Key findings

Defining AI leaders

Many AI projects fail to scale; legacy data architectures are the culprit

AI’s growing strategic importance is driving a significant increase in initiatives across businesses. Broad experimentation and education are and organizations would be remiss not to encourage it. However, the opportunity is being throttled by projects that lack a clear pathway to recognizing value, hampered by data challenges. AI projects risk stalling in a limited deployment purgatory, costing a company money, time and resources, while not seeing desired levels of use. Initiatives are becoming snagged on data siloes, poor data quality and ineffective data and model pipelines.

Key insights:

• In the average organization, 51% of AI projects are in production but not being delivered at scale.
• Data quality is the greatest inhibitor when moving AI projects into production environments.
• Storage and data management are the most common infrastructural inhibitors to AI initiatives, identified by 35% of organizations; however, those that have AI widely implemented feel these challenges less keenly.

As organizations invest to apply AI to an ever-growing set of objectives, a kink is emerging in organizations’ project pipelines. While more initiatives are funneled toward AI project teams, there is a buildup of initiatives that have only been partially deployed. As Figure 4 illustrates, respondent organizations, on average, have more projects classified as being in production with a limited deployment than scaled-up capabilities. In chasing new initiatives, many organizations may fail to maximize the value of their existing investments. The crux of the problem appears to be data quality and availability, with legacy data architectures causing this pipeline stoppage in many organizations.

Data quality is the most frequently identified challenge as organizations move their projects from pilots to production. As Figure 5 illustrates, data quality concerns — identified by 42% of organizations as among their top three barriers — are even more significant than skills shortages (32%) and budget limitations (31%). Organizations in media and entertainment (59%), higher education (53%), and aerospace and defense (48%) feel the data quality challenge particularly keenly.

Figure 4: Many projects fail to graduate from limited deployment to delivering at scale

Q. How many AI/ML projects do you currently have: In pilot/proof of concept; in production, limited deployment; in production, at scale?
Base: All respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

The data quality challenge is not a lack of data to build performant models but, rather, that the data is not set up in such as way that project teams can take full advantage of it. When asked specifically to rank the primary data challenges to move projects to production environments, respondents indicated that availability of quality data is a more notable impediment than identifying relevant data. With 34% of organizations perceiving availability of quality data as a top three data challenge, outranked only by data privacy concerns (35%), it is clear that many organizations are poorly set up for effective data management.

Legacy data technologies seem to be a leading cause of these data management shortcomings. Data management and storage are most commonly seen as the infrastructure components that inhibit AI application development. More than a third (35%) of respondents see them as a more serious issue than security (23%), compute (26%) and networking resources (15%).

Figure 5: Top three impediments to organizations moving an AI/ML application from pilot to production environments

Q. What are the primary challenges or impediments to moving an AI/ML application from proof-of concept/piloting stages to production environments?
Base: All respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

Figure 6: Organizations find the early data steps of the AI life cycle as challenging as model building

Proportion of respondents that identify AI life cycle stage as “most challenging”

Q. What stages of the AI/ML application life cycle are currently the most challenging (Rank 1)?
Base: All respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

Immature data management toolsets are a worrying backdrop for the increasingly data-hungry AI strategies many organizations are embarking upon. More than three-quarters (80%) of respondents forecast an increase over the next 12 months in the volume of data they use to develop their AI models, and just less than half (49%) are forecasting growth in data volumes of more than 25%. Perhaps more fundamentally, though, is that the challenge for organizations that underinvest in data management may come with new data-related pressures — in particular, the mix of data types organizations are employing for model training. The proportion of organizations using unstructured rich media and text data for AI initiatives has increased since 2023, and outdated data management technologies may prevent organizations from delivering these projects meaningfully.

Generative AI has rapidly eclipsed other AI applications

Organizations have rushed to invest in generative AI, with interest outstripping longer-standing forms of AI. As the dust settles on this explosion of investment, a small cohort of generative AI trailblazers has emerged. These organizations have more widely integrated capabilities and are seeing significant competitive benefits from the technology around new product development, enhanced innovation and faster time-to-market. These competitive advantages are likely to grow as generative AI trailblazers set out to establish a significant gap between themselves and others, shaped by their investment and infrastructural advantages.

Key insights:

• 88% of organizations are actively investigating generative AI.
• 24% already see generative AI as having graduated to an integrated capability across their organization.
• The majority of these generative AI trailblazers see a “high” or “very high” impact from generative AI initiatives on increasing the rate of innovation (79%), supporting new product introduction (76%) and improving time to market (76%), among other areas of competitive differentiation.

What are AI leaders doing differently?

Leaders are investing in a broader range of AI types with a greater likelihood of engaging with robotics, expert systems and classification models alongside generative AI. This expanded portfolio equips them to identify solutions that can meet diverse business needs and fosters a more holistic approach where these technologies can be brought together.

Generative AI adoption is progressing rapidly. As Figure 7 illustrates, a set of trailblazers — 24% of organizations — have already graduated generative AI investments into scaled-up production capabilities. In contrast, 11% of companies have not invested in generative AI, 29% are still experimenting with the technology and 37% have generative AI in production but not yet scaled. This is a remarkable level of uptake for a technology that only made it into the public consciousness with the launch of ChatGPT in November 2022.

Figure 7: Generative AI maturity and levels of investment

Proportion of respondents at each stage:

Q. Which of these statements most accurately reflects the use of generative AI at your organization?
Base: All respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

Organizations that have integrated and broadly deployed generative AI experience a wide range of benefits. Importantly, these benefits are commonly seen in areas that deliver competitive advantage. More than three-quarters (79%) of these trailblazers see generative AI as having a “high” or “very high” impact on their rates of innovation, 76% on their time to market, 76% in supporting new product introduction, 74% on improvements to their product or service quality, and 67% on their product and/or service differentiation. These levels outstrip organizations that are less “AI-mature” and suggests that relative adoption of generative AI may shape industry winners and losers. Organizations that fail to rapidly institute meaningful generative AI projects could end up losing out to those that can.

Generative AI trailblazers are more sophisticated in their enabling infrastructures and strategies. They use a wider array of venues for AI model training and inference. More fundamentally, however, they take far more factors into account when it comes to AI infrastructure planning. They are more likely to plan their infrastructure considering security, AI accelerator access, data privacy, scalability, customer support, and access to AI tools and frameworks than organizations that have not invested to the same degree. The only factor these trailblazers are less likely to account for than those experimenting with generative AI is up-front costs, which they see as less important than longer-term operating expenditures. By looking at these considerations at the outset of infrastructure decision-making, these organizations are ensuring these issues do not emerge as projects progress.

GPU availability continues to be constrained, shaping infrastructure decision-making

AI accelerators play an important role in optimizing the performance of AI. These specialized hardware devices — GPUs being the most prominent example — are designed to accelerate model training and inference; they are faster and more efficient than CPUs for AI workloads. Organizations can face challenges accessing GPUs, and that scarcity is elevating their position in infrastructure planning and is encouraging uptake of specialist AI cloud computing platforms.

Key insights:

• After security, the leading factor in infrastructure decision-making is accelerator availability, identified by 44% of organizations.
• Hyperscaler public clouds are one pathway to GPUs, but many are also turning to specialist AI clouds. GPU clouds are emerging as a key venue for both training — employed by almost a third, 32% of organizations — and inference, 31%.
• In some geographies, particularly in Asia-Pacific, lack of access to AI accelerators is already limiting organizations from moving models into production.

The leading infrastructure decision factors relate to security, AI accelerator access, and reliability and availability. As Figure 8 illustrates, access to AI accelerators ranks highly, outstripping even long-standing areas of concern such as operating costs and flexibility. Telecommunications companies (53%), higher education (53%) and manufacturing organizations (51%) prioritize this access particularly strongly.

Figure 8: Security and access to AI accelerators leading infrastructure decision-factors

Q. Which factors most influence the AI infrastructure decisions made at your organization?
Base: All respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

Hyperscaler public clouds offer an important avenue for organizations seeking GPUs, but they are not the only game in town. While hyperscaler cloud computing incumbents represent the most popular venue for AI training and inference, identified by 46% and 40% of organizations, respectively, specialist AI clouds have emerged as an auxiliary, or even alternative, venue. GPU clouds have seen an explosion in popularity that reflects the high demand for GPUs. Almost a third, 32%, of organizations that have invested in AI are executing training workloads using GPU clouds, and 31% for inference. These specialist cloud offerings are particularly popular with information technology and services companies, with 51% citing GPU clouds as a venue for training.

As the landscape of AI development and deployment widens, GPU clouds are poised to see further growth. Organizations are anticipating an increase in training and inferencing venues that will be employed over the next 12 months, and in this growth environment, GPU clouds are projected to grow to a 34% adoption rate for both inference and training. Higher education institutions appear to be a particularly fast-growing customer cohort. Our data highlights scalability as being the principal role organizations see GPU clouds as stepping into; the ability for organizations to easily and cost-effectively manage fluctuating AI workloads is a clear driver of adoption.

GPU availability challenges are felt keenly by organizations in some countries, including a number of major Asia-Pacific economies; India, Taiwan, New Zealand and Australia are more likely to rank GPU availability among their top three challenges to bringing a model into production. Sweden, where 39% identify it as a top three challenge, and UAE (35%) also stand out in this regard.

Figure 9: National differences in the level of impact GPU availability is having on bringing models into production

Q. What are the primary challenges or impediments to moving an AI/ML application from proof of concept/pilot stages to production environments? Ranks 1, 2 and 3.
Base: All respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

What are AI leaders doing differently?

Leaders are more likely to leverage GPU clouds for both training and inference and are particularly invested in how the technology can be used to reduce the time it takes to bring AI initiatives live. Leveraging specialist cloud-based services to expedite the development process by securing access to scarce GPU resources appears to be a clear avenue to drive through an AI advantage.

Concerns about AI’s environmental impact persist but are not slowing AI adoption; sustainable AI practices offer opportunity to mitigate emissions

The story this year is about expansion: expanding scope of AI, expanding workload requirements, expanding data infrastructure. In this context, sustainability practices are key. With many sustainability practices delivering measurable value and the potential of applying AI to address energy consumption, there is a clear opportunity to address the emissions challenge.

Key insights:

• Nearly two-thirds 64% of respondents say that their organization is “concerned” or “very concerned” with the sustainability of AI infrastructure.
• Popular sustainability measures include investing in energy-efficient IT hardware (42% of organizations), increasing investment in AI governance (40%), providing training and education on sustainability (37%) and establishing sustainability guidelines (35%).
• Nearly one-third (30%) of respondents say reducing energy consumption is a driver for AI/ML adoption at their organization.

Sustainability remains important to organizations, with almost two-thirds concerned about the impact AI/ML has on energy use and carbon footprints, including 25% that are “very concerned.” The topic is an influential factor in AI strategies, with 37% seeing sustainability as among the factors that most influence the AI infrastructure decisions made at their organization. As Figure 8 illustrates, this is comparable with operating costs, data privacy and scalability. This focus on sustainability is important in the context of expanding data demands, as well as venues for training and inference, forecast for AI workloads over the next 12 months.

In addition to exhibiting a clear appetite to invest in better optimized technology, many organizations are increasing investment in AI governance (40%), providing training and education on sustainability (37%), and establishing sustainability guidelines (35%). Less popular are initiatives that would represent rolling back AI investments. Just 5% say they have canceled AI initiatives in the past 12 months, and 19% report they have changed project scope.

Organizations largely find sustainability practices to be effective. When identifying the projects that are more likely to deliver high impact than minor or no impact, we see that practices are more likely to deliver impact than not, including those that are sometimes overlooked (see Figure 10). While just 19% of organizations had engaged in changing AI project scope to address environmental concerns in the past 12 months, 57% of those that did see it as “highly” or “very highly” impactful. Another practice that may represent significant opportunity is changing infrastructure vendor — a step taken by 27% but considered the most impactful practice overall.

Figure 10: Sustainability steps assessed by impact

Q. What impact have you seen from the following actions on the environmental impacts of your AI projects?
Base: Respondents from organizations that had invested in step over past 12 months, all respondents (n=1,519).
Source: S&P Global Market Intelligence 451 Research Global Trends in AI custom survey, 2024.

A number of organizations are already applying AI to energy consumption — countering the resource-intensive nature of AI workloads with AI-driven efficiency benefits. Nearly onethird (30%) of organizations treat reducing energy consumption and carbon footprint as a key driver for developing AI/ML applications. While currently the least popular objective for AI/ML applications, it is notable that almost a third of organizations may already be applying AI to better assess and predict emissions or to inform efficiency improvements that could contribute to energy savings. As workload pressures expand, we expect this proportion to increase.

The drive to reducing energy consumption is not just about meeting sustainability goals. While 11% of respondents see meeting enterprise sustainability goals as their priority in reducing energy consumption, that was less than improved operational efficiency (13%) and achieving cost savings (12%). Concerns about regulatory compliance (9%) and customer expectations (9%) are also apparent. In some instances, this may give sustainability advocates the ability to address sustainability impacts while couching the value in the context of wider business goals.

What are AI leaders doing differently?

AI leaders are taking more steps to address the environmental impact of their projects and are more likely to see these steps as “highly” impactful. In particular, AI leaders are significantly more likely to consider changing training or inferencing venues over the next 12 months, establish sustainability guidelines and implement offsetting methods. By engaging with multiple steps in parallel, the value of each appears to be magnified. While an emissions impact assessment may be useful, it delivers more value when that assessment can be converted into decisions about choice of vendor partner or AI training or inferencing environments.

Conclusions

The 2024 Global Trends in AI report represents a very different AI-adoption landscape than the 2023 edition. AI is gaining more widespread implementation, with greater focus on delivering product and service quality improvements and revenue growth. The maturation of generative AI is a key driver of this transition. Yet challenges remain. Many organizations are struggling to shift investments into capabilities that they can deliver at scale, and they acknowledge there is pressure on the sustainability of business operations.

Methodology

The findings presented in this report draw on a survey fielded in North America, Europe, the Middle East and Africa, and Asia-Pacific in Q2 2024.

The survey targeted 1,519 AI/ML decision-makers/influencers, filtering for respondents with AI/ML deployed in pilots and production environments across the following industries: aerospace and defense, automotive, energy/oil and gas, finance, government, healthcare, higher education, IT and services, life sciences, manufacturing, media/entertainment, telecommunications, transportation and logistics, and utilities. The most common respondent job roles were IT operations leadership, IT infrastructure leadership and executive management. This report also draws on contextual knowledge of additional research conducted by S&P Global Market Intelligence 451 Research.

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About the authors

Alex Johnston

Senior Research Analyst, Data, AI & Analytics

Alex Johnston is a senior research analyst on the 451 Research Data, AI & Analytics team at S&P Global Market Intelligence. He focuses on emerging technologies and how they can be applied in business contexts. Alex’s primary coverage areas are artificial intelligence, distributed ledger technology and event stream processing. Alex’s recent areas of concentration include monitoring the emerging generative AI market, tracking the evolution in blockchain use cases and investigating real-time architectures.

David Immerman

Consulting Analyst

David Immerman is a consulting analyst at S&P Global Market Intelligence. He is responsible for executing on a range of custom research initiatives and development of thought leadership across technology sectors including industrial IoT, digital transformation, edge computing, AI/machine learning and fintech, among others, and verticals such as manufacturing and automotive.