Enterprise artificial intelligence adoption has reached a decisive turning point, with 78% of organisations globally now using AI in at least one business function, according to recent industry findings.
The rapid rise—up from 55% in 2023 to 78% in 2025—is accelerating a major shift away from bespoke development models toward modular, pre-built generative AI platforms designed for large-scale deployment.
Technology analysts say the new model reflects growing pressure on enterprises to deploy AI faster while managing escalating development costs and complexity.
Traditional delivery approaches, which often require building customised systems for each use case, are increasingly proving difficult to scale as organisations expand their AI investments across multiple departments and markets.
Modular platforms, by contrast, allow companies to configure reusable GenAI components that can be deployed repeatedly with minimal redevelopment, significantly reducing implementation timelines.
Research highlights the urgency of the transition. Industry forecasts indicate that at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, largely due to poor data quality, inadequate governance, rising costs or unclear return on investment.
Analysts note that these failures are often tied to heavily customised builds that require large specialist teams and lengthy development cycles.
The economic case for modularisation is becoming clearer as adoption expands.
New research shows 65% of organisations report regularly using generative AI, roughly double the percentage recorded in earlier surveys, creating demand for scalable delivery models capable of supporting enterprise-wide deployments without repeated development overheads.
Platform-based solutions enable vendors to invest heavily in initial development while distributing ongoing improvements, regulatory updates and performance enhancements across multiple clients.
Financial institutions provide a clear example of the diverging outcomes. Banks deploying fully customised GenAI systems often require large ongoing maintenance teams—sometimes as many as twelve specialists—to manage upgrades and compliance changes.
By contrast, institutions adopting pre-built modular platforms report needing as few as three internal staff, with vendors handling system updates that are shared across all customers.
Industry strategists describe the emerging delivery model as “Service-as-Software,” an evolution of the Software-as-a-Service paradigm in which intelligence itself becomes a packaged, continuously updated product rather than a bespoke engineering project.
Instead of commissioning one-off AI systems, enterprises are beginning to adopt domain-specific GenAI platforms trained on industry datasets and delivered as configurable software modules that improve with each deployment.
The shift is also reshaping the IT services industry, prompting firms to move from project-based delivery structures toward product-based platform strategies.
Although building reusable GenAI platforms requires higher upfront investment and extensive domain expertise, providers gain long-term advantages as each deployment improves margins, reduces resource requirements and enhances shared platform capabilities.
Analysts say the transformation marks more than a technological upgrade—it represents a structural redefinition of enterprise software economics.
With 78% of organisations already using AI, rising to even higher levels in coming years, companies that transition early to modular, pre-built AI ecosystems are expected to benefit from faster rollouts, lower operating costs and continuous performance improvements
