McKinsey Report: Surge in Gen AI Adoption Drives Significant Value for Enterprises

Organizations are racing to adopt generative AI, one of the most significant technological innovations in recent history.

A recent McKinsey report reveals that 65% of organizations are now regularly using AI, nearly double the figure from just 10 months ago. This surge comes a year and a half after the launch of ChatGPT, which has dramatically reshaped our world. Expectations for generative AI are high, with many respondents anticipating “significant or disruptive” changes in their industries.

“In 2024, generative AI will no longer be a novelty,” stated Alex Singla, senior partner and global co-leader of QuantumBlack, AI by McKinsey. “The technology’s potential is evident. While most organizations are still in the early stages, we are beginning to understand effective implementation and value generation.”

Rapid AI Investment Growth

Half of the respondents reported their organizations have adopted AI in multiple business functions, with 67% predicting an increase in AI investments over the next three years. The greatest adoption has occurred in professional services, where generative AI is predominantly utilized in marketing and sales (for content creation, personalization, and sales leads), product and service development (for design and research), and IT (for chatbots and data management). Organizations are also witnessing substantial cost reductions in human resources. On average, firms reported a timeframe of one to four months to deploy generative AI.

Workers across all levels are becoming increasingly comfortable with AI tools both professionally and personally, with 41% of C-level executives using generative AI regularly at work. “The pace of innovation, the emergence of new companies, and the influx of investment have been remarkable,” said McKinsey associate partner Bryce Hall. “Leading companies are now capturing business value from these advanced AI capabilities.”

Three Approaches to Generative AI Implementation

McKinsey identifies three archetypes for generative AI implementation: “takers” who utilize off-the-shelf tools, “shapers” who customize these tools, and “makers” who develop models from scratch. The survey revealed that organizations typically adopt a mix of both—about 50% rely on off-the-shelf solutions while the remainder significantly customize or build their own. This trend is evident across sectors such as technology, media, telecommunications, retail, and financial services.

Looking ahead, a shift toward a “buy, build, and partner” approach is anticipated, moving away from the dichotomy of “build versus buy.” Alexander Sukharevsky, senior partner and global co-leader of QuantumBlack, AI by McKinsey, emphasized that a successful future enterprise will require a well-coordinated mix of solutions.

While simple, one-step solutions are often favored at the outset of new technology adoption, Sukharevsky cautions against this as generative AI matures. “The future enterprise's infrastructure will rely on a robust integration of various foundational models tailored to specific needs.”

Addressing Risks and Challenges

Despite the enthusiasm for AI, organizations remain cognizant of its risks. Forty-four percent of respondents reported experiencing negative consequences from generative AI, mainly in the form of inaccuracies, cybersecurity issues, and a lack of explainability. Other concerns include misuse of AI, data privacy, bias, and potential intellectual property infringements.

High-performing organizations particularly struggle with data challenges, including the availability of training data and the need for effective data governance. Alarmingly, only 18% of respondents indicated that their organizations have an enterprise-wide council focused on responsible AI governance, and just one-third recognized risk awareness and mitigation for AI tool users as necessary skill sets.

“Responsible AI must start at day one, and significant work remains in education and action,” warned Lareina Yee, senior partner with McKinsey and chair of the McKinsey Technology Council. She advocates for establishing clear principles for generative AI application, implementing guardrails, providing comprehensive training, and securing contracts with providers. Employees must also be educated to prevent accidental leaks of proprietary data into public models and to incorporate risk management into AI development.

“There is a growing awareness of responsible AI and urgency to address it,” Yee added. “Transitioning from awareness to action will be critical.”