What changes when a retailer can make a separate decision for every customer instead of placing thousands of people into the same campaign segment?

In this episode of Startup Builders & Backers, I speak with Ilyas Kurklu, CEO and co-founder of Replenit, about the company’s attempt to move retail CRM beyond fixed audiences, campaign calendars and generic promotions. Replenit calls its product Maestro, an AI CRM Manager designed to decide what should happen next for each customer and pass that decision to the retailer’s existing channel tools.

The distinction between prediction and decision runs through our conversation. A propensity score can estimate what someone might do, but a retailer still has to decide whether to act, which product to recommend, when to communicate and whether a discount is commercially sensible. Ilyas argues that this reasoning should be readable by a human CRM manager. For every decision, the team should be able to inspect why this customer was selected, why the timing was chosen and why a particular product was recommended.

That raises the question every enterprise AI project eventually faces: how do you prove the system created value rather than claiming credit for a purchase that would have happened anyway? Ilyas recommends measuring incremental margin per customer against a randomized holdout. He is skeptical of attributed revenue, influenced revenue and open rates when they do not reveal whether the business made additional money. We also discuss Replenit’s focus on cost per decision and the processing expense that comes with adding further data to an AI model.

Integration is another part of the story. Retailers may already use a customer data platform, a customer experience platform, analytics products and multiple communication channels. Replacing that stack can bring cost, delay, security reviews and regulatory work. Ilyas explains why Replenit has been designed as a reasoning and orchestration layer that works with the systems a retailer already owns.

The conversation also considers customer interest and commercial pressure. A retailer may have contractual brand rules, margin targets and limits on which products can be recommended together. Maestro records brand, product and customer memory, while the retailer sets the rules that govern its output. The human CRM manager then becomes responsible for reviewing outcomes, setting boundaries and questioning the system’s decisions.

For founders and backers, Ilyas offers a refreshingly practical reminder. Building an AI company still means doing the ordinary work of running a business, proving that the buyer can make or save money, answering why an incumbent cannot easily copy the idea and reducing the risk that stops an enterprise customer from saying yes.

Would you trust an AI CRM manager to make individual customer decisions, and what evidence or controls would you require before giving it that responsibility? Share your thoughts with me at Tech Talks Network.