GCC to the Power of AI: The AI-Native Global Capability Center

For three decades, the Global Capability Center existed to answer one question: how do you deliver the same work for less. Location strategy, headcount ramps, and process standardization did the heavy lifting. That model built real value, and it still runs much of enterprise IT and operations today.

It has also reached its limit.

ISG’s 2026 research on GCC services found that these centers have moved well past their origins as cost arbitrage vehicles, with enterprises now building them as hubs for innovation, AI-led operations, and long-term competitive advantage. The center that once absorbed transactional work is being asked to own outcomes. That is a different mandate, and it requires a different kind of center: one that is AI-native by design rather than automated at the edges.

Most GCCs still run on a shared-services logic: standardize the process, centralize the headcount, chase incremental efficiency. McKinsey’s State of Organizations 2026 research shows why that logic is running out of road. Eighty-four percent of leaders plan to expand the scope of their shared-services and capability centers within the next one to two years, but more than 40 percent have not yet started systematically adopting the technologies needed to do it. Only 6 percent of business services leaders report realizing full value from advanced technologies across multiple use cases.

The barriers are not mysterious. Integration with legacy systems and organizational resistance to change are cited most often as the reasons scaling stalls. Neither is solved by adding another automation tool to an already fragmented stack. Both are symptoms of a center that was architected for headcount efficiency, not for AI-native operation.

This is the gap between a GCC with AI in it and a GCC built for AI from the ground up.

An AI-native GCC is not a traditional center with a chatbot layered on top of its ticketing system. It is a center where AI agents and human specialists are designed into the same workflow from day one, with clear rules for what each does and how work moves between them.

McKinsey’s research draws a useful distinction here: organizations that simply automate an already-centralized process capture short-term savings that plateau quickly. Organizations that automate first and then centralize, redesigning the end-to-end process around AI before deciding where and how it runs, capture more durable gains. The sequence matters. Centralizing a broken process just makes the breakage consistent.

This is the practical meaning behind Ascendion’s framing of Engineering to the Power of AI: AI is not bolted onto engineering delivery, it multiplies it. Applied to a GCC, that means workflows are redesigned around a Carbon + Silicon operating model, where human judgment and AI execution are deliberately orchestrated rather than loosely coexisting. Agents handle the repeatable, high-volume execution. People own the exceptions, the judgment calls, and the accountability. Neither is a bolt-on to the other.

The financial case for the traditional GCC was straightforward: move the work to where it costs less to do. That case is not disappearing, but it is no longer the primary source of value.

McKinsey’s research puts numbers to the shift. Centers that move from a traditional shared-services model to an AI-native operating model see an additional 20 percent gain in cost effectiveness beyond what location strategy alone delivers, a 40-fold increase in access to innovation, and a roughly 50 percent improvement in productivity tied to operational excellence. Customer and user experience metrics improve by another 20 percent on top of that.

That is a different value equation, and it is the one behind what Ascendion calls AI Arbitrage: value created not by where the work is cheapest to staff, but by how effectively AI-augmented engineering capacity is orchestrated, wherever it sits. A GCC built on AI Arbitrage competes on the speed and quality of its output, not just its cost per hour.

That shift also changes what the center is selling internally. A traditional GCC sells capacity, measured in FTEs and hours. An AI-native GCC increasingly delivers Services-as-Software: reusable, governed, AI-driven capability that scales without a linear increase in headcount. The center stops being a labor pool and starts being a platform.

Agentic workflow orchestration. Routine, high-volume execution, whether it is IT operations, claims processing, or financial close, runs through AI agents that handle the full task end to end, with humans stepping in at defined exception points rather than every step. This is the operating model behind self-healing IT operations: agents detect, diagnose, and resolve incidents before they need a ticket, and engineers focus on the incidents that actually require judgment.

A governed platform foundation. Scattered pilots do not compound. An AI-native GCC needs a common platform layer, comparable to Ascendion’s AAVA™ (AI-native platform for the full engineering lifecycle), so that agentic capability built for one workflow is reusable across the next one, with consistent governance, security, and observability rather than a new one-off build every time.

Outcome-based governance. Traditional GCCs are managed against FTE counts and SLA adherence. AI-native centers are managed against business outcomes: cycle time, defect escape rate, time to market, cost per resolved issue. McKinsey’s research found that only 26 percent of shared-services centers currently focus on analytical or strategic tasks, with the rest still concentrated in operational and administrative work. Shifting the mandate requires shifting what gets measured first.

A hybrid talent model. The center needs people who can supervise and improve agentic systems, not just execute manual process steps. McKinsey’s research points to emerging roles like AI product owners and trust and safety leads inside these centers, and finds that roughly three-quarters of existing roles will need reshaping with new skill mixes that combine technical fluency with judgment and stakeholder management.

Enterprises rarely need to redesign an entire GCC at once, and trying to do so is usually how transformation programs stall. Three moves tend to separate the centers that make real progress from the ones stuck in pilot purgatory.

First, audit the current task mix honestly. If the center’s work is still overwhelmingly operational and administrative, that is the baseline to change, not the mandate to preserve.

Second, pick one or two domains for full agentic redesign rather than spreading automation thinly across everything. A center that fully reimagines one end-to-end process, IT service management or claims intake, for example, learns more and builds more reusable platform capability than a center that adds a chatbot to ten different workflows.

Third, build the governance and platform layer before scaling. Integration with legacy systems is the single most cited barrier to scaling AI-native shared services. That is an architecture problem, and it needs to be solved before the third or fourth use case goes live, not after.