AI readiness survey: Are companies ready for AI adoption?

Confidence in AI is soaring, but cracks are showing in the foundation. In this new Capital One survey of 4,000 business leaders and technical practitioners across industries, a surprising gap was revealed between perceived AI readiness and the reality of operational challenges. Responses indicate that organizations may be overlooking key challenges in implementing strong data management, fostering a robust data culture, acquiring the right technical skills and leveraging data to enable AI at scale. Companies that address these issues today will be set up for the future with more sophisticated AI that can produce tangible business value.

• Overconfidence vs. reality: 87% of business leaders see their data ecosystem as ready to build and deploy AI at scale, yet 70% of technical practitioners spend hours daily fixing data issues.
• Data culture disconnect: Leaders say data culture is a top indicator of AI success, but only 35% of respondents say they have a strong data culture, citing inconsistent support and education.
• Strategy misalignment: While 78% of tech practitioners and 82% of leaders agree on the importance of an AI strategy, only 53% of tech practitioners and 55% of leaders are fully familiar with their business’ AI strategy.

Capital One’s survey data shows that business leaders are highly optimistic about their companies' AI capabilities. An impressive 87% of leaders believe their organization has a modern data ecosystem capable of building and deploying AI solutions at scale, while 84% claim to have centralized processes, tools, and platforms available to manage and govern data. Similarly, 82% express confidence in their company’s data strategy to mobilize resources for AI adoption and 78% of leaders feel their organization is prepared to handle the growing volume and complexity of data required by AI.

However, the disparity between leaders’ perception of their data capabilities and the day-to-day reality of data management is striking.

On the one hand, 77% of business leaders claim it’s easy to use the data they need for their jobs, 78% say it's easy to understand the data and 80% believe it's easy to find the data they need to do their jobs. These figures suggest strong confidence in the effectiveness of their data systems.

However, the operational reality is more complicated. 70% of technical practitioners report spending up to four hours daily fixing data issues, conducting quality checks or correcting errors. These ongoing struggles not only slow down workflows, but also point to deeper issues with data management and governance to ensure high-quality data.

This contrast between perceived ease and the time spent resolving data problems highlights how many organizations overlook the result of poor data management in an increasingly complex environment. As the volume, variety (such as structured vs. unstructured data) and speed of data grow, it becomes even more crucial for companies to implement scalable data management processes. For example, as companies want to use more advanced AI like multi-modal AI, they will require the ability to process unstructured data in various formats and at massive scale. Without this ability, they risk falling behind in AI adoption and lose valuable time that could be devoted to innovation.

Data security adds another layer of complexity. 76% of business leaders rank data security as their top concern in AI initiatives, followed by data quality (73%) and data management (65%). Yet, while 53% of business leaders believe their organization prioritizes data management to mitigate risk, 38% admit it is given only moderate importance. Efforts to address security risks vary, with 58% of business leaders using encryption tactics, but only 20% using tokenization.

Most business leaders rank data culture as the top indicator of AI readiness, yet only 35% of survey respondents report having a strong data culture, and over 20% report a lack of strong data culture or inconsistent leadership support, talent development and education around data. 

Another disconnect: while 78% of tech practitioners and 82% of business leaders agree that a company-wide data strategy focused on AI readiness is essential, only 53% of tech practitioners and 55% of leaders are fully familiar with their organization’s AI strategy. This lack of familiarity raises concerns about whether strategies are clearly defined, communicated and followed—or if it is leading to gaps in implementation.

A strong data culture empowers teams to make data-driven decisions and equips them with the tools and knowledge to work effectively with data. Without this, AI initiatives will struggle to scale. When technical practitioners lack confidence in their organization’s data culture, it can lead to fragmented AI adoption efforts.

Many organizations also lack the capabilities to execute their AI strategies effectively. Only 36% of tech practitioners and 47% of business leaders strongly agree that their organizations’ talent has the necessary skills and expertise to implement complex AI projects. Just 51% of both groups are confident that their AI solutions are scalable enough to handle increasing data and user demands, and only 33% of tech practitioners and 41% of business leaders report that they are successfully scaling AI-driven solutions across the enterprise. This suggests that while there is a strong intent to embrace AI, the actual execution and implementation are falling short of expectations.

These figures reveal a broader insight: business leaders’ confidence in AI readiness may not align with the realities faced by the technical practitioners who must implement and scale these solutions. Without the proper talent and tooling, AI initiatives are likely to fall short of their potential.

Ultimately, the confidence gap revealed in the survey findings illustrates a significant disconnect within organizations. While leaders report confidence in their data ecosystems, the data management struggles of tech practitioners, lack of data-driven culture and lack of clarity around AI strategy reveal a more complex reality. 

Recognizing and bridging this disparity is essential to building a foundation for AI capabilities and use cases that can lead to meaningful business outcomes in the long term. Companies that close this gap will be better positioned to leverage AI’s full potential and unlock more durable, long-term value in the years ahead.

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This study consists of findings from a survey of nearly 4,000 business leaders and tech practitioners, conducted on behalf of Capital One by Morning Consult. The survey was fielded July 19-30, 2024 with a margin of error of +/- 2%. The business leader sample consists of approximately 2,100 respondents who currently work for organizations across various industries with over 500 employees as business executives, senior leaders, or managers. The tech practitioner sample consists of approximately 1,800 respondents who currently work for organizations across various industries with over 500 employees as data scientists, data architects, data analysts, data governance managers, data stewards, engineers, or database administrators.