\/vickyvalet\/2018\/05\/01\/after-a-year-of-secrecy-laszlo-bock-and-wayne-crosby-break-their-silence-on-humu\/#5ad39db61c94 [Accessed 12 Nov. 2018].<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

With $40 million dollars in venture backing and over 20% of Fortune 100 companies asking to use their product, will Humu, an enterprise relationship management system, make a lasting impact in the move to bring technology into HR functions?<\/p>\n","protected":false},"author":11477,"featured_media":32354,"comment_status":"open","ping_status":"closed","template":"","categories":[407,877,4741,346],"class_list":["post-32353","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-employee-satisfaction","category-human-capital","category-laszlo-bock","category-machine-learning","hck-taxonomy-organization-humu","hck-taxonomy-industry-information-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nCan ML replace Human Resources? - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-ml-replace-human-resources\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can ML replace Human Resources? 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