Momentum, Without a Map
In boardrooms and strategy sessions across the globe, artificial intelligence has moved from a distant aspiration to an immediate priority. Our extensive survey of 655 business leaders from countries including the U.S., U.K., Germany, Taiwan and Japan, reveals a notable reality: AI adoption is accelerating rapidly, with 82% of global businesses already deploying AI applications in their day-to-day operations. This broad adoption of artificial intelligence spans industries and regions, demonstrating that AI is no longer the exclusive domain of tech giants but has become an essential technology for enterprises of all types.
Customer service stands at the forefront of this adoption wave, with 63% of organizations deploying AI to enhance customer interactions. Document processing (54%), IT operations (51%), and security applications (51%) follow closely behind, showing how AI has rapidly infiltrated core business functions. This isn't merely experimentation—it's transformation at scale.
The enthusiasm for AI runs deep within organizational hierarchies. An overwhelming 90% of leaders report their organizations are receptive to AI-driven changes, with 83% considering AI adoption "urgent."
Most tellingly, 82% of businesses have secured executive sponsorship at the highest levels, with CEOs and senior management personally championing AI initiatives. This top-down commitment signals that AI has transcended technical curiosity to become a strategic imperative.
Financial commitments reinforce this strategic shift. Eight in ten organizations have established dedicated AI budgets, with regional variations highlighting different approaches to investment. The Asia-Pacific region leads in budget allocation, with 86% of APAC businesses dedicating resources to AI initiatives, compared to 76% in Europe and 75% in the United States. However, American organizations are investing more aggressively, with 57% committing more than 10% of their IT budgets to AI, surpassing European (46%) and APAC (45%) counterparts.
This financial commitment shows no signs of wavering, as 87% of business leaders anticipate increasing their AI budgets over the next three years.
What's driving this rush toward AI? In a word, efficiency. A compelling 63% of global leaders identify operational efficiency as the primary benefit they expect from AI, with 80% making it the central focus of their 2025 AI strategy. As one business leader articulated, "AI will eliminate repetitive work, allowing employees to engage in more strategic and creative tasks." Another explained how "AI advancements will impact our organization by enhancing efficiency in decision-making and innovation across operations while requiring continuous adaptation to new technologies and regulatory changes."
Yet beneath this momentum lies a concerning paradox.
Despite widespread adoption and executive support, only 39% of organizations have developed a clearly defined, comprehensive AI strategy. Even fewer—just 37%—have implemented a robust change management plan to guide AI implementation across their businesses. This gap underscores that while AI is deployed, many organizations may not fully understand how to integrate it effectively or ready their workforce for the changes ahead.
One Vice President in the financial industry's IT department acknowledged: "We have our work cut out for us in formalizing these toolsets. Their use has grown organically within the business units, so we have duplication. I think we're getting there, and that's certainly part of my role—to figure that out."
This lack of a cohesive strategy raises critical questions about the sustainability of current AI initiatives and their long-term impact on business performance.
Proceed with Caution
The enthusiasm for AI must be tempered with a sobering reality: many organizations remain ill-equipped to scale their AI initiatives effectively. Our research reveals significant shortcomings in three critical areas—infrastructure readiness, talent availability, and data quality—each representing a potential barrier to realizing AI’s full potential.
Infrastructure limitations are particularly concerning. Only 29% of organizations have systems or storage resources to meet growing AI demands. Even fewer—a mere 23%—have dedicated power infrastructure to handle the increased energy requirements of AI workloads. This means 77% of businesses have only begun upgrading their facilities—if they have any power management strategy. Additionally, 65% of leaders acknowledge that their organizations lack comprehensive energy efficiency optimization measures for AI systems, raising questions about operational costs and environmental impact as these workloads grow.
The talent gap poses an equally significant challenge. A third of business leaders (34%) report their organizations are "significantly under-resourced" or "under-resourced" when it comes to AI expertise, while nearly half (49%) identify a lack of skilled talent as a primary barrier to successful AI implementation. As one C-level executive in manufacturing lamented, "It's about manpower talent. It's so hard to find people who know AI and can talk about AI."
Despite recognizing the talent gap, organizations are taking inconsistent approaches to addressing it. While two-thirds (66%) of leaders express intentions to upskill existing employees to adapt to increased AI integration, 39% still don't have dedicated programs to develop AI skills among their workforce. This disconnect between recognized need and decisive action threatens to widen the talent gap rather than close it.
Perhaps most fundamentally, organizations struggle with data readiness—the essential foundation for effective AI applications. Nearly half of businesses leverage customer data (49%) and operational data (48%) for AI initiatives. However, data management practices remain largely rudimentary. Only about half (53%) of organizations have basic data automation processes for AI/ML models, while 18% rely on manual or ad hoc data cleaning procedures. Not surprisingly, 46% of leaders cite data quality and accessibility issues as a major barrier to successful AI implementation.
Industry leaders recognize these challenges. One Vice President in the financial industry's IT department explained: "Our data quality and cataloging is so critical when it comes to AI...That is work in front of us." Another C-level IT executive in manufacturing emphasized the need for integrated data platforms: "We need to get the data into one platform so we can try to leverage the other functions to use AI."
These infrastructure, talent, and data challenges cannot be overlooked. Without addressing these fundamental capabilities, organizations risk their AI investments delivering diminishing returns as they attempt to scale beyond initial proof-of-concept deployments.
Security Sensitivity
As organizations increasingly integrate AI into their operations, a new dimension of risk has emerged that demands urgent attention: data security and privacy. Security concerns are growing proportionally, with personally identifiable information (PII) rapidly becoming central to AI initiatives.
Nearly half (49%) of businesses already use customer data to power their AI initiatives, and the trend is accelerating. Looking ahead, 56% of leaders indicate their organizations plan to utilize personally identifiable information for future AI applications. This growing reliance on sensitive data introduces significant security considerations.
As one director in the life sciences and biotechnology industry explained: "I think when you get into some of these companies, ours in particular, we have to be a little bit more safeguarded because of the data sets we have. Not all of it can be shared in public knowledge, but AI makes looking at that data, interpreting that data, analyzing that data 1000 times easier."
The expanded use of sensitive data in AI systems introduces security considerations and ethical concerns about bias and fairness. AI systems are only as objective as the data they're trained on, raising important questions about how organizations ensure their AI applications make fair and unbiased decisions.
Our research reveals a significant gap in this area. Nearly half (47%) of business leaders acknowledge their organizations have limited bias detection and correction processes in their AI systems. Even more concerning, 17% reported having no formal bias correction process and relying instead on ad hoc bias checks. This inconsistent approach to bias mitigation poses significant risks, especially as AI becomes more deeply integrated into consequential business decisions.
Forward-thinking leaders recognize these challenges. As one Vice President in the financial industry observed: "We have to make sure that data is bias-free, and to do that, we have to come up with policies and standards controls that need to be implemented within the organization by support of data scientists and data stewards." This recognition drives 44% of leaders to identify AI ethics and data engineering as the most critical skills their organizations will need in the next five years.
On a more positive note, organizations are beginning to implement security measures for their AI systems. Only 5% of businesses report no specific AI security measures. The vast majority are taking at least some precautions, with 56% conducting regular security audits of AI models and infrastructure, 56% performing security testing of AI-powered applications, and 51% conducting vulnerability assessments for AI systems.
Despite these measures, significant concerns persist. Nearly half (48%) of business leaders identify data privacy breaches through model extraction as a top AI security concern. One Vice President of Marketing and Sales in retail observed: "I think there's a big open question on IT security that large companies are going to worry about."
As AI systems become more deeply integrated with sensitive business and customer data, organizations must evolve from basic security practices to comprehensive security strategies that address the unique vulnerabilities associated with AI technologies.
Preparing for an AI-Powered Future
One prediction seems certain as we look toward the horizon: AI adoption will continue to accelerate across all industries. Organizations that prepare effectively today will position themselves to capitalize on this technological revolution, while those that fail to address fundamental readiness gaps risk falling behind.
The business case for AI continues to strengthen among organizations using AI; 65% report that their AI applications are meeting or exceeding expectations for return on investment. This positive experience drives further adoption, with 92% of business leaders indicating plans to expand their AI usage in some capacity.
However, successfully scaling AI initiatives requires addressing several critical preparation gaps. A third (34%) of business leaders identify integration with existing security infrastructure as a top AI security concern. Meanwhile, 43% acknowledge their employees demonstrate only a basic understanding of AI at best, with additional training urgently needed. These challenges have led two-thirds (67%) of business leaders to identify AI security among the most critical AI-related skills their organizations will need to develop over the next five years.
Organizations must balance enthusiasm with strategic planning as they prepare for an AI-powered future. By systematically addressing infrastructure limitations, closing talent gaps, enhancing data quality, and strengthening security measures, businesses can move beyond initial experimentation to realize AI's transformative potential at scale.
The path forward is clear, if challenging. Organizations must translate their AI enthusiasm into comprehensive strategies that address foundational readiness gaps. Those who successfully navigate this transition will be positioned to reap the efficiency gains and competitive advantages that AI promises. For the rest, the gap between AI ambition and AI readiness may prove increasingly difficult to bridge.
