What GCC AI Delivery Gets Wrong at the Engineering Layer
The parent company approved the GCC to build AI capability at speed. The pilots have been impressive. The leadership presentation went well.
Now comes the part nobody put in the business case: actually running these systems in production, at scale, against real enterprise data, integrated into legacy infrastructure that was never designed for autonomous agents.
This is not an AI problem. It is an engineering problem. And most GCCs are not set up to solve it fast enough.
Here’s What’s Happening
Building an AI-native GCC requires a very specific combination of skills:
- LLM platform engineering,
- real-time data architecture,
- agentic workflow design,
- MLOps, and
- enterprise integration
And you need all these working together from day one.
The numbers tell the story of where most GCCs actually stand. According to the EY GCC Pulse Survey 2025, 58% of GCCs are investing in Agentic AI and 83% are scaling GenAI projects. The intent is unambiguous. But intent and execution are not the same thing.
The parent company's AI roadmap is already running. Pilots have been completed. Results have been presented to leadership. The next phase has been approved. The GCC is expected to take those pilots into production and the engineering foundation needs to be ready.
The smartest GCC leaders don't wait for a full hiring cycle to complete before delivery begins. They bring in embedded engineering partners who own these capability domains immediately, operate as part of the team, and transfer knowledge as the permanent organisation is built alongside them.
This is how the gap between AI mandate and AI delivery gets closed by accelerating capability to match the roadmap.
The Pilot-to-Production Gap Is an Engineering Problem
Most GCC AI CoEs are well-equipped for the strategy and experimentation phases. The architects understand the frameworks. The data scientists can build models. The AI leads can design agentic workflows that look impressive in a controlled environment.
What breaks down between pilot and production is almost never the AI itself.
It is the infrastructure underneath it.
The data pipelines that need to run in real time, not batches. The API integrations connecting the AI layer to legacy enterprise systems that were never designed with autonomous agents in mind. The observability stack that monitors what the agent is doing, catches anomalies, and escalates appropriately. The deployment infrastructure that lets the team ship updates without taking down live workflows.
These are not AI problems. They are production engineering problems — and they require a different kind of engineer than the ones who built the pilot.
The scale of this problem globally is striking. Research from IDC, commissioned by Lenovo, found that for every 33 AI proofs-of-concept an enterprise starts, only four reach production. That’s an 88% failure rate.
IDC's own research lead summarised the cause directly: "The high number of AI POCs but low conversion to production indicates the low level of organisational readiness in terms of data, processes and IT infrastructure."
This is precisely the engineering readiness problem GCCs face at speed.
What Happens When Delivery Slips
GCCs operate on credibility.
The parent company made a significant commitment (capital, leadership attention, strategic narrative) based on the promise that the India GCC would deliver AI capability at speed and scale. When the delivery timeline slips, the conversation changes.
Budget scrutiny increases. The transformation story gets questioned. The GCC, which was supposed to be the enterprise's AI hub, starts to be seen as a source of delays rather than acceleration.
This dynamic is more common than anyone publicly admits. The same EY pulse data that shows 58% of GCCs investing in Agentic AI also reveals the maturity gap: when it comes to GCCs having embedded Centers of Excellence with the governance and operational depth to manage production-grade AI systems, the numbers drop sharply. Investing in AI and running AI in production are two very different things — and the gap between them is wide.
MIT's NANDA initiative, in its State of AI in Business 2025 report, studied over 300 enterprise AI deployments and found that 95% of generative AI pilots fail to deliver measurable P&L impact. The cause, in almost every case, was not the model. It was flawed enterprise integration, data infrastructure gaps, and the absence of the production engineering layer that turns a working demo into a reliable system.
The Four Engineering Problems That Kill GCC AI Deployments
- Data platform readiness.
Most enterprise AI deployments fail not because the model is wrong but because the data feeding it is inconsistent, stale, or inaccessible without manual intervention. Getting to a production-grade, AI-ready data platform is a multi-month engineering program. Starting it after the AI roadmap is already running is too late.
- Agent orchestration at scale.
Agentic workflows that run cleanly in demo environments often fail in production because the orchestration layer (managing context, tool invocation, exception handling, escalation logic) is significantly more complex when operating across real enterprise systems with real data variability.
- Integration depth.
Enterprise AI doesn't operate in isolation. It reads from and writes to systems (ERPs, CRMs, data warehouses, operational databases) that were built long before autonomous agents existed. Making these integrations reliable, secure, and performant under production load is serious engineering work that is consistently underestimated.
- Observability and governance.
A production AI system without proper monitoring is a governance risk and a stakeholder trust risk. Building the instrumentation, dashboards, and alerting that give the parent enterprise confidence in what the AI is doing is its own engineering challenge and one that is frequently treated as optional until something goes wrong.
What the GCCs Getting This Right Are Doing Differently
The GCCs that are successfully moving AI from pilot to production share a common approach: they treat the engineering infrastructure as a prerequisite, not an afterthought.
They invest in data platform readiness before the AI roadmap demands it. They build the integration and observability layers alongside the AI systems, not after them. And crucially, they close the engineering gap quickly, rather than waiting for a full hiring cycle to complete.
The most effective way to do this is through embedded engineering partnerships. The MIT NANDA data makes this case directly: AI deployments built through external expertise and partnerships succeed 67% of the time, compared to just 33% for internal-only builds. That gap in outcomes — 2x — is not marginal. It reflects a structural reality: the combination of deep specialist knowledge and operational accountability that embedded partners bring is genuinely difficult to replicate through a hiring cycle alone.
Experienced AI and platform engineers who work directly alongside the GCC team, own specific capability domains, and operate as part of the delivery structure rather than as external consultants — this is how the credibility window gets protected. Deliver in the short term. Build the permanent team in parallel. Transfer capability as the organisation matures.
The Compounding Value of Getting This Right Early
There is a compounding advantage to solving the pilot-to-production problem early.
When the first agentic system runs reliably in production (monitored, governed, delivering measurable outcomes) the parent company's confidence in the GCC grows. The second AI program gets approved faster. The third builds on the infrastructure from the first two. The engineering patterns, the platform components, the observability frameworks become reusable assets.
The IDC finding that 88% of AI POCs never reach production means something specific for GCC leaders: the GCCs that do build this production foundation in the first eighteen months will be operating at a fundamentally different level of AI capability (and enterprise trust) than those that spent the same period running pilots that never shipped. The gap compounds. First-mover credibility in AI delivery is not recoverable once the narrative has turned.
The Question Worth Asking Now
Your AI mandate is real. Your team has the vision. The roadmap is approved.
The question is whether the engineering infrastructure (the data platform, the orchestration layer, the integrations, the observability) is being built with the urgency the mandate demands.
MIT's research is unambiguous on what separates the 5% that succeed from the 95% that stall: it comes down to how the production layer is built, and who builds it alongside you.
If the answer to that question is still being figured out, the gap between what was promised and what gets delivered will become the story. And that is a very difficult story to recover from.
Altzor is an AI acceleration partner for GCCs and enterprise technology teams — building the data and AI engineering infrastructure that takes programs from mandate to production.
