Five Management Strategies for Getting the Most From AI

A global survey of C-level executives finds that AI is delivering real value to companies that use it across operations and within their core functions.

Fueled by the buzz around powerful applications of artificial intelligence (AI), many business leaders are contemplating whether to introduce AI into their organizations. While practitioners and academics have outlined some of the strategic challenges of implementing AI, many executives are still seeking good models for how to generate competitive advantage from its application.

To find out more about what contributes to successful AI adoption, we helped lead a survey by the McKinsey Global Institute of 3,000 C-level executives across 10 countries and 14 sectors. From that research, we identified five fundamental strategies for how to get the most out of AI’s potential.

1. Plan to Grow, Not Just Cut

Executives should approach AI as an instrument to expand their businesses — creating new products or services, increasing productivity, or winning more market share — as much as a tool to cut costs. Companies with less experience in AI tend to focus on its ability to help cut costs, but the more that companies use and become familiar with AI, the more potential for growth they see in it.

Retailing executives in our survey, for example, mentioned cost cutting as often as increasing market share or market growth as their main objectives for implementing AI. But the subset of retailers who have adopted AI at scale — meaning, they deploy AI across technology groups, use AI in the most core parts of their value chains, and have the full support of their executive leadership — cited AI’s potential for business growth twice as often as its potential for cutting costs.

This same subset of retailers, the early AI adopters, reported that insight-based selling — using AI to review shoppers’ habits and suggest personalized promotions and tailored displays — increased sales by 1% to 5% in traditional stores. And they reported that personalization and AI-enabled dynamic pricing lifted online sales as much as 30%.

2. Invest in Both Technical and Managerial Talent Capabilities

In our survey, executives gave several reasons for not adopting AI. The largest share (30%) said they were uncertain of its business case. Another 21% cited the scarcity of AI-related human capabilities — and these same executives were 50% more likely to also say that AI presented an uncertain business case, suggesting that human capabilities are critically important to capture the returns from AI in new organizations.

The talent question is challenging for many organizations on two grounds. First is the need for new talent: When debating how AI may affect labor markets by automating parts of old jobs, companies have paid less attention to how AI is likely to require new technical job categories such as “DevOps Engineers” and “Next-Gen Machine-Learning Engineers.” Second is the need for managerial attention: Good return on AI will be captured only when the technology is embedded in business and workflow processes — a job that typically is complex and requires management from the highest-level leaders.

Regarding technical jobs, AI promises to be a great source of employment — but also of headaches. Filling new technical positions is expensive and time-consuming because we have not been turning out enough skilled professionals to keep up with the demand. In the United States, for instance, there were approximately 150 million workers in 2016, but only 235,000 data scientists. To circumvent the issue, companies should be using multiple paths for talent acquisition. Organizations that have been best at adopting AI are better at anticipating needs, starting with a few hires during pilots and then scaling their recruitment process just before they move from piloting to full-scale development.

The management of AI technology also involves new leadership skills, including those required to implement modern processes embedded with AI. Companies that are successfully embracing AI are committed to transformation programs, with top management embracing the change and cross-functional management teams ready to redefine their processes and activities.

Read the entire article on MIT Sloan Management Review

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