EPAM: Bridging the AI Perception–Implementation Gap to Deliver Enterprise Value

A comprehensive blueprint emerges from EPAM Systems’ recent study on enterprise AI adoption: a pronounced gap between perception and real-world execution persists, even as boards and executives push for broader AI-driven outcomes. The research, conducted across a diverse global footprint, reveals that companies are betting more on AI, yet face systemic hurdles that delay scalable value realization. As organizations move beyond pilots and proofs of concept, the focus shifts to strategic prioritization, governance, and the orchestration of people, data, cloud, and processes to secure measurable business impact. The findings underscore that successful enterprise AI requires more than technology — it demands disciplined governance, robust talent strategies, and a unification of business objectives with technical execution. In a world where AI is no longer a novelty but a strategic imperative, EPAM’s analysis urges executives to accelerate from experimentation to scalable, high-value deployments.

Overview of the EPAM Study and Key Findings

EPAM Systems conducted an expansive investigation into the state of enterprise AI, surveying 7,300 participants across nine countries and eight industries. The study, titled From Hype to Impact: How Enterprises Can Unlock Real Business Value with AI, provides a granular view of perceived capabilities versus real-world outcomes. The data shows a significant self-assessment gap: 49% of respondents rated their companies as “advanced” in AI implementation. Yet, among those leaders who identified themselves as advanced, only 26% have actually delivered AI use cases to market. This delivery gap highlights a persistent challenge within many organizations: the leap from experimentation to execution that yields tangible returns on investment.

The research depicts a multi-phase maturity journey for AI within enterprises. In the early phase, organizations experiment with AI and develop proofs of concept to target immediate gains in productivity and operational efficiency. This phase was prevalent in the 2023–2024 period, as noted by industry observers and participating executives. The new findings indicate that many firms are now transitioning into a more mature phase where success depends on identifying high-value use cases and prioritizing them in a way that produces broad, cross-organizational impact. The implication is clear: rather than pursuing a broad, shallow set of AI initiatives, leading enterprises are converging on a smaller number of strategic use cases with the potential to unlock sizable, scalable improvements.

EPAM’s leadership emphasizes that the current inflection point is not about more AI experimentation but about disciplined deployment. The study’s leadership commentary highlights a shift in emphasis from “quick productivity gains” to “strategic prioritization” and “governance-driven alignment.” As organizations move forward, the emphasis is on selecting use cases that provide durable, enterprise-wide benefits rather than isolated wins. This reframing aligns with the broader industry consensus that sustainable AI value requires coherence across business objectives, data readiness, technology platforms, and organizational culture. The overarching implication is that governance and alignment will determine whether AI investments translate into durable competitive advantages or simply deliver transient productivity improvements.

The scope of the EPAM study and its methodology bolster its credibility as a benchmark for enterprise AI programs. With nearly 7,300 participants spanning nine countries and eight industries, the findings mirror real-world diversity in regulatory environments, data policies, and operational complexities. The study’s robust sample provides an empirical lens to examine how advanced AI strategies translate into practical, market-ready outcomes. It also reveals the critical role of cross-functional governance in shaping the success of AI initiatives, as well as the need to overcome structural frictions that slow deployment. In addition, the research offers a forward-looking perspective on how investment patterns and labor market dynamics will influence enterprise AI adoption in 2025, laying the groundwork for a more deliberate, outcomes-driven AI strategy.

The leadership voices within EPAM add depth to the data, offering interpretive insights that connect the numbers to organizational realities. Elaina Shekhter, Chief Marketing and Strategy Officer at EPAM, points to the post-ChatGPT era as a turning point. Shekhter notes that after 2023 and into 2024, many organizations moved beyond mere experimentation to pursue proofs of concept that aimed for immediate productivity gains and operational efficiencies. The new research, however, indicates a maturation cycle where success hinges on identifying high-value use cases and prioritizing them strategically to achieve broad organizational impact. This perspective aligns with the broader industry understanding that AI initiatives must be anchored in concrete business problems that deliver scalable ROI, rather than isolated efficiency improvements that do not translate into strategic advantages.

The study’s conclusions set the stage for a rigorous discussion about how enterprises can bridge the gap between AI potential and realized value. The emphasis on governance, alignment, and strategic prioritization informs a practical roadmap for organizations seeking to maximize the ROI of their AI investments. The findings also underscore the necessity of an integrated approach where business leaders, technologists, data professionals, and security teams collaborate to ensure that AI initiatives are not only technically feasible but also economically justified and operationally sustainable. This integrated approach is presented as essential in a landscape where both the promise and the risk of AI are escalating, and where the pace of change continues to accelerate across industries and markets.

Investment Trends for Enterprise AI Adoption

The EPAM study reveals a continuing, multi-faceted commitment to AI investments, even as organizations acknowledge the challenges of scaling AI from pilots to production-ready deployments. Enterprises plan to increase AI spending by 14% year over year in 2025, signaling sustained confidence in AI-driven strategies despite persistent implementation hurdles. This steadfast commitment suggests that companies view AI as a strategic asset capable of driving productivity, differentiation, and long-term value creation, rather than a transient technology fad.

Within this investment trajectory, the study singles out a group EPAM labels as “disruptors”—leaders who occupy the forefront of AI adoption and experimentation. For these market frontrunners, the financial stakes are substantial. Disruptors attribute 53% of their anticipated 2025 profits to AI investments, underscoring a direct, material link between AI initiatives and financial performance. This quantified impact demonstrates that, for disruptors, AI is a central driver of profitability and competitiveness, rather than a marginal line item. It also highlights the intensifying pressure on leadership to ensure that AI programs yield measurable outcomes across revenue, margin, and market share.

The labor market dynamics accompanying AI investments reinforce the scale of organizational change required. The study shows that 43% of surveyed companies plan to hire for AI-related roles throughout 2025. This hiring momentum signals a robust demand for specialized talent, particularly in areas that bridge technical capabilities with business value. Machine learning engineers and AI researchers emerge as the most sought-after roles, reflecting the ongoing need for advanced analytical expertise to design, deploy, and optimize AI systems at scale. The demand signals also reflect the broader trend toward embedding AI into mission-critical business processes, where specialized skills are essential to ensure reliability, governance, and ethical considerations.

Dmitry Tovpeko, EPAM’s Vice President of Engineering, offers a practical lens on the relationship between productivity gains and transformation. He emphasizes that while improved productivity and operational efficiency are universal goals, the true transformation lies in bridging the gap between technology teams and the broader business. As AI continues to reshape enterprise ecosystems, developers are increasingly evolving from task-oriented users to strategic experts who can responsibly harness AI for end-to-end scenarios. This perspective underscores that success is not merely about the adoption of new tools but about cultivating a talent framework that aligns engineering capabilities with strategic business objectives to address real-world customer problems. The implication is that AI-driven growth depends on both the scale of investment and the strategic alignment of technical talent with market needs.

From a budgeting standpoint, the data imply that AI investments are becoming core to strategic planning in large enterprises. The 14% YoY increase in AI spending signals that boards expect AI to contribute to long-term value creation, rather than providing a temporary productivity boost. The expectation that AI-related roles will drive future capability indicates that companies intend to build, not just buy, AI capacity. In practice, this means that AI programs must be wired into hiring pipelines, training schemes, and graduate-to-professional pathways that sustain talent pipelines for the years ahead. The investment pattern also points to the necessity of robust governance and risk management mechanisms to ensure that growing AI spend translates into meaningful, measurable returns.

The 2025 financial outlook for disruptors also reflects a nuanced balance between investment intensity and hurdle rates. While AI is a major driver of profitability, it demands careful management of deployment risks, data quality, and security posture. Leaders who allocate funds toward AI in a disciplined, prioritized manner are more likely to unlock the intended value while mitigating potential downsides. Consequently, the enterprise AI investment narrative is shifting from a simple appetite for more AI to a structured, value-driven approach that emphasizes prioritization, governance, and operational integration. This approach ensures that the capital deployed is aligned with strategic business outcomes and capable of delivering sustainable competitive advantage in an increasingly AI-enabled market.

From Hype to Real-World Execution: Bridging Tech and Business

The EPAM study presents a clear narrative about the evolution of AI within large organizations: early enthusiasm and rapid experimentation gave way to a more measured, strategic deployment that requires cross-functional alignment. The journey from “proofs of concept” to market-ready AI capabilities hinges on bridging the long-standing gap between technology teams and business units. In practice, the transformation involves more than technical expertise; it demands governance structures, performance metrics, and organizational change that align AI activities with the overarching business strategy.

The leadership perspectives emphasize that true transformation demands a shift in mindset. Developers and data scientists must increasingly operate as strategic partners who can translate customer needs into end-to-end AI-enabled solutions. This requires a redefinition of success metrics, moving beyond traditional software delivery measures to metrics that capture customer impact, revenue lift, and operational resilience. The emphasis on end-to-end scenarios also highlights the need for cross-domain collaboration across data, cloud platforms, security, compliance, and user experience to ensure that AI deployments address real customer problems in a scalable, responsible manner.

As AI reshapes enterprise operations, the skillset required for success expands beyond pure algorithmic performance. Talent strategy becomes a cornerstone of enterprise AI programs. The market’s demand for machine learning engineers and AI researchers underscores the need for specialized capabilities, but equally important is the ability to integrate these capabilities into business processes and decision-making workflows. In this context, organizational readiness—encompassing data governance, cloud readiness, and process redesign—emerges as a critical determinant of AI maturity. Companies that align technical capabilities with business objectives, data governance frameworks, and operating models are more likely to realize sustained, enterprise-wide impact.

The shift from experimentation to deployment also implies a new internal governance posture. Governance is no longer a peripheral concern but a central enabler of scale. Enterprises must articulate a comprehensive AI strategy that encompasses risk management, regulatory alignment, data privacy, and responsible AI principles. The governance framework must be adaptable to diverse regulatory environments across markets while remaining rigorous in its controls. This tension — balancing innovation speed with risk management — requires thoughtful design of governance boards, decision rights, and escalation paths that empower teams to act decisively while maintaining oversight.

In this context, EPAM’s leadership notes that the most impactful AI programs will be those that tie technology choices directly to business outcomes. The emphasis on high-value use cases means that organizations must develop a robust vetting mechanism to identify opportunities with the potential for broad organizational impact. The selected use cases should be prioritized not only for their ROI potential but also for their ability to serve as archetypes that can be scaled and replicated across lines of business. The end goal is a portfolio of AI initiatives that collectively elevate customer experience, reduce time-to-market, enhance product quality, and improve overall enterprise performance.

Governance Frameworks and Modernization: The Core Barriers to Scale

Governance emerges as a principal obstacle in the journey toward enterprise-scale AI. EPAM’s findings reveal a striking discrepancy between advanced, aspirational strategies and the practical governance structures required to execute at scale. While 75% of advanced companies report having a clear AI strategy, a mere 4% of disruptors report having comprehensive governance frameworks. This governance gap persists despite widespread recognition of its importance, indicating that many organizations still operate without the robust oversight necessary to sustain AI initiatives in production environments.

A critical dimension of governance involves the timeline for implementing effective AI governance models. The study shows that businesses anticipate a minimum of 18 months to implement such governance structures. This extended horizon reflects the complexity of aligning AI governance with rapidly evolving regulatory requirements, data privacy concerns, cross-border data flows, and multi-stakeholder accountability. The 18-month timeline also implies that organizations must invest in ongoing governance efforts, including policy development, risk assessment, auditability, and change management, to ensure that AI deployments remain compliant and ethically sound as they scale.

Another notable obstacle highlighted by EPAM is technology modernization. Thirty-one percent of executives identify outdated technology infrastructure as a barrier to AI adoption. This revelation underscores that even when the strategic intent is clear, legacy systems, fragmented data platforms, and aging compute environments can impede progress. The modernization challenge is not solely about hardware but about creating an integrated, flexible technology stack that can support advanced analytics, real-time processing, and secure cloud operations. In practice, this requires a holistic modernization plan that covers data pipelines, model governance, monitoring, and the ability to scale AI workloads across distributed environments.

Security concerns also play a significant role in modernization challenges. Thirty-five percent of organizations report that insufficient security programs are a primary barrier to AI modernization. The absence of robust data protection, data quality controls, and cloud security architectures introduces risk that could undermine trust in AI systems and impede adoption. As AI deployments become more pervasive, organizations must embed security-by-design principles into every phase of AI development, from data sourcing and model training to deployment and ongoing monitoring. The study’s emphasis on security reflects a broader industry trend: as AI systems increasingly influence critical decisions, the value of governance and secure, auditable processes becomes central to enterprise risk management.

Organizational readiness factors further shape the path to AI scale. The research indicates that 65% of disruptive AI adopters understand the necessary skills for AI adoption, highlighting the importance of talent strategy in successful implementation. This readiness reflects a recognition that the capability to execute AI initiatives hinges on having the right mix of technical, data, and domain expertise, as well as the organizational culture that can absorb new processes and ways of working. The readiness statistic suggests that many enterprises are aware of the human capital required but still face gaps in capability development, recruitment, retention, and internal training programs.

Nir Kaldero, Chief AI Officer at EPAM NEORIS, emphasizes that the next phase of AI is about deployment at scale rather than mere experimentation. He stresses focusing on enterprise-wide, high-impact use cases while continuing efforts to align people and culture, data and cloud infrastructure, and new processes to unlock exponential business value. Kaldero’s perspective reinforces the governance-moderation concept: without scalable deployment, even the most promising AI strategies may fail to deliver sustained value. His point about alignment across people, data, cloud, and processes highlights the interconnected nature of governance, modernization, and execution.

From a governance perspective, the study suggests that the primary challenge is not just about deploying a new AI platform or selecting a toolset. It is about ensuring that governance structures enable consistent decision-making across the organization, provide transparent accountability, and establish the controls necessary to manage risk and compliance as AI use cases scale. The complex regulatory landscapes across different markets demand governance models that can adapt to diverse requirements while maintaining a consistent standard of ethical AI practice. This implies an ongoing, iterative governance process rather than a one-time implementation, with continuous evaluation, auditing, and refinement as AI capabilities and regulatory expectations evolve.

Technology modernization, while a barrier, should be viewed as an enabling condition for governance rather than a separate obstacle. The study suggests that the core issue lies in the misalignment between business objectives and the technical implementation. When the business goals are not clearly translated into technical requirements, modernization efforts may produce tools and platforms that fail to deliver expected outcomes. The remedy is a structured approach that links strategic objectives to data architecture, model governance, user experience, and operational processes. In this sense, governance and modernization are interdependent forces that must be developed in tandem to achieve durable AI scale.

EPAM’s analysis also highlights the importance of data quality and cloud security in the modernization equation. Data protection and cloud infrastructure readiness are critical to ensuring that AI systems operate safely, ethically, and reliably at scale. Organizations must implement robust data governance programs, including data lineage, quality controls, and access management, to maintain trust in AI-driven decisions. The security dimension, in particular, requires a proactive posture that anticipates evolving threats and ensures resilience in AI deployments across on-premises and cloud environments.

The study’s insights into organizational readiness and capability development reveal that leadership must invest in a strategic talent pipeline. A workforce that understands AI’s business implications, can collaborate with domain experts, and can translate insights into action is indispensable for achieving sustainable outcomes. The 65% readiness level among disruptors signals a favorable trajectory, but it also signals the need for ongoing education, cross-functional training, and the creation of internal career pathways that align with AI-driven transformation. When combined with governance and modernization, readiness becomes a practical, actionable driver of enterprise value rather than a theoretical aspiration.

Nir Kaldero’s reflections about aligning people, data, cloud, and processes connect governance to daily operations. By elevating the role of AI in enterprise strategy and ensuring that governance structures guide decision-making across functions, organizations can move beyond siloed implementations toward an integrated, scalable AI program. The next phase of AI adoption will require a careful orchestration of governance, modernization, and workforce readiness to unlock true exponential business value across the enterprise.

Governance and Modernization: Barriers to AI Adoption and Readiness Across Enterprises

Understanding the obstacles to AI adoption at scale requires analyzing the interplay between governance structures, technology modernization, and organizational readiness. EPAM’s study sheds light on how these elements interact and why many enterprises struggle to translate AI potential into broad, measurable business outcomes. The governance dimension stands out as a pivotal factor in determining whether an organization can translate AI investments into durable competitive advantage.

One of the most striking findings is the gap between strategy and governance. Although many advanced firms claim to have a clear AI strategy, the absence of comprehensive governance frameworks among disruptors suggests that many organizations rely on high-level plans without the concrete governance mechanisms necessary to operationalize them. Governance frameworks encompass policy development, risk assessment, accountability structures, and oversight processes that support scalable, compliant AI deployments. Without these elements, AI initiatives risk fragmentation, inconsistency, and risk accumulation. The 18-month minimum implementation horizon underscores that building robust governance is a long-term, iterative effort rather than a quick fix. Organizations must be prepared to invest in governance capabilities over an extended period, with ongoing reviews and adjustments to keep pace with regulatory changes and evolving best practices.

Technology modernization is another substantial barrier. The study identifies outdated infrastructure as a significant obstacle for 31% of executives. The implication is that even with mature AI strategies, legacy data systems, fragmented data stores, and insufficient compute capacity can bottleneck AI performance and hinder deployment at scale. Modernization requires a holistic approach that includes modern data platforms, streamlined data pipelines, scalable compute resources, and interoperable AI tooling. It is not merely a transition to the cloud but a thoughtful re-architecture of the data and analytics ecosystem to support real-time AI workloads, model monitoring, and secure data management.

Security concerns persist as a central risk element in AI modernization. Thirty-five percent of organizations report that insufficient security programs hinder AI modernization. The importance of data protection, data quality, and cloud security cannot be overstated in an era where AI systems process sensitive information and influence critical decisions. A secure AI operating model demands end-to-end security controls, continuous monitoring, vulnerability management, and transparent governance for data usage and model outputs. The study’s emphasis on security indicates that risk management is not ancillary but a core capability essential to the trust, adoption, and effectiveness of enterprise AI.

Organizational readiness remains a key enabler of AI scale. The finding that 65% of disruptors understand the necessary skills highlights that knowledge is essential, but it is not sufficient by itself. Organizations must translate that knowledge into a concrete talent strategy, including recruitment, onboarding, training, and retention plans that ensure the right skills are available when needed. The readiness metric reflects a proactive stance toward building capabilities rather than reacting to talent gaps after a project begins. It also underlines the importance of cross-functional collaboration between AI specialists, data engineers, software developers, and business leaders to ensure that AI capabilities are integrated into business processes and decision-making.

The perspectives from EPAM’s leadership illuminate the practical path forward. Nir Kaldero’s emphasis on deployment at scale and alignment of people, data, cloud, and new processes points to a holistic approach: governance and modernization must be designed to support scalable, enterprise-wide use cases. The goal is not merely to automate isolated tasks but to embed AI capabilities into end-to-end business processes that can be replicated and scaled across the organization. This approach elevates AI from a discrete project to a strategic capability that drives systemic improvements in customer outcomes, product quality, and operational efficiency.

In sum, the EPAM study presents a nuanced view of the barriers to AI adoption in large organizations. Governance and modernization are closely intertwined, and their success relies on a robust, long-term commitment to building the capability, culture, and technical foundation needed for scalable AI. The insights underscore the necessity of moving beyond tactical investments toward strategic, governance-driven programs that treat AI as a core operating capability with clear accountability, measurable outcomes, and a coherent plan for scaling across the enterprise.

Talent Strategy and Readiness: Building the AI-Ready Workforce

A critical and recurring theme in EPAM’s findings is the centrality of talent readiness to the success of enterprise AI programs. While many organizations articulate an understanding of the skills required for AI adoption, the real challenge lies in translating that knowledge into a sustainable workforce capable of delivering high-value, scalable AI deployments. The study notes that 65% of disruptors report a clear understanding of the necessary skills for AI adoption, signaling a solid awareness of the talent requirements. However, awareness alone does not guarantee execution; the practical deployment of AI at scale demands comprehensive talent strategies, integrated with governance, data strategy, and operating models.

The demand for specialized roles remains acute. Machine learning engineers and AI researchers are identified as the most sought-after positions, reflecting the ongoing need for advanced technical expertise to design, train, and optimize AI models that can operate within complex enterprise environments. Yet, the talent landscape extends beyond the core AI disciplines. Enterprises require data engineers to build and manage robust data pipelines, data scientists to translate business problems into analytical solutions, software engineers to integrate AI into production systems, and security specialists to ensure AI security and compliance. Each role plays a critical part in the AI value chain, and each must be embedded into cross-functional teams that can collaborate effectively to deliver end-to-end AI solutions.

Talent strategy is not limited to recruitment; it encompasses training, upskilling, and retention strategies that sustain AI capabilities over time. The study’s emphasis on readiness suggests that organizations recognize the importance of cultivating internal talent pipelines that can grow with AI maturity. This includes establishing formal training programs, certification tracks, and hands-on project experiences that accelerate the development of practical AI competencies. It also involves creating career pathways that reward domain knowledge and cross-disciplinary expertise, enabling staff to advance within the organization while contributing to AI-driven initiatives.

A consequential implication of the talent readiness findings is the need for synergy between AI teams and business units. For AI to deliver real business value, the people who understand the business problems must work closely with technologists who can translate those problems into data-driven solutions. This collaboration requires new ways of working, such as cross-functional squads, shared performance metrics, and governance structures that facilitate rapid decision-making while maintaining accountability. The study implies that without such alignment, even the most skilled AI professionals can struggle to produce outcomes that resonate with business leaders and customers.

The talent landscape also intersects with the broader labor market, where AI expertise is in high demand. The 2025 hiring momentum for AI roles indicates that competition for top talent will intensify, potentially driving up compensation and raising expectations for career development opportunities. Enterprises must develop competitive value propositions to attract and retain AI talent, including opportunities to work on strategically important projects, access to data, modern toolsets, and a culture that supports experimentation and responsible innovation. In this context, talent strategy becomes a strategic lever for AI adoption, enabling organizations to accelerate deployment, improve model quality, and sustain performance improvements over time.

Nir Kaldero’s perspective on deployment at scale reinforces the importance of a strong talent framework. He asserts that the next phase of AI hinges on enterprise-wide, high-impact use cases that require coordinated efforts across people, data, cloud infrastructure, and processes. This perspective underscores the reality that talent is not just about technical capabilities but about cross-functional collaboration, change management, and the continuous alignment of skills with evolving business needs. As organizations scale AI, they will need to continuously assess and adapt their talent ecosystems to match the complexity of deployment, regulatory requirements, and security considerations. The takeaway is clear: investment in human capital is as critical as investment in technology for achieving durable AI value at scale.

In practice, building an AI-ready workforce entails more than hiring specialists. It requires cultivating a culture of continuous learning, experimentation, and disciplined execution. It involves fostering partnerships with academic institutions and industry consortia to access fresh talent and the latest research, as well as implementing mentorship and knowledge transfer programs that accelerate the onboarding of new AI practitioners. It also requires robust governance that clearly defines roles, responsibilities, decision rights, and accountability for AI initiatives. Finally, it demands a scalable infrastructure that enables data sharing, model training, and secure deployment across the organization, ensuring that AI work is not isolated to isolated teams but integrated into the enterprise’s core operations.

The talent and readiness narrative is inseparable from the broader governance and modernization agenda. Talent strategies must be designed in concert with governance frameworks, data strategies, and security policies to ensure that AI initiatives are not only technically sound but also compliant, ethical, and aligned with strategic objectives. By building a robust, AI-literate workforce and aligning it with governance and modernization imperatives, enterprises can unlock the full potential of AI, deliver sustained value, and maintain competitiveness in a rapidly evolving market.

Enterprise AI Deployment: From Concept to End-to-End Value

The transition from isolated AI experiments to enterprise-wide deployment is a central theme in EPAM’s study. The report stresses that the next phase of AI involves deployment at scale, focusing on enterprise-wide, high-impact use cases while continuing efforts to align people, data, cloud, and processes to unlock exponential business value. This shift underscores a practical reality: AI success depends not merely on developing sophisticated models but on integrating AI into end-to-end business workflows that produce measurable outcomes.

A key insight from the study is that success hinges on the deliberate alignment of technology teams with business objectives. The mere accumulation of technical capabilities does not guarantee economic value; instead, the alignment of tech initiatives with strategic business goals is what unlocks real impact. This alignment requires cross-functional collaboration across product teams, data teams, and operations, ensuring that AI is embedded in core decision-making processes and customer-facing experiences. Organizations that achieve this alignment are better positioned to realize improvements in productivity, customer satisfaction, and competitive differentiation.

The role of end-to-end scenarios becomes a focal point in guiding deployment strategies. Enterprises must design AI solutions that address complete customer journeys and operational cycles, rather than isolated tasks. This approach necessitates an integrated architecture that spans data sources, data quality controls, model governance, and deployment pipelines. It also requires a clear understanding of how AI outcomes feed into business processes, how results are monitored for quality and bias, and how the organization handles model updates in response to feedback and changing conditions. The emphasis on end-to-end capabilities ensures that AI deployments create ripple effects across multiple functions and touchpoints, maximizing the potential for comprehensive value creation.

Nir Kaldero’s perspective reinforces the imperative to deploy at scale with a holistic view. He notes that as AI reshapes the enterprise, developers are transitioning from being mere implementers of algorithms to becoming strategic actors who can steward end-to-end, responsible AI solutions. This perspective highlights the growing importance of responsible AI practices, including transparency, fairness, and accountability, as core to durable deployment. The practical takeaway is that large-scale AI programs require not only technical excellence but also governance mechanisms that ensure ethical considerations and risk controls are integrated into every deployment phase.

A crucial component of enterprise deployment is the integration of data, cloud, and processing capabilities. Kaldero’s emphasis on aligning data and cloud with business objectives points to a condition for success: AI is a cross-domain initiative that must be supported by a resilient data foundation and scalable cloud infrastructure. The deployment strategy should prioritize data quality, reliable data pipelines, secure access controls, and auditable model governance to ensure reliability and trustworthiness. This approach also reinforces the need for continuous monitoring, performance measurement, and feedback loops that allow AI solutions to evolve in response to changing customer needs and business priorities.

In practice, enterprises should pursue a phased deployment plan that prioritizes high-value use cases with clear, scalable architectures. This plan should include a rigorous evaluation framework to assess ROI, reliability, and customer impact before expanding to additional use cases. It should also incorporate a governance spine that defines roles, responsibilities, and decision rights for AI initiatives, as well as a risk management strategy that addresses security, privacy, and regulatory considerations. By anchoring deployments in end-to-end value, organizations can maximize the likelihood that AI programs deliver durable, enterprise-wide benefits and transform how they create value for customers and stakeholders.

Actionable Roadmap for Enterprises: Turning Insights into Impact

Based on EPAM’s findings, enterprises can translate insights into a practical roadmap that governs AI deployment, governance, and talent development. The following principles provide a structured path from concept to scale, focusing on deliverable outcomes, risk management, and sustainable growth.

1. Establish a clear, actionable AI strategy anchored in business goals. The strategy should identify high-value use cases across functions and prioritize them based on impact, feasibility, and alignment with core objectives. A transparent framework for prioritization helps ensure that resources are directed toward initiatives with the strongest probability of broad organizational impact. The strategy must be revisited regularly to reflect new data, evolving customer needs, and changing market conditions.

2. Build robust, scalable governance from the outset. Governance should prescribe decision rights, accountability structures, risk management protocols, and ethical guardrails for AI. This includes defining model governance processes, data governance standards, security controls, and compliance measures that adapt to diverse regulatory contexts. Governance must be designed to scale with the organization, enabling consistent decision-making as AI initiatives proliferate across lines of business.

3. Modernize the technology stack in a holistic manner. Organizations should pursue modernization efforts that modernize data platforms, streamline data pipelines, and provide flexible compute and storage capabilities to support AI workloads. Modernization should also address inter-operability between on-premises and cloud environments, ensuring data security and reliability as AI models are deployed at scale. A modernized stack supports faster experimentation, more reliable deployments, and easier governance.

4. Invest in talent and organizational readiness. Enterprises must implement a comprehensive talent strategy that includes recruitment, training, and retention programs for AI roles, as well as reskilling opportunities for adjacent functions. Building cross-functional teams that include AI specialists, data engineers, software developers, and business domain experts is critical to achieving end-to-end value. Ongoing learning, mentorship, and career development opportunities should be embedded in the culture to sustain AI maturity.

5. Embrace end-to-end deployment with measurable outcomes. Enterprise AI initiatives should be designed to deliver end-to-end value across customer journeys and business processes. This requires aligning data, cloud infrastructure, and operational processes with strategic objectives and ensuring continuous monitoring of model performance, bias, and compliance. A phased rollout approach allows for learning and adjustment as deployments scale and new use cases emerge.

6. Prioritize security, privacy, and risk management. Security programs must be embedded in AI modernization from the outset. Enterprises should implement comprehensive data protection, secure data handling practices, and cloud security controls to reduce risk and preserve trust in AI systems. Regular risk assessments, audits, and incident response planning are essential to maintaining resilience as AI deployments expand.

7. Measure, learn, and iterate. Establish robust metrics that capture both process improvements and business outcomes. Track ROI, productivity gains, customer impact, and operational efficiency to evaluate success and inform future investments. Use a feedback-driven approach to refine models, adjust governance, and optimize deployment strategies based on real-world results.

8. Foster a culture of responsible AI and ethics. Organizations must embed ethical considerations into the fabric of AI programs, including fairness, transparency, accountability, and bias mitigation. This cultural orientation supports trust among customers, employees, and regulators, enabling broader adoption and longer-term success.

9. Align executive sponsorship with operational delivery. Leadership must champion AI initiatives, remove organizational friction, and ensure alignment between strategic goals and day-to-day execution. Strong executive sponsorship helps secure the funding, resources, and cross-functional collaboration required to scale AI across the enterprise.

10. Plan for the long horizon, not only the next wave. Given the 18-month governance timeline and the ongoing need for modernization, enterprises should adopt a long-range plan that accounts for regulatory shifts, market dynamics, and evolving technology landscapes. A future-ready AI program anticipates changes, enabling a proactive rather than reactive stance.

The practical implication of these recommendations is that enterprise AI success rests on more than technical prowess. It requires a coherent, integrated program that combines governance, modernization, talent, and strategic alignment to deliver enduring value. By following a structured, phased approach that prioritizes high-value use cases and scales with rigorous governance, enterprises can transition from hype to impact and secure durable competitive advantage in an AI-enabled economy.

Conclusion

The EPAM study presents a comprehensive, evidence-based picture of the current state of enterprise AI: widespread optimism, rising investments, and a pronounced gap between perceived advancement and actual, market-facing outcomes. The data show that while nearly half of respondents view their organizations as advanced in AI, only a minority of these leaders have successfully delivered AI use cases to market. This delivery gap signals the need for a more disciplined approach that prioritizes high-value use cases, strengthens governance, modernizes technology, and solidifies talent strategies to support scalable, end-to-end AI deployments.

Investments in AI remain robust, with a projected 14% YoY spending increase in 2025 and a meaningful share of profits tied to AI investments among disruptors. The labor market signals are clear: demand for AI specialists will continue to grow, underscoring the importance of talent pipelines, training, and cross-functional collaboration to sustain momentum. Leadership perspectives from EPAM emphasize that true transformation hinges on bridging the gap between tech teams and business units, ensuring that AI initiatives address real customer problems and deliver broad organizational impact.

Governance and modernization remain the most salient barriers to scale. The substantial governance gap—where many firms report clear strategies but few have comprehensive governance frameworks—needs to be addressed through a structured, long-term program. Likewise, modernization challenges—outdated infrastructure and security concerns—underscore the importance of building a resilient, scalable AI operating model that integrates data, cloud, and processing capabilities with business objectives. Organizational readiness and talent strategy are essential levers; the right skill mix, cross-functional collaboration, and ongoing training are prerequisites for sustained AI success.

Going forward, enterprises should adopt a pragmatic, phased roadmap that marries strategic prioritization with rigorous governance, disciplined modernization, and a strong talent framework. By focusing on end-to-end deployments that deliver measurable outcomes, organizations can convert AI investments into durable value and establish a scalable model for enterprise-wide AI adoption. The insights from EPAM underscore that the real opportunity lies not in increasing the number of AI pilots, but in orchestrating a coherent portfolio of high-value, scalable AI initiatives that align with business goals, governance standards, and customer needs. The path to enterprise AI maturity is thus a disciplined, collaborative journey—one that requires leadership commitment, cross-functional teamwork, and a steadfast focus on measurable business impact.