The State of AI: Generative AI Adoption Starts to Generate Value

In 2023, the world discovered the potential of generative AI (gen AI). In 2024, organisations are not just exploring this technology but actively leveraging it to derive significant business value.

According to a recent survey, 65 percent of respondents report that their organisations are regularly using gen AI, nearly double the percentage from just ten months ago.

The expectations for gen AI’s impact remain high, with three-quarters of respondents predicting significant or disruptive changes in their industries in the coming years.

Organisations are already seeing material benefits, including cost reductions and revenue increases, as they deploy gen AI across various business functions.

AI Adoption Surges

A Global Uptick in AI Adoption

The interest in generative AI has heightened attention towards a broader set of AI capabilities. Over the past six years, AI adoption hovered around 50 percent. However, this year, adoption has jumped to 72 percent. This increase is truly global, with more than two-thirds of respondents in nearly every region reporting the use of AI. The professional services industry has seen the most significant rise in adoption.

Functional Deployment of Gen AI

Sixty-five percent of respondents say their organisations are using gen AI in at least one business function, up from one-third last year. The average organisation uses gen AI in two functions, most commonly in marketing and sales, product and service development, and IT. The most substantial increase from 2023 is in marketing and sales, where adoption has more than doubled. Furthermore, respondents are increasingly using gen AI in their personal lives, indicating a broader cultural shift towards AI integration.

Benefits and Value Creation

Tangible Business Benefits

Organisations are beginning to see significant value from their investments in gen AI. The survey highlights that investments in gen AI and analytical AI are starting to pay off, particularly in human resources, supply chain, and inventory management. For example, respondents report meaningful revenue increases and cost decreases from AI deployments. Human resources is the function where the largest share of respondents report seeing cost decreases, while supply chain and inventory management show the most significant revenue increases.

Investment Trends

Organisations are budgeting for gen AI in a way that parallels their investment in analytical AI. Many industries report spending over 5 percent of their digital budgets on gen AI, with larger shares investing more than 20 percent in analytical AI. This trend indicates a balanced approach to leveraging both generative and analytical AI capabilities. Looking ahead, 67 percent of respondents expect to increase their AI investments over the next three years.

Risks and Mitigation Strategies

Addressing the Risks of Gen AI

As generative AI adoption grows, so do the associated risks. The most recognised risk is inaccuracy, followed by cybersecurity concerns and the explainability of AI models. Nearly half of the respondents report experiencing negative consequences from gen AI use, including data privacy issues, bias, and intellectual property infringements.

Governance and Responsible AI Practices

Responsible AI governance is crucial for mitigating these risks. However, only 18 percent of respondents say their organisations have an enterprise-wide council with the authority to make decisions involving AI governance. Embedding risk mitigation controls and fostering AI risk awareness among technical teams are essential steps that many organisations still need to implement. Leading companies incorporate risk practices into the development of their AI applications, ensuring robust testing and validation before deployment.

Best Practices for AI Implementation

Customisation and Integration

The survey identifies three archetypes for implementing gen AI solutions: takers (using off-the-shelf solutions), shapers (customising tools with proprietary data), and makers (developing their own models). While many organisations start with off-the-shelf solutions, the most successful ones invest in customisation to address specific business needs. This approach creates a competitive moat that off-the-shelf solutions cannot provide.

Building a Comprehensive AI Ecosystem

Organisations must move beyond the binary “build versus buy” mindset to a “buy, build, and partner” strategy. Successful AI integration involves a blend of proprietary, off-the-shelf, and open-source models. This comprehensive ecosystem ensures that the enterprise can leverage the full potential of gen AI. Effective AI implementation also requires the right organisational structure, relevant skill sets, and robust operating models to unlock the full impact of AI.

Achieving High Performance with Gen AI

Characteristics of High Performers

Only a small subset of organisations currently attributes a significant share of their EBIT to gen AI. These high performers use gen AI across multiple business functions, including marketing, sales, risk, legal, compliance, strategy, corporate finance, supply chain, and inventory management. They are more likely to customise or develop proprietary models, ensuring that their AI applications are tailored to their specific needs.

Emphasising Risk Management

High performers pay more attention to gen AI-related risks and embed best practices into their operations. They involve legal functions early in the development process, conduct thorough risk reviews, and follow strategy-related practices that ensure scalable and sustainable AI deployment.


The rapid adoption of generative AI in 2024 marks a significant shift from exploration to value creation. Organisations that strategically invest in gen AI and analytical AI, while mitigating risks through robust governance and customisation, are beginning to see substantial benefits. As AI continues to evolve, the ability to innovate, deploy, and improve AI solutions at scale will become a key differentiator for success. Embracing a holistic approach that includes vision, technology, organisational structure, and risk management will enable companies to unlock the full potential of AI and achieve lasting competitive advantages.

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