Go into the wrong store and you can feel it. The associate proposes what you already have. Someone has sent you an email about a product which you returned last week. The shelves of the store are bare where you wanted to go and there is a shelf a few aisles away that just bursts with merchandise that no one wants. They are not some petty inconveniences. They are an indicator of a fundamental malfunction between what retailers know and what they actually do with such knowledge.
The most effective retailers have discovered that there is one thing the other retailers are still understanding; AI in retailing is best when the customers do not even realize it.
The Inventory Problem That Reports Couldn't Solve
An international clothing retailer years took part in observing the identical pattern unfolding. Some of the stores were out of fast-moving items and some were left with dead stock that was to be discounted. There were reports every week which were received too late to rectify. Forecasts that were done manually failed miserably when consumer behavior changed.
The workaround was not a larger spreadsheet. It consisted of real time analytics and machine learning demand models which were constantly adjusted according to real time sales data, geographical patterns, weather, and the presence of promotional activity. Reviewed inventory recommendations daily, or even hourly. Stockouts reduced significantly and the rates of clearance went down within a single season.
This is what the AI-based inventory management really looks like. Not a drastic change, but a silent, ongoing layer of decision support which eliminates the delay between what is going on the floor and what is ordered off the warehouse.
The same can be said in distribution centers, when physical operations are concerned. Robotics used by retailers to pick and pack goods during high demand times are not substituting workers in bulk numbers they are quenching the demand volume that only human staff would have been unable to handle. After the chain of grocery stores was unable to fulfill online orders by the same day, one of them deployed the system of automated order fulfillment. Turnaround of orders hastened. Picking errors fell. There was an increase in volume without proportional increase of the headcount.
Why Retail Personalization Has Failed So Many Customers
Retailing Personalization is not a new concept. The difference is doing it in a non-hollow manner.
The vast majority of the customers have used the malfunctioning one: ads promoting products that have been bought, suggestions that do not take into account anything they have viewed, the follow-up messages so out of touch with the real world that they are clearly computer-generated — and that is what they are. Its issue is hardly ever intentional. It is disjointed information that never gets to the appropriate system at the appropriate time.
This is what was found by one electronics retailer. The customers were also doing a lot of shopping on the internet and then fulfilling their purchases physically in the stores. But those online messages never found their way into store associates or marketing platforms. Staff had no context. Emails were irrelevant. It was impersonal in the experience even when it had been greatly invested in technology.
Data unification and real-time machine learning were the solution. Associates would be able to view recent browsing history and build recommendations based on real interest. Marketing campaigns were dynamically adapted depending on the current behavior and not based on the traditional audience groups. This was observable to customers, not due to any interface redesign, but because the dialogue no longer seemed predetermined, but knowledgeable.
The same has been the case of AI-delivered customer services. Chat bots that deal with standard queries price checking, policy, account support questions, etc. release human operators to deal with cases where they are really needed. A fashion brand used this model when making a big sale in the future despite the fact that its support staff was exhausted by past peaks. Most queries were solved with the help of robotic answers. Even with increased volume of orders, satisfaction scores were better.
The movement, in this case, is substantial. AI can help retailers broad assumptions about what customers can be interested in and shift to moment-by-moment responsiveness to what they are doing in real time..
Fraud Detection and the Trust Problem Retailers Can't Ignore
The development of digital commerce opens up a similar amount of exposure to fraud. A single online market place had to cope with a sudden surge of fraudulent transactions over a holiday season. Detecting systems based on rules detected the more evident cases but failed on lesser cases. Still, better, false positives prevented genuine clients at the checkout-counter, which is a sort of harm in itself.
Fraud detection using machine learning altered the calculus. The system was not reliant on fixed thresholds, but the context of transactions was evaluated on a real-time basis: purchase velocity, device behavior, historical trends, and anomalies that can be perceived only by rules. Fraud losses dropped. So did false positives. The number of real customers was less that were interrupted during valid purchases.
Fraud prevention is not the only way to trust retail AI. Customers are becoming more concerned with the way their information is managed. When retailers implement the use of personalization and predictive systems without providing transparency, they fester doubt about the very systems that they are supposed to protect. The organisations that do it right consider responsible data use to be an element of the customer experience in itself, rather than a compliance box ticked. Clarity of consent, clearly explainable recommendations, and uniformity of data management can all help to build the type of trust that can withstand an announcement of a data breach.
The Sequencing Problem Most Retailers Get Wrong
Ambition is the least frequent error in adoption of AI in retailing and sequencing is the most frequent. Those organizations are fast to automate decisions without developing a real level of trust in the data behind it. Whenever automated outputs give contradictory information to what frontline teams observe every day, the confidence will quickly disappear. Associates cease to depend on the system. Managers work around it. The technology which was expected to assist, is an issue to deal with independently.
It is hardly ever to give up the technology. It is to take its time, reintroduce transparency, and allow humans and systems to build mutual understanding. Confidence restores when teams assess the results collaboratively, doubt the assumptions of the models, and maintain the human judgment in cases when a sense of nuance is indeed required.
What Good Actually Feels Like
The most successful retail experiences in the current world do not seem automated and this is precisely the trick. The intelligence that they possess is used sparingly. People do not have their judgment substituted with systems. AI in retail can be constructed by considering the real needs of both customers and employees, at which point the technology becomes invisible.
It is right to have the experience. It is more difficult to construct than any dashboard. And it is far more than worth.
