At TechSparks 2026, CleverTap co-founder Anand Jain delivered a pointed message to marketing leaders chasing the latest wave of artificial intelligence: if the tool does not improve business outcomes, it is merely adding noise. Jain argued that many teams are still using generative AI as a productivity layer for drafting copy, summarising data or accelerating routine tasks, but that this approach falls short of what modern customer engagement demands.
Profit Over Productivity
Jain's central thesis was that marketing has moved beyond the era in which AI could be judged only by how much time it saved. In his view, the next competitive advantage will come from systems that can act autonomously in real time, interpreting customer behaviour and making decisions that directly influence conversion, retention and monetisation. That shift, he suggested, separates experimental AI use from technology that can be tied to measurable profit.
The comments reflect a broader recalibration underway across startups and enterprise software. For the past two years, many marketing teams have adopted generative AI tools to produce campaigns faster and at lower cost. But as budgets tighten and boards demand clearer returns, the question has changed from whether AI can help teams work faster to whether it can help companies earn more. Jain's remarks place CleverTap firmly in the camp arguing that marketing technology must be judged on commercial impact, not novelty.
He also underscored a practical limitation of generic AI tools: they are often reactive, not decisioning engines. They can generate content, but they do not necessarily know when to send a message, which customer segment to prioritise, or what intervention is most likely to convert a user at a given moment. In Jain's framing, that distinction matters because modern customer journeys are dynamic, fragmented and highly time-sensitive.
Real-Time Decisioning Shift
Jain's argument points to a larger evolution in the marketing stack. Traditional automation systems were built to execute pre-set rules, while generative AI has largely focused on language, creative output and internal efficiency. The emerging layer, he said, is autonomous decisioning: software that can ingest behavioural signals, evaluate context and trigger the right action without waiting for manual intervention.
For startups in the customer engagement and martech space, this is a meaningful strategic pivot. It suggests that the market will increasingly reward platforms that can connect AI to outcomes such as reduced churn, higher repeat purchases and better campaign attribution. In other words, the value proposition is moving from "help teams do more" to "help businesses decide better."
That distinction is especially important in India's fast-growing digital economy, where consumer brands, fintech firms, e-commerce companies and subscription businesses are all competing for attention in crowded channels. In such an environment, incremental gains in timing and targeting can have an outsized effect on revenue. Jain's remarks imply that AI systems which merely generate content may be easy to adopt, but they are unlikely to become mission-critical unless they can influence those outcomes at scale.
What Marketing Teams Need
The broader message from Jain was not a dismissal of generative AI, but a warning against overestimating its business value when used in isolation. He appeared to argue that marketing leaders should demand stronger proof of impact before treating AI as a strategic transformation rather than a tactical convenience. That means asking whether a tool can improve conversion rates, reduce customer drop-off, personalise engagement in real time and ultimately drive profit.
For investors and founders, the implication is equally clear. The next generation of AI startups may be judged less by the sophistication of their models and more by the precision of their decisioning systems. In a market crowded with AI-first claims, the winners may be those that can demonstrate not just automation, but measurable commercial lift.
Jain's comments at TechSparks 2026 capture a growing industry consensus: marketing AI will not be defined by how much content it can produce, but by how intelligently it can act. As enterprises move from experimentation to accountability, the pressure is rising for AI tools to justify themselves in the language that matters most to business leaders — profit.