Arun Kumar Ramchandran, CEO of QBurst – Interview Series

Arun Kumar Ramchandran, CEO of QBurst, is a veteran technology and services executive with 25+ years of leadership experience spanning global consulting, large-deal sales, P&L ownership, and enterprise transformation. He became CEO in April 2025 and is responsible for leading QBurst across the business while shaping its strategy as an AI-led technology services and digital engineering firm. Prior to QBurst, he held senior roles at Hexaware Technologies (including President and GenAI consulting leadership), Capgemini/Sogeti (executive client and sales leadership), and Infosys and Virtusa, where he built and scaled business units, led major strategic programs, and drove growth across multiple geographies and industry verticals.

QBurst is a global digital engineering partner positioning itself around “High AI-Q,” combining AI-enabled delivery with applied AI and data-driven approaches to help enterprises modernize, build, and scale. The company emphasizes end-to-end digital experience engineering, modernization, and product engineering—supporting clients with initiatives such as composable digital platforms, conversational and customer experience solutions, and AI-ready data foundations—aimed at producing measurable outcomes like improved productivity, faster delivery, and stronger customer experiences across a broad international client base.

You’ve taken on the CEO role at QBurst after a long leadership career across Hexaware, Capgemini, Infosys, and other global organizations. What drew you to QBurst at this moment in its growth, and how does your background shape the direction you want to take the company?

The decision to join QBurst was a confluence of opportunity and potential. What drew me to QBurst was a combination of its inherent strengths and a unique market opportunity. Qburst’s entrepreneurial culture and success with cutting-edge technology in delivering to demanding clients both impressed and intrigued me.

With the convergence of disruptive changes and shifting environments across technology, industries, and regulations, a focused and differentiated firm like QBurst has a once-in-a-generation opportunity to break away from the pack and create a new technology & engineering services firm and delivery model for the AI-driven future.

With more than 25 years in tech-driven transformation across multiple industries, how has your experience influenced the way you think about scaling an AI-led services platform today?

I’ve observed that the main innovation and adoption of technology occur after the hype cycle has cooled down and real business problems start to get solved at the enterprise level. There are three specific points I would like to make here in terms of scaling an AI-led services platform.

1. Crossing the “PoC Stage.”

The biggest challenge I see today is crossing the PoC stage. Scaling requires a shift in mindset: we don’t just build AI; we provide production-grade solutions. At QBurst, we help clients grow past the PoC stage by focusing on agility – adopting new models with larger context windows rather than being locked into yesterday’s tech.

2. No AI Without a Strong Foundation

A lesson I’ve carried through every tech cycle – from the early days of mobile in 2009 to the cloud revolution—is that you cannot automate chaos. AI is only as powerful as the data feeding it. QBurst is driving growth by ensuring that the “boring but essential” work is done, namely Digital Modernization and Advanced Data Engineering.

3. The ‘High AI-Q’ Vision

To lead this change, we’ve repositioned ourselves as a ‘High AI-Q’ company. This reflects the integration of Generative AI and Agentic AI into all our core services, driving AI-native enterprise transformation. At QBurst, AI is not an additive feature but the core fabric of our strategy and delivery. It blends custom machine learning models with intelligent automation to ensure that as the business grows, its intelligence scales with it.

We’ve been forerunners since the dawn of Android, and we’re applying that same proactive DNA to lead the AI era. At QBurst, we aren’t just a tech-first company; we are a results-first partner whose growth is driven by customer satisfaction.

You’ve emphasized ‘High AI-Q’ as a defining framework for QBurst. How should enterprise leaders interpret this concept, and why is it an important differentiator in the current AI landscape?

QBurst’s ‘‘High AI-Q’’ journey is a conscious decision: running fast on the operational layer with AI-Driven SDLC, and making bold moves on the strategic layer with Managed Agents. Most importantly, it anchors the entire enterprise in the slow, foundational change of culture, values, and human capability.

While there are risks and concerns about AI, if implemented securely, AI can create abundance and innovation. Enterprises will see value not just in terms of Productivity, but also Growth and Transformation.

From a delivery standpoint, we’re seeing this play out daily through our AI-Driven SDLC framework. This is the “how” of transformation, where we’ve embedded AI into every stage of development, from user story generation to self-healing test scripts. The results      speak      for themselves:

• Time-to-Market: Significant reduction in development and testing cycles.
• Quality: A remarkable 25-35% reduction in post-release defects.
• Efficiency: A consistent 20-30% improvement in overall delivery.

The strategic layer is where we move beyond optimizing parts to optimizing the whole ecosystem. This demanded a rethink of our solution pillars, leading to the creation of Managed Agents, a fusion of Enterprise Agentic AI and Managed Services. For our clients, this means AI agents handle front-end and back-end tasks, workflows, and operations, driving both efficiency and continuous innovation. We’re not just delivering services; we’re orchestrating a seamless value network.

Many enterprises are accumulating what you call “AI Debt” — significant spending on GenAI pilots that don’t scale or generate value. What are the root causes of this problem, and how can organizations break out of that pattern?     

Enterprises accumulate “AI Debt” when GenAI investments stop at pilots and fail to scale into real business value. The root cause is what we call the retrofitting trap – an attempt to bolt GenAI capabilities onto legacy systems that were never designed to support AI-native workflows. In these environments, data, architecture, and governance simply aren’t ready, so pilots stall or break under scale.

This is compounded by a lack of foundational readiness. Many organizations rush to experimentation while bypassing essential investments in data strategy, data engineering, and governance. Without modernized data foundations and clear control frameworks, GenAI initiatives remain isolated proofs of concept rather than enterprise capabilities.

Breaking this pattern requires a shift to AI-first design. Instead of asking where AI can be added, organizations must design systems with AI outcomes in mind from day one by aligning architecture, data flows, and governance to support intelligent automation at scale.

Practically, this starts with data engineering. Building robust, well-governed data pipelines and models upfront creates the conditions for GenAI to scale sustainably. When the foundation is right, AI moves from experimentation to impact. Thus, AI Debt gives way to long-term value creation.

The traditional Time & Materials contract model is increasingly seen as misaligned with the realities of AI-driven efficiency. Why is this model becoming outdated, and how might approaches like “Managed Agents” or “Service-as-Software” provide a more sustainable path forward for enterprise IT?     

The traditional Time & Materials model was built for an era of resource scarcity, where value was directly tied to human effort. In the AI era, that assumption no longer holds. Intelligence and execution are becoming abundant, and as abundance increases, value shifts from effort to outcomes. AI fundamentally breaks the logic of hourly billing.

This is why the industry is moving toward outcome-based models. Metrics such as tickets resolved without human intervention or workflows completed end-to-end by AI provide clear, measurable value. These models treat capability as software, not labor, which can be described as “service-as-software.”

Approaches like Managed Agents and Service-as-a-Software offer a more sustainable path forward. They shift the focus from paying for effort to paying for intelligent results, enabling predictable costs, continuous improvement, and shared upside from automation. Managed Agents allow human engineers and AI agents to work together toward business goals, while Service-as-a-Software makes value measurable through outcomes rather than hours spent.

In an AI-driven world, the most aligned commercial models are those that reward results, not effort—creating a win-win for both enterprises and service providers.

Your ‘High AI-Q’ methodology focuses on Talent, Application, and Impact as the three critical layers for AI readiness. How can CIOs assess their maturity across these layers before scaling GenAI initiatives?

Before scaling GenAI, CIOs need a clear view of maturity across the three ‘High AI-Q’ layers of talent, application, and impact and not just the technology stack.

At the talent layer, maturity is about people-readiness. CIOs should assess AI skills, openness to change, and whether employees have secure, governed access to LLMs that enables safe experimentation.

At the application layer, the focus is on data and governance fundamentals such as data quality, architecture, security, and the maturity of policies and guardrails across LLM access and AI development practices.

At the impact layer, CIOs should evaluate use cases by effort versus business value. Identifying low-effort, high-impact opportunities enables early wins and supports an iterative approach to scaling GenAI.

For organizations still operating on legacy architectures, what foundational modernization steps are required to prepare for agentic workflows and AI-native delivery models?

Here are the three steps that can prepare organizations as they move towards agentic workflows.

1. Prioritize Data Foundation Modernization: For organizations operating on legacy architectures, the first step is modernizing the data foundation to enable metadata, lineage, and data quality metrics for siloed data. This ensures agents have the contextually rich, explainable data they need. The introduction of GenAI-based tools has made this modernization faster and straightforward.  While using GenAI with legacy architecture is possible, the token requirement for getting meaningful results would be extremely high.

2. Establish Enterprise Knowledge Layers: Organizations that have not modernized their systems will have a lot of accumulated knowledge undocumented. Building the knowledge layers to capture this transient accumulated knowledge within the system would be the second high-priority task. This is the missing layer in many organizations’ AI adoption journey.

3. Define Agent Boundaries and Ways of Work: The third step is to ensure that agents adhere to all best practices and security compliances currently followed in the organization. Governance frameworks, security policies, and observability frameworks enable agents to think and act effectively within the boundaries and the established ways of the organization’s work.

When preparing for “AI readiness,” what does that require beyond tooling — in terms of data, processes, governance, and team capabilities?

AI readiness goes far beyond selecting the right tools. In practice, AI adoption succeeds or fails on an organization’s ability to capture tribal knowledge, such as the unwritten processes, decision logic, and key relationships that exist only in employees’ heads. This knowledge must be documented in natural language that AI systems can reason with it, not just process data in isolation.

Data readiness is equally critical, but quality alone is not enough. What truly determines success is metadata which includes the context, lineage, and meaning behind the data. Without this, even the most advanced models produce shallow or unreliable outcomes.

Enterprise AI adoption also lags consumer AI for a reason: governance, security, and compliance are non-negotiable. These are not obstacles to work around, but requirements to build for. Organizations must establish trust frameworks that include guardrails, GenAI observability, explainability, and human-in-the-loop workflows to ensure AI outputs are safe, repeatable, and accurate.

Finally, teams need to develop AI intuition. Readiness means upskilling employees in AI literacy so they know how to prompt effectively, validate results, and audit outputs rather than blindly trusting a “black box.” AI works best when humans stay firmly in the loop.

The technology services sector is crowded with legacy players. What do you consider QBurst’s strongest differentiators when competing for enterprise transformation mandates?

QBurst differentiates itself in a crowded technology services market by pairing deep engineering expertise with the agility of a much smaller, innovation-led firm.

Our competitive edge is defined by five key pillars:

1. Engineering Depth with a Design Thinking Mindset – We do not just write code. We solve business problems through holistic, user-centered solutions.

2. Agility and Ownership – We are large enough to scale but lean enough to care – our flexibility and adaptation to quick changes is something that our clients have provided testimony for. Our teams take true ownership of client success. You would see the delivery ownership run up to the senior leadership level.

3. Cultural fluency: Whether it’s LINE mini-apps in Japan or integrated pricing systems for American grocery chains, we tailor not just the tech—but the experience—to each market.

4. AI-First Vision – We’re embedding AI into our delivery, our operations, and our client solutions—not as a buzzword, but as a capability multiplier.

5. Culture of Innovation and Experimentation – Our leaders are tech-savvy and love to solve customer problems using the latest and emerging tech. We are not afraid of failure and have created meaningful impact for our clients by taking a start-up approach in many cases.

We’re also not afraid to disrupt ourselves. We’re experimenting with outcome-based models, composable delivery frameworks, and co-innovation labs for enterprise clients.

Looking ahead three to five years, how do you expect enterprise IT operating models to evolve with the rise of agentic workflows and AI-native organizations, and what should leaders prepare for now?

The next wave of innovation will belong to those who can marry powerful AI capabilities with thoughtful systems of control, supervision, and trust. That’s why the emerging conversation around enterprise agentic frameworks feels so important—and so urgent.

Some of the key insights for me are:

• AI datacenter construction is accelerating, not slowing; sentiment in the datacenter world is highly optimistic, with capacity, demand, and investment all surging.
• Enterprise AI adoption will be slower than consumer AI (Organizational data is often messy, fragmented, and distributed across many systems rather than clean and centralized; today’s models are not yet accurate enough for highly specific company situations and functions without adaptation to each organization’s unique context; to unlock real value, models will need to be trained and fine-tuned on proprietary enterprise data, especially in the “last mile” of specific workflows and use cases)
• Before truly autonomous agents can thrive in the enterprise, there is a bigger challenge: building the equivalent of supervisory structures, approvals, and guardrails that exist for employees, which allows the human workforce to execute reliably and scale.

Leaders should prepare by keeping the following in mind:

• Agents should be treated like new hires, with clearly defined scopes, explicit oversight, and mechanisms to contain mistakes while they “learn” the organization’s written and unwritten rules.
• There is a need for an “agent bus” or coordination layer where agents register, obtain write permissions, and have their actions monitored by supervisory agents.
• Recreating the checks and balances that make human organizations robust will be critical to achieving safe, accurate, and reliable execution in an agentic enterprise world.
• Managing human talent and reskilling is another important aspect as the Human-AI interfaces and collaborations change with Agentic systems & frameworks.
• The most exciting frontier is the emergence of advanced Enterprise Agentic Frameworks—beyond what exists today—that can turn this vision into a practical, scalable reality, when combined with strong domain understanding and solutions.

Thank you for the great interview, readers who wish to learn more should visit QBurst.