AI-Native GCC 3.0
for Enterprises

TechBlocks delivers GCC 3.0 as Global Capability-as-a-Service, without the CapEx-heavy commitments of the past

Start with a single pod, prove ROI in 6–8 weeks, then scale into a self-evolving, AI-native delivery engine.

We had the privilege of hosting David Boone, CEO of Michaels Stores, Uma Bhemisetty, EVP & CIO, and Wei Dong, VP & CISO, at our TechBlocks GCC in Hyderabad.

David’s inspiring words on innovation, collaboration, and customer-first thinking, deeply resonated with our teams. A big thank you to David and the Michaels leadership for their continued trust and partnership as we build the future of retail technology together.

We’ve spent over a decade operating
GCCs for Fortune 100 enterprises

Backed by 2,500+ engineers across AI, data, cloud, DevOps, and QE, we bring the scale of a global integrator with the precision of a boutique transformation partner.

Talk to a GCC 3.0 strategist

Proven real-world success running large-scale GCCs

Built and operated a 300+ member GCC for an Apollo Global–backed specialty retailer based in North America
Scaled 260+ engineers across U.S., Canada, India within months
Delivered 19 cross-functional pods covering Checkout, Catalog, Marketplace, and Store Ops
Enabled 4× faster releases, 45% lower engineering cost, and 24% higher online conversions
Implemented AI-powered SRE, autonomous delivery, observability dashboards & microservices modernization

Why legacy GCCs fail in an AI-first world

GCC 1.0: The Original

Optimized for labor, not outcomes

GCC 1.0 was designed for efficiency and arbitrage. It’s slow to stand up, rigid to change, and measured in headcount. AI initiatives get stuck in pilots and POCs because the GCC 1.0 model can’t absorb automation at scale.

The result is delayed value, low innovation, and high cost.

GCC 2.0: The Evolution

Digital-enabled, but still linear

GCC 2.0 added cloud, DevOps, and data, but the operating model stayed the same. Scaling still means adding more people. Pods exist, but they aren’t AI-augmented or self-improving. CoEs exist, but they don’t drive automation or predictive operations.

The result is efficiency gains, but transformation is capped.

GCC 3.0: The AI-Ready Future

Outcome-led, self-evolving, & built to scale velocity without scaling cost, GCC 3.0 redefines global capability for the AI-native enterprise.

TechBlocks GCC 3.0 delivers:

2–5× engineering velocity
Higher throughput, fewer defects
Predictive, proactive operations
Consistency & reuse
Faster time-to-value
Scale without increasing cost

From Cost Centers to Capability Engines

AI-native engineering and autonomous operations tied to product and business metrics.

From Digital Enablement to Intelligence & Automation

Copilots, predictive workflows, automated QA, and self-healing systems built into delivery—not bolted on.

From Staffing to Modular Pod Ecosystems

Modular, cross-functional pods augmented by agents that mature into domain CoEs over time.

Client Success

$70M

In savings over 5 Years

Faster release cycles, moving from high-risk to modern AI delivery velocity

45%

Lower engineering costs, with streamlined GCC 3.0 POD-based execution

View full case study

Scaling Retail Transformation & Velocity with an AI-Native GCC for North America’s Largest Arts & Crafts Retailer

Challenges

Our client, North America’s largest arts and crafts retailer, invested $30M into a digital transformation initiative that spiraled into a $120M overrun, with two years of delays and little progress to show. Their offshore delivery center in China had become a liability—burning $2.2M per month, dependent on in-person releases, and burdened with undocumented systems and Chinese-only documentation. Fragmented U.S. and Canadian tech stacks, legacy systems with no transparency, and a vendor model that ran on ticket-driven delivery slowed every release and amplified risk. At the same time, a 185% year-over-year surge in product listings and rising omnichannel expectations required faster iteration, better system resilience, and improved customer experience. Without a reset, they risked losing both competitiveness and customer trust.

Solutions

TechBlocks transitioned the client’s operations from chaos to control by launching an AI-native Global Capability Center (GCC) and modernizing their retail technology stack end to end:

Launched an AI-native GCC in 90 days and executed a right-shoring strategy that cut monthly burn from $2.2M to $1M
Reverse-engineered critical applications and established a scalable reference architecture with embedded DevOps practices
Scaled the GCC to 260+ specialists across Canada, the U.S., and India, organized into 19 cross-functional PODs
Modernized delivery by splitting key monoliths into microservices, deploying CI/CD with DevSecOps, and introducing AI-powered SRE agents for 24/7 monitoring and automated incident response
Gave executives real-time visibility into sprint and release health through delivery telemetry
Optimized Cloud infrastructure as code (IAC) and FinOps, replatformed 12+ Canadian digital platforms, and enhanced omnichannel experiences with BOPIS, curbside pickup, last-mile delivery, and automated self-checkout

The GCC 3.0 Operating Model

AI-Native CoEs + AI-Augmented Pods + AI-Led Business Transformation

AI-Native CoEs

Capability centers keep delivery consistent & scalable across pods.

This is where standards, guardrails, and reusable platforms live, so teams move faster without trading speed for risk.

Cloud CoE

Standardized landing zones, security, observability, and FinOps controls reduce risk and accelerate delivery enabling you to ship faster on a cloud platform your board can trust.

Data & Analytics CoE

Governed pipelines and data quality controls improve decision accuracy, reduce rework, and unblock AI so you get one version of the truth across products and teams.

AI & ML CoE

Turn AI into a repeatable delivery capability with production-grade MLOps and model governance accelerate rollout and improve reliability at scale.

DevOps CoE

Increase release velocity without increasing incidents by running a governed software supply chain that strengthens DevSecOps, reduces rollback pain, and improves audit readiness.

Quality Engineering CoE

Reduce defects while releases accelerate through risk-based automation and AI-assisted testing that improve stability and protect conversion and revenue.

Platform Engineering CoE

Remove delivery friction with self-serve platforms by building golden paths and reusable services that boost engineering throughput and scale output without linear cost growth.

AI-Augmented Pods

Pods are built to ship outcomes, not tickets.

Each pod brings 10–30 cross-functional experts, augmented by AI copilots, AI-powered SRE, and automated QA. Performance is measured on the KPIs that matter: velocity, stability, conversion, and cost.