Executive Summary
As software adoption becomes increasingly widespread across industries,
Vertical SaaS is emerging as an important component of digital transformation in
industrial sectors. Rather than relying on horizontal, general-purpose enterprise
software, organisations are progressively adopting specialised platforms that align
with industry-specific workflows, regulatory requirements, and data structures. In
this context, Vertical SaaS is increasingly recognised not only as a tool for
improving operational efficiency, but also as an enabling asset that supports
industrial competitiveness. The global Vertical SaaS market is projected to expand
from approximately USD 90.1 billion in 2024 to USD 205.6 billion by 2030,
corresponding to an average annual growth rate of over 14%. This growth
markedly exceeds that of on-premises industrial software, suggesting that
cloud-based operating models have entered a phase of structural diffusion across
industries. While early adoption was largely concentrated among large enterprises,
market expansion from 2026 onward is expected to be increasingly driven by
uptake among small and medium-sized enterprises.
This expansion reflects the combined effects of regulatory, economic, social, and
technological developments. In sectors characterised by high regulatory intensity—
such as healthcare, financial services, and manufacturing—organisations face
growing incentives to integrate compliance and risk-management functions directly
into core software systems, rather than relying on ex post controls. From an
economic perspective, persistent labour shortages and rising labour costs are
strengthening demand for subscription-based SaaS models that support automation
and productivity improvements, while offering clearer cost–benefit outcomes. Social
factors, including chronic workforce constraints and the wider adoption of remote
and hybrid working arrangements, are further increasing the relevance of industry-specific
digital tools. In parallel, advances in generative artificial intelligence and large language