With over 78% of organizations globally leveraging AI in at least one business function, we are at a pivotal inflection point in the enterprise AI journey. A new path forward has emerged, one that doesn’t rely on endlessly building custom solutions from scratch for every use case. This paradigm is powered by Generative AI-driven, modular, pre-built platforms designed to scale, harnessing the best of GenAI and adapting seamlessly to enterprise needs.

Beyond efficiency: Modularity is the new market fit

The shift from bespoke to modular AI solutions represents more than operational efficiency. It's a fundamental redefinition of market fit. The latest research by McKinsey also found that AI implementation has jumped from 55% in 2023 to 78% in 2025. Today, organizations are betting heavily on GenAI implementation, but such rapid growth requires a delivery model that can scale with the rising demand while being adaptable and delivering outcomes faster.

Given the complexity of GenAI development and implementation, the traditional delivery models are struggling to keep pace. These models are inherently linear, requiring a fresh customization for each use case, additional manpower and extended timelines. As a result, fragmentation grows, efficiency shrinks and innovation stalls.

Even when GenAI solutions serve similar business functions across clients, the delivery approach often treats them as unique builds. This "reinvent-the-wheel" mindset leads to diminished ROI and higher execution risks, particularly as the complexity of use cases increases.

At HCLTech, we believe it's time to rethink the enterprise solution delivery blueprint. We offer a different approach that leverages GenAI’s greatest strength over traditional software development, which is the capability to learn and remember.

We advocate for modular GenAI solutions, pre-built to address specific problems, operating on principles of exponential scalability. While the initial platform development requires investment, each deployment increases margins and reduces resource requirements. More importantly, each implementation enhances platform capabilities, creating a virtuous cycle where value and competitiveness grow over time.

Research from Gartner underscores the urgency of this transition. At least 30% of generative GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value. These failures stem less from technology and more from unsustainable economics and extended timelines of custom development approaches.

Understanding how GenAI pre-built solutions add business value

To help understand how modular GenAI solutions would work, let’s explore a case study of two banks. Both banks sought to implement comprehensive GenAI-driven customer service platforms, fraud detection systems and personalized financial advisory tools. Their approaches, however, are entirely different.

After both solutions went live, Bank A’s competitive advantage remained limited, as the GenAI capabilities, while custom-built, weren't substantially superior to what competitors were deploying.

In Bank B’s case, the solution went live with sophisticated GenAI capabilities that had been tested and refined across dozens of previous banking implementations. A year after implementation, Bank A’s solution still needed a team of twelve specialists for upkeep. In contrast, Bank B required just three internal staff, with the vendor managing updates and improvements shared across clients. When compliance rules changed, Bank B received updates automatically, while Bank A began another custom development cycle.

Service-as-Software and the GenAI revolution

The rise of Service-as-Software marks a critical inflection point in the evolution of enterprise technology delivery. Just as the Software-as-a-Service (SaaS) revolution replaced bespoke software builds with agile, cloud-based applications, today’s pre-built GenAI platforms are overcoming “custom build from scratch” trap. It is redefining how enterprises consume digital intelligence not as custom projects, but as packaged, modular services embedded in scalable software frameworks.

Historically, enterprises invested heavily in building one-off IT systems, such as CRM tools and HRMS, crafted to fit perceived "unique" business needs. Today, that same standardization principle is being reimagined in the GenAI era through the lens of Service-as-Software.

What drives Service-as-Software

• Industry-specific intelligence as a competitive advantage: Service-as-Software doesn’t treat GenAI as a model-building exercise for every client. Instead, it packages GenAI services trained on rich, domain-specific datasets into ready-to-deploy software modules that solve industry problems at scale. This specialization creates natural competitive moats for IT services providers, helping them create GenAI solutions that are difficult to replicate. 
  For the end user, industry specialization enables continuous improvement that benefits all platform users. For example, a retail industry solution is deployed across multiple retailers, it learns from collective data patterns, adapts to industry trends and incorporates best practices from high-performing implementations.
• Implementation speed and efficiency: The transition to Service-as-Software isn't merely an operational improvement; it's becoming a competitive necessity. As McKinsey's latest research shows, 65% of respondents report that their organizations are regularly using GenAI, double the percentage from a previous survey. This explosive adoption rate creates market pressure that traditional custom development approaches cannot sustainably address.
  Leading IT services providers are recognizing that their strength isn’t in writing custom code from scratch, but in their capacity to deliver proven, industry-specific solutions quickly and effectively. This requires a fundamental shift in organizational structure, from project-based teams to product-based platforms. The initial building cost is higher and requires deep domain expertise, extensive testing and robust infrastructure. However, the end product becomes increasingly attractive as solutions are deployed across multiple clients.
• Mindset shift: Crucially, the industry’s earlier resistance to modularity, rooted in the belief that “every use case is unique,” is falling apart due to speed, cost and scale of impact. As Gartner notes in its 2025 AI Market Guide, “Enterprise AI success will increasingly depend not on model novelty but on operational repeatability.” Service-as-Software will enable enterprise-scale adoption by abstracting complexity behind proven, configurable GenAI services.

The biggest differentiating factor for Service-as-Software is that it represents a fusion of intelligence with software engineering, where the service is not merely hosted in the cloud but embedded as a productized component, governed by SLAs, version control and continuous updates. These offerings represent repeatable IP, not custom projects that are built from scratch.

Pre-build advantage: The way forward

The path from longer customization cycles to agile modular development requires careful navigation of several key challenges. Client expectations must be managed, as many organizations initially resist modular solutions, believing their requirements are unique. Internal teams must be retrained to think like product managers rather than project managers. Organizational structures must be redesigned to support platform development rather than project delivery.

The most successful IT services providers are adopting a "platform plus" approach that captures the efficiency benefits of modularity while preserving the ability to deliver unique value. They develop modular platforms that address the majority of common requirements, then offer specialized customization services for differentiating capabilities.

As this transformation accelerates over time, ecosystems of pre-built GenAI solutions will emerge, letting enterprises configure industry-grade intelligence like building blocks that can be combined and configured to meet specific client needs. Clients will have access to sophisticated GenAI solutions that would have been impossible to develop independently.

The shift from bespoke solutions to modular, industry-specific platforms marks more than a technological evolution; it signals a redefinition of how enterprises create value. Those who lead this transformation won’t just navigate the GenAI era; they’ll shape it.

The results are clear: faster rollouts, lower costs, higher performance and built-in continuous improvement. Enterprises that embrace this shift will not only future-proof their operations but will set the pace for what comes next.

The future belongs to those who turn the art of bespoke development into the science of pre-build solutions.