PNN
New Delhi [India], April 29: As artificial intelligence continues to dominate conversations across industries, a familiar narrative is beginning to take shape that businesses may soon no longer need software product companies. The logic seems simple, with AI now "Saksham" and capable enough to generate applications on demand, organizations can build and manage their own systems internally. This thought is gaining traction, and leads to apprehension of "Why rely on external vendors at all?".
This shift comes at a time when enterprises globally are experimenting with AI-led development, with many exploring whether internal tools can replace traditional SaaS investments.
However, this assumption overlooks a fundamental aspect of how enterprise software actually works in practice.
While AI has significantly reduced the time and effort required to write code, according to Kunal, software development is only a small part of the overall lifecycle of a product. In most real-world scenarios, coding accounts roughly for just 15 to 25 percent of the total effort. The rest lies in implementation, customization, training, support, and continuous adaptation to business needs. Observations by McKinsey & Company reinforce a similar reality, faster development does not automatically translate into business impact. Adoption, integration, and process alignment ultimately determine whether a system delivers value.
"The idea that businesses can replace software vendors by building tools internally using AI sounds efficient," said Kunal. "But once you move beyond the first version, the complexity starts catching up. Building something is one step, running it reliably across teams is a different challenge altogether."
The shift toward AI-assisted development has made it possible to create functional prototypes within hours that once took months to build. Yet, the complexity of real-world operations, ranging from edge-case workflows to cross-functional dependencies, requires a depth of domain understanding that extends beyond code generation. This evolving landscape is beginning to divide software product companies into three distinct categories.
The first includes legacy providers that have been slow to adopt AI-driven development practices. While many of these organizations have established products and customer bases, their inability to evolve at the pace of technological change may impact their competitiveness.
The second category comprises a new wave of AI-first builders, including startups and independent developers who leverage generative tools to rapidly create applications. While these players benefit from speed and lower development barriers, they often face challenges in scaling products due to limited domain expertise and the absence of structured implementation and support systems.
Between these two sits the third and most resilient category consists of companies that combine deep domain knowledge with the strategic adoption of AI. These organizations are not only accelerating development but are also improving how products are delivered, supported, and evolved over time.
"AI will undoubtedly improve how software is built, it will reduce timelines, enhance quality, and expand what products can do," Kunal added. "But it does not eliminate the need for expertise in implementation, support, and long-term system evolution. Businesses don't need more code, they need outcomes."
The broader implication for enterprises evaluating software solutions is a shift in focus. Rather than assessing whether a system can be built using AI, organizations may need to consider who can ensure its sustained performance and alignment with business objectives over time.
Historically, similar waves of technological disruption, from cloud computing to no-code platforms, have followed a pattern of initial overestimation, followed by practical recalibration. AI appears to be following a comparable trajectory, where its role as an enabler is becoming clearer than its ability to replace entire ecosystems.
As the market continues to evolve, the gap between surface-level functionality and deeply integrated, business-ready systems is expected to become more pronounced. In this environment, companies that combine technological advancement with operational understanding are likely to emerge as long-term leaders.
For software buyers, this shift is less about whether AI can build tools and more about choosing partners who can ensure those tools deliver consistent business value.
About EAZY Business Solutions
EAZY Business Solutions provides unified ERP, DMS, and Sales Force Automation (SFA) platforms designed to help businesses streamline operations, improve visibility, and drive execution across sales and distribution networks. The company works with manufacturing and distribution businesses across industries to deliver scalable, customizable, and outcome-driven software solutions.
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