Top Tech Trends Shaping 2026: AI, Automation & More

Hello everyone and welcome to a new episode of Letho Retail Re-Imagined podcast. I'm Victor Novak and today we're looking ahead to the technology set to reshape the global retail in 2026. Uh artificial intelligence is uh moving faster than any retail innovation we've seen in the past decade. It's uh expected for the AI retail market to grow by over $40 billion by 2030. Uh and what is really important is u AI is not anymore only analyzing the data but it acts on it. In this episode, we break down the top five retail technology trends with uh AI for 2026, focusing on what's emerging right now. In particular, we are covering aic and autonomous AI, unified AI powered retail platforms, AIdriven pricing and

promotions, predictive supply chains, and AI powered security and fraud prevention. And to help us uh unpack these uh trends, we have an outstanding team with us on board. Uh Brad Mitcheller, uh Leio, we of North America, uh Victor Hart, our global sales manager, Cecilia Flores Castillo, uh our retail supply chain expert joining us from Brazil, and Andy Marino, retail efficiency experts across EMIA markets. So let's dive right into the first trend and uh it's a big one. Aantic and autonomous AI. AI has uh officially moved from uh being a decision support tool to uh becoming a decision execution mechanism engine. Uh this is basically where Agentic AI comes in. Systems that plan, act, and self adjust in real time. So Brad, I'm

curious how is this change is uh uh playing out across uh North America? >> Yeah, thanks Victor. In North America, you know, retailers are under, I think, more pressure than ever before to make faster, more accurate decisions. And so, you know, they're looking for a gentic AI to really become the answer for them. Uh retailers are no longer satisfied with just dashboards that tell them what happened in the past. They want technology that shows them what the problem is, you know, identifies it and then actually becomes the, you know, fixes the problem, becomes the answer on the fly in real time. And so what we're seeing here is, you know, autonomous systems that can do a lot of things. They can cut prices when seasonal items sort of slow down, right? They can redistribute inventory automatically between their stores and they can initiate replenishment as soon as sellrough patterns and demand changes in their business. um we want to be able to coordinate promotions with actual inventory availability as well. And so we're

seeing Agentic AI play a role in all these areas. I think if I look forward to 2026, it will be dramatically different uh from the past couple of years. Retailers are expecting AI to act for them in real time, not just in retrospect. Right? We want to be proactive instead of reactive. >> And this isn't just theoretical. Decision-making agents are handling replenishment cycles. markdowns and even new product launches. For example, a system can detect that a summer item is losing positions in minutes, not in days, and it can immediately adjust its price, trigger a transfer to a higher demand store, update its forecast, and notify the category manager. And these micro actions add up to millions in protected margin across a vast network. A good example from Leafio's ecosystem is the shelf AI assistant, which scans huge volumes of reporting data and flags shelf assortment or availability problems instantly and proposes fixes

right away. This model is becoming common across all of the retail industry. >> Thanks, Victor. That's really uh that's uh really insightful and I'm curious how does it play out across Brazil, Sicilia? And let's not forget the after sales process automation. So not only in Brazil but in Latin America, this process has been transforming customer support and this using of AI service agents allows now the retailers and the overall industry to do a lot of automated actions. for example, sending post purchase instructions, answering product questions, managing delivery updates, automated returns, recommending add-ons, and also escalating complex cases to humans. So, this is creating nowadays a continuous personalized touch point that retailers could never deliver manually. And customers absolutely love the 247 responsiveness. >> Absolutely. So this uh convinces me one

more time that AI is taking center stage not only in informing the retailers about the necessary changes but actually carrying them out. So right now it's a real transformation where humans set up uh and configure the systems but and set up the strategy but the system carries it out executes a set the set strategy. Uh so now let's move to the next trend that enables all of these uh possible uh unified retail platforms. So again based on what we discuss with the customers with uh the retailers across the globe uh we see more and more than retailers are becoming more aware that these connected solutions are actually having a huge impact on their operations and actually slows them down. uh at the same time the unified AI platforms are becoming more and more significant and are playing more crucial role nowadays

in ensuring the uh optimum performance of retailers and achievement of their goals. >> Yeah, absolutely Victor I cannot agree with you more. The same in EMIA. We see more and more companies are trying to eliminate the Frankenstein architecture where you have a number of solution just stitched together but actually not producing anything that is going to be really viable and you understand if you are not having the unified architecture you are not going to be able to compete in terms of speed in terms of efficiency and of course customer edge. So imagine again a very simple situation. You've got one team planning for the promotions and another team having no idea like supply chain team having no idea what's going to be happening. What you are going to end up with is of course very dissatisfied customers who are going to come for the promotion but not going to receive goods they wanted. And therefore, one of the strongest trends we see in the EMIA region is customers

looking for a unified platforms that help to automate for multiple and crucial business processes such as category management, such as inventory, such as promotional management and of course merchandising because having this unified uh infrastructure helps us to make a better decision and really it helps with a mentality shift. It helps us rather than reactively working issue on issue. It helps us to have also unified goals which is also very important and provides number of our customers with a big competitive edge. >> Yeah. And and it's essential in North America too for sure. You know retailers they want ecosystems not apps not applications. They want future demand demand to drive you know how they lay out their shelf. They want their shelf performance to drive how they update their assortment. and they want assortment to automatically drive how they plan and replenish their inventory. For example, if a category manager decides to approve a new product and launch it into the stores, they need a

unified platform that instantly updates planagrams, you know, pushes it down to the store managers to actually go and make those planagrams compliant and then ultimately trigger updates to our forecast and our replenishment plan so that stores actually have the new inventory that aligns with the category manager's decisions. That's the kind of you know speed and real-time planning that you know retailers are expecting and will be expecting in 2026. >> Exactly. And unified platforms are proving to be really the only scalable approach. So retailers are typically starting small maybe with inventory optimization as an example and then expanding into planagrams then promotions then advanced analytics because everything shares the same data structure. The ROI simply compounds as more modules are added on. And we see this all the time with Leafio's ecosystem. Forecasting, planagrams, assortment, shelf analytics loop endlessly uh reinforcing each other just

driving better and better results over time. >> Well, absolutely. And uh important thing uh that we spot as well with our customers when the entire retail chain um is able to communicate in real time. It uh allows to react faster, take faster decisions, plan smarter and obviously this all comes up with reduced cost of um time consuming and costly manual work. Uh as the next trend is discussed, let's look at one of the hottest areas where AI is rewriting the rules. Uh pricing and uh promotions. Uh so pricing is becoming extremely dynamic. Uh in 2026, static price tags are becoming more and more as an exception, not the rule. uh artificial intelligence is uh enabling retailers to drive realtime adjustments based on the demand, competition and uh stock levels.

Yeah, exactly. Uh dynamic pricing has finally hit mainstream adoption which is awesome. Now artificial intelligence engines can detect demand drops, analyze competitor moves, read local elasticity, evaluate stock levels, apply predefined margin rules, and much more. And this helps them to automatically adjust prices through digital shelf labels, which can mean thousands of micro updates per day, which you can imagine is simply not possible when it's being managed um by humans. And also it's important to mention that promotions are becoming even more scientific. So now artificial intelligence systems can identify the cannibalization risks, the halo effects, promotional fatigue, optimal depth and duration and also the crosscategory uplift. Just to give a quick example here in Brazil, we are seeing retailers now achieving 20% higher availability of products also reducing their uh even in

50% their over stocks and finally achieving a 15% higher forecast accuracy. So promotional science is now becoming a real competitive advantage and shoppers feel the impact instantly. >> Uh I totally agree with you Celia. Pricing is becoming a living system. adjusting uh by the hour uh and it's really important that companies are able to adapt to this to these dynamics uh seamlessly. Uh now let's shift to the backbone of retail that AI is reshaping the most supply chains. And I think what's important here is in the recent years companies around the world retailers around the world uh uh realized that uh the traditional supply chains were built for optimization not for volatility. But actually volatility is something that happened and uh retailers faced a lot

during these years. uh so the value of uh AI in today's conditions and why it's so important is because it can predict the disruptions even before they happen and uh makes enables retailers to get prepared to ensure the minimized risks minimized losses for them and actually executing their strategy uh with no delay with no uh actual losses to the planned effects. Mhm. >> Uh >> yeah, I couldn't I couldn't agree more, Victor. And it's important to mention that today uh artificial intelligence can scan millions of signals of that of data across a global ecosystem. So now we can consider within the supply chain efficiency, the weather patterns, the port congestion, geopolitical instability, many factors that back in the day were not possible to even consider. Now we also can include in

these factors the social sentiment and news cycles shipping and custom logs and also the supplier re reliability and lead time. So whenever a risk emerges which is a constant in the supply chain the system can flag potential issues weeks in advance. So now instead of reacting to delays retailers can prepare and actually do this strategy in a planned way. So now with these tools, the retailers can reroute the shipments, refine the order timing, suggest alternative suppliers and also adjust their network flows. What used to require days of manual analysis and coordination now happens in seconds and this level of prediction is becoming a competitive advantage. >> In the US, you know, retailers are taking it, I think, even further. Uh predictive supply chain data isn't just for logistics. you know, it's really feeding into every aspect of retail planning uh across pricing, promotions, assortment, you know, replenishment

logic, even things like workforce scheduling and you know, automation amongst all these components is becoming really paramount for the retailers here in the US. Uh let me give you an example. An example, let's say a storm is threatening our deliveries, right? Our punishment to our stores. the system can and should be able to automatically increase safety stock, you know, move around labor hours, delay promotions that we have planned, adjust pricing, etc., all without waiting for the crisis to unfold. And now we're seeing, I think, a new powerful dimension to that, which is AI enabled sustainability and waste reduction, right? I think AI can play a great role in sort of sustainability. Uh, and retailers are using these predictive models to reduce spoilage. um especially in the perishable categories uh by by ordering smarter and with better timing. We're minimizing excess miles traveled by optimizing the routes that we're taking. We can consolidate shipments to lower emissions uh and then balance write-offs

so that our inventory is very aligned with our demand uh to eliminate those write-offs ultimately those perishable categories. things like shrinking safety stocks without you know increasing the risk of stockout and aligning fresh delivery deliveries with true consumption patterns all plays a role in sort of making our supply chain uh sustainable and AI is is really boosting this it's not just about cost optimization though it's really environmental responsibility driven by data in our business a more predictive supply chain reduces food waste lowers the carbon footprint and helps re retailers meet ESG targets without sacrificing profitability and that's where I think I see a gentic AI and supply chain kind of converging uh right now uh at this time the system just just doesn't predict a disruption in the future it actually you know looks at it and executes adjustments to overcome it it's sort of becoming this

self- steering supply chain that optimizes around availability cost and sustainability all simultaneously. So this is happening right now. I think it's very exciting. >> Absolutely. Uh I personally think that uh companies who are uh late on implementation of these uh technologies for predicting the future and actually avoiding those disruptions or avoiding the heavy impact of those disruptions will ultimately lose a lot of competitiveness if they don't keep up with the tempo with the uh evolution of the uh today's technologies. uh and uh as the final one which personally for me is the most interesting uh because that's something that we face when we speak about space planning that we uh hear from the customers more and more often it's uh computer vision and image recognition. So basically it all started uh with uh standard recognition of the pictures. But right

now uh the technology is uh evolving so rapidly that uh the computer vision allows retailers not only to run a simple uh comparison between the pictures between uh what's uh in the system and what's in the store but actually uh enables them to also run the com the full compliance of the planagrams. the uh stock availability of the store of the goods on the shelves and in particular run checkout optimization and uh get real time customer behavior insights which proved to be extremely important for successful realization of the space planning strategy for each uh company. Uh so it's totally understandable why for many retailers computer vision is becoming the bridge between the physical store and uh an AIdriven decision making indeed and I would like to also add

Victor that I think that's been the most revolutionary part when we are speaking about instore operations because if you look at the typical store operations there could be a number of people who are continuously busy they need to check if the products are placed correctly. if they're placed at all, if every single product which needs to be shelfd is actually there, they need to check on pricing, they need to make sure that they have the right quantity and they have to do a lot of this work. Uh sometimes as a result if your store is underst stuffed uh as a result you might not be able to provide the level of customer service that is expected from you and therefore despite that it helps it helps store employees to automate so many different manual activities which would otherwise take them hours and hours during the daytime. Store employees can actually focus more on the customers, provide better, more personalized service and of course at the same time increase not only decrease cost for the stores on those manual operations but also increase revenue uh because the customers are going to be

more satisfied and going to get the quality of the service that would be required. So I believe if you're looking at the email markets that's one of the most revolutionary changes that happened with regards to AI. Yeah, I'm so excited about computer vision. Computer vision, I think, really should go far beyond just compliance, right? It's turning into a dynamic performance engine. Image recognition models uh can do a lot. It's really amazing. We can read product labels and package designs with extreme accuracy. Analyze uh you know, shelf space productivity, identify which items are, you know, touched but not purchased is incredible data to have. um we can evaluate brand visibility, track how long products are remaining on the shelf and ultimately map customer movement paths which help us optimize layouts. One of the biggest leaps we're seeing in North America is the integration with autonomous actions um you know coupling with this computer vision. So for an example, if the system detects an out of

stock, it can trigger an immediate replenishment task to make to you know overcome that out of stock and we can update the forecast, notify the distribution center, evaluate if we should change the assortment because of availability and ultimately you know once those decisions are made we can then confirm that compliance is um you know refilled on the shelf. And so this really turns I think you know hours of manual delay process and mitigation into a closed loop which is you know detecting the problem taking the right actions to solve it and then verifying that we've solved that action. So this is where computer vision is really becoming the eyes of the entire retail ecosystem. Uh thank you for this insights colleagues and uh before we close it's uh important to acknowledge the challenges retailers face in adopting these technologies because obviously it takes some effort some time to uh make the technology up and running. uh and uh the most frequent

challenges retailers face during the implementation is fragmented lowquality data uh lack of skilled users, skilled personnel to actually utilize those technologies. Uh privacy and regulation pressure on the legislative side. Uh upfront investment obviously is also one of the uh key driving factors for the retailers. uh it's sometimes it may be harder to measure ROI from the start and you can only understand the impact this technology had after the implementation and and the usage of the solution and uh employees concerns around automation because obviously automation comes with uh uh the reduced amount of time reduced amount of employees may come with these uh challenges for certain retailers but we see retailers overcoming these challenges through unified data platforms. Uh pilot first

rollouts where we can focus on the limited scope to give the customer the ability to test everything before committing to scaling the system fully on the whole chain. Uh modular cloud ecosystems where you can start with one module and after implementation start scaling. So there is always a potential to grow and increase your the suit of solutions and optimize your supply chain even farther. Uh training employees and that's something we provide when we implement the solutions. It's a full on boarding full uh support in terms of using the system and sharing methodology. How to make it efficient and at the same time how to uh not just uh replace employees but how to enable employees to use the technology in a smart way to deliver the expected results to the chain. Uh it's obviously specific governance and compliance frameworks and transparent communication

being transparent about what's possible, what has to be done, what are the uh potential results and uh how much effort should be uh should should be committed to achieve those results. So uh one important conclusion is that the retailers who approach AI as a strategic partner and not just as a tool or uh just an add-on to the existing infrastructures are the ones who are winning already and the ones who will win eventually if they're starting this journey today. Uh so thank you everyone. Today we explored the five biggest AIdriven retail technology trends shaping uh the coming 2026 and agentic AI unified platforms predictive supply chains uh dynamic pricing uh and intelligent security uh and uh those are the areas which are reshaping how retailers work today uh

how retailers plan today and how retailers serve their customers to ensure for the best uh quality and the best customer experience. And uh I want to thank you once again to my colleagues who joined me on this call to discuss uh these extremely interesting uh areas and topics. Uh Brett Mitchell, uh Victor Hart, Celia Flores, Castillio, and Annie Marenko. Thank you so much. Thank you for sharing your expertise, your insights. It was extremely valuable and I hope it will be valuable as well to our listeners and viewers. Uh and uh to our listeners and viewers, thank you for listening uh watching us. Uh you can explore more insights on the Lithio AI blog. Uh and if you want to see how artificial intelligence can transform your retail operations, visit us at Lethio AI. uh leave a simple request and we'll reach out to you with further details to tell you more to share more

about the possibilities and uh share our expertise with you. Uh until next time, keep reimagining the future of retail.