{"id":211,"date":"2023-04-12T07:40:00","date_gmt":"2023-04-12T05:40:00","guid":{"rendered":"https:\/\/www.theftexplained.com\/?p=211"},"modified":"2023-04-12T09:09:24","modified_gmt":"2023-04-12T07:09:24","slug":"can-ai-make-the-workplace-more-humane","status":"publish","type":"post","link":"https:\/\/www.theFTexplained.com\/index.php\/2023\/04\/12\/can-ai-make-the-workplace-more-humane\/","title":{"rendered":"Can AI make the workplace more humane?"},"content":{"rendered":"\n

This post marks a (temporary) conclusion<\/strong> to my series on the impact of AI on society at large and the job market specifically, with the first two posts accessible here<\/a> and here<\/a>. This week, I want to highlight the positive contributions that well-managed AI, including Large Language Models<\/a><\/em> (LLMs), can offer the HR realm<\/strong>, producing advantageous outcomes for all stakeholders, particularly current and prospective employees.<\/p>\n\n\n\n

Before delving into the details, let me recap the primary ‘competitive’ advantages of AI:<\/p>\n\n\n\n

    \n
  1. Sift through and analyse vast amounts of data to extract insights with minimal guidance or supervision;<\/li>\n\n\n\n
  2. Generate authentic (though this term is debatable in light of ongoing AI and copyright infringement discussions<\/a>) and customized content, thereby enhancing efficiency and reducing costs and time expenditures.<\/li>\n<\/ol>\n\n\n\n

    To organize my thoughts, I now examine how AI could potentially assist each crucial HR function in achieving strategic goals, adhering to the conventional employee lifecycle. While the list is already extensive, it is by no means comprehensive<\/strong>; I welcome your thoughts and suggestions via email<\/a>. Importantly, I am discussing the perceived potential of AI; many features mentioned in this article remain aspirational given the current state of AI development. However, I am confident these goals can be attained in the long run, which often arrives sooner than anticipated in this field.<\/p>\n\n\n\n

    Talent Acquisition
    <\/strong>Primary strategic objective: Identify and attract suitable talent to fulfil the organisation’s present and future skill requirements<\/em>
    How AI can help:
    <\/span>– Map the existing organization to pinpoint skill gaps and determine recruitment needs accordingly;
    – Utilise current employee data and job requirements to craft job descriptions;
    – Detect (e.g., by browsing social media) and proactively engage with appropriate candidates, properly valuing non-traditional or non-linear career trajectories that will become increasingly common;
    – Maximise recruitment campaign efficiency by implementing and\/or advising on most suitable recruitment channels and associated activity;
    – Employ both internal and external data to recommend recruitment locations, capitalizing on hybrid work models if relevant and consistent with company policy;
    – Propose enhancements to the interview process (e.g., quantity, nature, and pacing of interviews, types of interviewers) to maintain high levels of candidate engagement;
    – Increase interview efficiency to optimize individual and collective fit assessments, helping answer questions about the candidate’s ability to perform tasks satisfactorily and integrate well with the organization; AI could potentially suggest real-time questions for human interviewers based on the candidate’s responses;
    – Augment and personalize the candidate experience, offering near-instant updates on processes, performance feedback (incorporating both human and machine-generated content), and reducing disengagement risks (e.g., through VR-powered company previews or chatbot usage).<\/p>\n\n\n\n

    Compensation & Benefits<\/strong>
    Primary strategic objective: Optimize financial and non-financial compensation packages to attract and retain the right talent within budget constraints.
    <\/em>How AI can help:<\/span>
    – During the recruitment process, analyse job descriptions and candidate profiles to propose fitting compensation packages that align with company policy, skill requirements, and the candidate’s personal circumstances (e.g., nationality, marital status, family situation);
    – Facilitate periodic staff compensation reviews and recommend personalized adjustments;
    – Locate and analyse external data sources to provide customized compensation benchmarks;
    – Simplify compliance with local regulations by collecting and analysing legal information, including local language translations, which is particularly valuable for multinational corporations.<\/p>\n\n\n\n

    Talent onboarding<\/strong>
    Primary strategic objective: Sustain high levels of new employee engagement and streamline productivity acceleration.<\/em>
    How AI can help:
    <\/span>– Use data acquired during the interview process to design personalized onboarding experiences, incorporating virtual learning and automatically scheduled in-person meetings with specific internal and external stakeholders;
    – Streamline administrative tasks by automatically retrieving and categorizing documents collected throughout the recruitment process, and identifying missing documents based on the new employee’s unique situation.<\/p>\n\n\n\n

    Performance management<\/strong>
    Primary strategic objective: Fairly assess and enhance employee performance<\/em>
    How AI can help:
    <\/span>– Review and ensure consistency of objectives across teams and departments;
    – Streamline the entire process, from target setting to performance measurement, using innovative quantitative data sources (e.g.     organisational network analysis<\/a> or ONA) as appropriate \u2013 increased reliance on hard metrics would make reviews more objective and coherent at the group level;
    – Offer guidance on internal mobility, providing managers with customized development options for each team member;
    – Suggest tailored performance improvement actions, informed in part by evaluating the impact of similar actions on other employees (connected to Learning & Development).<\/p>\n\n\n\n

    People Engagement
    <\/strong>Primary strategic objective: Maintain a high level of individual engagement across the organisation<\/em>
    How AI can help<\/span>:
    <\/span>– Detect early signs of employee disengagement at individual or team levels;
    – Implement a customized engagement measurement strategy, sending targeted surveys to specific groups at particular times (both periodic and ad hoc);
    – Proactively recommend and\/or execute mitigating actions;
    – Engage line management as needed with improvement suggestions.<\/p>\n\n\n\n

    Learning & Development (L&D)
    <\/strong>Primary strategic objective: Identify skill gaps in the workforce, develop mitigation plans, and provide employees with the most effective tools to address those gaps.<\/em>
    How AI can help:
    <\/span>– Advise on suitable individual learning and development pathways, connecting them to performance, career aspirations, and the L&D program portfolio;
    – Offer personalized, near-instant feedback on improvements;
    – Facilitate measurement of ‘Return on Investment’ for specific training programs;
    – Support negotiations with training providers through methods such as fact base gathering and benchmarking competition.<\/p>\n\n\n\n

    HR operations
    <\/strong>Primary strategic objective: Guarantee efficient delivery of all HR-related activities at minimal cost.<\/em>
    How AI can help:
    <\/span>– Streamline data collection and utilisation;
    – Save time by generating drafts of standard documents (e.g., salary certificates, recommendation letters);
    – Establish a comprehensive, single point of contact for all administrative HR-related inquiries using chatbots.<\/p>\n\n\n\n

    Employee offboarding<\/strong>
    Primary strategic objective: Maintain an optimal employer image for departing employees.
    <\/em>How AI can help:<\/span>
    – Streamline and expedite administrative procedures associated with offboarding;
    – If applicable, determine and coordinate the implementation of the most appropriate employability enhancement measures (e.g., connecting with internal and external stakeholders, outplacement), tailored to the employee’s skills, aspirations, performance, and desired career path;
    – Track the progress of former employees and identify potential ‘boomerang employees’ when appropriate.<\/p>\n\n\n\n

    In this scenario, the People Analytics<\/a> (PA) department holds a vital role<\/strong>. Before undertaking any advanced analyses, the PA team collaborates with business leaders and the IT department to gather and organize data in a business-focused manner. Enhanced by AI rather than replaced<\/strong> (as is the case for most of the activities mentioned above), the PA team can swiftly pinpoint areas of concern and access the organization’s abundant knowledge and experience (frequently unconscious and concealed) to suggest remedial actions. As mentioned earlier<\/a>, the capacity to ask pertinent questions and critically assess the machine’s output will be more important than ever.<\/p>\n\n\n\n

    All of these initiatives, taken together, should allow companies to offer a true strategic ‘service’ that meets the individual needs of each of their employees, and allow HR team members to offload a large number of low-value-added tasks to focus more on interacting with their ‘customer’. Paradoxically, the irruption of AI in our workplaces could inject a large dose of humanity<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

    This post marks a (temporary) conclusion to my series on the impact of AI on society at large and the job market specifically, with the first two posts accessible here and here. This week, I want to highlight the positive contributions that well-managed AI, including Large Language Models (LLMs), can offer the HR realm, producing […]<\/p>\n","protected":false},"author":1,"featured_media":212,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[41,44,10,46],"class_list":["post-211","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-non-classe","tag-ai","tag-automation","tag-employment","tag-hr"],"_links":{"self":[{"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/posts\/211","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/comments?post=211"}],"version-history":[{"count":10,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/posts\/211\/revisions"}],"predecessor-version":[{"id":223,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/posts\/211\/revisions\/223"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/media\/212"}],"wp:attachment":[{"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/media?parent=211"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/categories?post=211"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.theFTexplained.com\/index.php\/wp-json\/wp\/v2\/tags?post=211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}