The AI race – Who is leading?

Artificial intelligence (AI) has become a ubiquitous topic of discussion among executives spanning diverse industries. One can’t help but wonder: who is emerging as the frontrunner in the competitive landscape of AI adoption and innovation? This question not only reflects the growing importance of AI across sectors but also underscores the desire to understand which companies or industries are leading the charge in harnessing its transformative potential.

Introducing our AI Index

To answer this question, we have crafted an AI index designed to empower companies to assess and compare their AI and foundational capabilities. Previous research efforts aimed at measuring companies' AI capabilities have primarily focused on isolated proxies captured in one-off snapshots. Consequently, we seek to develop a more holistic framework, employing a 360-degree perspective. This framework will encompass not only AI-related metrics but also the vital capabilities essential for companies to foster and scale their AI endeavors:

• Say: This measurement assesses how companies discuss AI in their earnings calls, focusing on strategic aspects such as future trends, investments and use cases. It also evaluates the firms' Responsible AI practices by examining mentions of Responsible AI, their talents on responsible AI and their commitment to business ethics and transparency.
• Do: This measure evaluates companies' innovation performance by analyzing their share of AI-related venture capital (VC) and merger and acquisition (M&A) investments. It also considers their performance in AI related innovation through patent with metrics including quantity, intensity, economic value, influence, success rate, innovativeness, and the number of AI talent they currently employ and are actively recruiting.
• Enable: Technology Index is a comprehensive assessment that takes into account various factors, including research and development partnerships, enabling and next-generation technologies, cloud adoption, data & AI adoption and cybersecurity to gauge a company's technological prowess and readiness in the modern business landscape.
• Care: The board technology quotient (TQ) assesses the percentage of board members who have a background in technology education or work experience. Meanwhile, the Workforce Index measures how effectively companies are managing and treating their employees, reflecting their commitment to workforce well-being and development.

Our AI index incorporates over 30 individual metrics sourced from more than 10 diverse data sets, encompassing a broad spectrum of industries, and spanning over 2000 large companies worldwide. By using third-party data sources to measure companies’ ambition and investment in AI, the index provides a valuable out-side-in view to companies. With this extensive dataset, we can uncover the most recent trends in how companies are developing AI and foundational capabilities with a few highlights.

Growing interests in AI

In Chart 1, we can see this phenomenal growing interest of companies on AI as measured in the Say dimension. Especially when ChatGPT came out in Q4 2022, companies start to talk about GenAI much more intensively. Here we are not just measuring companies talking about AI generally but in a strategic way. We have employed GPT + human methodology to classify the AI sentences in different categories, those that align with future trends, strategy, investment, or use cases are classified as strategic ones, as they signal strong intention in developing AI capabilities.

Growing investment in AI

When we dig into the Do dimension which measures companies’ AI assets, we can observe the escalating significance of AI within organizations in recent years. The three metrics we consider represent various avenues for companies to invest in AI. M&A and VC funding as an external way for growth, aren't the sole methods for companies to pursue AI investment. However, factors like increasing interest rates in recent years have greatly affected the availability of funds in the VC market. Innovation through patents can vary significantly by industry, such as Life Sciences and Chemicals, prioritizing patents to safeguard their innovations. Conversely, talent acquisition is a cross-industry concern, with all sectors requiring AI expertise in the current AI-driven era to foster the development and diffusion of AI capabilities. The AI talent we monitor thus encompasses individuals with AI-specific skills, such as data scientists who develop AI solutions, as well as professionals in various industries or functions who possess the necessary AI-related skills to effectively utilize these solutions, including roles like automotive engineers or sales planners.

Tech industries leading the pack

It comes as no surprise that technology companies, particularly those specializing in software, platforms, and high-tech solutions, are at the forefront of AI adoption. Given that AI aligns closely with their core business operations, they not only discuss AI more frequently but also actively develop more AI assets. However, the AI phenomenon is extending its reach beyond the tech sector and permeating various industries (See Charts 4a-d).

Based on the industry rankings across these four key metrics outlined (see Table 1), distinct patterns emerge delineating four groups of industries.

• At the forefront, the technology sector (Group 1) includes Software & Platform and High-Tech, as the key providers for AI solutions, consistently occupying the top position across all metrics, showcasing its dominance.
• Following closely are industries (Group 2) like Life Sciences, Banking, Communication & Media, which lead among non-tech sectors across most metrics, positioned at the top half of at least 3 of the 4 metrics, demonstrating their collective commitment to adopt AI at scale.
• The next group (Group 3) comprises industries positioned within the top half of all industries in only one or two specific metrics. Examples include Industrial, Utilities, and Consumer Goods & Services.
• Finally, the last group (Group 4) encompasses industries that consistently trail behind across all metrics, suggesting that they are allocating relatively fewer resources to building AI capabilities compared to their counterparts in other sectors.

Any AI financial premium?

Thanks to our AI Index, we can pinpoint leaders in all 4 key dimensions within each industry. This naturally leads to a fundamental question: do these leaders also demonstrate stronger financial performance?

We do observe a strong correlation between robust AI and foundational capabilities and consistent financial performance, as measured by metrics including revenue growth, profitability, and enterprise value (as the sum of market capitalization and net debt). It is reasonable to assume that investors would exhibit greater confidence in companies that demonstrate robust signals of investment in AI and are actively developing AI assets, thus it would not come as a surprise that leaders in AI index ranking perform better on market cap and enterprise value. However, it might be challenging to assert a direct cause-and-effect relationship between AI capabilities and growth or profitability, particularly considering the time required to realize these benefits. Indeed, most companies are still in the process of implementing proofs of concept of various (Gen)AI use cases or evaluating their return over investment. This correlation might actually suggest an alternative interpretation: financially robust companies possess the resources to invest in AI capabilities, enhancing their prospects for future performance and competitive advantage.

We also observed that certain dimensions within the framework exhibit a stronger correlation with growth than others. When contrasting the Say and Do dimensions of the framework, our data highlights that actions could speak louder than words, and perhaps even more notably, relying solely on words might be less associated with consistent financial performance.

As depicted on Chart 5, we've categorized companies into three groups: those excelling in both Say and Do (ranking in the top quartile of both the Say and Do index simultaneously within each industry), those excelling solely in Say (ranking in the top quartile for Say only but not for Do), and the remaining companies. It's evident that companies excelling in both Say and Do consistently achieve significantly better revenue growth across all years. As discussed previously, if we cannot definitively establish a causal link between companies’ AI assets and their growth based on this analysis, an alternative hypothesis for this trend could be that financially challenged companies tend to hype their AI initiatives in corporate communications more to compensate for their lackluster financial performance.

Who will come out on top in the AI race?

In any case, companies still have much ground to cover in fully developing their AI and foundational capabilities to emerge victorious in the AI race. We see this dataset as a valuable tool for companies to strategically position themselves and uncover winning strategies and solutions. It has already initiated numerous discussions with different companies and received positive feedback. This dataset will be a dynamic asset with regular updates.

A heartfelt thank you to Philippe Roussiere Praveen Tanguturi, Ph.D. Yingchuan Zhu and Jenna Jiang for their invaluable contribution in building this index. A special acknowledgment goes to Professor Rob Seamans from New York University’s Stern School of Business for providing invaluable guidance throughout the development of the analysis.

Stay tuned for more news from our team on this exciting topic:

• Philippe Roussiere on Responsible AI
• Praveen Tanguturi, Ph.D. on CEO driving continuous reinvention
• Deeksha Khare Patnaik on AI Talent
• Sandra Najem on AI-enabled Thought Exchange

The views and opinions expressed are those of the author and do not necessarily reflect the official policy or position of Accenture.?