For years, AI sat on enterprise roadmaps as a futureaspiration. In 2025, that changed. AI moved decisively from “what if” to “howdo we make this work?”

Across industries, organizations accelerated adoption by launchingpilots, experimenting with copilots, and investing heavily in platforms andtools. Yet despite this momentum, most enterprises still struggle to take AIbeyond isolated use cases. Very few have successfully operationalised AI atscale.

The reason is becoming increasingly clear: AI failure israrely a technology problem. It is a readiness problem.

The Enterprise AI Readiness Gap

Modern enterprises are not digitally immature. Most havealready invested in cloud infrastructure, API-first applications, DevOpspipelines, and sophisticated data platforms. On paper, they appear wellprepared for AI.

And yet, AI exposes fault lines that traditional digitalsystems never did.

Unlike conventional software, AI doesn’t just follow rules. It learns, adapts, and depends on livedata to stay relevant. It requires ongoing monitoring, retraining,governance, and security controls that go far beyond standard applicationmanagement.

AI must also operate directly within business workflows,influencing decisions rather than simply supporting them.

This creates a fundamental mismatch: digital foundationsexist, but AI foundations do not.

Why AI Programs Break Down in Practice

Enterprise AI initiatives typically fail for a few recurringreasons:

• Siloed architectures where infrastructure, data,and applications evolve independently
• Inconsistent data quality and ownership, makingAI outputs unreliable
• Lack of governance, especially around access,risk, compliance, and explainability
• Too much focus on pilots and too little on scale,limiting production thinking
• Poor integration into workflows, which limitsadoption and real impact

Unless you have a cohesive system that connects intelligenceto operations, AI remains experimental rather than transformational.

Moving from Experiments to Enterprise AI Systems

To succeed, enterprises must stop treating AI as a set ofdisconnected tools and start building it as a system-level capability.

At Parkar, we see successful AI programs anchored in aunified architecture that brings together trust, data intelligence, andapplication engineering. This is the thinking behind our AIONIQ framework,which focuses on the following three foundational layers enterprises needed toscale AI responsibly.

TRiSM - Trust, Risk & Security Management (An industry-standard Gartnerframework)

AI introduces new identities, access patterns, and riskvectors. Enterprises need guardrails that ensure AI systems are secure,auditable, and compliant from day one. This includes strict access control,protection of sensitive data, and continuous monitoring of AI behavior.

DAIR (Data, Analytics, Intelligence, Responsibility) - Intelligence Builton Reliable Data

AI is only as effective as the data that feeds it.Enterprises must ensure that insights are derived from current, contextual, andgoverned data—so AI responses are not just fast, but also relevant andaccountable.

CAPE (Composable Application & Platform Engineering) AI Embedded intoEveryday Work

True value emerges when AI is woven directly intoapplications and workflows. This means enabling automation, copilots, andintelligent agents within the tools teams already use, rather than forcingadoption through standalone interfaces.

Together, these layers allow enterprises to move fromfragmented pilots to scalable AI operating models.

Why Global Capability Centers Are Central to This Shift

As enterprises rethink how AI should be built and scaled,Global Capability Centers (GCCs) are emerging as the natural leaders of thistransformation.

Once viewed primarily as cost or delivery centers, GCCstoday operate at the heart of enterprise technology ecosystems. They managecloud platforms, build and run applications, engineer data pipelines, andenforce cybersecurity and compliance standards.

This gives GCCs several structural advantages when it comesto AI:

• End-to-end ownership across infrastructure,data, and applications
• Deep engineering talent spanning analytics,platforms, and product development
• Proven ability to iterate quickly and deploy atscale
• Established governance models covering access,risk, and compliance

Because AI cuts across every layer of the technology stack,GCCs are uniquely positioned to orchestrate it holistically—not as supportunits, but as strategic AI engines for the enterprise.

The Opportunity Ahead for Enterprises and GCCs

AI is rapidly becoming a core determinant of enterprisecompetitiveness. The organizations that succeed will not be those that run themost experiments, but those that design AI as a durable, enterprise-widecapability.

This is where GCCs can redefine their mandate—from executionto ownership, from delivery to leadership.

At Parkar, we partner closely with GCCs to help them playthis role by:

• Building AI-ready engineering and product teams
• Designing unified enterprise AI architectures
• Creating intelligence supply chains that connectdata to decisions
• Deploying secure, scalable AI workflows
• Establishing long-term AI operating models

The next decade of enterprise innovation will be shaped byhow effectively organizations operationalize AI. And increasingly, thatresponsibility will sit with GCCs.

The groundwork for that future is being laid today.

‍