Rewiring Contact Centers for AI-Driven Operations

By: Shikha Murali, ITES Operations & Process Excellence Leader

In today’s rapidly evolving operational landscape, leadership at scale demands more than technology adoption; it requires a fundamental shift in mindset and capability. For ITES Operations & Process Excellence leader, Shikha Murali, the focus is on reframing AI as an enabler of human potential, while applying a disciplined, data-driven approach to transformation that balances process rigor with agility across diverse global markets.

Shikha is a dynamic ITES professional with over 20 years of experience in operations management, client relationships, and business process transformation. A Six Sigma Master Black Belt, she specializes in driving process excellence, continuous improvement, and digital transformation initiatives. She has successfully led secure work-from-home implementations using advanced technologies, while consistently aligning business objectives with operational efficiency and enhanced customer satisfaction.

In conversation with Women Entrepreneurs Review Magazine, Shikha talks about how AI is transforming global operations by shifting mindsets, strengthening processes, and focusing on outcomes. She highlights the importance of combining technology with human skills to build resilient teams, improve efficiency, and deliver better customer experiences.

For deeper insights, read the interview below.

When you stepped into leading global operations at scale, what transformation challenge defined your early priorities, and how did you start reshaping operational excellence?

When I stepped into leading global operations at scale, the defining transformation challenge was navigating the structural shift driven by Artificial Intelligence.

Contact centres are no longer just operational engines — they are becoming digitally augmented ecosystems. The biggest early priority was not technology adoption itself, but mindset shift. Like many large-scale transformations, the real challenge was helping teams accept that change is inevitable and, more importantly, beneficial.

I have been transparent about what AI means for our industry. While AI will increasingly handle routine, transactional queries over the next few years, it is unlikely to replace human capability. Instead, it shifts the value curve. Basic service interactions will become self-serve, allowing our teams to focus on more complex, judgment-based customer needs.

My focus has been to contextualize this shift for operational teams — positioning AI not as a threat, but as an enabler of upskilling and role elevation. This transformation improves customer experience through faster resolution, while simultaneously driving structural cost efficiency.

Operational excellence in this new environment is no longer just about volume and productivity — it is about capability building, digital fluency, and designing human-plus-AI service models that are sustainable at scale.

As you began reshaping that vision, how did your Six Sigma Master Black Belt approach influence the first strategic shifts you introduced across teams and processes?

My Six Sigma Master Black Belt training fundamentally shaped how I approached the transformation. Six Sigma trains you to see patterns in data that are not immediately obvious and, more importantly, to approach change in a disciplined, structured way. That mindset was critical when integrating AI into operations at scale. AI deployment is often treated as a technology rollout. I viewed it as a process redesigning exercise. We applied a structured methodology — clearly defining problem statements, identifying high-impact use cases, breaking implementation into controlled phases, and establishing measurable success criteria. Another critical shift was bridging the gap between technical capability and operational reality. AI tools generate large volumes of data and recommendations, but value is created only when teams can interpret and act on those insights effectively.

So, the focus wasn’t just on enabling AI — it was on building analytical capability within the organization. We trained teams not only to use AI tools, but to question outputs, validate assumptions, and integrate insights into decision-making.

Six Sigma provided the framework; AI became the accelerator.

While executing those shifts, what complexities surfaced during global transitions, and how did you create alignment across diverse geographies and service lines?

During global transitions, the primary complexity was uneven AI maturity across markets.

Certain regions — the United States being a strong example — are significantly ahead in AI integration within their broader enterprise ecosystems. Clients operating in these environments tend to be more receptive to AI-enabled contact centre solutions. In contrast, other markets are still building foundational digital capability, which requires a different pacing and engagement model.

Another critical complexity has been data governance. As countries rightly increase scrutiny around consumer data protection, questions around data residency, cross-border storage, and regulatory compliance become central to AI deployment. In many cases, the constraint isn’t technological capability — it’s legal and policy alignment.

To create alignment across geographies and service lines, we focused on three principles:

Contextual deployment — adapting AI maturity models to each region rather than forcing a uniform rollout.

Strong partnership with legal and compliance teams — ensuring solutions were designed with regulatory realities in mind from the outset.

Shared value narrative — aligning stakeholders around common outcomes: improved customer experience, operational resilience, and cost efficiency.

Ultimately,

In industries like energy and BFSI, how do you sustain rigorous process discipline while enabling agility and innovation within large operational ecosystems?

Industries like Energy and BFSI operate in highly regulated environments, where process discipline is non-negotiable. At the same time, operational ecosystems must remain agile to respond to evolving customer expectations, regulatory shifts, and technological disruption. For me, the balance lies in being agile in thinking, but process-driven in execution. Process discipline ensures repeatability, auditability, and regulatory compliance — particularly critical in regulated sectors. However, agility comes from how we design and deploy change. Large-scale transformation must be broken into clear, digestible problem statements at the team level, with defined ownership and measurable outcomes.

My approach has been strategic and outcome-driven:

• Clearly define the objective.
• Decompose it into actionable components.
• Establish governance and accountability.
• Create feedback loops for rapid course correction.

In regulated sectors, every decision must pass two filters — regulatory compliance and customer impact. Innovation cannot be at the expense of either. Sustainable agility comes from building guardrails that allow experimentation within defined risk boundaries.

Ultimately, operational excellence in Energy and BFSI is about institutionalizing discipline while empowering teams to innovate responsibly.

As analytics and automation evolve, how are you redefining continuous improvement from efficiency programs to strategic value creation for clients?

As analytics and automation mature, continuous improvement can no longer be defined purely by efficiency gains. It must translate into measurable strategic value for clients. Artificial Intelligence is fundamentally reshaping the BPO landscape. We are no longer simply service delivery hubs processing transactions — we are partners in value creation. Historically, commercial models were built around FTE-based pricing. Today, we are seeing a clear shift toward outcome-based pricing (OBP) and even share-based pricing (SBP), where commercial alignment is tied directly to performance metrics or enterprise value indicators. The conversation has evolved from:
“Can you process this transaction?”
to
“Can you resolve this efficiently, improve customer experience, and share in the upside?”

Recently, I worked on a commercial construct where our compensation was partially linked to improvements in client performance metrics tied to market outcomes. That kind of model fundamentally changes the nature of the partnership — it requires deeper operational integration, analytics maturity, and shared risk appetite.

As simpler queries increasingly move to automation, outsourced environments are handling more complex, judgment-intensive interactions. This creates an opportunity. Handling complex transactions onshore is expensive; by combining AI-enabled workflows with skilled global operations, we can deliver both cost efficiency and improved resolution quality.

Continuous improvement, therefore, is no longer about incremental productivity. It is about:

• Embedding analytics into decision-making
• Aligning commercial models with outcomes
• Sharing risk and reward
• And positioning operations as a strategic lever for enterprise growth

That is the shift from efficiency programs to strategic value creation.

LAST WORD: Advice on Driving Large-Scale Operational Transformation

My advice to leaders driving large-scale operational transformation is threefold.

First, stay intellectually current. The AI and automation landscape is evolving rapidly — and so are client ecosystems. Leaders don’t need to be technologists, but they must be informed enough to ask the right questions. The quality of your questions will determine the quality of your deployment at scale.

Second, bridge the gap between technology and practical execution. Transformation fails not because of poor tools, but because of poor translation. Leaders must convert technical capability into operational clarity — defining use cases, setting guardrails, and ensuring teams understand not just how to use tools, but why they matter.

Third, build resilience through capability, not control. Large-scale change creates uncertainty. The most sustainable impact comes from upskilling teams, creating psychological safety around experimentation, and establishing clear accountability structures. When teams understand the direction and feel equipped to navigate it, resilience follows naturally.

Operational transformation is not about speed alone. It is about disciplined execution, adaptive thinking, and delivering measurable impact that endures beyond the initial rollout.

Disclaimer: The views expressed in this article are solely those of Shikha Murali.