Globally, artificial intelligence (AI), and in particular generative AI (gen AI), is changing the business landscape. Although there is a lot of enthusiasm about AI’s potential, adoption and value creation in practice are complex.

Recent studies by the Boston Consulting Group (BCG), the U.S. Census Bureau, and McKinsey provide thorough insights into how businesses are implementing AI, the industries driving adoption, and the difficulties businesses encounter in scaling AI value.

In order to give a clear picture of AI adoption in 2024, its effects on businesses, and the characteristics that set AI leaders apart, this article compiles the most important findings from these studies.

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The Explosive Growth of Generative AI in Business

According to the most recent McKinsey Global Survey, generative AI tools are expanding quickly. One-third of survey participants report routine use of many gen AI tools in at least one business function within a year of their launch.

Nearly 25% of C-suite executives personally use gen AI tools, and more than 25% see gen AI on their boards’ agendas, indicating that company leaders are becoming more involved. Forty percent anticipate that advances in generative AI will result in higher overall AI investments.

Businesses with integrated AI are spearheading the use of gen AI, especially in product development, marketing, sales, and service operations—domains that have been demonstrated to produce the majority of AI’s business value.

Although gen AI is becoming more and more popular, only 55% of organizations report using AI, indicating that overall adoption of AI is stable. With only 23% attributing at least 5% of last year’s EBIT to AI, usage is still quite restricted, suggesting substantial unrealized potential.

Variations in AI Adoption Across Companies and Regions

According to research conducted by Kristina McElheran and the U.S. Census Bureau, the adoption of AI varies unevenly among American businesses. According to recent surveys, less than 4% of businesses use AI for production, down from just 6% in 2017.

AI use is more prevalent in larger businesses and industries like manufacturing, information services, and healthcare than in retail and construction.

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Geographically, AI use is concentrated in superstar cities like San Francisco and Nashville, but it can also be found in unexpected locations like manufacturing hubs in the Midwest. The results highlight the differences between AI invention or commercialization hubs and AI adoption in production.

Characteristics of AI-Adopting Startups

Among startups, AI adopters tend to have younger, highly educated, and experienced leaders. These firms often have venture capital backing and focus on process innovation, enabling them to leverage AI effectively.

McElheran points out that AI functions more as a point solution improving specific tasks rather than a sweeping overhaul, meaning firms must innovate broadly to integrate AI without destabilizing existing systems.

Overcoming Barriers: Inertia and Adjustment Costs in AI Adoption

The costs of changing workflows and organizational inertia are the main obstacles to the adoption of AI. Change is resisted by routine work practices, and businesses must pay for possible job displacement, restructuring, and training.

The potential disadvantage of digital transformations for older workers highlights the need for practical, evidence-based strategies that strike a balance between the advantages of AI and its social and economic effects.

AI Adoption and Value Creation: Insights from Boston Consulting Group

According to a recent BCG report, only 26 percent of businesses have the skills necessary to go beyond AI pilots and produce tangible value. Of these, 4% are leaders in cutting-edge AI, and another 22% are headed for big profits. The remaining 74% find it difficult to scale and attain AI value.

Leaders in AI exhibit a number of unique characteristics:

• They give priority to core business operations over sporadic increases in productivity.
• They make significant investments in workforce enablement and AI.
• They incorporate AI into operations that generate income and expenses.
• They concentrate on fewer, more impactful projects with a higher anticipated return on investment.
• They place more emphasis on people and procedures than on technology alone.
• They swiftly embrace generative AI, taking advantage of its capabilities in system orchestration and content production.

The industries that saw early digital disruption, such as fintech, software, and banking, have the highest concentration of AI leaders.

Where AI Generates the Most Value

Despite popular belief, operations, sales and marketing, and research and development account for 62% of AI value. The rest comes from support services like procurement, IT, and customer service.

Industry-specific insights include:

• AI value creation in software, media, telecom, and travel is driven by sales and marketing.
• AI greatly aids R&D in the biopharma, medtech, and automotive industries.
• AI significantly improves customer service in banking and insurance.
• AI-enabled personalization is advantageous for retail and consumer goods.

The Critical Role of People and Processes in Scaling AI

According to BCG’s research, people and process-related problems account for 70% of AI implementation challenges, followed by technology (20%) and algorithms (10%).

Many businesses mistakenly concentrate on technical difficulties while ignoring human aspects such as governance, talent development, workflow optimization, and change management.

Accordingly, AI leaders devote roughly 70% of their resources to people and procedures, 20% to technology and data, and 10% to algorithms. Most businesses run the risk of lagging behind in their AI endeavors if they neglect these human-centric factors.

Conclusion: The State of AI Adoption in 2024

Rapid advancements in generative AI are a defining feature of AI adoption in 2024, but broad value realization is still scarce. Adoption is driven by large corporations and specific industries, but many businesses must deal with substantial inertia and adjustment costs.

Investments in people, procedures, and targeted strategic initiatives are just as important to success as technology.

As AI develops, businesses that strike a balance between aspiration and practical implementation strategies—especially those that prioritize core business transformation—will reap the biggest benefits and maintain their competitive edge in a digital economy.