Dozens of software firms have sprung up to sell companies AI recruitment tools, which they promise will cut bias out of their clients' hiring processes. In promotional materials and press releases, they argue that human recruiters are irredeemably biased while machines are objective, so companies and job candidates alike will benefit from AI-driven hiring. AI algorithms are not inherently objective, and hiring software can introduce new layers of bias and discrimination, excluding qualified job-seekers and leaving companies open to negative headlines. But if companies apply AI in thoughtful ways, and maintain a healthy dose of skepticism toward AI vendors' commercial claims, there are ways to use algorithms to cut down on bias in hiring. Textio uses machine learning to help hiring managers optimize job descriptions.

The vast potential AI holds for businesses worldwide is of little doubt. But flawed strategy, poor approaches to process change, expertise shortfalls and a general lack of technical understanding prevent many enterprises from deriving value from artificial intelligence. Among the 90 percent of companies that have invested in AI fewer than two out of five say they've made any business gains, according to "Winning With AI: Pioneers Combine Strategy, Organizational Behavior and Technology," a survey of 2,500 business executives conducted by MIT Sloan Management Review and Boston Consulting Group (BCG). AI includes associated technologies such as machine learning (ML) and natural language processing (NLP), both of which aim to ape human thought. Get the insights by signing up for our newsletters.

Most companies that say they're using artificial intelligence have yet to gain any value from their A.I. investments. A survey from MIT Sloan Management Review and Boston Consulting Group released Tuesday found that companies that view A.I. as merely a "technology thing," akin to a product rather than a business overhaul, fail to gain financial results. The survey's authors defined the "value" of an A.I. project as lifting sales, reducing costs, or creating a new product. The survey, based on responses from nearly 2,500 executives, found that seven out of ten companies report little to no impact from their A.I. projects so far. Overall, 40% of the surveyed companies that have made "significant investments" in A.I. have yet to report any business gains.

Bias is everywhere, including in your data. A little skew here and there may be fine if the ramifications are minimal, but bias can negatively affect your company and its customers if left unchecked, so you should make an effort to understand how, where and why it happens. "Many [business leaders] trust the technical experts but I would argue that they're ultimately responsible if one of these models has unexpected results or causes harm to people's lives in some way," said Steve Mills, a principal and director of machine intelligence at technology and management consulting firm Booz Allen Hamilton. In the financial industry, for example, biased data may cause results that offend the Equal Credit Opportunity Act (fair lending). That law, enacted in 1974, prohibits credit discrimination based on race, color, religion, national origin, sex, marital status, age or source of income.