TECHNOLOGY
Beyond the AI Hype: Abhishek Rungta on Entrepreneurship, Innovation and Business Value
INT. Founder Abhishek Rungta reflects on entrepreneurship, AI adoption and digital transformation, explaining why technology must move beyond hype to deliver measurable business value and sustainable growth.
Beyond the AI Hype: Abhishek Rungta on Entrepreneurship, Innovation and Business Value
Q1. INT. began its journey in 1997, when India's technology industry was still evolving. What inspired you to start the company, and how has your vision changed as INT. has grown into a full-stack digital transformation organisation?
Interestingly, INT. was not born out of a grand business plan. My entrepreneurial journey began with experimentation, setbacks and a series of unexpected circumstances. In 1996, while I was in college, I attempted to start an email service business. That venture failed before generating any revenue. However, the experience introduced me to internet technologies and encouraged me to explore what was possible with the emerging digital ecosystem. I began learning through practical assignments, understanding how internet connectivity worked and identifying opportunities around web technologies.
A motorcycle accident subsequently became an unexpected turning point. During my recovery, I spent time learning animation and HTML, which eventually led me towards website development. In September 1997, I attended a computer exhibition in Kolkata, persuaded an exhibitor to share some space and began offering website design services. That experience brought my first customers and laid the foundation for Indus Net Technologies. Even the company's name emerged spontaneously during that period. Looking back, the journey was shaped as much by curiosity, timing and experimentation as by deliberate planning.
Over the years, INT. has evolved from a small web services venture into an organisation working across enterprise engineering, AI and data, cloud, cybersecurity and digital experiences. We have also expanded into technology products. My perspective has evolved with the business. In the beginning, the challenge was finding customers and delivering solutions. Today, it is about building an organisation that can solve complex enterprise problems and continue adapting as technology changes. The underlying principle remains the same: technology must create genuine business value.
Q2. Building a technology company over nearly three decades comes with significant changes in markets, customer expectations and technology. What have been the most defining turning points in INT.'s journey, and which leadership decisions helped the company navigate them?
One of the most important lessons I have learned is that the business model that makes a company successful at one stage may eventually become the very thing limiting its growth. When we started, the internet itself was a new opportunity. Website development and related services were meaningful businesses because organisations were only beginning to establish their digital presence. Over time, those requirements evolved into enterprise applications, mobile platforms, cloud infrastructure, data engineering and now AI-driven business transformation.
Each transition required us to question our existing assumptions. We had to recognise when a service was becoming commoditised, when customers needed more sophisticated capabilities and when continuing with a familiar model would restrict future growth. Some of these decisions involved moving away from established revenue-generating activities to invest in capabilities that were more relevant to the next stage of the market. Reinvention is rarely comfortable, particularly when the existing business is still performing reasonably well.
Another important development has been our expansion beyond traditional technology services into products, including OneSpace and MobiLearn. This reflects our broader evolution towards developing technology capabilities and solutions that address specific business requirements. From a leadership perspective, the defining decisions have involved building complementary capabilities, investing in people and accepting that the organisation must change as it grows. The objective is not simply to survive technology cycles but to remain relevant to customers throughout them.
Q3. INT. has evolved from traditional technology services to an integrated model covering AI, data engineering, cloud, cybersecurity and digital experience. What drove this evolution, and why do you believe the definition of a digital transformation partner needs to change?
The traditional technology services model was largely centred on execution. A customer defined a requirement, a technology provider developed the solution, and success was measured by whether the project was delivered according to the agreed specifications. That approach made sense when technology requirements were relatively contained. Today, however, enterprise problems are interconnected, and a technically successful implementation does not necessarily translate into a meaningful business outcome.
Consider a customer experience platform. Its effectiveness depends not only on the interface but also on the underlying data, enterprise applications, integrations, infrastructure and security architecture. If those components do not work together, the organisation may have a functional application without achieving the intended improvement in customer experience or operational efficiency. This is why I believe technology partnerships must evolve from delivery-centric relationships towards greater accountability for business outcomes.
At INT., our full-stack approach reflects this interconnected reality. AI, data engineering, enterprise applications, cloud, cybersecurity and digital experience cannot operate as isolated capabilities. The emergence of Agentic AI makes this even more important because autonomous systems need access to reliable information, connected workflows and appropriate governance. Technology partners must understand the customer's operating environment and help ensure that the solutions they implement produce measurable results.
Q4. Artificial intelligence has become a major investment priority for Indian enterprises, yet many organisations struggle to move beyond pilot projects. What are the biggest reasons behind this gap, and what should CEOs prioritise to achieve measurable business outcomes?
At INT., we have undertaken more than 40 AI pilot projects, and that experience has reinforced an important observation: the difficulty is often not getting AI to demonstrate a capability, but making that capability commercially and operationally viable. We have seen organisations encounter challenges involving insufficient structured data, accuracy concerns, infrastructure costs, integration complexity and the availability of skilled people. A demonstration may work well in a controlled environment, but deploying the same solution across real business processes introduces an entirely different set of requirements.
Another recurring problem is that organisations begin with the technology rather than the business problem. There is pressure to introduce AI because competitors are experimenting with it or leadership expects an AI initiative. However, without a clearly defined outcome, even a technically impressive pilot can struggle to justify further investment. We have also observed that companies sometimes underestimate the cost of making AI solutions reliable, secure and scalable. These issues can create a significant gap between the excitement surrounding AI and the actual returns organisations achieve.
My advice to CEOs is to prioritise effectiveness before efficiency. Before asking how AI can automate a process, examine whether the process is necessary, whether it can be simplified and what outcome the organisation actually needs. Automating an inefficient or unnecessary workflow may only make the wrong activity happen faster. Start with a measurable business problem, establish a baseline and evaluate the full cost of implementation. Involve business leaders, technology teams and finance from the beginning. The objective should be to improve decisions, reduce costs, strengthen customer experiences or create new value, rather than accumulate AI pilots.
Q5. You have spoken about the growing importance of Agentic AI. How do you see it changing enterprise operations, and what distinguishes organisations that are genuinely prepared for autonomous AI systems from those simply experimenting with the technology?
Agentic AI represents a significant transition because we are moving from systems that primarily generate information to systems capable of executing actions within defined boundaries. A conventional AI assistant may retrieve information, summarise documents or recommend a response. An AI agent connected to enterprise applications could potentially coordinate several steps in a workflow, retrieve relevant data, initiate permitted actions and escalate exceptions to human teams. This creates opportunities in customer service, financial operations, insurance processes and internal enterprise functions.
However, the moment an AI system begins taking actions, the nature of organisational responsibility changes. It is no longer sufficient to evaluate whether the model produces an accurate answer. Leaders must understand which systems it can access, what decisions it is authorised to make, how its actions are recorded and when human approval becomes necessary. An organisation with fragmented data, unreliable integrations or weak governance cannot become AI-ready simply by deploying an agentic platform.
I believe genuine readiness is determined by architecture, governance and accountability. Enterprises must establish clear permissions, audit trails, security controls and mechanisms for intervention. A useful question for any CEO is whether the organisation can identify and correct an inappropriate action taken by an autonomous system. The most successful enterprises will not necessarily be those giving AI the greatest independence. They will be those designing autonomy around clearly defined business objectives and operational safeguards.
Q6. INT. works across sectors such as banking, insurance, life sciences and retail. Which industry-specific challenges are creating the most meaningful opportunities for AI-led transformation, and how do technology requirements differ across these sectors?
One of the most important lessons from working across industries is that enterprise AI cannot be approached as a standardised solution. Different sectors operate within distinct regulatory environments, business processes and customer expectations. In banking, for example, opportunities are emerging around risk intelligence, fraud prevention, customer experience and operational efficiency. However, these applications depend on reliable information, secure systems and the ability to integrate AI with existing financial infrastructure.
Insurance presents another set of challenges because information is often distributed across underwriting, policy administration, claims processing and customer engagement. AI can help analyse information and support faster decisions, but its effectiveness depends on how well these functions communicate. Life sciences introduces additional complexity involving scientific data, clinical workflows, privacy requirements and human oversight. Retail, meanwhile, is increasingly connecting customer intelligence with demand forecasting, inventory planning and omnichannel experiences.
Although the applications differ, the underlying challenge is remarkably similar. Organisations need to connect fragmented information, modernise existing systems and ensure that AI operates reliably within established business processes. This is why industry understanding is becoming as important as technical expertise. The real opportunity is not simply introducing AI into different sectors but understanding how it can address the operational problems that matter most within each industry.
Q7. As enterprises increasingly adopt AI-driven solutions, concerns around data privacy, cybersecurity, regulatory compliance and accountability are becoming more important. How should business leaders balance the need for faster innovation with responsible technology adoption?
I do not see responsible AI and innovation as competing priorities. In fact, weak governance can become one of the biggest barriers to innovation because organisations hesitate to scale systems they cannot adequately monitor or control. When AI is introduced into sensitive business processes, leadership teams need to understand how information is accessed, how decisions are generated, what actions are permitted and who remains accountable for the outcomes.
I believe organisations should adopt a risk-based approach rather than applying identical controls to every AI application. A tool that summarises internal documents presents a different level of risk from a system influencing a financial transaction, an insurance decision or a healthcare-related workflow. The level of testing, monitoring, access management and human oversight should reflect the potential consequences of an error.
Cybersecurity, privacy and governance should therefore influence system architecture from the beginning rather than being addressed after deployment. Enterprises also need clear accountability across business and technology functions. The objective is not to slow innovation unnecessarily but to create the conditions in which it can scale responsibly. As intelligent systems become more deeply embedded in operations, trust and reliability will increasingly distinguish successful implementations from short-lived experiments.
Q8. With AI becoming more capable, how do you see the relationship between human expertise and intelligent automation evolving? What skills and organisational changes will be most important for building an AI-ready workforce?
I believe the most significant workforce transformation will involve redesigning how people and intelligent systems work together. AI can increasingly support information retrieval, repetitive analysis, documentation and structured workflows. This creates opportunities for employees to devote more attention to problem-solving, customer relationships, complex decisions and activities requiring contextual judgement. However, organisations should not assume that providing employees with AI tools will automatically improve productivity.
Technology changes the way work can be performed, but businesses must also reconsider their processes, responsibilities and performance expectations. If an organisation continues operating through the same inefficient workflows while adding AI tools, the productivity gains may remain limited. Leaders need to examine which activities should be automated, which should be redesigned and where human judgement remains essential.
The priority should be building AI fluency across the organisation rather than confining it to a specialised technical team. Business leaders need to understand what AI can realistically deliver, employees must learn how to work with intelligent systems, and technology teams need stronger capabilities in integration, security, data engineering and governance. Managers will also need to become more effective at supervising work performed jointly by people and AI agents. Ultimately, workforce development must advance alongside technology adoption if organisations want sustainable results.
Q9. Having built and led INT. through multiple technology cycles, what have been your biggest entrepreneurial lessons, and what advice would you give founders looking to build sustainable technology businesses in India?
The first lesson is that entrepreneurship is far less predictable than it appears when people look back at a successful company. INT. itself emerged through a series of unexpected events. My first business failed, an accident introduced me to new technical skills, and an opportunity at a computer exhibition helped establish the company. Luck and timing certainly played a role. However, what matters is being curious enough to experiment, prepared enough to recognise an opportunity and persistent enough to continue when something does not work.
Starting as a bootstrapped business also taught me to think carefully about capital allocation. When resources are limited, every hiring decision, investment and expansion must be considered in relation to the value it can create. Financial discipline is important, but the deeper lesson is learning to distinguish between activities that contribute to long-term growth and those that merely create the appearance of progress. Revenue is important, but customer trust, profitability, delivery quality and the ability to survive difficult periods are equally meaningful measures of business strength.
Perhaps the most difficult lesson is that founders must evolve as their organisations grow. The skills required to build a company from nothing are not necessarily the same skills needed to lead a much larger enterprise. In the early stages, being involved in every decision can be an advantage. Later, that same behaviour can make the founder the biggest bottleneck in the organisation. You have to learn to delegate, build leadership capacity, trust people and sometimes step away from responsibilities you once handled personally. My advice to entrepreneurs is to remain adaptable, question their assumptions and recognise that building a sustainable company also requires continuously reinventing themselves.
Q10. Looking ahead to the next five years, what is your vision for INT., and which emerging technologies or changes in enterprise expectations do you believe will have the greatest influence on India's digital transformation industry?
I believe the next five years will fundamentally change how enterprises evaluate technology partners. Organisations are becoming less interested in isolated implementations and more focused on whether technology investments improve business performance. Artificial intelligence will accelerate this transition, particularly as generative and agentic capabilities become more deeply integrated with enterprise applications, information systems and operational workflows.
However, I do not believe the defining opportunity will be limited to building increasingly sophisticated AI models. The greater challenge will be integrating intelligence into the way organisations actually operate. Businesses will need reliable data, connected applications, appropriate governance and teams capable of translating technological possibilities into practical improvements. This will create opportunities for technology companies that understand both engineering complexity and business execution.
For INT., the priority is to continue strengthening our integrated capabilities across AI, data, enterprise engineering, cloud, cybersecurity and digital experience while evolving our products and solutions in response to changing market requirements. Having built the company through several major technology transitions, I believe long-term relevance comes from recognising what is changing without abandoning the fundamentals of good business. The companies that succeed will be those that combine innovation with disciplined execution, develop strong teams and remain accountable for the value they create for customers.
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