Generative AI: The new frontier for US business ingenuity

US businesses are placing hefty bets on generative AI. Our recent study reveals planned expenditures of over $67 million per company on this powerful technology this year alone—well over the global average of $47 million. And the pressure is on: A huge majority (74%) believe they aren't moving fast enough with their generative AI strategies, and many (66%) believe these delays will result in a competitive disadvantage.

This show of enthusiasm is not surprising in a country renowned for its innovation, entrepreneurial mindset and robust high-tech ecosystem. Indeed, our study also shows a higher-than-average level of confidence in what the US has to offer in terms of the key factors needed for succeeding with a generative AI strategy.

The fact is, regional variances—regulatory compliance, infrastructure and available expertise, for instance—as well as internal factors like the business’s own technology foundation, will influence success with implementing generative AI strategies and how businesses use generative AI. As a result, the pace of generative AI uptake and the way in which it’s used will be uneven across the globe.

To better understand what generative AI adoption will look like globally, we conducted a study of 2,200 business leaders in 23 countries and 15 industries, including 500 in the US. The study assessed a wide range of generative AI adoption trends, including investment levels, use cases, how critical gen AI strategies are to business success and organizational readiness to adopt the technology. 

We also analyzed 18 regional and internal business factors that will either inhibit or accelerate business adoption of gen AI (see the end of the report for the full list of factors). Respondents evaluated each factor’s potential impact on their generative AI strategy, rating it as either positive or negative on a scale of high to low impact.

From the results, we calculated a “momentum score” for each country or region. The momentum score represents the level of confidence business leaders have about being able to roll out their generative AI strategy based on internal business factors and the prevailing local conditions of their country or region.

For all the regions covered, inhibitors to adoption outranked accelerators, meaning that all momentum scores skewed negative. In effect, businesses globally feel constrained by their operating environment.

But to understand how different regions varied relative to each other, we averaged the ratings to establish a baseline global momentum score. This approach enabled us to identify regions that are more optimistic about their ability to adopt the technology compared with a global average.

For the US, the momentum score is 11% higher than the global average. The factors contributing to this score vary, but the most impactful are a more optimistic view of market demand and data readiness compared to global responses. The region also holds a less pessimistic view of several inhibitors, including national infrastructure and sustainability. That said, businesses in the region are marginally more pessimistic than global peers about their technology infrastructure and the flexibility of their existing business model.

US gen AI scorecard

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 1

As for where businesses’ generative AI investments will be aimed in the near term, we looked at two distinct uses of the technology: productivity, such as helping people work more quickly and get more done, and innovation, which involves bigger changes to business and operating models. Overall, the US mirrors the global trend: Over the next two years, more respondents expect to use generative AI to boost productivity than drive disruptive change (see Figure 2).

However, our study also reveals a change in what productivity means when pursued with generative AI. The end goal is not efficiency and cost-cutting as has been the case with previous automation endeavors. This new dynamic requires fresh thinking around understanding business use cases of generative AI, which we’ll address later in this report.

Greater focus on productivity than innovation

Q: Which of the following best describes the role generative AI will play in your organization's business strategy in the next two years? (Percent of respondents naming each as a top-3 choice)

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 2

This report identifies the regional and business factors that could either inhibit or accelerate generative AI momentum in the US. It also provides an industry-specific look at how generative AI will be used, a regional focus on business readiness and strategies for US businesses to successfully implement generative AI.

Inhibitors and accelerators: The forces driving AI momentum

To dig deeper into these mechanics, rather than comparing to a global average, we’ll now examine how business leaders rate inhibitors and accelerators within their region. By doing so, our study provides a detailed temperature check on what respondents view as the main inhibitors and accelerators to generative AI in their region. 

With this assessment, leaders can take advantage of what’s working well in their local environment, while strategizing on overcoming challenges.

A look at US gen AI accelerators

Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage point difference to the country's momentum score compared to the global baseline.

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 3

A top accelerator of generative AI adoption in the US is high market demand. Expectations are high among US businesses and consumers for generative AI to be integrated into products and services. This widespread recognition is fueling a sense of urgency among businesses to adopt and leverage AI to meet evolving customer expectations and maintain a competitive edge.

Data readiness also ranks as an  accelerator. The US has undergone a significant digital transformation over the past few decades. Many large businesses have accumulated vast amounts of well-managed data, providing a rich foundation for generative AI applications. Whether this data is accessible enough for optimal generative AI use, however, is another matter, which we’ll address later in the report.

The availability of compute power is another key driver. With many of the largest technology companies, including hyperscale cloud providers based out of the US and with considerable operations in the region, it’s no wonder US business leaders are confident. The US also boasts a thriving startup ecosystem and a robust venture capital landscape. This fertile ground for innovation has led to a proliferation of AI tools and models, making it easier for businesses to access and experiment with cutting-edge technologies. The abundance of readily available solutions lowers the barriers to entry, encouraging broad adoption.

Understanding US gen AI inhibitors

Respondents were asked which factors inhibit or accelerate their organization's adoption of generative AI. Score represents a percentage point difference to the country's momentum score compared to the global baseline.

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 4

Despite the confidence and promising environment in the US, several challenges remain. The lack of skilled AI professionals is a major obstacle that applies to businesses worldwide, and the US is no exception. A significant percentage of US respondents (58%) identify the shortage of expertise as a key barrier to adopting AI. While just over half (56%) are focused on internal training programs to develop AI skills within their workforce, many (38%) intend to hire externally, which will be difficult amid global scarcity of talent.

Consumer perception of generative AI is a significant inhibitor to generative AI momentum, according to US respondents. Many consumers may view this technology as a threat to their jobs or question the accuracy of AI-generated content.

Every few weeks, a story of an LLM hack emerges, from getting the model to reveal its training data, to a rogue agent bypassing restrictions to get the LLM to do its bidding. Recent high-profile controversies include a General Motors chatbot being manipulated into selling a hacker a Chevy Tahoe truck for $1.00, and an Air Canada chatbot giving erroneous advice to a customer on the airline’s refund policy—which the company ultimately had to honor.

To accelerate adoption, businesses will need to demonstrate the reliability and accuracy of AI outputs and communicate transparently about their intended use of the technology, including its impact on jobs.

The maturity of generative AI technology is another top inhibitor. Although AI has been around for decades, the generative aspect is still in its nascent stages, leading to fluctuating accuracy in outputs and raising ethical concerns that are often clumsily addressed. Businesses are still grappling with the full scope of generative AI's capabilities, potential applications and optimal deployment. This ongoing exploration and experimentation phase naturally slows down the pace of adoption compared with more established technologies.

While US respondents named their operational flexibility as an accelerator for generative AI adoption, they’re less sure about the flexibility of their business models, which they named as an inhibitor. This challenge stems from the inherent nature of AI, which often necessitates fundamental shifts in processes, workflows and decision-making mechanisms.

Companies with less adaptable business models will struggle to accommodate these changes due to entrenched practices, bureaucratic hurdles and resistance to change. In contrast, companies with more agile and flexible structures are better equipped to experiment, learn and adapt quickly to the evolving capabilities of AI.

Sector spotlight: Stark differences in industries’ gen AI priorities

Of course, there are many use cases and strategies for using generative AI. As we’ve said, US businesses are primarily focused on realizing productivity gains with generative AI, at least in the next two years. However, a look at what’s driving their business cases sheds a new light on productivity from how it’s been seen historically.

Traditionally, businesses have equated automation productivity gains with cost-cutting: driving down the cost of output by reducing the number of people needed to get work done.

While generative AI-driven automation will likely lower headcount to some degree, that is no longer the end goal. Instead, as seen through the metrics respondents will use to drive business cases, we see a shift toward redirecting productivity gains into funding endeavors that increase revenues or lead to entirely new revenue streams.

The metrics US businesses say will be most important for justifying generative AI expenditures include increasing revenues, discovering new revenue sources and creating new products and services, all of which were named by at least 44% of respondents (see Figure 5). Conversely, metrics like cost savings, time-to-market and productivity were cited by less than 35% of respondents. In other words, the concept of productivity no longer stops at cost-cutting—businesses appear to be redirecting productivity gains into initiatives aimed at growth.

Revenue is a top metric for justifying gen AI use cases

Q: Which of the following metrics are most important in terms of justifying your organization’s generative AI business cases? (Percent of respondents naming each as a top-3 choice)

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 5

Using this more granular view of productivity goals and business drivers, we analyzed the differences in how industries intend to use the technology.

Rather than focusing on the distinction between productivity vs. innovation, we grouped the metrics into two high-level categories of business use cases:

• Enhancing current business performance (revenue, cost savings, time-to-market, productivity)
• Building something new (new revenue sources, new or improved products, innovation)

We then assigned each of the metrics a score to see the relative gap between a number-one-ranking metric and a number-three-ranking metric. By calculating the average score across industries, we could clearly see how each industry’s responses deviated from the baseline. 

Our analysis reveals stark differences among US industries in terms of the business use cases they’ll likely prioritize (see Figure 6).

Industries diverge on business cases

Note: This figure depicts each industry’s relative deviation from a baseline of “zero,” using a ranked scoring of the top-three metrics respondents cite as important for justifying their generative AI use cases. It reveals a weighted view of each industry’s overall priorities for gen AI deployment.

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 6

• Healthcare organizations, burdened by high spending and worrisome patient outcomes, are turning to generative AI to build new tools that allow practitioners to focus on what really matters. By automating administrative tasks like managing contracts and handling appeals resolutions and automating clinical documentation, healthcare providers can dedicate more time to patient care, enhance care quality and lower costs.
  
  Doctors at national healthcare provider HCA Healthcare, for instance, have been using AI to record audio of their conversations with patients. The system converts the recordings into medical notes and automatically transfers them to the patient’s electronic health record. HCA is expanding its use of generative AI to automate documentation that nurses hand off to each other after each 12-hour shift. Doing so alleviates nurses from a laborious task and improves accuracy, leading to improved patient outcomes.
  
  
• Retail and consumer goods companies are similarly focused on new and improved experiences with generative AI. These companies face challenges like thin profit margins, supply chain disruptions and fluctuating consumer demands, and in a hyper-competitive market, innovation is a differentiator.
  
  Hawaii’s largest grocer Foodland Super Market, for instance, plans to use generative AI for supply chain and category management processes, with the goal of better meeting shopper needs and avoiding stock-outs.
  
  Retailers are also exploring the use of generative AI to enhance customer engagement. Walmart, for example, is creating smarter search functions that allow shoppers to find products based on themes or specific use cases. This not only improves the shopping experience but also helps customers discover products they might not have otherwise found.
  
  
• The life sciences sector, with its significant research and development costs and the constant pressure to be first to market, is increasingly turning to generative AI to accelerate drug discovery and development. Large biopharma companies such as Amgen are harnessing generative AI to quickly identify promising drug targets and speed clinical trials and manufacturing, vastly compressing the traditionally lengthy and expensive process of bringing new treatments to market.
  
  
• The transportation and logistics sector, on the other hand, is heavily focused on leveraging generative AI to improve their time to market and increase the productivity of both staff and customers. From FedEx to Ryder to UPS, large T&L organizations are using the technology to optimize routes, improve the accuracy of delivery timeframes and accelerate last-mile routing.
  
  In other cases, T&L businesses are creating entirely new services for customers. Penske Truck Leasing, for instance, has created a generative AI platform that enables customers to get real-time insights into fleet performance. The platform compares a fleet’s performance to similar fleets across Penske’s live database of hundreds of thousands of vehicles in real-time, enabling customers to unlock fuel efficiency opportunities and optimize utilization using targeted insights.

Business constraints: Talent and tech infrastructure

A remaining question is whether businesses are ready to drive real value from these use cases.

The answer, according to our research, is mixed. To better understand how prepared executives believe their business is to adopt generative AI, we asked respondents to rank their organization’s maturity on a scale of 1 to 4 by selecting a statement that best described their organization in the following five areas, from low maturity to high:

• Organizational agility
• Leadership commitment
• Skills and talent
• Strategy and approach
• Technology and infrastructure

The output reveals where business leaders believe they are already mature, and areas where they feel the need to evolve capabilities considerably to make generative AI investments work.

Leadership support is sound, but fundamentals are lacking

Respondents were asked to rate the maturity of their organization's operations in relation to generative AI. (Percent of respondents rating each as a 3 or 4, with 4 representing the highest level of maturity).

Base: 500 senior business leaders in the US
Source: Cognizant and Oxford Economics
Figure 7

US respondents highly rate strategy and leadership commitment to generative AI, with a majority giving both an average rating of 3 out of 4. However, this enthusiasm is counterbalanced by a more critical view of their organizational agility and technology infrastructure (see Figure 7). 

A key element undercutting confidence in technology infrastructures is data. Although over half of respondents (55%) believe their data is in good condition, only 20% say it can be easily accessed—a key requirement for effectively using generative AI, which relies not only on data cleanliness but also on the availability of information and knowledge.

Without seamless accessibility, generative AI algorithms may encounter limitations in extracting valuable information, resulting in inaccurate or incomplete outputs.

Path to success: Strategic recommendations for US businesses

The US has much to offer when it comes to generative AI momentum. But businesses here will still need to apply focused attention to take full advantage of the accelerators of generative AI and overcome the inhibitors. To navigate the road ahead, executives should prioritize the following actions:

• Prioritize data accessibility: Our study reveals a significant gap between the perceived quality of business data and its actual usability for AI applications. This discrepancy hinders the effective implementation of AI.
  
  To bridge this gap, businesses must invest in comprehensive data management solutions that ensure data is not only clean and reliable but also structured in a way that facilitates easy retrieval and analysis for AI algorithms. By implementing robust data access controls and security measures, organizations can protect sensitive information while also enabling authorized users to seamlessly access the data they need, fostering a data-driven culture that empowers innovation and informed decision-making.
  
  
• Leverage the country’s unique innovation environment: The US boasts a unique competitive advantage, thanks to its thriving startup ecosystem and a legacy of innovation, particularly in Silicon Valley. By actively fostering collaboration between established businesses and agile startups, US companies can tap into the cutting-edge advancements emerging from this hub of technological ingenuity.
  
  This collaborative approach would provide access to a talent pool brimming with fresh perspectives and specialized expertise in AI, effectively addressing the skills gap that many businesses face.
  
  Further, established businesses can learn from the nimble and adaptable nature of startups, enabling them to overcome internal barriers and create a more responsive environment for generative AI adoption. This dynamic exchange of knowledge and resources can help businesses drive innovation and embrace the transformative capabilities of generative AI.
  
  
• Balance leadership enthusiasm with measurable results: One of the key insights from our study is the strong leadership commitment in the US to AI, demonstrated by significant investments and strategic initiatives. However, many businesses are still in the early stages of AI adoption and must balance this enthusiasm with tangible results.
  
  Leaders should focus on practical use cases and pilot projects to identify viable business cases and demonstrate real value. Businesses can build a solid foundation for AI adoption that aligns with their strategic objectives by iterating through experiments and refining their approach.
  
  
• Shore up consumer trust: In the US, building and maintaining consumer trust in generative AI is paramount for its widespread adoption. This requires addressing three key areas of concern: economic security, the technology's inner workings and societal repercussions. To mitigate these concerns, companies need to prioritize transparency, providing clear explanations of how AI is being used and its impact on employment.
  
  By showcasing AI's positive applications in areas like education and innovation, companies can create a more optimistic narrative and foster greater trust in this transformative technology among US consumers.

*The full list of regional factors we evaluated includes: the flexibility of the existing operating model, market demand for gen AI-enabled products and services, data readiness, quality of output from gen AI, availability of compute power, cost/availability of gen AI-related technologies, shareholder/investor sentiment, regulatory environment, sustainability, national infrastructure, cost/availability of capital, data privacy and security, existing technology infrastructure, current and prospective employee perceptions, flexibility of the existing business model, maturity of gen AI-related technologies, consumer perceptions and cost/availability of talent.

Learn about the impact of generative AI on jobs and the economy in our report New Work New World.