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Organizations are in a wild race to adopt generative AI — it is one of the most significant technological innovations in a generation (or multiple generations).
In fact, just a year-and-a-half after ChatGPT was announced — changing the world as we know it — 65% of organizations are regularly using AI, according to a new report from consulting firm McKinsey. This is nearly double the percentage from the firm’s previous McKinsey Global Survey only 10 months ago.
Looking ahead, expectations are high: The majority of respondents predict that gen AI will lead to “significant or disruptive” change in their industries.
“In 2024, gen AI is no longer a novelty,” said Alex Singla, senior partner and global co-leader of QuantumBlack, AI by McKinsey. “The technology’s potential is no longer in question. And while most organizations are still in the early stages of their journeys with gen AI, we are beginning to get a picture of what works and what doesn’t in implementing — and generating actual value with — the technology.”
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AI investment increasing at a rapid pace
Half of the respondents to the survey said their organizations have adopted AI in two or more business functions, and 67% expect AI investment to increase in the next three years.
The biggest increase in adoption is in professional services, and gen AI is (today at least) most often being used in marketing and sales (for content, personalization and sales leads); product and service development (for design development, scientific literature and research review); and IT (for help desk chatbots, data management, real-time assistance and script suggestions). Also, organizations are seeing the greatest cost reduction in human resources.
On average, respondents also reported that their organizations required one to four months to get gen AI into production.
Also, workers at all levels are growing increasingly comfortable with AI tools not just at work, but at home. Today, they are using gen AI across their professional and personal lives. Surprisingly, 41% of C-level execs report using gen AI regularly at work.
“The pace of innovation, the evolution of new companies and capabilities and the wave of investment have been remarkable,” said McKinsey associate partner Bryce Hall. “Now we’re seeing how leading companies are capturing business value from these often-dazzling AI and gen AI capabilities.”
Takers, shapers and makers
McKinsey identifies three archetypes for implementing gen AI: “takers” who use off-the-shelf tools; “shapers” who customize those publicly available tools; and “makers” who develop their own models from scratch.
Interestingly, the survey found that most organizations are half-and-half: Roughly 50% of gen AI uses were from off-the-shelf tools, while the other were “significantly customized” or built from scratch. This is across technology, media and telecommunications, consumer goods and retail, financial services and business and legal and professional services.
Going forward, we will see a shift to “buy, build and partner” (away from “build versus buy”), where organizations will build ecosystems that mix proprietary, off-the-shelf and open-source models, said Alexander Sukharevsky, senior partner and global co-leader of QuantumBlack, AI by McKinsey.
While adopting simple, one-step solutions is a natural tendency in the early days of any technology, “it’s not a sound approach as gen AI becomes more widely adopted,” said Sukharevsky. “The spine and brain of the enterprise of the future will rely on a well-orchestrated mix of multiple foundational models — both off-the-shelf solutions and tools that have been finely tuned to the enterprise’s specific needs.”
Challenges with data, explainability, security
Still, organizations aren’t blind to the inherent risks in AI: In fact, 44% of respondents say their organization has already experienced negative consequences from gen AI use. This has typically been inaccuracy in outputs, cybersecurity and lack of explainability. Other issues include incorrect use of AI and data privacy, bias or intellectual property (IP) infringement.
Additionally, “high performers” identified by McKinsey are specifically experiencing challenges with data — they report insufficient amounts of training data, a struggle to define processes for data governance and quick integration of data.
While they recognize these challenges, though, just 18% of respondents said their employers had an enterprise-wide council or board focused on responsible AI governance. Further, only one-third identified gen AI risk awareness and mitigation controls as required skill sets for workers interacting with AI tools.
“Responsible AI needs to start on day one, and there is still much work to be done in terms of education and action,” said Lareina Yee, senior partner with McKinsey and chair of the McKinsey Technology Council.
Organizations must establish clear governance and principles for applying gen AI, apply guardrails, perform “robust training” and have secure contracts with providers, she said. Employees also have to be educated so that proprietary data are not inadvertently leaked to public models. It is also important to incorporate risk practices into AI development.
“What we see in the survey results and in our conversations with clients is a growing awareness of responsible AI and an urgency to get it right,” said Yee. “Moving from awareness to action will be critical.”
