Rohit Kapoor is the cofounder, Chairman and CEO of EXL, a global data and AI company.
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As the inexorable march of AI continues, companies are growing confident that their AI efforts are keeping up with or even ahead of competitors. However, according to my company’s third annual study on enterprise AI, reality tells a different story—one that suggests many companies don’t have an accurate perception of where they stand in the AI race.
Our study found significant enthusiasm and optimism for AI from virtually all participating companies. Reflecting on the progress they’ve made, 76% of participants in the study believe their company is ahead of its competition in AI.
But it turns out this is largely a case of wishful thinking. When looking further, only 10% of companies meet the criteria of an AI leader—those that are making the greatest company-wide progress in moving beyond pilots and integrating AI into high-impact workflows across core business functions.
Most companies still struggle to fully develop AI in more than a handful of business functions or to accelerate the shift from pilots to full-scale production.
AI Is Generating Significant Returns For Leaders
True AI leaders are those deploying AI broadly across their businesses and, importantly, seeing significantly greater impact. They are more likely than laggards to report that AI is advancing nearly all 16 business priorities measured in the study.
These priorities include both operational outcomes such as operational and process efficiencies, data processing speed, stability in uncertain or changing markets and agility to adapt to market changes. The priorities also include financial results such as enhancing customer engagement, improving pricing accuracy and reducing operating costs.
Three Key Factors Distinguish How Leaders Approach AI
What distinguishes AI leaders from laggards? One factor our study highlighted is how leaders manage data. Seven in 10 participants overall cited data as a challenge to scaling AI, and data-related issues were the top three most frequently mentioned barriers to AI implementation.
1. Data Management
Leaders are addressing the data challenge more effectively by building a stronger data foundation that’s critical to effective and expansive AI use. Forty-four percent of AI leaders, versus 17% of laggards, have data that’s accessible enterprise-wide. Conversely, 83% of laggards still struggle with data siloed in business functions, which limits AI use and impact. Leaders’ greater maturity in data management is a big reason for this discrepancy.
2. Operating Model Transformation
Another factor in leaders’ performance is their operating model. While a majority of laggards have made significant changes to their operating model to align with AI’s requirements, leaders are more likely to have taken the next step: completely redesigning their enterprise-wide operating model to deploy and benefit from AI.
Why does this matter? Adapting an existing model preserves legacy constraints such as approval hierarchies, handoff points and role definitions that were designed for human-paced work. Leaders understand that AI fundamentally changes how work gets done and who does it and that transforming the operating model accordingly is key to unlocking AI’s power.
3. Context
Beyond data management and operating model transformation, a third factor has emerged as critical to successful AI implementation, one that is often overlooked. This is context, or knowledge and expertise that goes beyond robust AI and technology skills.
In our experience, AI leaders increasingly recognize that significant business outcomes remain elusive without two contextual elements:
1. Deep Industry-Specific Knowledge: This includes insights on customer segments, markets, products, sector dynamics and applicable regulations.
2. An Understanding Of How Things Work In A Specific Enterprise: This includes the workflows/systems, the way the company is organized, its policies and procedures, its customer preferences and its overall strategies.
While companies certainly can deploy AI without such context, doing so won’t materially move the operational or financial needles.
The Widening Gap
AI continues to gain momentum across enterprises and industries, promising to reshape businesses in ways that not many people were thinking about only a few years ago. Now, as AI enters its next era of explosive growth driven by agentic AI, the hopes as well as the stakes are even higher.
I see agentic AI emerging as a once-in-a-generation development that takes the power of AI to the next level as companies are able to embed AI agents into core operational workflows and processes to achieve order-of-magnitude improvements in business performance.
To capitalize on that opportunity, though, companies must accelerate their own efforts to address shortcomings in data management—improving the quality of and access to data enterprise-wide—while shifting from traditional ways of working and capabilities that tend to stifle AI’s use and limit its impact.
Context needs to be at the core of these efforts to bridge the gap between the potential of AI as a technology and the real-world impacts businesses need to achieve.
We are now firmly in the age of AI, with new leaders and new challenges emerging all the time. Companies that don’t commit fully to creating an environment in which AI can thrive are vulnerable to being left behind.
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