The Cognitive Enterprise Advantage: How GCCs Can Learn to Think in Systems

Global capability centers (GCCs) have pushed process-level automation as far as it will go, and the returns are tapering. For years, they tightened workflows, improved KPIs and embedded analytics inside individual functions. That plateau, says Rohail Qadri, President of Professional Services and IT Consulting at Trigent Software, is why GCCs now face a choice between refining fragments and building intelligence as a connected system.

Qadri says the implications are clear. GCCs that keep tuning isolated processes risk becoming “smart in pockets and dumb at scale.” The next phase, he argues, belongs to organizations that treat intelligence as a system that moves across functions and reshapes how the enterprise senses and responds.

Automation accelerates workflows by following predefined rules. A cognitive system, in contrast, learns, adapts and interacts. “Automation makes a task faster,” Qadri says. “Cognitive systems redesign how the entire business senses, understands and responds at scale.”

He says the widening gap between the two approaches explains why the old divide between rules-based tasks and judgment-led work no longer holds. Today, pre-configured accelerators and what Qadri calls Trigent’s “GCC-in-a-Box” model allow mid-market enterprises to deploy cognitive centers from day one. 

Metrics That Miss the Point

Qadri believes the issue starts with how performance is measured. GCCs lean on KPIs that track cycle time, accuracy, throughput and cost per transaction. These indicators reward functional efficiency but hide the value that emerges when insights flow across domains. 

He says the metric that matters now is Time to Intelligence. It captures how quickly a team can move from identifying a problem to deploying a working AI solution.

Qadri points to Ops Excellence platforms such as QMetry 360 that measure innovation velocity rather than labor efficiency. A governance center might deliver flawless SLA performance, but if it takes a quarter to move from concept to deployment, it is celebrating the wrong victories. GCCs need measures that reflect system adaptability, reusable insights, model evolution and the quality of human and AI collaboration.

Digitization compounds this problem. Many GCCs adopt digital tools and assume they have transformed. Digitization converts manual steps into digital formats but often preserves the underlying flaws. Transformation requires redesigning value creation through AI-native models and connected workflows. Organizations that confuse the two lock in technical debt and miss the opportunity to build new capabilities.

Qadri argues that GCCs need experimentation environments where they can prototype and validate cognitive models. Trigent’s AXLR8 Labs is designed for this. He says real transformation begins with redesigning work end-to-end and allowing intelligence to flow across the value chain.

How GCCs Shift From Processes to Systems

Future GCCs will link models across functions and locations to create an enterprise that learns and responds as one system.

If AI rewards systems thinkers rather than process owners then GCCs need to organize around outcomes. Systems thinkers understand how signals move across supply chain, finance, customer insights, risk and operations, and how choices in one area reshape performance elsewhere.

Qadri recommends building cross-functional intelligence pods that focus on business outcomes rather than narrow KPIs. Data has to be managed as a shared product with clear ownership and quality standards. Governance should make it easier to move from an idea to a working trial and then to a call on whether it scales.

He says pod-based delivery is emerging as the most effective structure. These teams bring together data engineers, AI specialists, and DevOps talent. They work with real autonomy and can build new capabilities in ten to twelve weeks instead of the twelve to eighteen months many enterprises still take.

Architecture councils provide direction on interoperability, while pods focus on system behavior, feedback loops and raising intelligence across the workflow.

Why AI Has to Learn Across the System

AI has to learn from the whole system and not just from individual processes.

Interconnected intelligence means models that share signals in real time so decisions in one area strengthen decisions in another. A demand spike picked up by forecasting should shape procurement, inventory allocation, financial projections and marketing. A shift in customer sentiment can also strengthen churn models, inform product decisions and refine sales outreach. These flows only work when data moves cleanly across the enterprise, which calls for unified pipelines, shared semantic layers and continuous learning.

Tools such as John Snow Labs’ Generative AI Lab and Spark NLP support this kind of environment by letting models draw on shared knowledge and improve through feedback. Each prediction becomes a signal that refines other models, creating an intelligence fabric that adapts as conditions change.

Building Systems That Learn Together

GCCs that build cognitive loops will shape how global enterprises sense, decide and act in real time.

The move from isolated models to interconnected ones starts with a unified data foundation. GCCs need consistent definitions, standard APIs and engines that coordinate workflows and track model updates. A shared coordination layer helps models exchange insights cleanly across HR, finance, logistics and other domains. Retrieval-augmented techniques let models draw on enterprise knowledge without retraining. Governance ensures the system learns as one rather than through scattered experiments.

Learning systems require strong governance and ethics. Qadri stresses the need for audit trails, clear explanations and visibility into model behavior. He rejects the idea that organizations must choose between cognitive capability and regulatory safety. Compliance frameworks such as ISO certifications, SOC2 security standards and GDPR protections can be built into the foundation. Human oversight remains critical for high-impact decisions and for addressing bias or drift.

Why Fragmented Automation Will Fall Behind

GCCs that stay in siloed automation risk being overtaken by platforms that already think across the enterprise.

Qadri sees GCCs shifting from delivery centers to intelligence hubs. Near-term priorities include automating decisions and linking predictions across functions so that insights in one area strengthen choices elsewhere. As data standards improve and shared meanings settle, models will trade signals that shape planning, forecasting, customer management and resilience.

Over time GCCs will run decision ecosystems that learn continuously. This shift requires leaders to prioritize learning velocity over short-term efficiency and to guide teams as system architects rather than process managers. The next generation of GCCs will operate lean innovation hubs that can launch high-value cognitive capabilities in weeks rather than years. GCCs that make this transition will influence enterprise strategy. Those that remain in fragmented automation will be tied to an older operating model.

This article is brought to you as part of a branded content partnership with Trigent Software, which supports global enterprises with AI, cloud and data-led GCC transformation.