By Rohit Kedia, Chief Executive Officer, Xoriant
In 2025, AI startups captured roughly 53% of total global venture capital funding, accounting for more than half of all global startup investments in the first half of the year, according to a Crunchbase analysis. At the same time, nearly 92% of Indian SaaS companies integrated AI into their products, with approximately 87% identifying themselves as AI-powered or AI-enabled.
Yet, beneath this widespread adoption lies a growing disconnect. While most SaaS companies now use AI in some form, only a small fraction apply it in ways that meaningfully influence business outcomes for their customers. For many, AI has become a layer of embellishment added to match up to competition or address margin pressures rather than helping customers with value realization.
This difference between using AI to ‘create’ value vs. peanut buttering it to just ‘do’ has become one of the defining challenges for SaaS leaders today.
For many startups, this gap is already visible in results. Despite increased investment in AI-led features and experimentation, converting these efforts into measurable business impact remains elusive. A recent MIT, 2025 research reveals that nearly 95% of AI initiatives fail to generate tangible value, underscoring how difficult it is to move from adoption to application.
The Problem with AI Applications
In many SaaS companies, AI applications remain limited to narrow, standalone use cases because it is often treated as an add-on rather than a core part of how the business operates. Shiny AI features sit in silos, with no shared objectives or alignment with business outcomes. While they may improve efficiency within their small radius of focus, they rarely lead to meaningful transformation for the company.
This mismatch becomes especially visible from the customer’s point of view. Users encounter AI as a wrapper present on the surface, serving a limited purpose. Support may feel faster, but not more effective. Insights may appear smarter, but not more actionable. Over time, this disconnect erodes trust, weakens product stickiness, and increases the risk of churn — particularly in a SaaS environment where switching costs are low and expectations are high.
As AI innovation accelerates, this challenge only intensifies. New models and tools continue to emerge rapidly, but without being embedded into the entire technology value chain, they fail to improve the outcomes customers actually care about.
How Applied Intelligence closes the gap between AI intent and impact
Applied Intelligence is the integration of artificial intelligence, advanced analytics, and human intelligence to reimagine business processes and platforms to deliver tangible outcomes. It is not about slapping on more AI tools or features to SaaS platforms but about embedding it into the core of how products are built, how decisions are made, and how customer outcomes are delivered.
Traditional SaaS systems were designed around fixed rules and predefined workflows that produced predictable results. But as platforms scale across users, geographies, and use cases, this deterministic approach begins to break down. Customer behavior becomes increasingly variable, contexts change constantly, and static logic struggles to keep pace. Modern AI addresses this with a fundamentally different operating model. Instead of rigid rules, it works in probabilities — learning from usage patterns, adapting in real time, and improving decisions as data evolves.
When embedded thoughtfully, this enables SaaS platforms to respond intelligently to real-world customer behavior rather than forcing every user into the same predefined path. Applied Intelligence ensures that this probabilistic capability remains grounded in the business context, which when combined with deep domain understanding makes workflows explainable, trusted, and aligned to meaningful outcomes, not abstract model outputs.
Most important of all, Applied Intelligence does not address problems in isolation. Instead, it connects intelligence across the entire value chain — from product design and customer engagement to operations and strategic planning — allowing insights to compound over time and strengthen decision-making consistency at scale.
Humans and AI: Better Together
It’s no secret that AI brings speed, scale, and pattern recognition while humans bring context, creativity, empathy, and accountability. But unlocking this partnership requires more than basic AI awareness. This shift also demands organizational readiness. SaaS companies must move beyond basic AI literacy toward true AI fluency, where teams understand how to frame the right problems, critically interpret insights, and translate intelligence into actionable outcomes. Without this fluency, even advanced models risk becoming underutilized features rather than drivers of impact.
As intelligence becomes embedded into products and workflows, professionals increasingly operate as creator–engineers —combining domain expertise with the ability to guide, shape, and refine intelligent systems. Rather than writing rigid instructions, they orchestrate outcomes using AI to augment thinking, accelerate innovation, and improve decision quality.
In this model, intelligence does not replace human judgment; it amplifies it.
In the years ahead, SaaS success will not be defined by how advanced technology appears in demos, but by how reliably it delivers value in everyday use. As Microsoft CEO Satya Nadella has noted, technology should empower people and extend human capability, not just automate tasks or add efficiency. Those who embrace Applied Intelligence will move beyond incremental improvement to architecting the operating system powering the next wave of SaaS.