Report: AI Separates 'High-Performer' Firms from the Rest of the Pack
New research underscores the distinct advantages of organizations that wholeheartedly embrace advanced AI, shedding light on what sets these industry leaders apart from their less successful counterparts.
A survey-based report published on Aug. 1 by management consulting firm McKinsey & Company ("The State of AI in 2023: Generative AI's Breakout Year") paints a picture of the AI landscape in 2023. Among other things, the report examines the unique approach taken by AI leaders, identified as "high performers," in contrast to less prosperous enterprises.
It's worth noting that McKinsey has diligently monitored high performers for several years, predating the emergence of OpenAI's ChatGPT chatbot and the subsequent surge of interest in generative AI, which took off in late 2022. For instance, their 2021 report examined high performers and their distinctive approaches to advanced AI—albeit in those times, "advanced AI" primarily referred to machine learning operations (MLOps) rather than generative AI. High performers, as defined, are organizations where survey respondents attribute a minimum of 20 percent of earnings before interest and taxes (EBIT) to their utilization of AI.
Here's a snippet from the 2021 report: "With adoption becoming ever more commonplace, we asked new questions about more advanced AI practices, particularly those involved in MLOps, a best-practice approach to building and deploying machine-learning-based AI that has emerged over the past few years. While organizations seeing lower returns from AI are increasingly engaging in core AI practices, AI high performers are still more likely to engage in most of the core practices. High performers also engage in most of the advanced AI practices more often than others do."
It was a similar story in the 2022 report (still before ChatGPT), which indicated high performers achieved superior results mostly by boosting top-line gains, being more likely than other orgs to report that AI is driving revenue, though they also report cost reductions.
Here's a snippet from the 2022 report: "High performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes. Also important, they are engaging more often in 'frontier' practices that enable AI development and deployment at scale, or what some call the 'industrialization of AI.'"
And then came ChatGPT and generative AI, and the 2023 report.
"These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management," the new report says. "When looking at all AI capabilities -- including more traditional machine learning capabilities, robotic process automation, and chatbots -- AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization."
Other high performer traits marked by McKinsey include:
- AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI.
- High performers use AI capabilities more broadly throughout the organization.
- Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities.
- For one example related to the item above, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.
- Don't Forget MLOps
Notably, MLOps is still important in the post-gen AI era, at least for McKinsey.
"The findings offer further evidence that even high performers haven't mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so," said the report in discussion about the second item above. "For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that's a much larger share than the 19 percent of respondents from other organizations who report that practice.
"Many specialized MLOps technologies and practices may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver -- and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents."
In addition to the report section discussing how leading companies are already ahead with Gen AI, other sections of the report were titled:
- It's early days still, but use of gen AI is already widespread
- AI-related talent needs shift, and AI's workforce effects are expected to be substantial
- With all eyes on gen AI, AI adoption and impact remain steady
David Ramel is an editor and writer for Converge360.