Is your marketing AI tool driving profit or just noise? CleverTap co-founder Anand Jain weighs in

Jain argued that marketing AI must move beyond productivity. Drafting emails, creating image variants, and summarising documents can make work faster, but they do not necessarily improve company profits. The real test is whether AI changes business results.

He wants systems that understand what each customer is likely to need next. Instead of pushing a company’s chosen product to a broad audience, the system should interpret live behaviour and select a relevant message, offer, or action for one person.

This requires real-time decisioning, not just content generation. A decisioning engine can learn from customer responses, operate across many campaign options, and support genuine commercial profit. Marketers then set goals and guardrails while the AI handles much of the execution.

Real-time decisioning is the most advanced AI maturity level described by Jain. It means a marketing system makes automated choices for each individual, rather than sending one generic campaign to a large audience. The system focuses on what a customer wants to buy next.

For example, it might avoid immediately sending a discount. It could first explain product benefits, build trust, or suggest an action that fits the person’s current behaviour. The system uses a single data hub to understand both context and history.

Its decisions change continuously as customers respond. Reinforcement learning helps it learn from those responses, while uplift modelling identifies which specific nudge, message, or channel could encourage the next positive step. This lets marketing operate like a helpful shop assistant at digital scale.

Jain describes three stages of marketing AI maturity. Level 1 is productivity, where AI saves time on tasks such as drafting emails or summarising documents. Level 2 is experimentation, including A-B tests that compare two versions to find a stronger performer.

Level 3 is decisioning. At this stage, the system automatically chooses actions for individual customers. It can select different messages, offers, or channels instead of applying one winning campaign to everyone. Jain calls this the hardest stage to build.

The progression moves from helping marketers work faster to helping them learn what works, then to making decisions automatically. The goal is a “party of one” system that can serve a single person within an audience of several million, using live responses to improve future actions.

Traditional A-B testing compares two campaign versions and usually stops after choosing a winner. That winner may then be shown broadly, even though customers have different needs, behaviours, and reactions. The method is useful for experimentation but does not continuously personalise decisions.

Real-time decisioning works differently. It combines a customer’s historical profile with immediate live actions, then selects the next message, offer, or channel for that individual. It may begin with trust-building or product education instead of a discount.

The key mechanism is continuous learning. Reinforcement learning observes how users respond and adjusts choices on the fly. Decisioning also uses uplift modelling, which asks what specific intervention could change a person’s next step. This makes the system more adaptive than a fixed A-B-test result.

A real-time decisioning system must combine a customer’s historical profile with immediate live actions. The profile supplies accumulated information about the person, while live actions reveal current interest, behaviour, and reactions. Jain says both belong in a single data hub.

For example, a customer’s past preferences may suggest what they usually like, while a current visit or response may show a different need. The system can use both signals to decide whether to explain a benefit, build trust, recommend an item, or present an offer.

This combination matters because marketing decisions cannot rely only on broad audience groups. The system must understand the individual’s present context while remembering their history. It can then select a relevant next step and adjust future actions as the customer responds.

Automation changes the marketer’s job from execution to strategic direction. Instead of manually drafting and delivering thousands of creative variations, marketers define the outcomes the system should pursue. The AI then manages campaign delivery across many possible messages and offers.

Their most important work becomes setting boundaries. Jain highlights budget guardrails, brand guardrails, and regulatory guardrails. For example, a bank should not use the word “free” when offering something if that violates its operating rules.

This shift does not remove human responsibility. Marketers still decide what the system may do and what it must avoid. Within those limits, a decisioning engine can work independently, learn from customer responses, and pursue genuine commercial profit. The marketer becomes the person directing a safe, accountable system rather than operating every campaign detail.

Reinforcement learning is the system that learns continuously from user responses. When a customer reacts to a message, offer, or channel, the system uses that response to adjust future choices. It does not simply select one permanent campaign winner; it keeps adapting on the fly.

Uplift modelling answers a different question. Instead of only calculating how likely someone is to buy, it identifies the specific nudge, message, or channel that could encourage that individual’s next positive step. The focus is the likely effect of an intervention.

Together, these methods make decisioning more useful. The system observes what happened, estimates which action could improve the next step, and changes its approach as new responses arrive. This supports personalised marketing across thousands of options while marketers maintain strategic and regulatory limits.