Lessons from INT.’s 28-year journey of reinvention, enterprise AI, and long-term value creation
Technology has a strange way of making yesterday’s advantage feel ordinary.
Every few years, the industry finds a new center of gravity. Infrastructure gave way to the cloud. Cloud accelerated digital transformation. Digital transformation made data central to business decisions. And now, artificial intelligence is reshaping how enterprises think, operate, compete, and grow.
For business leaders, this pace of change creates pressure. For technology companies, it creates an even deeper challenge: how do you stay relevant when the market itself keeps changing?
This question was at the heart of a recent conversation between Maria Perez and Abhishek Rungta, Founder & CEO of Indus Net Technologies (INT.), for The Guardian’s India Special Report. The discussion explored INT.’s nearly three-decade journey, its evolution through multiple technology waves, its approach to enterprise AI, and the growing opportunity for India-UK technology collaboration.
But beyond the interview, one larger idea stood out clearly:
Sustainable technology companies are not built by chasing every trend. They are built by learning how to evolve before the market forces them to.
Executive Summary
INT.’s journey offers a practical view of what it takes to remain relevant in an industry defined by constant change. Since 1997, the company has evolved from digital infrastructure to digital engineering, digital transformation, and now enterprise AI transformation.
The key themes from this perspective are:
- Relevance depends on continuous reinvention, not occasional adaptation.
- Enterprise leaders need clarity, not more technology noise.
- Customer-centricity must be reflected in long-term technology decisions.
- Enterprise AI requires context, governance, security, and integration.
- India’s technology story is moving beyond cost advantage toward IP, platforms, and measurable outcomes.
- The India-UK partnership presents a strong opportunity for co-development and technology-led growth.
For INT., staying relevant has never been about adding new labels to services. It has been about staying close to client problems, investing in people, and building capabilities that continue to create value as business needs change.
Relevance Is Harder to Build Than Growth
Growth is often visible. Relevance is harder to measure.
A company can grow because the market is expanding, demand is strong, or a particular technology cycle is favorable. But staying relevant for nearly three decades requires something more disciplined.
- It requires the ability to question what worked yesterday.
- It requires the humility to keep learning.
- It requires the courage to move away from capabilities that are losing value.
- Most importantly, it requires a deep understanding of what clients will need next, not just what they are buying today.
This has shaped INT.’s evolution since 1997.
The company began in digital infrastructure, expanded into digital engineering, moved into nterprise AI transformation, and is now helping enterprises navigate AI-led transformation. This journey reflects more than a change in service categories. It reflects a deliberate pattern of reinvention.
For INT., reinvention does not mean abandoning the past. It means keeping the principles stable while allowing capabilities to evolve.
The principle is simple: solve real client problems.
The capabilities around that principle must keep changing.
Enterprises Need Signal, Not Noise
Every major technology wave creates noise.
Cloud created noise. Digital transformation created noise. Data platforms created noise. Generative AI and agentic AI are creating noise now.
Noise can create awareness and urgency, but it becomes dangerous when it replaces strategy. Many enterprises today are under pressure to “do something with AI.” Boards are asking questions. Competitors are launching pilots. Employees are experimenting with public tools. Vendors are making aggressive claims.
In this environment, the most valuable role a technology partner can play is not to add more excitement. It is to create clarity.
Enterprise leaders need to know:
- Which trends are relevant to their business
- Which use cases are mature enough to pursue
- What risks must be managed early
- Where technology can create measurable business outcomes
- How investments today will affect flexibility tomorrow
This is where INT.’s philosophy of separating signal from noise becomes important.
Technology should not be adopted because it is fashionable. It should be adopted because it improves how a business works. That is especially true for AI.
The issue is not whether enterprises should adopt AI. They should. The real question is whether they are prepared to adopt AI in a way that is secure, useful, scalable, and connected to business value.
That is where thoughtfulness matters more than speed.
Customer-Centricity Is Tested in Long-Term Decisions
Customer-centricity is one of the most overused phrases in business. Almost every technology company claims it. Very few define what it means when difficult decisions need to be made.
In enterprise technology, the wrong recommendation can be expensive. A cloud decision can shape infrastructure costs for years. An AI model choice can influence flexibility, governance, and operating cost. A software architecture decision can affect scalability and maintainability. A third-party tool can become either an accelerator or a constraint.
For INT., customer-centricity means technology recommendations should be based on what is right for the client, not on commissions, preferred alliances, or short-term commercial convenience.
This is not just an ethical stance. It is a strategic one.
Enterprises need partners who can think beyond the first implementation. They need partners who understand lifetime total cost of ownership, long-term maintainability, and future flexibility.
The cheapest solution is not always the most cost-effective. The best solution is the one that continues to create value across its lifecycle.
This long-term view has shaped INT.’s client relationships. The company’s average client relationship extends beyond seven years, while its average employee tenure is over six years. These numbers reflect something that cannot be manufactured through marketing.
They reflect trust.
And in enterprise transformation, trust is not a soft metric. It is a business advantage.
Enterprise AI Cannot Be Another Isolated Tool
The current AI conversation is often too tool-centric.
New models are launched. New interfaces are introduced. New use cases are promoted. New promises are made. But enterprises do not operate in clean environments.
They operate across ERP systems, CRMs, HR platforms, data repositories, cloud infrastructure, shared drives, emails, documents, chats, spreadsheets, legacy applications, and industry-specific systems. Their data is structured and unstructured. Their workflows are distributed. Their permissions are complex. Their compliance obligations are serious.
This is why enterprise AI cannot be treated like individual AI usage. A public AI tool can help an individual write, summarize, or research faster. But enterprise AI must do something far more complex. It must work within the organization’s own context.
Enterprise AI must:
- Understand internal knowledge
- Respect role-based access
- Protect sensitive data
- Connect with existing systems
- Support real workflows
- Maintain governance and accountability
This is the challenge INT. is addressing with INT. OneSpace.
OneSpace is designed as a secure enterprise AI layer that connects structured and unstructured organizational data, enabling companies to create company-specific GPTs, intelligent agents, and AI-led workflows within proper governance boundaries.
The idea is not to force enterprises to replace every system they already use. That would be unrealistic for many organizations.
The more practical opportunity is to make existing systems more intelligent, connected, and usable.
An enterprise does not always need another platform. Often, it needs a better intelligence layer across the platforms it already has.
That is where AI begins to move from experimentation to transformation.
India’s Technology Story Is Moving Beyond Cost Advantage
For decades, India’s technology industry has been associated with scale, talent, and cost-effective delivery. These strengths remain important, but they are no longer enough to define the future.
The next chapter will be shaped by platforms, intellectual property, AI-led productivity, co-development models, and outcome-driven innovation.
This shift is visible in INT.’s own direction. While services remain an important foundation, INT. is increasingly building platform-led capabilities such as OneSpace, Learning OS, and salesforce automation platforms to create clearer, measurable value for enterprises.
The strategic direction is not about moving away from services. It is about combining services, software IP, automation, AI, and skilled support to deliver stronger business outcomes.
This also strengthens the India-UK opportunity.
The UK brings deep strengths in research, education, intellectual property, global business leadership, and innovation ecosystems. India brings engineering talent, execution capability, digital scale, and a growing base of technology-led enterprises.
Together, these strengths create a compelling opportunity for co-development, especially in enterprise AI, digital engineering, product modernization, data platforms, cybersecurity, and industry-specific transformation.
The future of India-UK technology collaboration should not be limited to buying and selling services. It should be about building platforms, products, capabilities, and companies together.
The Companies That Endure Keep Evolving
Technology will continue to change.
AI models will improve. Enterprise platforms will evolve. New tools will enter the market. Business expectations will rise. The pace of transformation will only become faster.
In such an environment, companies face a choice.
They can chase every trend and risk losing focus. Or they can stay anchored to clear principles while continuously evolving their capabilities.
INT.’s journey points to the second path.
Its capabilities have changed, services have changed. platforms have evolved and clients’ expectations have grown. But the core principles have remained consistent:
- Solve real problems
- Stay close to clients
- Invest in people
- Avoid unnecessary hype
- Build for long-term value
- Create technology that improves business outcomes
That is what staying relevant really means.
It is not about being the loudest voice in every technology cycle. It is about being useful when clients need clarity, dependable when transformation becomes complex, and forward-looking when the next wave of change begins.
For enterprises preparing for AI-led transformation, technology adoption alone will not define success.
The winners will be the organizations that combine technology with context, governance, trust, and measurable value.
Because technology will continue to change.
But the need for meaningful business impact will not.
Looking to build enterprise AI capabilities that are secure, scalable, and aligned with real business outcomes? Connect with us, to explore how we can help your organization move from technology adoption to measurable transformation.
