Most marketing teams today use AI to save time on tasks such as drafting emails, creating image variants, and summarising long documents. However, Anand Jain, Co-founder and Chief Marketing Officer of CleverTap, a customer engagement and marketing automation platform, says basic tools that do these jobs rarely improve company profits.
In a masterclass on marketing strategy at TechSparks 2026, Jain urged leaders to look beyond simple shortcuts.
“We are going to talk about how you go from using AI for fun and games all the way to using AI to generate actual business results.” While creating content using AI makes the job faster and saves time, Jain questioned whether it truly drives growth, asking, “Is it actually going to impact business results or it is just more of the same?”
Traditional digital campaigns usually operate on a push model wherein companies decide what they want to sell instead of understanding what consumers want to buy. Whereas, a local shop assistant in an offline channel remembers a customer’s personal taste, notices immediate reactions, and suggests items tailored to their budget and style. Digital marketing must adopt a similar mindset.
“Not what I want to sell, what my customer wants to buy. So you flip this around,” Jain said.
Instead of broadcasting generic promotions to large groups, modern marketing engines should interpret live customer actions to match individual needs.
This approach marks a shift towards the highest level of AI maturity, known as 'real-time decisioning'.
Marketing technology generally progresses through three stages. Level 1 focuses on basic time-saving tasks, Level 2 covers experimentation such as A-B testing (comparing two ad versions to see which works better), and Level 3 achieves true automated decisioning.
“You start with productivity, then you do experimentation, and the hardest thing is decisioning. When you actually are building an entire system for a party of one and that one person could be one out of several million that show up on your app or your website,” Jain noted.
Instead of bombarding users with discount codes, decisioning offers gentle helpful steps first, such as establishing brand trust or explaining product benefits.
Real-time decisioning relies on a single data hub that combines a customer’s historical profile with their immediate live actions. Traditional A-B testing usually stops after selecting one fixed winner, but decisioning uses reinforcement learning, a smart system that learns continuously from user responses and adjusts offers on the fly.
Furthermore, instead of relying only on propensity scoring (calculating how likely someone is to buy), decisioning uses uplift modelling. This method identifies the specific nudge, message, or channel needed to encourage an individual user to take their next positive step.
As automated systems manage campaign delivery across thousands of message options, the role of the marketer shifts from execution to strategic direction. Rather than manually drafting creative variants, marketers define high-level goals and establish strict operational limits.
“Your job is to create budget guardrails, brand guardrails, regulatory guardrails. Do not use the word 'free' if you are offering something, if you are a bank,”Jain emphasised.
By establishing clear boundaries, marketing leaders empower smart decisioning engines to work independently, keeping brand messaging safe while driving genuine commercial profit, he added.
Edited by Swetha Kannan