From Hype to Boardroom Agenda: McKinsey Study Shows a 40% Surge in AI Investment Due to Generative Tech

The report opens with the context of the explosive growth of generative AI tools, emphasizing their rapid adoption and the increased focus on AI by company leaders and C-suite executives. 40% of organizations are planning to increase their investment in AI due to the advances in generative AI, highlighting the perceived value and potential of these tools in organizational settings.

TLDR

Goal: The study, conducted by McKinsey & Company through a global survey, aims to assess the rapid adoption and impact of generative AI (gen AI) tools within organizations and their influence on business functions and investment strategies.

Methodology:

• McKinsey executed an online survey from April 11 to 21, 2023, with 1,684 participants across various regions, industries, and corporate levels.
• Respondents included those from organizations that have adopted AI in at least one function, focusing on their use and perspectives on gen AI.
• The survey data were weighted by global GDP contribution to adjust for varying response rates, ensuring a balanced representation of global economic impact.

Key Findings:

• One-third of respondents indicate their organizations regularly use gen AI in at least one business function, with a notable presence in marketing, sales, and service operations.
• AI high performers, organizations with significant AI-driven EBIT, are leading in gen AI adoption, particularly in product/service development and risk/supply chain management.
• There is a significant expectation of gen AI causing disruptive industry changes within three years, especially in knowledge-intensive sectors like technology and financial services.
• Hiring for AI-related roles has become somewhat easier, although finding machine learning engineers and AI product owners remains challenging.
• Investment in AI is expected to increase, with 40% of respondents indicating their organizations plan to bolster AI funding due to gen AI advancements.

Recommendations:

• Organizations should focus on integrating gen AI where it can add the most value, such as marketing, sales, and product development, as evidenced by current usage trends.
• Companies, especially those classified as AI high performers, should continue to invest in AI capabilities broadly to maintain a competitive edge.
• There is a need for a strategic approach to gen AI adoption, including identifying opportunities, establishing governance models, and managing risks.
• Talent acquisition strategies should adapt to the changing demands of AI roles, emphasizing the reskilling of existing workforces.
• Businesses should consider gen AI’s potential for creating new revenue streams and enhancing the value of existing offerings rather than solely focusing on cost reduction.

Think Critically

Implications:

• The integration of generative AI (gen AI) into business functions, as highlighted by high-performing organizations, suggests a shift towards more strategic and value-focused applications of AI. If widely adopted, this could lead to a significant transformation in how businesses operate, prioritize investments, and innovate, potentially increasing the value of offerings and creating new revenue streams.
• The report indicates that industries based on knowledge work may experience more disruption from gen AI. This could have broad economic implications, potentially reshaping sectors like banking, pharmaceuticals, and education, while manufacturing might see less immediate impact.
• The underpreparedness of many organizations in mitigating risks associated with gen AI, such as inaccuracy and cybersecurity, could have social and political implications. If these risks are not adequately addressed, it could lead to wider issues of trust and safety in AI applications, affecting consumer confidence and regulatory landscapes.

Alternative Perspectives:

• While the report suggests that gen AI will drive significant business transformation, it’s possible that the actual impact may be overestimated due to the current hype around the technology. The true test will be the long-term integration and value generation of gen AI in various industries.
• The focus on high-performing organizations may overlook the challenges faced by smaller or less technologically advanced companies, potentially widening the gap between AI leaders and laggards and leading to market consolidation.
• The report’s emphasis on the potential for gen AI to create new revenue streams and increase the value of offerings might overshadow the need for a balanced approach that also considers ethical implications and long-term societal impacts.

AI Predictions:

• Given the current trajectory, gen AI is likely to become a catalyst for new product and service development, with a significant number of companies creating AI-driven offerings within the next few years.
• The need for reskilling and workforce adaptation will become more pressing, as gen AI automates more knowledge-based tasks, leading to a shift in job roles and the emergence of new types of employment.
• Regulatory frameworks for AI use, particularly gen AI, are expected to become more stringent and widespread as organizations and governments aim to mitigate the risks associated with these technologies.

Glossary

• Machine-learning-operations (MLOps): A set of practices that high-performing organizations use to adopt AI capabilities, including assembling existing components and live-model operations for transformative gen AI applications.
• Live-model operations: Monitoring systems with instant alerts for gen AI systems to enable rapid issue resolution and maintain system integrity.
• Taker approach: A strategy where companies consume existing AI services to gain efficiencies and speed without developing new capabilities in-house.
• Shaper approach: A strategy where companies develop their own AI capabilities, such as tuning models and training them on proprietary data, to create a competitive advantage.
• Knowledge graphs: Data structures that high-performing organizations embed in products or business functions to enhance AI capabilities, particularly in relation to gen AI and natural language processing.