---
title: The Future of Smart Stores
description: Explore how smart technology is transforming retail stores by enhancing operations, improving customer experience, and driving efficiency through AI.
image: https://blog.telaid.com/hubfs/Blog%20Post%20Featured%20Image%20(7).png
---

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# The Future of Smart Stores

![Chase Smith](https://blog.telaid.com/hubfs/Chase%20Smith-%20larger%20file.jpg)

 24 July, 2026   
 by [Chase Smith](https://blog.telaid.com/blog/author/chase-smith)

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Retail transaction counts have been flat for years. If a store wants more revenue or lower  
cost, the tech on the floor has to do the work. Chase Smith, Director of Enterprise Sales at  
Telaid, breaks down where AI cameras, smart checkout, and automation are actually paying  
off, and why most retailers are still leaving value on the table by treating each of these  
systems as its own island.

Stores Are Finally Catching Up to Ecommerce  
For years, ecommerce has had real time visibility into what customers are doing, and physical  
retail has been stuck relying on point of sale data and manual audits. That gap is closing.  
Camera and edge computing are turning the store floor into a live data source in its own right.

The pressure behind this shift is simple math. Instore transaction counts have been flat  
industry wide, according to Anax research, so retailers need technology that either drives  
more revenue per visit or cuts cost. That single constraint is behind most of the trends  
reshaping the store today.

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From Security Camera to Operations Tool  
The biggest shift Smith points to is cameras moving from a passive security role into an active  
operations role. Stinker Stores is a clear example: they put vision AI on their roller grills this  
year to track sell through and cut waste, a job that used to require someone walking the floor  
with a clipboard.

The customer feels that change too. Shelves stay fuller, and employees spend less time on  
manual checks and more time actually helping people. That is the real payoff of moving  
cameras out of the security office and into daily operations.

Where AI Video Analytics Is Actually Paying Off  
Smith names three areas where adoption is strongest: shelf compliance, queue and labor  
optimization, and loss prevention. Loss prevention in particular has an easy business case.  
About a third of retail shrink happens at self checkout, so catching a skipped scan in real time  
has a direct dollar value attached to it.

That clear dollar figure is exactly why adoption concentrates there first. Smith's advice for  
retailers evaluating video analytics is to pick one or two use cases, prove out the return, and  
only then scale. He is equally direct about the part retailers still get wrong: collecting the data  
is the easy half of the problem. Routing it to the person who can actually act on it is the part  
that determines whether the investment pays off.

Smart Checkout Is About Choice, Not Elimination  
Customer expectations around checkout have shifted, but maybe not in the direction retailers  
assumed a few years ago. Smith says customers want a choice between fast self checkout  
and a staffed lane, not to be forced into one path. Fully cashierless concepts had a rough  
stretch, and some high profile efforts scaled back once the infrastructure and accuracy  
requirements proved tougher than expected.

The more durable trend is making both lanes faster and more accurate, so that whichever  
option a customer picks, the experience holds up.

Scaling Smart Checkout Takes More Than a Good Pilot  
A model that hits ninety nine percent accuracy in a single pilot store can slip once it meets the  
diversity of a real chain. Lighting, layout, and product mix all vary from store to store, and what  
works in one location does not guarantee results across five hundred or a thousand of them.

Smith's guidance for retailers scaling smart checkout is to run tight pilots with clear KPIs  
across a deliberately varied set of sites, not just one format or one location. Testing across  
that variety up front is what keeps a rollout from breaking once it leaves the pilot store.

Automation's Real Constraint Is Labor, Not Technology  
Store automation is being driven less by flashy new capability and more by a basic labor  
constraint. Back office work like inventory audits and equipment monitoring eats staff time  
without ever touching the customer. Every minute an employee spends walking the floor with  
a clipboard is a minute they are not available to help someone, grow the basket size, or bring  
that customer back for a future visit.

RaceTrac's approach to remotely managing food court equipment is a good example:  
catching equipment downtime before it costs a sale is not a glamorous use case, but it is a  
real one, and it is exactly the kind of problem automation is suited to solve.

The Biggest Misconception: Automation Replacing People  
Smith is direct on this point: the biggest misconception retailers have is that automation  
replaces staff. In practice, the store team remains the differentiator, especially as food service  
continues to grow inside convenience retail. Rutter's expanded seating and bar concepts are  
one example of where that human element matters most.

The real value of automation is freeing staff from repetitive monitoring tasks so they can be  
present for customers instead. An overloaded staff makes for a worse experience every time,  
while a store where employees are visibly ready to help is the kind of place customers choose  
to return to.

Connecting the Systems That Already Exist  
Cameras, smart checkout, and automation are often discussed as separate categories, but  
Smith says none of them work in isolation. A camera that flags a shrink event only matters if it  
is tied to a task system that gets a person to act on it. The retailers building genuinely smart  
stores treat video analytics as an operational product with a clear owner, connected into  
inventory and point of sale systems, rather than a security feed sitting in its own silo.

What Defines the Next Generation of Smart Stores  
Looking three to five years out, Smith expects the defining shift to be connection rather than  
addition. Most stores already have cameras, point of sale systems, and equipment sensors in  
place. The gap is that these systems are not talking to each other yet.

He also expects convenience store specific AI to keep pulling ahead of generic retail  
technology, since food service throughput and forecourt uptime are different problems than a  
grocery store faces, particularly as convenience retailers keep expanding their food service  
offerings.

Ready to take the next step? Contact Telaid to see how we can partner on your next project to enhance employee safety by integrating smart technology.

 

 

[Previous New York Retail Safety Act](https://blog.telaid.com/blog/new-york-retail-safety-act)

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