Biddappa Muthappa, Co-Founder & CEO, Sash.Ai

Biddappa Muthappa is an AI and data science expert with extensive experience driving transformative AI solutions across industries such as Retail, Technology, and AdTech. He has led high-impact teams, scaled Data Science centers, and delivered measurable business growth. Recognized with multiple awards, he focuses on human-centered AI, mentoring emerging talent, and shaping innovative, real-world AI strategies.

In a recent interaction with M R Yuvatha, Senior Correspondent at siliconindia Startupcity, Biddappa Muthappa shared his insight that GCC growth relies on human-centered AI leadership, not just technology.

Global Capability Centers (GCCs) are shifting from cost arbitrage to becoming strategic innovation hubs, and artificial intelligence (AI) is a key catalyst for this transformation. The challenge, however, is not just piloting proof-of-concepts (POCs) but scaling adoption responsibly and sustainably. For GCC leaders, this means navigating governance, workforce readiness, and organizational trust in ways that accelerate not hinder innovation.

Governance That Accelerates, Not Delays

Governance is often misunderstood as a brake on innovation, but in reality, it should act as a steering mechanism. GCCs must build frameworks aligned with their specific industries and regulatory environments. For instance, BFSI operates under very different compliance expectations than healthcare, making a one-size-fits-all model ineffective.

Instead of treating governance as an afterthought, leaders should embed it into the development lifecycle, ensuring AI systems are transparent, explainable, and reliable. Done well, governance does not slow adoption it builds trust and speeds deployment by reducing risks before they escalate.

 Ethical AI should never be treated as a compliance checkbox, it is, in fact, a prerequisite for adoption and growth. By embedding transparency, fairness, and accountability as upfront design parameters rather than leaving them for post-deployment audits GCCs can create systems that are both trustworthy and scalable.

Responsible AI is not just about cultural alignment, it is about building the organizational confidence required to scale AI use cases into production. Governance frameworks must balance global consistency with local relevance, ensuring AI decisions align with both enterprise-wide values and regional realities. When done right, ethical AI governance delivers measurable ROI by reducing risks, accelerating trust, and improving model performance through cleaner, bias-aware data practices.

Reskilling as a Growth Enabler

AI is reshaping job roles across GCCs. The traditional data scientist profile, for example, is evolving into the full-stack AI engineer, with responsibilities spanning engineering, deployment, and model lifecycle management. Leaders must anticipate such shifts and redesign workforce strategies accordingly.

Reskilling should extend beyond AI-specific roles to create adjacent skills and new functions that complement AI and add strategic value. This requires a commitment to AI literacy across all levels of the organization, preparing employees to adapt and thrive in a future where human and machine collaboration defines success. Far from being a cost burden, reskilling must be treated as a core driver of adoption velocity and organizational resilience.

Responsible AI governance and human-centered leadership are essential for GCCs to scale innovation, build trust, and unlock sustainable competitive advantage.

Human-Centered AI Leadership

AI has the potential to be either a multiplier of inefficiencies or a catalyst for scale and the difference lies in leadership. Human-centered AI leadership ensures that technology is not deployed in isolation but anchored in a broader redesign of processes, culture, and governance. Instead of applying AI on top of legacy inefficiencies, leaders can use it as an opportunity to reimagine workflows end-to-end, integrating automation, data intelligence, and human expertise.

 This shifts AI from being a tool that merely accelerates existing systems to one that reshapes operations, enabling GCCs to deliver new value at scale. The emphasis here is not on technology alone, but on aligning AI with human judgment, trust, and creativity so that it becomes a true enabler of transformation.

One of the biggest risks in AI adoption is not technological it is human bias. Left unchecked, leadership assumptions and cognitive blind spots can seep into data models, decision frameworks, and governance, turning AI into a mirror of personal or organizational biases.

Human-centered AI leadership addresses this risk by fostering diverse decision-making forums, stress-testing AI outputs, and encouraging open challenges from employees across levels and functions. Leaders who model humility acknowledging where they might be wrong create an environment where biases can be surfaced and corrected before they scale.

Also Read: How GCCs Are Quietly Powering Global Tech Strategy

Balancing Machine-Driven Speed with Human Creativity

As AI takes on more cognitive functions, GCC leaders must evolve beyond traditional management skills and embrace new leadership capabilities that balance machine-driven efficiency with distinctly human strengths. Every leader must develop at least a high-level understanding of AI’s architecture, opportunities, and limitations. This knowledge is essential to navigate trade-offs, make informed decisions, and engage confidently in AI-driven transformation.

Equally important is adaptive risk-taking. GCC leaders must be willing to experiment with emerging technologies, evaluate their pros and cons, and adapt quickly without being paralyzed by fear of disruption. Partnerships with startups, academia, and global teams play a critical role here, allowing GCCs to stay at the forefront of innovation while continuously refreshing skills and capabilities across the workforce.

The leadership quality that will differentiate the best from the rest is empathetic foresight. As AI changes the nature of work, leaders must anticipate human concerns such as job displacement, erosion of creativity, or fear of irrelevance. By addressing these proactively through reskilling pathways, transparent communication, and recognition of uniquely human contributions leaders can preserve trust and foster creativity even in highly automated environments.

Cultural Reinvention Through AI

As GCCs scale, the pressure from parent organizations is shifting from efficiency toward strategic decision-making and innovation. Leaders cannot view AI integration purely as a technical rollout they must approach it as a cultural reinvention of work. The true competitive advantage will not come from speed alone, but from the ability to embed AI in a way that amplifies human strengths such as trust, collaboration, and creativity.

Trust becomes a strategic asset in this journey trust in data, in AI systems, and most importantly, in leadership. Building this trust requires transparency in how AI is used, clarity on its limitations, and consistent communication about its role in decision-making. Collaboration becomes a competitive edge when leaders encourage human-AI teaming rather than substitution. By positioning AI as a collaborator, employees are empowered to focus on problem-solving, storytelling, and creativity while machines handle scale, speed, and pattern recognition.

Looking Ahead

When leaders embrace this mindset, AI integration moves beyond automation and efficiency into the realm of cultural reinvention. It creates organizations where human creativity is unleashed, innovation thrives, and competitive advantage is sustained. Far from slowing down the race for adoption, this balance between machine-driven speed and human-centered strengths allows GCCs to scale AI in ways that are both fast and future-proof.