In a new, industry-spanning examination of enterprise AI adoption, EPAM Systems reveals a persistent gap between how organizations perceive their AI capabilities and how successfully they implement AI in real-world use cases. The study highlights the critical role of governance, strategic alignment, and scalable deployment in turning AI hype into tangible business value. As companies plan to increase AI investments and expand their AI workforces in the coming years, leaders must move beyond proofs of concept toward enterprise-wide, high-impact deployments that deliver measurable returns.
The AI Adoption Gap: Perception versus Reality
Across nine countries and eight industries, EPAM’s study canvassed 7,300 participants to dissect the journey from AI curiosity to market-ready outcomes. A striking finding emerges early: nearly half of respondents—49%—evaluate their organizations as “advanced” in AI implementation. Yet, among those self-identified leaders, only 26% have actually delivered AI use cases that reach the market. This reveals a substantial delivery gap, underscoring a fundamental challenge many enterprises face as they move from experimentation to scalable, real-world applications that generate observable ROI.
This gap is not simply a matter of building models or integrating algorithms; it reflects a complex mix of organizational, technical, and strategic factors that must align to realize commercial value. The research demonstrates that initial productivity gains and operational efficiencies, often achieved through proofs of concept, are not sufficient by themselves to secure broad organizational impact. Instead, success hinges on identifying high-value use cases and prioritizing them in a way that aligns with overarching business goals and customer needs. The transition from isolated pilots to enterprise-scale programs requires disciplined governance, cross-functional collaboration, and a clear roadmap for deployment, monitoring, and optimization.
Elaina Shekhter, EPAM’s Chief Marketing and Strategy Officer, emphasizes this shift in emphasis. She notes that “Following the release of ChatGPT and throughout 2023 and 2024, we witnessed companies across industries experiment with AI and develop proofs of concept, primarily targeting immediate gains in productivity improvements and operational efficiencies.” The current research, however, makes it clear that a new phase is upon us: success will depend on systematically identifying high-value use cases and prioritizing them to achieve broad, lasting organizational impact. In effect, enterprises must elevate their AI programs from isolated wins to strategic initiatives that ripple across functions, products, and customer experiences.
From governance misalignment to missed deployment opportunities, the study points to a range of impediments that contribute to the practical gap. It is not merely about technology; it is about translating insight into scalable action, ensuring that technical teams are oriented toward solving real customer problems, and embedding AI into core decision-making processes. The implications for leadership across the enterprise are profound: without a concerted focus on prioritization, governance, and scaling, even the most promising AI capabilities may remain confined to isolated experiments rather than delivering sustained business impact.
The takeaway for executives is clear. To close the delivery gap, organizations must reframe AI from a portfolio of experiments into a disciplined program that prioritizes strategic value, coordinates across the business, and maintains a relentless focus on measurable outcomes. As AI reshapes the enterprise landscape, bridging the gulf between capability and execution becomes the defining task for successful digital transformation. The path forward demands a robust governance framework, a business-led alignment of technical work, and a deployment playbook that scales use cases with tangible returns.
Investment Trends and Economic Impact of Enterprise AI
In the face of implementation challenges, the economic signal around enterprise AI remains distinctly optimistic. EPAM’s findings show that companies intend to increase AI spending by 14% year over year in 2025, signaling a continued commitment to AI-driven initiatives despite the friction involved in moving from pilots to production. This spending trajectory suggests that organizations expect long-term value from AI, even as they tackle governance, modernization, and talent challenges.
Among market leaders—whom EPAM classifies as “disruptors”—the financial stakes are particularly pronounced. Disruptors anticipate that AI investments will account for a substantial portion of their profit growth in 2025, with 53% of their expected profits attributed to AI-related activities. This figure illustrates a direct link between AI initiatives and financial performance, reinforcing the argument that strategic AI deployment can be a differentiator in competitive markets. The research, therefore, reinforces the notion that the most forward-looking enterprises are integrating AI deeply into their value propositions, revenue models, and operational capabilities, rather than treating AI as a peripheral enhancement.
The labor market dynamics accompanying these investment trends further emphasize AI’s growing centrality in business strategy. The study reports that 43% of surveyed companies plan to hire for AI-related roles in 2025, signaling a continued expansion of the AI workforce as organizations scale their capabilities. Within the talent mix, machine learning engineers and AI researchers stand out as the most sought-after positions, highlighting the ongoing demand for specialized technical expertise necessary to develop, tune, and deploy AI systems responsibly and effectively.
Dmitry Tovpeko, EPAM’s Vice President of Engineering, offers a pragmatic lens on these dynamics. He notes that while improved productivity and operational efficiency are universal objectives, the essence of true transformation lies in bridging the gap between technology teams and the business. As AI reshapes the enterprise, developers are increasingly evolving from task-oriented users into strategic experts who can craft end-to-end AI solutions. This shift requires more than technical acumen; it demands a reframing of AI as a tool integrated into core business processes, enabling teams to tackle real-world problems with precision and scale. In Tovpeko’s view, success is less about the sophistication of tech stacks or cloud infrastructure and more about aligning technology disciplines with business objectives to solve customer problems in meaningful ways.
The broader takeaway from investment and workforce data is that spending and talent acquisition are not ends in themselves but enablers of a deeper aspiration: to turn AI investments into sustainable competitive advantage through enterprise-wide deployment. Organizations that can harmonize funding, people, and processes around high-priority use cases are better positioned to translate AI activity into measurable outcomes, whether in customer satisfaction, operational resilience, or revenue growth. The economic narrative around enterprise AI, therefore, is not simply about the scale of investment; it is about the quality of deployment, governance, and cross-functional collaboration that ensures AI activities translate into durable value.
Key implications for strategy and execution
- Prioritize high-value use cases with clear business impact, ensuring alignment with customer needs and strategic goals.
- Invest deliberately in AI capabilities that bridge productivity gains with market-facing outcomes, rather than pursuing isolated tech experiments.
- Recognize AI talent as a core strategic asset; plan for specialized roles and continuous upskilling to sustain momentum.
- Build a financial model that links AI projects to measurable ROI, including improvements in throughput, quality, and customer outcomes.
- Balance innovation with governance and risk management to support scalable, compliant AI deployment.
Governance, Modernization, and Security: The Core Barriers
One of the most consequential findings from EPAM’s study is the prominence of governance as a critical obstacle to AI scale. While a sizeable majority of advanced companies—75%—report that they have established clear AI strategies, a strikingly small fraction of disruptors—just 4%—say they have developed comprehensive governance frameworks. This stark contrast underscores a fundamental misalignment: even as many organizations acknowledge AI’s strategic importance, only a minority have codified governance that can steward deployments across the enterprise.
The governance gap is not merely a theoretical concern. It has practical and long-term implications for an organization’s ability to scale AI responsibly. Effective AI governance encompasses roles, decision rights, measurement frameworks, regulatory alignment, ethical considerations, data governance, and risk management. Without an integrated governance model, deployment efforts may suffer from inconsistent standards, fragmented data sources, and misaligned incentives, all of which can erode value and create friction across business units. The study further reveals that businesses anticipate a minimum of 18 months to implement effective AI governance models. This timeline reflects the inherent complexity of coordinating across diverse markets, product lines, and regulatory regimes, emphasizing that governance is a strategic, multi-year endeavor rather than a one-off initiative.
Compounding governance challenges is the modernization barrier associated with technology infrastructure. The study shows that 31% of executives identify outdated technology infrastructure as an impediment to AI adoption. This signals that even when leadership recognizes the strategic importance of AI, legacy systems, fragmented data architectures, and incompatible platforms can slow progress. Modernization is not simply a numerical upgrade; it is a comprehensive rethinking of how data flows, how tools interoperate, and how security and resilience are built into core systems. As AI models require access to diverse data sources with high quality, the reliability and compatibility of the underlying tech stack become decisive factors in deployment success.
Security concerns also persist as a significant modernization challenge. Thirty-five percent of organizations cited their lack of sophisticated security programs as a primary obstacle to AI adoption. This reflects a broad anxiety about data protection, data quality, and cloud security in AI environments. In practice, this means that enterprises must embed robust security controls—ranging from data governance and encryption to secure development practices and continuous monitoring—into the fabric of their AI initiatives. The stakes are high: AI systems can amplify risk if governance, data handling, and access controls are not designed and enforced across all stages of the lifecycle.
Beyond governance and modernization, organizational readiness emerges as a key differentiator. The study finds that 65% of disruptors understand the necessary skills for AI adoption, highlighting the centrality of talent strategy in achieving scale. This aligns with the broader industry consensus that successful AI programs require not only technical capability but a shared understanding of roles, responsibilities, and collaboration patterns that enable cross-functional delivery. The capability to deploy AI at scale is thus as much about people, processes, and governance as it is about algorithms or cloud platforms.
Nir Kaldero, Chief AI Officer at EPAM NEORIS, provides a practitioner’s perspective on the path forward. He asserts that “The next phase of AI is not just experimentation but deployment at scale—focusing on enterprise-wide, high-impact use cases while continuing the effort to align people and culture, data and cloud, and new processes to unlock true exponential business value.” This insight echoes a central theme of the study: scalable AI requires a holistic transformation that encompasses leadership, culture, data management, and operational processes in addition to technology. The governance challenge is not solely about policy creation; it is about institutionalizing responsible AI practices, ensuring regulatory alignment, and enabling speed and agility within a compliant framework.
The security dimension cannot be separated from governance and modernization. Enterprises must navigate data privacy regulations, cross-border data transfers, and the evolving threat landscape as AI capabilities expand. A sophisticated security program is no longer a luxury but a baseline requirement for any enterprise AI initiative. The absence of such programs can undermine trust, hamper adoption, and risk regulatory penalties. Therefore, the governance-modernization-security triad stands as the central axis around which successful enterprise AI programs rotate.
Recommendations for strengthening governance and security
- Develop a comprehensive AI governance framework that specifies decision rights, accountability, and escalation paths across all business units.
- Align governance with regulatory requirements and ethical considerations to uphold customer trust and brand integrity.
- Invest in modernization initiatives that unify data architecture, streamline data access, and enable secure, scalable AI workflows.
- Implement security-by-design practices across the AI lifecycle, including data protection, model monitoring, access controls, and incident response planning.
- Build organizational readiness through targeted upskilling, cross-functional teams, and clear incentives that reinforce alignment between business objectives and technical execution.
Talent, Culture, and Organizational Readiness
The EPAM study highlights the crucial role of people and culture in moving from AI experimentation to enterprise-scale value. While a majority of disruptors demonstrate awareness of the necessary skills for AI adoption (65%), the literature consistently shows that technical capability alone does not guarantee success. The path to scale requires a deliberate talent strategy that fuses specialized expertise with a culture of collaboration, experimentation, and disciplined execution.
One of the most telling shifts during the AI journey is the transformation of developers from task-oriented users into strategic experts who can design and implement end-to-end AI scenarios. Dmitry Tovpeko emphasizes this evolution, noting that “as AI reshapes the enterprise, developers are evolving from task-oriented users to strategic experts, responsibly harnessing AI for end-to-end scenarios.” This transformation is not merely about hiring more data scientists; it is about enabling cross-functional teams to work together in new ways. It requires rethinking roles, establishing new collaboration patterns, and creating feedback loops where insights from business units continually inform model development and deployment.
The emphasis on alignment between business objectives and technical execution is reinforced by Tovpeko’s broader observation: success hinges not on tech stacks or cloud infrastructure alone, but on aligning tech teams with the strategic goals of the business to solve real-world customer problems. This viewpoint reinforces a central truth of AI transformation: technology is a means to an end, and the end must be defined by customer outcomes and business strategy. Without a shared understanding of goals and a governance-friendly operating model, AI initiatives risk becoming isolated experiments with limited impact on core metrics.
Nir Kaldero’s perspective adds a practical dimension to the talent conversation. He frames AI deployment at scale as an enterprise-wide effort that demands the alignment of people and culture, data and cloud, and new processes. This alignment is not a one-off activity; it is an ongoing program that evolves as AI capabilities mature and as business needs shift. Talent strategy, in this sense, must be dynamic, reflecting ongoing learning, collaboration, and adaptation across the organization. The readiness of an organization to adopt AI at scale is therefore a function of its people, its processes, and its ability to nurture a culture of continuous improvement.
Beyond the structural and cultural considerations, the job market signals a sustained demand for AI expertise. The study’s hiring projections for 2025—coupled with the ongoing need for ML engineers and AI researchers—underscore the importance of building a robust pipeline of specialized talent. Enterprises should consider not only recruitment but also retention strategies, partnerships with academic institutions, internship programs, and internal upskilling pathways to ensure a steady supply of qualified professionals who can drive the organization’s AI ambitions forward.
Practical steps to strengthen talent and culture
- Create cross-functional AI squads that bring together product, engineering, data science, security, and governance experts to drive end-to-end initiatives.
- Develop clear career paths and development plans for AI roles, emphasizing both technical mastery and business acumen.
- Invest in training programs that cover not just algorithms and tools but also ethics, risk management, and regulatory considerations.
- Foster a culture of experimentation with structured reviews, post-implementation assessments, and a bias toward learning from both successes and failures.
- Build partnerships with external experts and research institutions to supplement internal capabilities and accelerate learning curves.
Pathways to Scale: Strategic Roadmap for Enterprise AI Transformation
The culmination of EPAM’s insights points to a strategic pathway that moves beyond speculative gains toward sustained, enterprise-wide impact. The core directive is to deploy AI at scale by focusing on high-impact, organization-wide use cases while maintaining a steadfast commitment to aligning people, culture, data, cloud infrastructure, and new processes. This integrative approach is designed to unlock exponential business value that goes beyond isolated improvements and permeates across all facets of the enterprise.
The study’s narrative reflects a practical synthesis of momentum, capability, and governance. It acknowledges the early productivity and efficiency wins achieved through proofs of concept but asserts that the true opportunity lies in orchestrating a broad transition to scalable AI. The objective is not a one-time acceleration but an ongoing, systemic transformation that continually expands the portfolio of high-value use cases, strengthens the data and security foundations, and enhances organizational readiness to sustain momentum.
From a programmatic perspective, organizations should adopt a deployment playbook that can be replicated across functions and geographies. This entails standardized processes for model development, testing, validation, deployment, monitoring, and retirement. It also requires a governance mechanism that keeps alignment with business outcomes, ensures compliance with evolving regulations, and implements continuous improvement loops. The deployment plan must be resilient, adaptable to shifting regulatory environments, and capable of absorbing new data sources and use cases as they emerge.
Nir Kaldero’s closing emphasis captures the essence of this strategic pathway: “The next phase of AI is not just experimentation but deployment at scale—focusing on enterprise-wide, high-impact use cases while continuing the effort to align people and culture, data and cloud, and new processes to unlock true exponential business value.” In other words, the transformation is comprehensive and ongoing. It requires a convergence of technology, governance, talent, and organizational culture that sustains value creation over time. The roadmap for scale is thus not a single initiative but a disciplined program that nurtures alignment, builds capabilities, and relentlessly pursues measurable outcomes that matter to the business.
Steps to operationalize AI at scale
- Establish a formal AI portfolio management function that prioritizes use cases with clear business value and deploys resources accordingly.
- Build a scalable data foundation and cloud strategy that supports rapid provisioning, secure data sharing, and governance across the organization.
- Implement a multi-year governance roadmap that evolves with regulatory changes and emerging risk considerations.
- Create objective metrics and dashboards that measure AI impact on customer outcomes, productivity, and financial performance.
- Develop a change-management plan that engages leadership, informs stakeholders, and sustains adoption across teams and regions.
Conclusion
EPAM’s extensive study illuminates a critical inflection point in enterprise AI: the distinction between perceived sophistication in AI adoption and the ability to translate that sophistication into market-ready outcomes. The data reveal a substantial delivery gap, underscoring the need for deliberate prioritization of high-value use cases, robust governance, and scalable deployment models. While the appetite for AI investment remains strong, with a 14% projected spend increase in 2025 and a growing AI workforce, the path to sustainable advantage lies in moving beyond proofs of concept toward enterprise-wide, high-impact deployments that deliver measurable returns.
Governance, modernization, and security emerge as pivotal barriers that must be addressed in parallel with talent development. The study’s insight that 75% of advanced companies have defined AI strategies but only 4% of disruptors have comprehensive governance frameworks signals a pivotal shift: without governance that coordinates across functions, regulation, and risk, AI efforts cannot scale effectively. The 18-month timeline to implement governance models further emphasizes the strategic, long-range nature of responsible AI adoption. Equally important is the modernization imperative—outdated infrastructure and insufficient security programs pose tangible obstacles to progress. Enterprises must treat these as inseparable components of the AI transformation, integrating governance, technology modernization, and security into a cohesive program.
At the human level, organizational readiness and talent strategy play a central role in unlocking AI value. The shift toward developers becoming strategic end-to-end solution builders, and the emphasis on aligning technical work with business objectives, reflect a broader redefinition of roles and workflows. A culture that supports collaboration, continuous learning, and disciplined execution will enable organizations to translate AI capabilities into real customer outcomes. As Nir Kaldero reminds us, the future of AI is anchored not only in technological prowess but in the disciplined integration of people, processes, data, and cloud into scalable, value-focused operations.
For enterprises seeking to translate AI investments into durable competitive advantage, the roadmap is clear: identify high-value use cases, construct robust governance and modernization foundations, secure data and compute assets, and cultivate the talent and culture needed to sustain enterprise-wide impact. By embracing a holistic transformation that unites strategy, governance, technology, and people, organizations can move from a phase of hype to a stage of meaningful, measurable business value that endures across markets and industries. The era of enterprise AI is not about isolated wins; it is about building resilient capabilities that empower the entire organization to solve real-world problems and deliver sustained growth.