For more than two decades, enterprise SaaS has been a rational response to a structural constraint: software was expensive to produce, difficult to test, and costly to maintain. Vendors absorbed that complexity and provided solutions to their customers that also optimized software development. But the underlying condition is now changing.
As AI in software development grows, its impact is often framed in terms of productivity gains. While those are real, organizations often fail to recognize the true reconfiguration of software production, maintenance, and evolution beyond the initial AI hype.
There are already tangible results. Under controlled conditions, AI-assisted development is increasing delivery speed while maintaining acceptable quality thresholds. However, enterprises with weak engineering foundations are experiencing the opposite effect: elevated throughput accompanied by reduced stability. This reflects amplification rather than replacement. While speed increases, weaker teams tend to produce more errors at that same pace. As the cost of iteration decreases, long-standing assumptions about building and scaling begin to erode.
Underutilization and cost concentration
One of the main changes regarding this matter covers the cost profile of enterprise software. Different studies show that software is taking up a growing share of technology budgets, even as utilization remains uneven. Across product environments, a significant portion of functionality is rarely used in practice. While the exact figures vary, the pattern is stable: organizations consistently pay for capability that exceeds their operational needs.
Under earlier assumptions, this imbalance was acceptable because the alternative –building and maintaining internal solutions– was prohibitively expensive.
Nevertheless, the transition towards software as a production system is compressing costs. Copilots, assistants, and generative interfaces now form part of a software development lifecycle that is increasingly supported by agentic components. These systems can translate structured requirements into functional outputs, generate and maintain test coverage, and support the evolution of existing codebases, among others.
In this context, human intervention does not disappear; it is merely redistributed. The emphasis moves toward defining intent, validating outputs, and managing risk. As iteration and documentation become less resource-intensive, the advantage historically held by large, feature-rich platforms narrows to specific contexts.
Execution at scale
The structural change to an agentic SDLC factory combines exponential automation with human expertise. Its adoption by G2000 companies is projected to rise 10x by 2027.
Companies are implementing AI agents to move at scale, whether with a conservative or a strong investment. The bottom line is that this investment restructures the core of work: shorter iteration cycles, more tightly scoped solutions, and continuous evolution of systems that would previously have required significantly larger operational overhead.
This shift also introduces new challenges. Without clear governance or adequate risk controls, over 40% of agentic AI projects will be canceled by the end of 2027. This is where the capital allocation equation becomes altered.
Engineering maturity is a prerequisite for strategic decisions that leverage this emerging technology. Regulatory and risk frameworks are evolving alongside technology, with NIST and standards such as ISO/IEC 42001 already applied to ensure traceability and accountability in AI-enabled systems.
Reliability and compliance are non-negotiable, as SaaS vendors will deploy the same agentic capabilities internally, reducing their own costs and enabling deeper automation in their platforms. When only a limited portion of a SaaS platform is actively used, and the cost of building and evolving a focused alternative decreases, the long-term equation becomes less predictable.
This does not immediately invalidate SaaS. That said, it introduces credible alternatives where none previously existed.
When buy versus build is no longer enough
SaaS will continue to play a central role in enterprise architectures, with many organizations remaining dependent on external solutions for reliability and scale. What is changing, however, is not the existence of SaaS, but the conditions under which it is the most efficient choice. As mentioned, the transition toward a platform factory will show its first proof points in bounded internal workflow domains, not in core financial systems. The logic of capital allocation is more cautious than ever, requiring a granular evaluation of usage, flexibility, and long-term costs to avoid risking it all.
The buy-versus-build question has expanded into a broader context: how can the viability of alternative production models be increased? The way organizations answer this question will define the new era of leadership, as a broader spectrum of options is becoming operationally feasible. For those actively testing these models, the focus is no longer on validating the possibility of change, but on understanding where it can be applied strategically.
That distinction marks the transition from speculation to execution.
A new logic for software decisions
With the cost of building and maintaining software decreasing, SaaS stops operating as the default choice and becomes a strategic one. This changes how systems are evaluated, as well as how organizations approach control and long-term cost.
At Globant, this transition is already being operationalized through AI Pods. By combining agentic AI capabilities with the expertise of architects and engineers, these modular units are designed to accelerate delivery while maintaining governance, quality, and scalability. Rather than positioning AI as a separate layer, AI Pods embed it directly into the software production lifecycle, reshaping how enterprise systems are built, evolved, and operated in practice.
