After agentic AI’s breakout in 2025, leading enterprises will be asking more from the technology in the coming years: how can it actively automate workflows, continuously optimize operations at scale, and drive business decisions that create value and sharpen the competitive edge?

With global spending on digital transformation projected to reach $3.9 trillion by 2030, this shift reflects a broader market reality. Yet for many, the promise of agentic AI remains frustratingly out of reach. This gap between aspiration and reality boils down to a business’s technology stack, underscoring the urgency to move decisively from experimentation to enterprise-wide impact.

Even in the midst of this agentic AI revolution, the technology is too often introduced as an overlay—bolted onto legacy systems, fragmented data environments, and processes never designed for intelligent automation. In these conditions, even the most advanced AI models are constrained to isolated use cases and pilots.

Legacy architectures cannot support the data velocity, integration requirements, or real-time decision-making that agentic operations demand. Without the right foundations, enterprises cannot fully experience the enterprise-level benefits made possible with agentic AI.

AI transformation from the ground up

To operate as an agentic enterprise, organizations must rethink where AI transformation truly begins. Rather than viewing agentic AI as a capability to be added later, enterprises need to focus first on the foundational layers of their digital and technology stack.

This means establishing modern digital platforms that enable seamless integration, cloud-native infrastructure that provides the computational power autonomous systems demand, and unified and trusted data architectures that eliminate silos, and implementing processes designed for AI-driven decision-making.

Without these foundational elements in place, enterprises risk testing capabilities that they can never scale.

Breaking out from pilot, towards enterprise-wide agentic transformation

Sustained agentic transformation is not accidental. It requires a deliberate, structured approach that moves organizations from readiness to scale, and then to continuous innovation.

1. Laying the right foundation

Transformation begins with diagnosing current digital maturity and identifying capability gaps. Enterprises must prioritize investments that align with long-term agentic goals rather than chasing short-term wins. It may seem impressive for a large retail chain to open digital storefronts, but without the proper infrastructure for real-time sales and inventory updates, bottlenecks and stockouts can quickly break delivery promises and undermine its transformation efforts.

A comprehensive digital assessment framework provides the necessary roadmap for this journey, ensuring foundational work supports autonomous operations rather than reinforcing existing constraints.

2. Modernize infrastructure for scale

A robust technological backbone is non-negotiable for AI transformation. This means modernizing applications and infrastructure to eliminate technical debt and migrating to architectures that support agility and scale. Application modernization and migration strategies enable enterprises to embrace flexible, API-driven architectures.

Equally critical is cloud adoption. Cloud infrastructure provides the elasticity and computational power required to integrate AI-driven applications, optimize workloads dynamically, and maintain resilience under changing demands.

It’s also important to note that successful cloud integration requires centralized oversight. This lack of visibility may be why 44% of executives say at least one-third of their cloud spend is wasted. To better manage spend, enterprises can consider establishing cloud cost governance for better control over cloud spending across providers.

3. Sustain innovation and intelligence

Transformation is an ongoing journey of refinement and evolution. Enterprises must continuously enhance existing applications to stay aligned with business needs and technological advances. To stay competitive, enterprises must treat modernization as a foundation to layer upon and maintain a continued momentum to meet the demands of a rapidly changing digital economy.

Meanwhile, agentic AI thrives with high-quality data. This is why it is necessary to break down data silos, improve data quality, and embed governance to accelerate business value. Data engineering ensures that AI models have access to clean and unified data pipelines, so that AI agents can deliver consistent, trustworthy outcomes at scale.

Finally, traditional testing methodologies can no longer keep pace with AI-driven systems. Research has shown that 88% of users are less likely to return to a website if they had a bad experience. With digital quality assurance, defects are flagged ahead of time, so iterations can move fast enough to keep pace with autonomous systems and end-user behaviors.

Measurable outcomes from foundational investment

Enterprises that invest intentionally in digital and AI foundations are already seeing measurable results. Comprehensive transformation frameworks reduce operational friction, unlock new efficiencies, and position organizations as leaders within their industries.

Mastercard, for example, has significantly reduced false-positive fraud cases through its AI-powered fraud detection system, which analyzes nearly 160 billion transactions each year. Hyundai has streamlined operations from procurement to production by deploying autonomous guided vehicles and robotic automation across its manufacturing processes.

The capabilities of agentic AI are immense and full of potential, but it cannot be realized on legacy foundations. The organizations that build modern digital and AI-ready capabilities today will be the ones that define competitive advantage tomorrow.

For a comprehensive playbook on establishing AI-ready capabilities across assessment, modernization, cloud strategy, and sustained innovation, visit the Illuminating Intelligent Enterprises microsite.