India’s enterprise technology story is moving into a new phase. Over the last decade, digital transformation has largely been about modernisation, like migrating workloads, improving customer experience, building mobile-first journeys, and moving faster through agile delivery. But in 2025, transformation is no longer only about building better products or platforms. It is increasingly about changing how work gets done, how decisions get made, and how organisations scale intelligence responsibly across functions.
In an exclusive interaction with Express Computer, Justin Marcucci, President, Apexon, speaks about what this moment means for enterprises navigating AI-led transformation and why India continues to shape their global delivery strength and innovation agenda. From the readiness gap many organisations still face to the shift from proof-of-concepts to production-grade deployments, Marcucci’s perspective highlights an increasingly important enterprise truth: AI value does not come from ambition alone; it comes from architecture, governance, and the discipline to scale.
Marcucci begins with a simple acknowledgement. He views these conversations as more than just business updates; they are opportunities to explain how they think about digital engineering at a time when enterprises are being forced to rethink both their foundations and their future. For him, their India growth is not a recent story of market expansion. It is a deeper story of heritage and strength that has existed for decades and continues to define the organisation’s trajectory today.
They are shaped by the thoughtful aggregation of several organisations that date back to the late 1990s and early 2000s, each with deep Indian roots in delivery and engineering talent. Even the North American side of those businesses carries a strong Indian heritage, with many having been founded by Indian expats. That legacy, he believes, gives them an enduring advantage: the company stays closely connected to Indian culture and continues to benefit from the talent momentum built around the country’s computer science ecosystem.
Over time, they lean into India not only as a delivery base but also as a growth engine. The organisation continues to expand its infrastructure across multiple cities, and Justin sees this long-term investment paying off through sustained ability to attract, train, and retain engineering talent at scale. India, in his view, remains their largest growth region from a staffing perspective and continues to be fundamental to the company’s global delivery model. As they expand worldwide, he expects India to remain its largest talent population area, not as a matter of legacy but as a matter of strategic necessity.
The India story
What makes India particularly relevant today is not just the scale of engineering talent but the speed at which enterprises want to move. He points to their work across industries, with strong traction in banking and financial services and in life sciences and healthcare. In one example, he describes supporting a Fortune 100 investment banking client with demanding expectations for top engineering capability. For them, India’s role in delivering value at that level is defined by three factors: the diversity of talent the company can attract, the speed at which it can assemble high-performing teams, and the strength of engineering execution once those teams are in place.
The AI and generative AI wave adds a new layer to this advantage. Marcucci notes that teams trained in modern AI techniques enable notable improvements in performance and outcomes, and he credits India’s talent pool and their training capacity for the organisation’s ability to meet aggressive milestones. He suggests that it would be difficult to move at the same speed elsewhere in the world today, both in building teams and in sustaining the leadership and delivery depth needed to turn AI ambition into measurable results.
Yet even as AI dominates enterprise conversations, Marcucci believes organisations are still learning a crucial lesson: wanting AI outcomes and being ready to deliver them are not the same thing. From his vantage point, most enterprises already understand AI’s potential to enable work to happen faster and more efficiently. The challenge is that their ambition often outpaces their readiness.
That readiness gap appears in two places. The first is technology. Many organisations still need foundational work in modern data architecture before AI can be deployed at scale. The second is behavioural. As AI tools enter workflows, enterprises face an unavoidable organisational change management challenge. Staff must adopt different ways of working, and leaders must guide teams through mindset shifts that redefine what “good work” looks like in an AI-enabled environment. In other words, the transformation is not just technical. It is cultural.
For organisations that are further along, the maturity curve looks different. Marcucci observes that many enterprises have already run proof-of-concepts and pilots, validating that AI can drive meaningful efficiency improvements in narrow, controlled use cases. The shift happening now, especially in the last quarter or so, is a move away from proving that AI works and towards building the capability to scale it. That means establishing governance frameworks, enterprise architecture readiness, and production-grade pathways that allow AI to be deployed confidently beyond the sandbox.
This is where he sees the enterprise conversation becoming more serious. AI is no longer a boardroom topic discussed as a future possibility. It is becoming the lens through which leaders rethink operating economics. Organisations want scaled value, not isolated success stories. And once they see that value materialising, investment naturally increases. Marcucci believes this marks the beginning of a more sustained cycle: scaled AI implementation drives visible outcomes, which then justify continued funding, expanding adoption and deeper architectural transformation.
AI expectations from stakeholders
As they work across both IT and business stakeholders, Marcucci notes that AI expectations evolve differently depending on which side of the organisation leads the initiative. On the IT side, CIOs and CTOs are increasingly focused on building the right frameworks for responsible deployment. Here, the conversations are about observability, governance, and guardrails about protecting enterprise data and customer confidentiality while ensuring AI initiatives scale without destabilising core systems.
On the business side, expectations split into two tracks. When the objective is top-line growth, Marcucci still sees significant experimentation. Organisations are exploring how AI can unlock new revenue streams or create differentiated customer experiences, and the industry is collectively still learning what those models look like at scale. But where operational performance is the priority, AI is moving faster. Cost optimisation and business process automation are attracting increased investment, and Marcucci believes this momentum is enabled by the foundational preparation happening on the IT side.
In a crowded digital engineering market, Marcucci positions their differentiation not around AI as a buzzword, but around the practical reality of what enterprises are buying. He argues that clients do not buy AI for its own sake. They buy the speed and power that AI enables. Their approach, he says, is grounded in the belief that speed without governance creates risk and that governance without speed creates stagnation. The balance must be designed in from the start.
He also points to their early push in generative AI and agentic AI and notes recognition from Gartner placing the company among emerging leaders in GenAI consulting and adoption. For Marcucci this validation reflects their insistence on starting in the place many organisations initially overlook: the foundational security and stability layer. Instead of beginning at the top with shiny use cases, they start from the bottom up, ensuring that observability, data security, and orchestration governance are in place before production-scale AI is deployed. That approach, he believes, enables faster movement into production for clients compared to competitors still working top-down.
The regulatory ecosystem
As regulatory expectations rise globally, governance becomes even more central. With India’s evolving data protection landscape and the growing prominence of frameworks such as the DPDP Act, enterprises are becoming more deliberate about how AI systems are deployed and monitored. Marcucci’s view is that whether the legislation is Indian, European, or eventually American, the responsibility is the same: service providers must understand restrictions deeply, design architectures that comply by design, and produce documentation and measurable security metrics that give enterprises confidence in what is being built.
He notes that many Indian clients already operate with a global mindset and are used to corporate governance requirements, meaning that the principle of data protection is not new. What is changing is that regulation is becoming more explicit and more prominent in India, pulling security and compliance into sharper operational focus.
In that context, Marcucci is candid about their approach to technology ecosystem choices. He does not believe it is realistic or responsible for a services organisation to attempt building everything in-house when hyperscalers and specialised platforms have invested heavily in advancing security, data management, and compute. Apexon therefore works closely with partners such as AWS, Microsoft, Google Cloud, Databricks, and Snowflake, investing in certification and deep capability to architect solutions that leverage the best of what these platforms provide.
Yet they avoid becoming tied to a single technology stack. Marcucci describes the company as technology-partner agnostic because clients come with different architectures, skills, and organisational realities. The right partner choice depends on the enterprise context, and their integrity lies in retaining flexibility to recommend what fits rather than what is convenient.
The AI advantage for enterprises
This same design philosophy shows up in their platform thinking. Marcucci highlights AgentRise, their platform built to accelerate agentic AI adoption and production-grade deployments at scale. AgentRise, he explains, is born from two years of production-level AI work with their largest clients. It reflects the same fundamentals he repeatedly returns to throughout the discussion: scalable architecture, governance readiness, and operational confidence.
AgentRise is built to be extensible across client environments rather than forcing a rigid data approach. It is designed to plug into diverse enterprise architectures across hyperscalers and data platforms while maintaining architectural sanctity and providing accelerators that support production implementation. For Marcucci, this is the difference between AI that looks impressive in a demo and AI that consistently delivers in the enterprise.
The value, he says, is measurable. In one engagement in investment banking, AgentRise enables a generative AI orchestration that cuts audit cycles by 70 per cent. In clinical research, an AgentRise-led programme accelerates clinical operations by 40 per cent by allowing broader deployment of automation across production workflows. In an automotive SaaS environment, AgentRise transforms credential management, an area previously considered unchangeable, reducing effort by 99 per cent and cutting onboarding timelines from weeks to days. For enterprises, these kinds of outcomes reframe AI from innovation theatre into operational advantage.
In India specifically, Marcucci sees the strongest AI programme traction in life sciences, where adoption is accelerating rapidly and they scale teams to support it. BFSI continues to be another major focus, where AI adoption is strong but balanced by the industry’s need for responsible deployment. Across both sectors, governance and trust remain non-negotiable, and AI acceleration depends heavily on the foundation beneath it.
But even with investments into modern data architectures, Marcucci believes many enterprises still struggle to unlock data value. The barrier is rarely the absence of tools. The barrier is the organisation’s attachment to legacy frameworks and the leadership’s hesitation to evolve foundational structures. Apexon therefore spends significant effort helping enterprises move through change management, building leadership understanding of modern data architecture expectations, and then translating that into behavioural change within teams. The technology is available. The transformation lies in the willingness to modernise thinking.
Looking ahead: All about value creation
Looking ahead, Marcucci expects 2026 to be defined by enterprises continuing to build the frameworks and architecture required to support AI ambitions at scale. The market will still see extensive work on use cases and automation, but the deeper value creation will come from enabling architectures that allow repeated deployment safely and efficiently across the organisation.
Beyond that, he sees another shift approaching that will reshape the technology landscape: quantum computing. While quantum is not yet available at enterprise scale, he believes the industry must begin preparing for quantum-driven algorithms and compute shifts that could fundamentally change how AI and agents operate. Their focus is therefore on ensuring teams, especially in India, build foundational capability in quantum algorithm thinking so the organisation can make meaningful recommendations as the ecosystem matures. The learning curve, he suggests, will be steep once quantum becomes more democratised, and preparation must begin now.
In the end, his view is pragmatic and enterprise-first. He does not present AI as a magic layer that automatically transforms organisations. He presents it as an operating change that demands readiness, responsibility, and repeatable deployment pathways. India, in that journey, is not just a delivery location. It is a strategic advantage—one that enables speed, capability-building, and scale at the moment enterprises are moving beyond experimentation into production.
For technology leaders, the takeaway is clear. AI transformation is no longer a question of “if”. It is a question of “how fast”, “how safely”, and “how consistently”. And for digital engineering partners, the new standard is no longer the ability to deliver pilots. It is the ability to engineer trust at scale.