In the past decade, the use of data insights has evolved from being a specialized "science" activity to becoming an integral part of nearly every business role. Today, employees are expected to generate insights in near real time and incorporate them into their daily responsibilities. As a result, it’s no surprise that companies have prioritized implementing advanced analytics to help them use data to improve their operational and financial performance. Those efforts have taken on even greater urgency with the emergence of generative artificial intelligence (GenAI), with its power to transform how businesses and entire industries operate. In a world where AI increasingly serves as the gateway to insights, the quality and consistency of data must be accurate at its source. Poor-quality or inconsistent data can lead to inaccurate insights and flawed decisions. Unlike traditional analytics, where manual intervention such as data cleansing could mitigate issues, AI systems process data automatically at scale, amplifying any errors or inconsistencies present. As AI becomes more integrated into real-time decision-making processes, the need for high-quality, consistent data at the source is essential to ensure reliable and trustworthy outcomes.

Given this change, organizations often focus on the “cool” part—building AI use cases and exploring the art of possible. Some GenAI use cases are:

• Productivity-enhancing point solutions
• Strategic use cases to create vision
• Innovation through business model transformations

However, the real challenge lies in the “hard” part—driving enablers to ensure AI use cases can find a path to scale. Some common pitfalls include:

• Lack of functional alignment and expertise
• No clear understanding of AI
• Unawareness of risk and audit implications
• Absence of robust cost management frameworks

Our research provides insights on how analytically mature organizations are tackling the “hard” part and therefore are at a better position in the AI implementation curve compared to their peers.

So, after all this work, where do companies stand today? And how can organizations that are less mature catch up?

According to new Kearney research, while companies have made progress, for the most part they still have a lot of work to do to get to the point where they have the AI and analytics capabilities that can have a game-changing impact on the business. More than half (51 percent) of companies participating in our research still have what we call immature capabilities—meaning, while they may have developed strategies and identified use cases for AI and analytics, they’ve struggled to build and scale the necessary capabilities for execution and put in place the management practices to sustain them. One slice of this group, comprising 3 percent of our survey sample, are what we deemed Laggards, who are struggling to capitalize on the power of AI and analytics to transform their business.

In this report, we present the results of our comprehensive survey of more than 1,000 companies around the world on where they stand with respect to AI and analytics. We discuss the progress companies have made in how they think about, build, scale, and govern their AI and analytics capabilities and the challenges they face in doing so. We also take a look at how companies are allocating their budgets to AI and analytics initiatives, as well as explore the key trends and best practices that emerged from our analysis of what the Leaders in our survey have achieved on their way to AI and analytics maturity.

Around the world, becoming a data-driven business has risen to the top of the executive agenda. More than ever, companies are looking to use data, paired with AI and analytics, to equip their business, employees, and leaders to make faster, more accurate, and more relevant decisions, and improve decision outcomes, in complex and fast-changing business contexts. And now with GenAI taking AI to the next level, the opportunities for companies to use data to drive innovation, growth, and competitive advantage are virtually endless. The key question companies need to answer is: How do we do it?

To be sure, despite the progress companies have made in the past 10 years in advancing AI and analytics in their enterprise, our survey shows much work remains to be done. With a little more than half of participating companies reporting only, at best, moderate maturity across our think–build–scale–govern framework, this much is obvious.

On the other hand, the other half of companies, the analytically mature, have built truly robust AI and analytics capabilities. These companies—in particular, the small group of Leaders in our study—have the right strategies, infrastructure, leadership, talent, and operating model necessary to maximize their use of data. This foundation is becoming even more important as they begin broadly implementing GenAI. With mature AI and analytics capabilities, the Leaders and Explorers in our study are in a position to transform their business and rewrite the rules of competition—and others must use these companies’ experiences and approaches to help accelerate their progress to avoid being left behind.

The authors would like to thank Aadarsh Thakur, Akansha Baruah, and Ankit Mohanty for their valuable contributions to this report.