Digital engineering firm QBurst experienced a leadership transition last year when Arun ‘Rak’ Ramchandran assumed the role of CEO. Taking the helm with existing strengths in digital experience capabilities, Ramchandran believes that his leadership has laid the groundwork for substantial growth. “This set the firm up for breakthrough growth,” he stated.
Breakthrough growth, according to Ramchandran, entails tripling revenues over the next four to five years and doubling the workforce. This expansion, he notes, will leverage AI-driven disruption, non-linear business models, increased investment in intellectual property and platforms, and a focus on enhancing customer value. The financial objectives also underscore a broader industry trend wherein revenue growth is no longer solely dependent on a linear increase in human resources.
The ‘5x5x5’ Strategy and High AI-Q:
QBurst’s prior successes in technology, client engagement, and team collaboration inform Ramchandran’s targets, which concentrate on enterprise and marketing clients. To “aspire big and different, and accelerate growth,” his strategy encapsulates “5x5x5—5 geographies, 5 verticals, and 5 solutions for focus.”
Additionally, QBurst introduced a branding initiative termed High AI-Q. Ramchandran identified two notable observations: first, most of QBurst’s projects were on customer-facing fronts, creating innovations that translated directly into tangible business results. The second insight was cultural; the engineers at QBurst were already well-versed in AI. Developers utilized tools like Copilot, while testers relied on AI for automation, illustrating that AI was inherently woven into the company’s operational fabric rather than being an external addition.
This built-in expertise manifested in significant outcomes: AI-assisted solutions improved patient monitoring and compliance within healthcare, decreased product returns in retail, and optimized energy consumption in industrial manufacturing. These applications were not merely experimental; they represented solid, production-grade solutions deployed across various sectors, including hospitality and healthcare.
Recognizing this intrinsic capability, the leadership articulated it through the High AI-Q branding. This concept, analogous to IQ and EQ, signifies QBurst’s fusion of deep technical knowledge, strong client understanding, and applied AI proficiency. Now trademarked, High AI-Q symbolizes a strategic emphasis on AI throughout the organization, encompassing talent development, internal tools, service delivery, and industry-specific solutions.
Agentic AI: From Model to Muscle
Expectations surrounding AI in enterprises are shifting from trial phases to yielding real business results. “Agentic AI will become table stakes,” Ramchandran asserts. As technology evolves from mere information processing to actionable insights, enterprise leaders’ inquiries are altering. Rather than focusing solely on which large language model to implement, leaders are now more concerned with identifying AI-native platforms that can manage entire business workflows.
CIOs are concentrating on measurable outcomes: determining which workflows can benefit from AI agents, how to gauge value, and the speed at which investments yield tangible returns. Ramchandran highlights that the return on investment (ROI) now plays a crucial role in enterprise AI decision-making, leading managers to scrutinize use cases, evaluate risks, and invest in workforce readiness for scalable and responsible AI integration.
At QBurst, this evolution is already taking place. The firm has begun deploying AI agents across HR and finance while incorporating them into service delivery, especially in areas like testing, coding, and engineering.
Mid-size Firms’ Advantage:
With AI democratizing growth opportunities amid a market largely dominated by larger organizations, mid-sized firms like QBurst must distinguish themselves to capitalize on emerging prospects. Ramchandran notes, “A firm like QBurst can be more agile, make quicker decisions, and maintain closer relationships with our customers.”
He predicts a potential cannibalization of conventional IT service revenue streams as AI adoption accelerates, suggesting that larger firms may be slower to adapt due to existing business models. Conversely, mid-sized companies see this shift as a unique chance to innovate and redefine technology and engineering service delivery for an AI-centric landscape.
Focused Markets, Outcome-Led Growth:
QBurst’s strategy for sustainable development hinges on concentration rather than expansion, with North America identified as a primary growth market, and Japan as a strategic focus. “We have a premium position in Japan, leading in luxury commerce and partnering with high-end fashion and retail brands,” Ramchandran explains.
The company aims to enhance investments in data, AI, and cloud technologies, leveraging its established strengths—robust engineering capabilities, adaptable business models, and results-oriented client engagements. Ramchandran believes these investment strategies will be key to expanding the client base and achieving long-term growth, with the High AI-Q positioning playing an essential role.
AI’s Industry Narrative is Changing
In the next 12 to 18 months, CIOs are expected to face increasing pressure to go beyond pilot programs and demonstrate clear value from AI investments. “We are transitioning from what I would call AI fascination and AI novelty to a period of AI accountability,” Ramchandran states.
As the AI infrastructure landscape develops, he emphasizes the need for technology leaders to prioritize foundational elements, beginning with readiness in enterprise data and tighter integration between technology initiatives and genuine business challenges.
The introduction of Agentic AI brings an additional level of responsibility; these AI agents, Ramchandran notes, will need to be managed similarly to employees—registered, authorized, trained, onboarded, and continuously supervised within established governance frameworks. For CIOs and CTOs, this represents a significant shift in designing and managing digital workforces.
Ramchandran expresses a realistic view of the industry’s AI narrative: “A lot of people are using the term AI-first,” he remarks. “But it’s going to be results-first. Technology is a means to an end. AI is a means to an end.”
