Now more than ever companies are rethinking their talent acquisition strategies as a changing workforce meets enabling technologies [1]. With that said, the US market in particular is ripe for leveraging job matching services as jobs have become more transactional in nature. In a recent Deloitte study, millennials hold the largest share of the US labor market and 66% of surveyed millennials said they would leave their jobs by 2020 [2]. The migrant nature of modern talent poses challenges for companies as they seek to replace and hire new talent.<\/p>\n
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LinkedIn\u2019s core value proposition is to \u201cconnect talent with opportunity at massive scale\u201d [3]. To effectively connect talent, LinkedIn must build out an intricate network that maps out companies, jobs, network members, schools, and skills. At the core, LinkedIn\u2019s use of machine learning to make these connections is what has allowed the company to be successful thus far and it\u2019s the key to their success going forward. Through their use of machine learning, they can streamline the complex talent acquisition process for talent-seeking companies and improve the job-seeking process for candidates \u2013 thus tackling the modern talent acquisition challenge.<\/p>\n
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LinkedIn has been able to successfully leverage their capabilities and this led them to their IPO in 2011 and acquisition by Microsoft in 2016 [4] [5]. With the latter, LinkedIn now has the potential to integrate itself within the platforms that Microsoft owns. By combining LinkedIn\u2019s machine learning capabilities with Microsoft\u2019s platforms, LinkedIn can continue to develop products that will further connect talent at an even broader scale.<\/p>\n
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In the short and middle-term and post-integration, LinkedIn has focused on fully integrating its capabilities with the Microsoft platforms. An example of this can be seen with the LinkedIn \u201cresume assistant\u201d that pops up in Microsoft Word as a candidate edits his or her resume [6]. This assistant uses LinkedIn\u2019s network to provide resume wording suggestions and offers potential jobs based on the resume\u2019s content. In the short-term, this business-to-customer approach will enable LinkedIn to continue enhancing their algorithm because they\u2019ll be able to collect even more data as customers\/professionals use LinkedIn across Microsoft\u2019s platforms (e.g. connecting Outlook directly to LinkedIn profiles).<\/p>\n
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For talent-seeking companies, LinkedIn uses its in-house talent-search system called LinkedIn Recruiter to help companies find optimal talent for roles [7]. In the matching process, however, outputs of machine learning can result in biases that may disadvantage already disadvantaged groups. The firm has made an effort to build a representative talent search by adjusting the architecture of the technology to avoid unintended biases. While this gender-neutral approach has been developed, removing biases is still an undergoing effort.<\/p>\n
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In some ways, LinkedIn is providing a public service as they aim to connect talent across different countries and industries. In the short-term, LinkedIn should ensure that as they integrate with the Microsoft platforms, they devote enough resources to data privacy controls because once this is breached, they will have to deal with the effects of public distrust. As they continue growing, they should also think about segmenting their business by industry and function. Right now, a job-seeker relies on the best match algorithm to find a suitable role but what if the job-seeker doesn\u2019t want a role that\u2019s tied to his or her previous experience? Going forward, LinkedIn should break down the world of possibilities to make the process more digestible for job-seekers.<\/p>\n
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In the medium to long-term, LinkedIn may have to pivot to a more business-to-business model because talent-seeking companies will want this data synthesized in a way that enhances their talent acquisition strategies. In addition, as LinkedIn continues to build out its capabilities, they will have to be cognizant of emerging competitors. There are already several HR tech companies (e.g. Mya, Ideal, Entelo, Restless Bandit) using artificial intelligence to connect talent with opportunities [8]. These competitors will not have a robust set of network data like LinkedIn does but given their expertise, they can synthesize and customize what they do have for talent-seeking companies.<\/p>\n
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LinkedIn is well positioned to accomplish its mission of connecting talent on a massive scale. Its technology, however, leads to several questions. As society moves to embrace diversity (ethnic, sexual orientation, etc.), how will LinkedIn\u2019s machine learning incorporate these aspects into its algorithm? What legal implications will this uncover? LinkedIn will have to be cautious and ensure equal and fair representation \u2013 tasks not easily auditable by artificial intelligence.<\/p>\n
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Additionally, how will talent management and HR teams at firms react to LinkedIn\u2019s advanced capabilities? Will they push against it and drive forward their own proprietary systems or maintain old-fashioned approaches? Will they pull in LinkedIn and fully rely on their technology to determine the future of their firm\u2019s talent? Whatever approach firms choose will ultimately determine their livelihood.<\/p>\n
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Citations: <\/strong><\/p>\n [1] 性视界 Business Review, \u201cThe Future of Talent Acquisition,\u201d https:\/\/hbr.org\/resources\/pdfs\/comm\/Kelly_OCG\/TheFutureOfTalent.pdf<\/a>, accessed November 2018.<\/p>\n [2] Deloitte, \u201cThe 2016 Deloitte Millennial Survey Winning over the next generation of leaders,\u201d https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/global\/Documents\/About-Deloitte\/gx-millenial-survey-2016-exec-summary.pdf<\/a>, accessed November 2018.<\/p>\n [3] Jeff Weiner, \u201cFrom Vision to Values: The Importance of Defining Your Core,\u201d https:\/\/www.linkedin.com\/pulse\/20121029044359-22330283-to-manage-hyper-growth-get-your-launch-trajectory-right\/<\/a>, accessed November 2018.<\/p>\n [4] LinkedIn, \u201cA Brief History of LinkedIn,\u201d https:\/\/ourstory.linkedin.com\/<\/a>, accessed November 2018.<\/p>\n [5] Rick Gillis, \u201cMicrosoft’s Acquisition Of LinkedIn Changed The Job 性视界 Industry In Ways We Don’t Even Know Yet,\u201d https:\/\/www.forbes.com\/sites\/forbescoachescouncil\/2017\/03\/29\/microsofts-acquisition-of-linkedin-changed-the-job-search-industry-in-ways-we-dont-even-know-yet\/#22edc6641300<\/a>, accessed November 2018.<\/p>\n [6] LinkedIn, \u201cLinkedIn Talent Solutions,\u201d https:\/\/business.linkedin.com\/talent-solutions\/product-update\/recruiter\/resume-assistant#<\/a>, accessed November 2018.<\/p>\n [7] Sahin Cem Geyik and Krishnaram Kenthapadi, \u201cBuilding Representative Talent 性视界 at LinkedIn,\u201d https:\/\/engineering.linkedin.com\/blog\/2018\/10\/building-representative-talent-search-at-linkedin<\/a>, accessed November 2018.<\/p>\n [8] Ascendify, \u201cHow AI is Disrupting (and Enhancing) Talent Acquisition,\u201d https:\/\/resources.ascendify.com\/hubfs\/Ascendify%20Guide%20-%20How%20AI%20is%20Disrupting%20(and%20Enhancing)%20Talent%20Acquisition.pdf<\/a>, accessed November 2018.<\/p>\n