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Tag: Harvard Business School

  • Harvard Business School: ‘Current Hiring System Is Broken’

    Harvard Business School: ‘Current Hiring System Is Broken’

    Harvard Business School has released a new report highlighting just how “broken” the current hiring system is.

    Virtually everyone has had the experience of applying for a job, seemingly the perfect candidate, only to be excluded from consideration with no good explanation. According to Harvard Business School, that scenario is more reality than suspicion.

    Many companies rely on automated hiring systems to aid in the process, but those very systems are creating much of the problem.

    These systems are vital; however, they are designed to maximize the efficiency of the process. That leads them to hone in on candidates, using very specific parameters, in order to minimize the number of applicants that are actively considered. For example, most use proxies (such as a college degree or possession of precisely described skills) for attributes such as skills, work ethic, and self-efficacy. Most also use a failure to meet certain criteria (such as a gap in full-time employment) as a basis for excluding a candidate from consideration irrespective of their other qualifications.

    As a result, they exclude from consideration viable candidates whose resumes do not match the criteria but who could perform at a high level with training. A large majority (88%) of employers agree, telling us that qualified high-skills candidates are vetted out of the process because they do not match the exact criteria established by the job description. That number rose to 94% in the case of middle-skills workers.

    Harvard Business School recommends a number of changes in the hiring process, including changing evaluation filters from negative criteria to affirmative, customizing the hiring approach for hidden workers, establishing new evaluation metrics and more.

    The full report is well worth a read for anyone involved in the hiring process.

  • AI Still Not Paying Off For Most Companies and Why It Does For Some

    AI Still Not Paying Off For Most Companies and Why It Does For Some

    A new reports suggests that, despite its promise for the future, artificial intelligence (AI) is still not paying off for most companies.

    A study by BCG GAMMA, the BCG Henderson Institute and the MIT Sloan Management Review found that a mere 11% of companies reported significant financial benefit from deploying AI.

    Surprisingly, this low return rate was despite widespread attempts to use AI. In fact, the study found that 71% of respondents understood how AI would impact their business, 59% had an AI strategy and 57% had already deployed it to some degree or another.

    One of the key differentiators appeared to be the degree to which an organization adopted and used AI. Companies that merely saw it as a quick fix, such as a way to improve automation, were the companies seeing very little return.

    “Our survey analysis demonstrates that Leaders share one outstanding feature: They intend to become more adept learners with AI,” reads the report. “Organizations that sense and respond quickly and appropriately to changing conditions, such as a new competitor or a worldwide pandemic, are more likely to take advantage of those disruptions. They view AI as more than a tool for cost cutting and automation.”

    As a result of this approach, those companies the report labels “Leaders,” fully integrate AI with their entire approach, learning from it while it learns from humans. This degree of change is often time-consuming and requires a fundamental shift in how many companies operate.

    “As more and more of the core of a company is built around software and data, the nature of the organization changes,” says Marco Iansiti, the David Sarnoff Professor of Business Administration at Harvard Business School. He goes on to caution: “It’s an architectural transition that takes a lot of time for a traditional organization. It’s a massive change.”

    The full report is a fascinating read and should be a top priority for any executive involved in AI deployment.