GCCs are evolving from low-cost delivery units to strategic command centres for agentic AI, emphasising industrialisation over innovation theatre to deliver sustainable business value.
Global Capability Centres are moving into a new role inside large enterprises: less back-office utility, more strategic command hub. That is the central argument of HCLTech’s paper on scaling agentic AI, which says these centres are increasingly the place where business knowledge, engineering capability and access to enterprise data converge.
The shift matters because the hard part of agentic AI is no longer proving that models can complete multi-step tasks or make useful decisions. According to IBM’s recent research on scaling agentic AI, many companies are still stuck in proof-of-concept mode because they have not yet aligned infrastructure, governance and workforce readiness. In other words, the technology has advanced faster than the operating model around it.
That gap helps explain why so many pilots fail to become durable enterprise value. HCLTech says common stumbling blocks include weak data quality, brittle links to legacy systems, unclear governance and security controls, and an inability to manage autonomy safely once AI moves into live business processes. Information Services Group has made a similar point, arguing that GCCs are well placed to move enterprises from experimentation to production, but only if they address data readiness and trust issues.
The broader industry view is that GCCs are no longer simply low-cost delivery locations. Boston Consulting Group has described them as strategic assets capable of driving enterprise transformation, with advanced AI use cases acting as accelerators for maturity. EY has also argued that, in banking, GCCs can function as a kind of second headquarters, combining technology, talent and strategy in one operating model.
What distinguishes the HCLTech proposal is its focus on industrialisation rather than innovation theatre. The paper argues that scaling agentic AI requires more than better models or more pilots; it requires redesigning how work flows across business, technology and risk teams. Its recommended approach brings together domain expertise, implementation capability and research depth in one co-ordinated squad, so that use cases are built with production constraints in mind from the outset.
HCLTech also recommends a disciplined lifecycle for moving from discovery to deployment and continuous improvement, including clear autonomy thresholds, evaluation criteria, governance templates and monitoring rules. That emphasis on operating discipline aligns with wider thinking across the GCC market, where analysts increasingly see the next phase of value creation coming not from isolated experiments, but from centres that can repeatedly convert AI into governed, repeatable business outcomes.
Source Reference Map
Inspired by headline at: [1]
Sources by paragraph:
Source: Noah Wire Services
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
10
Notes:
The article was published on May 1, 2026, and is the latest available information on the topic. No earlier versions or similar content were found, indicating high freshness.
Quotes check
Score:
10
Notes:
The article does not contain any direct quotes, ensuring originality and eliminating concerns about reused or unverifiable content.
Source reliability
Score:
10
Notes:
The article is published on HCLTech’s official website, a reputable source in the industry. No signs of derivative content or reliance on lesser-known publications were found.
Plausibility check
Score:
10
Notes:
The claims made in the article align with current industry trends and are supported by reputable sources. No inconsistencies or implausible statements were identified.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): HIGH
Summary:
The article meets all verification standards, with no significant concerns identified in any of the checks. It is fresh, original, and from a reliable source, with all claims being plausible and independently verifiable.
Supercharge Your Content Strategy
Feel free to test this content on your social media sites to see whether it works for your community.
