Groceryshop 2026 shows that retail robots are ready to scale and use artificial intelligence

It’s been more than ten years since I first helped bring robots to mall stores. Long gone are retailers’ concerns about robots stealing jobs, scaring children, accidentally bumping into shoppers or simply disrupting the grocery shopping experience.

It’s great to see how advanced the industry has become; we have come a long way. Now that we’re seeing scalability, we can start using near-real-time shelf information across more retail solutions. In this age of AI revolution, there is no excuse not to prioritize and take advantage of these insights.

Is the broader retail solutions space ready to advance and make this data actionable? I attended Groceryshop 2026 last month to find out.

Simbe continues to grow with the robot Tally

The biggest announcement came from Simbe Robotics, which has now completed the rollout of 3,000 Tally robots globally to retailers and countries. With implementation underway at a Tier 1 retailer, that number will grow rapidly, marking another major achievement for robotics in retail.

Simbe has also been proactive in building capabilities to make its data actionable, from analytics dashboards to virtual store tours. This proactive approach will be critical to staying relevant as the retail technology environment continues to evolve.

How far can Simbe go alone in developing these capabilities before they start to overlap with established solutions? And how quickly will established solutions start consuming this new source of data off the shelves? A new way of thinking is certainly needed across the industry. It remains to be seen whether this disruption will trigger the necessary change.

Badger Technologies creates a layer of intelligence for retail

Badger Technologies appears to be making a similar transition. In my discussion with the new CEO, John Gehre, at Groceryshop, he described Badger as evolving from a robotics company to a retail intelligence company.

The robot remains the platform for capturing shelf and store data, but the focus is shifting from making that information actionable to how it can improve decisions and execution at the individual store level.

Going beyond the basics of identifying out-of-stocks and price tags, Badger is exploring whether continuous data collected as the robot moves through the aisle can be applied to estimate how much product remains on the shelf, when replenishment is needed, and when it will reach peak freshness.

The company continues to distinguish itself through its multifunctional robotic platform, as well as Jabil’s engineering and manufacturing scale. The question is whether it can now use these advantages to move faster.

ShelfOptix rethinks the distribution model

ShelfOptix is ​​taking a slightly different approach to scaling retail robotics. While it continues to strengthen its BrainOS-based computer vision and actionable shelf intelligence, the most interesting differentiator may be its deployment model.

The company is targeting small-format retailers, including drugs, convenience stores, dollar stores and regional groceries, with a foldable robot that can travel between stores escorted by a human rather than remaining permanently deployed in one location.

This model reduces the hurdle of dedicating a robot to each store and opens the door to recurring audits over a larger area. This is a practical approach to matching the economics of robotics to the needs of different retail formats.

Blue Collar Robotics Offsets Retail Slowdown

Newcomer Blue Collar Robotics attended the kickoff presentation and was selected as a finalist. The company is tackling a more difficult but potentially more transformative retail use case: physically picking products in the store.

Instead of focusing on shelf scanning, the startup is developing a mobile robot designed to move products between shelves and carts, with initial customer trials planned as it works towards commercialization. The challenge is significant and requires navigation, vision, picking, controls and back-end integration to work together reliably.

That’s ultimately what I believe the long-term role of robots in retail stores will be: moving from observing retail execution to actually executing it. This is an exponentially more difficult use case that the industry is starting to pursue, and it will take time to mature before robots can deliver results accurately, repeatedly, at scale, and with speed.

MUSE expands modular approach to retail

MUSE, winner of last year’s Shark Reef Startup Pitch, has returned to the Groceryshop as it continues to expand the capabilities of its modular robotics platform. The robot moves the product through the store, allowing store staff to focus on replenishment.

The company has added modules to guide customers to item locations and support in-store promotions. It is also developing additional modules for picking and replenishment, security and floor cleaning.

With additional funding and an ongoing pilot project with a small U.S. retail chain, MUSE is looking to grow and gain a stronger position in the U.S. market. It’s a very aggressive and wide-ranging approach to expanding the robot’s capabilities.

I’ll watch as the company leverages what it learns from implementations, along with recent technological advances, to respond to a similarly aggressive roadmap.

The path to end-to-end shelf automation

As robots become increasingly capable of continuously collecting pricing, inventory, RFID, and visual data, simply providing another data feed or API is no longer sufficient. The next competitive battleground will be how effectively intelligence is translated into actions that improve store execution.

This will accelerate as legacy solutions begin to capture this data. I still believe that many of these decade-old systems will become much more relevant when they start leveraging actual shelf conditions instead of relying on floor data, random compliance checks, and point-of-sale data without a true understanding of the retail real estate that generated the sale.

This will be an important step towards end-to-end shelf automation. It will make some existing shelf solutions obsolete – a positive outcome – and make others much more relevant.

The future is here, more than ever. Which was to be experienced and watched as innovation begins to deliver results accurately, repeatedly, at scale and with speed, at a speed still unknown to this industry.

About the author

Georges Mirza has been at the forefront of retail and consumer packaged goods (CPG) innovation, creating market-leading category management and retail analytics solutions that have achieved a majority of market share. It has pioneered advances in robotic data collection and image recognition, addressing challenges such as stock-outs and inventory accuracy.

Today, Mirza advises and partners with technology and retail leaders to define and scale next-generation growth strategies. Follow him on LinkedIn or X.