Artificial Intelligence (AI) is rapidly redefining how enterprises select, deploy and measure software-as-a-service (SaaS) platforms. What began as an era of digital efficiency and cloud-native applications has quickly morphed into a landscape where AI is no longer an optional enhancement but a strategic imperative. According to Deloitte’s Tech Trends 2025 India perspective, AI is becoming the foundational layer for technology adoption across industries, shaping innovation, efficiency, and digital transformation at scale.
This shift reflects broader changes in enterprise expectations. Enterprises no longer seek software that simply digitises workflows; they seek platforms that help teams deliver measurable outcomes with speed, accuracy and accountability. The transition from pilot projects to mission-critical use cases is well underway. Recent data from an EY-CII report shows that 47% of Indian enterprises now have multiple generative AI use cases in production, marking a decisive move beyond experimentation. Another 23% remain in pilot stages, highlighting the momentum of adoption across industries. Crucially, 76% of Indian business leaders believe generative AI will have a significant business impact, underscoring the strategic value organisations expect from AI-integrated SaaS solutions.
McKinsey’s Global State of AI Survey provides further context for how enterprises are integrating AI into SaaS and broader business operations. In 2025, 88% of respondents reported regular use of AI in at least one business function, up from 78% the previous year. The most common applications are in IT and knowledge management, reflecting a shift toward intelligent systems that organise, interpret and act on data at scale. Yet despite wide adoption, only about one-third of organisations have begun to scale AI programmes across the enterprise. This gap between adoption and scaled impact highlights a core challenge: Moving from isolated use cases to integrated, workflow-first deployments.
This pattern is especially relevant for SaaS platforms because AI-enabled features are increasingly integral to what users expect from SaaS solutions. Traditional SaaS added value by standardising processes and enabling cloud access; AI-augmented SaaS extends this value by automating insight generation, predicting outcomes, and even executing complex tasks once reserved for skilled human operators. Companies scaling AI at enterprise level are more likely to allocate substantial portions of their digital budgets toward AI technologies.
The broader economic implications are significant. Research estimates that generative AI could create up to $ 4.4 trillion in annual productivity gains globally, a figure that rivals the economic impact of major industrial revolutions. This potential has accelerated SaaS evolution from data repositories and user interfaces to intelligent co-pilots assisting knowledge work, customer engagement and decision-support processes.
Enterprise buyers today expect AI-enabled SaaS to move far beyond task automation. The demand is for platforms that actively reduce friction in decision-making, compress response timelines, and drive clear improvements in business performance. Increasingly, this expectation is shaping interest in agentic AI systems that can manage multi-step workflows and execute tasks autonomously rather than merely assist users. What was once experimentation is now steadily progressing toward real operational deployment, signalling a shift in how organisations view automation and value creation.
In the Indian context, AI adoption is closely tied to core operational priorities rather than peripheral innovation. Organisations are integrating AI most actively into functions such as operations, customer service, and marketing, where speed, accuracy, and consistency directly affect outcomes and customer experience. This growing focus on operational AI has also sharpened expectations around trust. Enterprises are looking for technology that embeds governance, accuracy, and ethical safeguards by design. Without these guardrails, AI risks creating misalignment between system outputs and business objectives, ultimately eroding confidence in automation rather than strengthening it.
Despite strong momentum, scaling AI across enterprise workflows remains complex. While AI usage is widespread, deep integration into end-to-end processes is still uneven. Many organisations are discovering that deploying AI tools is not the same as transforming how work gets done. Real impact depends on aligning AI capabilities with business priorities, redesigning workflows, and building execution discipline across teams. Without this alignment, AI initiatives risk remaining fragmented or failing to translate into sustained performance gains.
Resource allocation further complicates this transition. There is broad conviction among Indian leaders about AI’s strategic importance, yet investment levels often lag ambition. Many organisations continue to fund AI conservatively relative to its expected impact, creating a gap between intent and execution. This imbalance can slow the pace at which enterprises move from isolated successes to enterprise-wide value realisation.
Across industries, one principle remains consistent: Human expertise is central to unlocking the full value of AI-enabled SaaS. AI excels in environments defined by structure, repetition, and scale. It can process vast information sets, identify patterns, and automate operational tasks at speeds no human can match. However, areas such as interpretation, strategic judgment, persuasion, and trust remain fundamentally human.
The most effective SaaS models recognise this balance. AI serves as an accelerator, not a substitute, for human decision-making. Organisations that succeed are those that combine AI’s computational strength with human oversight, ethical accountability, and contextual understanding. This hybrid approach is increasingly visible as enterprises move from experimental deployments toward programmes that prioritise governance, reliability, and measurable performance.
The evolution of SaaS in the AI era reflects a deeper shift in enterprise expectations. Software is no longer evaluated solely on functionality or efficiency, but on its ability to deliver outcomes, reduce cognitive and operational load, and integrate seamlessly into strategic workflows. As organisations mature in their AI adoption, success will depend less on deploying the latest tools and more on embedding AI thoughtfully into how work is executed. Those that balance automation with human expertise, governance, and business alignment will shape the next phase of digital transformation.
This article is authored by Shankar Lagudu, co-founder and COO, Responsive.