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Software-as-a-Service (SaaS) has become deeply embedded across the construction and infrastructure value chain, powering everything from project controls and document management to procurement, HSE reporting, contractor management and financial planning
For developers, EPC contractors, consultants and asset owners delivering multi-billion-dollar projects, cloud-based platforms now underpin virtually every stage of the project lifecycle. Common Data Environments (CDEs), Building Information Modelling (BIM) collaboration platforms, field management systems, document control, digital quality assurance, ESG reporting, workforce management and financial planning applications have largely been delivered through subscription-based SaaS models.
What began as an attractive pay-as-you-go proposition has evolved into one of the largest recurring operational expenses within digital transformation budgets, often outpacing the growth of the business itself. This includes annual licencing fees, implementation costs, scope creep charges, customization charges, per-tenant and per-environment pricing, and compute-driven overages. As organizations scale and data volumes grow, additional charges related to performance, storage, and computational power come into play.
Global technology market pressures are adding to those costs. The sector-wide correction dubbed the "SaaSpocalypse", driven by weaker SaaS earnings, declining valuations and the rapid adoption of autonomous AI, has prompted many vendors to move from subscription pricing to consumption- and outcome-based charging models.
These global trends are coinciding with regional economic pressures. For example, the US-Iran conflict has disrupted supply chains, tightened liquidity, and forced companies, especially those in capital‑intensive sectors, to scrutinise every line of operational expenditure.
“In capital-intensive sectors, the cumulative impact and cost drag of SaaS subscriptions has become impossible to ignore, especially for long‑term projects, where a recurring overhead compounds over project lifecycles,” said Adinath Kadam, a US-based FP&A systems architect and cloud native finance infrastructure researcher.
“As a result, CXOs are reassessing the balance between long‑term SaaS commitments and other digital investments such as AI, automation, digital twins, and cloud‑native analytics. The question is no longer whether SaaS is useful, but whether the economics of perpetual subscription models still make sense in an environment where cost discipline, data control, and operational resilience have become strategic priorities,” he said.
Why construction companies struggle to move away
According to Kadam, reducing SaaS expenditure has become increasingly attractive for contractors and project owners seeking to improve operating margins, but migrating away from established platforms remains challenging.
He said: “Implementations often take 18–24 months, cost millions of dollars, and involve extensive scoping, configuration, and change‑management cycles. By the time a system is live, employees, consultants, workflows, and reporting structures are fully built around it. Operational dependence and fear of disruption make enterprises heavily reliant on their existing SaaS platforms, even when the economics no longer make sense or better alternatives emerge.”
Cloud-native alternatives
The changing economics are prompting some organisations to evaluate cloud-native architectures for selected business functions instead of relying entirely on proprietary SaaS platforms.
Kadam said financial planning and analysis (FP&A) is among the most practical functions for migration because its workloads rely primarily on aggregating financial and operational data rather than compute-intensive processing.
FP&A workloads built on high‑volume transactional fact data such as sales, costs, payroll, and operational quantities, accompanied by a small set of descriptive data such as time, business unit, legal entity, product, and sales channel. FP&A workflows also involve frequent re-computation of derived metrics such as margins, growth rates, variances, and ratios, across multiple planning cycles and scenario versions, often using consistent formulas applied to changing underlying data.
“From a data‑engineering perspective, FP&A workloads are aggregation‑dominant rather than compute‑intensive. Performance bottlenecks arise not from complex algorithms, but from repeated data movement, unstable transformations, and redundant recalculation logic embedded across spreadsheets,” he said.
A cloud-native FP&A architecture also supports the heavy reliance of FP&A teams on spreadsheets such as Microsoft Excel. It allows finance teams to work with multi-million-row financial datasets while preserving familiar Excel workflows and avoiding premature migration to complex data warehouses or business intelligence platforms.
“Despite the emergence of new analytics platforms, financial decisions are still predominantly reviewed, debated, and approved within spreadsheets. That said, modern data engineering principles can be applied within Excel-centric environments to improve reliability, transparency, and scale in FP&A operations,” said Kadam.
Impact of AI
Looking ahead, Kadam expects Artificial Intelligence (AI) to transform financial decision-making across construction and infrastructure companies. He said the integration of AI with cloud-native data warehousing can redefine FP&A workflows by making financial analytics more accessible, scalable, and responsive.
“Instead of relying on analysts to interpret questions, write queries, and prepare reports, an intelligent FP&A system allows users, including non-technical users, to ask questions in natural language, such as 'which region is underperforming?' and receive immediate, data-driven insights. This democratises access to financial insights, reduces dependency on specialists, and creates a fundamentally different relationship between finance teams and their data," he concluded.
(Reporting by Dennis Daniel; Editing by Anoop Menon)
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