For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption. Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage. Only 20% of respondents used any AI-related technology in a core part to their business. A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave.
The McKinsey 2018 survey garnered responses from over 2,000 participants from a wide range of company sizes across 10 industry sectors. Overall, the 2018 survey found that while the business world had begun to adopt AI, few companies had in place the foundational building blocks that would help them to generate value from AI at scale. 30% were conducting AI pilots. Nearly half, 47%, had embedded at least one AI capability in their standard business processes, but only 21% reported using AI across multiple business functions. AI investments were still quite small, with almost 60% of respondents saying that less that one-tenth of their digital budgets went toward AI.
The most recent Global AI Survey was published in November of 2019. McKinsey garnered responses from over 2,300 participants, nearly 80% of whose companies were using AI in some capacity. Overall, the survey showed that “Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce.” AI is becoming more mainstream, with 58% of respondents using AI in at least one function, - a nearly 25% increase over the previous year; and 30% reported using AI across multiple business units, - a nearly 50% increase.
One of the survey’s major findings is that a small share of high performers, around 3% of companies using AI, are already achieving outsize business results. High performing companies are much further along in scaling the use of AI in their organizations, with an average of 11 different use cases compared an average of 3 among the other companies.
Let me summarize the key findings of the 2019 survey.
Most respondents are seeing returns from AI. To ascertain whether AI is delivering meaningful value, the survey asked about 33 specific AI use cases across eight different business functions: marketing and sales, product and service development, supply-chain management, manufacturing, service operations, strategy and corporate finance, risk management, and HR.
Overall, 63% report revenue increases in the business unit where the AI use case is deployed. Revenue growth is highest in marketing and sales, - pricing, prediction of likelihood to buy, and customer-service analytics; in product and service development, - creating new AI-based products and enhancements; and in supply-chain management, - sales and demand forecasting, and spend analytics. At the same time, 44% report cost savings in the business units where the AI use case is deployed. Cost decreases are highest in manufacturing, - yield, energy and throughput optimization; and in supply-chain management, - spend analytics, and logistics-network optimization.
AI high performers had significantly higher revenue increases and cost reductions, - being nearly three times likelier than other companies to report revenue gains of more than 10%, and more than four times likelier to report cost reductions of at least 10%.
AI adoption is increasing in nearly all industries. Survey results show increases in AI adoption in just about every industry over the past year as measured by the percentage of respondents whose companies are using at least one AI capability in one or more business units. Not surprisingly, high tech leads in AI adoption (78%), followed by automotive and assembly (76%), and telecom (72%). Retail had the largest year-to-year increase in adoption, 60% compared to 25% in the 2018 survey. Travel, transport, and logistics also saw a big increase, 64% compared to 38% in the previous survey.
The fast pace of adoption is expected to continue. 74% of companies that have adopted AI say that they will increase their AI investments in the next 3 years, with over half expecting an increase of 10% or more. High performing companies plan to invest significantly more, with nearly 30% saying they will increase their AI investments by 50% or more over the next 3 years, compared with 9% of other companies who say the same.
AI high performers tend to engage in key value-capturing practices. Based on McKinsey’s previous research, a few core practices are essential to capture the value of AI at scale. These include aligning AI strategy to business goals, investing in AI talent and training, collaborating across functions, applying strong data practices, and establishing company-wide standards and practices.
Survey results show that AI high performers are far more likely to apply these practices. For example, 72% of respondents from high performing companies say that their AI strategy aligns with their corporate strategy compared to 29% from all other companies; 62% of high performing companies have cross-functional teams of AI and business professionals working together on specific problems compared to 23% from all other companies; and 65% of high performers have a clear AI data strategy to compared with 20% from all others.
High performers are also more likely to recognize and work to reduce AI-related risks. The survey asked specifically about 10 of the most widely recognized AI-related risks, including cybersecurity, regulatory compliance, personal privacy, and explainability. Only a minority of respondents, 41%, say that their companies have comprehensive plans to identify and mitigate these AI risks.
Respondents from AI high performing companies are more likely to have such plans. For example, 86% of high performers have plans to mitigate cybersecurity risks, compared with 48% from all other companies; 76% have plans to mitigate personal privacy risks compared to 30% from other companies; and 42% are addressing the explainability of AI recommendations compared to 19% overall.
High performing companies report greater emphasis on workforce retraining. The survey found that concerns that AI is leading to workforce reductions have largely not been realized so far. But 34% of respondents expect AI to drive a decrease in employment over the next 3 years compared to 21% who expect an increase and 28% who expect AI to have little impact.
Nevertheless, the survey shows that a majority of companies are preparing for AI-related work-force changes. 60% of companies using AI report that at least some of their employees have been retrained in the past year, while 83% expect such retraining over the next 3 years.
AI high performers report much higher retraining efforts. 97% retrained at least some employees over the past year, and 45% retrained more than a quarter of all employees. Over the next three years, 70% of high performing companies plan to provide retraining to over a quarter of their workforce; 42% plan to retrain over 50%; and 30% plan to retrain over 75%.
“With the research showing that companies now use AI more often than not, the technology appears to have reached another stepping stone in its ascent in business,” said McKinsey’s 2019 AI Survey in conclusion. “Along with it comes a ratcheting up of the urgency to scale AI among those still early in their adoption journeys. However, while the survey results indicate that some companies are further ahead in realizing AI’s impact, they also suggest a path for lagging companies to catch up.”
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