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Rapid Adoption and Challenges of Generative AI in Business

The latest McKinsey Global Survey indicates a rapid adoption of generative AI (gen AI) in various business sectors, with one-third of participants reporting regular use within a year of introduction, extending beyond tech departments to include a quarter of C-suite executives. Despite the enthusiasm and increased investment in gen AI, the survey highlights a lack of preparedness in managing related risks, particularly inaccuracy, and suggests significant industry disruption and workforce changes ahead, with varying impacts across different sectors. More about it you can read in our last blog post

Characteristics of ‘AI High Performers’

The survey paints a clear picture of ‘AI high performers’ – organisations where a significant portion (at least 20%) of their 2022 EBIT was due to AI usage. These high achievers are deeply invested in AI, both in generative AI and more traditional forms. They’re ahead of the curve, deploying gen AI across more business areas, notably in product and service development and in managing risk and supply chains.

These AI-savvy organisations don’t just stop at gen AI. They’re also leveraging a broader range of AI technologies, including traditional machine learning, robotic process automation, and chatbots. They’re significantly more engaged than their peers in using AI for product development, optimising product-development cycles, enhancing existing products, and innovating new AI-driven products. Additionally, they’re applying AI more extensively in risk modelling and within HR, optimising performance management, organisational design, and workforce deployment.

Strategic Focus and Investment in AI by High Performers

A striking distinction is their approach towards gen AI: unlike their peers focusing on cost reduction, AI high performers prioritise creating new businesses or revenue streams and enhancing the value of existing offerings with AI-driven features. Financially, these organisations commit substantially more to AI, with many spending over 20% of their digital budgets on AI. They also adopt AI more comprehensively across multiple business functions and embed a diverse array of AI capabilities.

Despite their advanced position, AI high performers still face challenges in maximising AI’s value, though these tend to reflect their more mature stage of AI integration. They often grapple with technical aspects, like monitoring and retraining models, as opposed to other organisations that struggle with foundational AI strategies and resources.

Challenges and Best Practices in AI Integration

The survey also highlights that even these leading organisations haven’t fully mastered AI adoption best practices, such as machine-learning-operations (MLOps). However, they are still more likely than others to follow efficient practices, like reusing existing components where possible.

Regarding more complex and transformative gen AI applications, specialised MLOps technologies and practices are essential for safe and effective deployment. Live-model operations are critical here, with monitoring systems and instant alerts for rapid issue resolution. AI high performers are more advanced in this area, yet there’s still room for improvement. For instance, a quarter of these high performers have comprehensive monitoring and alert systems, compared to just 12% in other organisations.

Evolving Talent and Workforce Dynamics in AI

The latest survey findings highlight evolving talent requirements as organisations bolster their AI strategies. In the past year, the most sought-after roles in AI-using organisations were data engineers, machine learning engineers, and AI data scientists. Interestingly, the demand for AI-related software engineers has decreased (28% in the latest survey, down from 39%). Prompt engineering roles are gaining prominence, reflecting the growing need for this skill set with the rise of generative AI.

Hiring for AI roles remains challenging but appears to have eased somewhat, possibly due to the layoffs in the technology sector from late 2022 through early 2023. While hiring difficulties for roles like AI data scientists and data engineers have lessened, finding machine learning engineers and AI product owners remains as challenging as before.

Looking ahead, the survey predicts significant workforce transformations due to AI adoption over the next three years. Reskilling is expected to be more prevalent than workforce reductions. Notably, 40% of respondents anticipate over 20% of their workforce will need reskilling, while only 8% foresee a workforce reduction of more than 20%. Service operations is one area where a decrease in workforce size is anticipated, a reflection of gen AI’s impact. Although gen AI could automate up to 70% of worker activities, this does not necessarily mean entire roles will be automated. Organisations with advanced AI capabilities (AI high performers) are expected to undertake extensive reskilling initiatives.

AI Adoption Trends and Investment Outlook

Despite the rapid spread of gen AI tools, the overall rate of AI adoption remains constant, with 55% of respondents reporting AI adoption in their organisations. The scope of AI usage still seems limited, with less than a third of respondents saying AI is used in more than one business function. Product and service development and service operations are the most common areas of AI adoption. Surprisingly, only 23% of respondents report a significant contribution of AI to their organisations’ EBIT, suggesting ample scope for value capture.

In terms of investment, over two-thirds of respondents anticipate increased AI spending over the next three years. Returns on AI investments are evident across various business functions, with many respondents reporting AI-related revenue increases. This optimism is reflected in the plans for increased AI investment in the coming years.