Outdated Inventory Woes: Traditional inventory management practices, like recurrent physical counts and manual data entry, lead to inefficiencies.
Manual Mayhem: Reliance on manual order processing introduces operational errors and delays.
Predictive Pitfalls: Using historical sales data for demand forecasting results in reactive and often inaccurate predictions.
Stockout Struggles: Inefficiencies in inventory management contribute to frequent stockouts, affecting customer satisfaction.
Overstock Overload: Outdated methods can lead to overstocking, tying up capital and increasing storage costs.
Recurrent physical counts. Manual data entry and order processing. Reactive demand forecasting using historical sales data.
These inefficient practices plague traditional inventory management. This outdated approach gives rise to stockouts, overstocking, and other operational challenges.
At worst, it can tank an entire business. Operational efficiency specialist Jon Naseath shared this cautionary tale on LinkedIn that you should consider:

If you’re already using inventory management software, you’re already off to a good start. But there’s still so much potential you can tap into with technology.
Case in point: Using AI for inventory management. Doing so can transform your ecommerce business in many ways, which we’ll explore in depth in this blog post.
But for now, let’s talk about how AI made its way to inventory management processes and revolutionized them.
What Role Does AI Play in Inventory Management?

Early attempts to incorporate machine learning into inventory systems were cumbersome, basic, and heavily logistical.
But when the 1980s rolled in, the US Air Force’s Inventory Management Assistant (IMA) developed rule-based systems that used predefined “if-then” rules for spare parts forecasting and replenishment.
AI and machine learning gained momentum in the 2000s as computing power increased and hardware became more accessible.
This data-driven era transformed supply chain management (SCM) for better forecasting and smarter stock management.
By the 2010s, the Industrial Internet of Things (IIoT) emerged, interconnecting devices—machines, equipment, vehicles, and products—with sensors linked to the Internet.
IIoT data science then enabled real-time inventory tracking, predictive equipment maintenance, and optimal delivery routes.
Industry 4.0 artificial intelligence and automation leveraged this data-rich environment to drive more responsive processes, such as automatically generating purchases when a popular item is running low.
This is where AI inventory management is at today. Technology has progressed from basic automation to a powerhouse of predictive analytics.
Benefits of AI-Driven Inventory Management
Modern AI can now cut across the entire supply chain, reducing inefficiencies and inventory issues. This can lead to:
Improved accuracy and reduced human error
Data entry and inventory tracking errors can skew your view of stock levels and production schedules.
AI inventory management helps develop more precise demand planning and deliver consistent service levels even in the face of fluctuating customer demand.
For example: Wellness brand Semaine Health achieved a 99.95% order accuracy rate after leveraging Shipbob’s AI-driven inventory placement.
Reduced costs
AI-enhanced inventory management systems take automation to a whole another level with machine learning and predictive technology.
Since robots don’t need breaks, vacations, or sick days, you can optimize their efficiency.
They operate error-free round the clock, which translates into higher productivity (53%) and cost savings (48%) for businesses.
Enhanced scalability
AI can process large, diverse datasets without requiring additional resources.
You don't have to hire new personnel or sign up for new tools to accomplish important tasks, such as understanding customer demand and supplier behavior.
Increased customer satisfaction
AI-powered personalization systems analyze customer behavior patterns to suggest relevant products based on real-time inventory data.
Doing so incites shopper interest while ensuring products are ready for immediate purchase, improving the customer experience as a whole.
Minimization of stockouts and overstocking
A study noted AI’s potential to improve inventory replenishment, safety stock optimization, and accurate delivery predictions.
AI can monitor what’s flying off your shelves and what’s sitting too long in real-time. This precision means fewer wasted dollars on excess inventory and missed sales opportunities.
Data-driven decision-making
51% of businesses and 82% of those using emerging technologies reported that AI improved their decision-making.
AI for inventory management minimizes the risks associated with gut-driven decisions by leveraging data insights and advanced algorithms.
7 Ways You Can Use AI For Inventory Management Optimization

AI recognizes data patterns and market trends and takes it a step further by taking your input into account.
This ability to adapt is a good thing because supply chains aren’t linear. Over time, this feedback loop creates an ever-smarter, more efficient system.
With such sophisticated intelligence, AI enables seven effective ways to optimize inventory management. It can help:
1. Improve demand forecasting accuracy
Stocking the closest number of product units to sell is key to maximizing profits. But Excel and forecasting formulas can only take you so far.
Eventually, you’ll hit a wall, especially with the rate at which ecommerce brands are growing (predicted CAGR stands at 9.49%). Some ecommerce brands don’t even have years of history to match the current market’s trajectory.
Statistical models struggle to capture anything other than linear relationships. They can’t extrapolate patterns from one product to another.
Nicolas Vandeput, Supply Chain Data Scientist (Source)
Predictive AI inventory management allows you to factor in relevant data including historical and real-time sales figures, and business drivers like seasonality, competition, trends, and customer behavior patterns.
Andrei Newman, CEO of luxury home spa brand Designer Home Spas, can attest to its effectiveness:
With better demand forecasting, we've minimized both stockouts and overstocking.
We no longer waste money on excess inventory that sits unsold or faces the high costs of rush orders to meet unexpected demand.
This efficiency helps maintain steady cash flow and reduces financial waste.
Couple this capability with machine learning, and data limitations become less of an issue.
This comes in handy when you don’t have enough data to work with, such as predicting holiday demand for a product that's only been on the market for almost a year.
AI-powered demand forecasting can employ scenario analysis to estimate future demand in a variety of hypothetical situations.
You can also use it to assess the impact of different external factors, such as economic conditions.
2. Automate inventory replenishment
Harvard Business Review declared: stockouts cause walkouts.
In ecommerce, walkouts are as simple as pressing a button.
For this to not happen, you have to be on top of your merchandise's optimal reorder points to maintain the correct inventory quantities, including safety stock.
But it’s not an easy feat for many online brands because factors influencing this parameter vary.
As a result, 60% of global online shoppers had reported running into product unavailability issues.
AI inventory replenishment provides an efficient way to handle this part of your business. Orders are placed when needed, not when the calendar tells you to.
Instead of having set reorder points, the process goes hand-in-hand with demand forecasting (just like JIT inventory management in manufacturing).
According to Nick DeGiacomo, CEO of AI inventory tool Bucephalus:
Replenishment is a complex interplay of prediction, context, simulation, and continuous learning.
It requires not just excellent forecasts, but a deep understanding of the entire supply chain ecosystem.
Top-tier predictions are the foundation, without accurate demand forecasting, even the most sophisticated replenishment system will falter.
Advanced replenishment systems analyze ideal order quantities and reorder points based on this information, generate the orders, and transmit them to suppliers.
IBM reported that companies that leverage this technology typically experience 40% in labor savings and 35% inventory reduction.
3. Use real-time inventory tracking
“Real-time” is a buzzword we can all get behind because it means having access to the latest data at all times.
Who wouldn’t want that when making an important business decision?
Most supply chain decision-makers definitely do. A 2023 survey indicated that 77% of them believe that real-time inventory visibility is a necessity.
A real-time inventory tracking system—offered by inventory management tools like Cin7 Core and Helcim—lets you track what's coming and going in warehouses or distribution centers, and even physical stores (if you have them).
According to SMB merchants using such platforms, 91% agree that they provide comprehensive real-time inventory control.
Using advanced ERP systems is also a necessity at this point. Traditional ERP systems don’t capture much of the critical information needed for supply chain planning and inventory updates.
DeGiacomo compared these systems to looking through a dirty rearview mirror because they provide fragmented and incomplete data. With AI, you can rely on multimodal models for processing and understanding multiple types of input data simultaneously.
“In supply chain land, so much of communication and planning are in documents, text, or audio, while product information is encoded in photos, reviews, or long unstructured feature lists. Multimodal models allow us to quickly and easily extract this information and make sense of it,” he explained.
After Precision Watches implemented an intelligent ERP system and RFID technology to sync their online and in-store inventories during a Rolex sale, Marketing Manager Sergey Taver shared:
Our real-time inventory display raised conversions by 20% because customers trusted its availability. This transparency reduces overselling and increases customer confidence.
4. Optimize inventory categorization

Sleeper products. Slow movers. Zombie products. Whatever you call them, you want these budget-eaters out of your product portfolio.
Inventory optimization means focusing on winning goods.
The ABC analysis is a popular inventory management technique that helps in this process, and it’s based on the Pareto Principle (as in 80% of your revenue comes from 20% of your products).
It lists your products from A to C—A being the most lucrative and C being the least.
Many inventory management tools and ERP systems offer automation that handles this part for you—even setting the appropriate service level for each group.
AI inventory management, however, takes it up a notch by looking beyond how each item generates revenue.
This study proved that the use of AI in ABC analysis allows the consideration of multiple criteria, such as cost information, purchase lead time, production, and criticality.
This helps with optimizing inventory by classifying products based on more nuanced criteria.
For instance, Cin7 Omni’s Inventoro can classify product portfolios by product category, warehouse, and suppliers.
Let’s say, you manage a large ecommerce brand with multiple distribution centers.
AI can analyze sales, restocking times, and item popularity in each warehouse. With the available information, you can identify a product that’s generally a slow seller online that might be a bestseller in a specific region.
5. Enhance warehouse management
SMB ecommerce warehousing often involves outsourcing to 3PLs. You can skip to the next section if you fall into this category.
But if your retail business manages its own warehouse—well, have I got good news for you. Artificial intelligence is advancing warehouse management in more ways than one.
Hardik Chawla, Senior Product Manager at Amazon, SCOT (Supply Chain Optimization Technology) shared, “Image processing and artificial intelligence are improving the efficiency of receiving processes and operational workflows.”
AI warehouse management optimizes and improves:

- Labor management. 37% of supply chain leaders reported experiencing high to extreme labor shortages (drivers and warehouse workers). With real-time data, AI can assign tasks to workers based on current demand, worker location, and past performance.
- Dynamic slotting. AI-powered dynamic slotting tools like Lucas, simulate scenarios to see if there’s a better slot for a product, improving warehouse layout. Features, such as similarity detection, are particularly useful to prevent avoidable mistakes such as picking errors caused by placing similar items near each other.
- Picking efficiency. Innovations such as AI-powered autonomous mobile robots (AMRs) can move across warehouse floors with remarkable accuracy. Meanwhile, sensors and RFID tags make use of AI and IoT to keep tabs on inventory movement.
- Predictive maintenance. Predictive technology detects potential problems before they disrupt the business and cause financial harm. Deloitte reported that it can lead to up to 20% higher productivity, 15% less downtime, and 5% cost savings on new equipment.
- Inventory monitoring. AI-powered drones, such as Gather AI, can reduce inventory errors by an average of 66%. These devices can fly by themselves and take pictures of inventory. AI then steps in to analyze these images and reconcile them with WMS data. They can also reveal underutilized warehouse space.
DeGiacomo identified one of the key reasons AI in warehouse management is essential—Simulation.
AI helps “simulate countless potential scenarios to identify optimal strategies. Advanced simulations can test and refine warehouse layouts, picking strategies, and staffing levels before implementation, dramatically reducing risk and increasing operational efficiency.”
6. Streamline supplier management
91% of organizations that have leveraged AI to improve supply chain activities have positively impacted decision-making and business operations.
Accurate demand forecasts can pave the way to improve partnerships with your suppliers.
If you know in advance how much merchandise you'll need in the foreseeable future, and over what period, you'll be in a stronger negotiating position.
AI also helps you stay ahead by analyzing data to note down any irregularities in supplier behavior, such as inconsistent delivery times.
This information provides a basis for mitigation.
This is great because research shows that only 26% of chief procurement officers could confidently forecast risks within their supply bases.
And if the worst occurs and your primary supplier drops the ball last minute, AI can swoop in to identify replacement suppliers on the fly.
According to Harvard Business Review, tools such as Scoutbee, can scrape websites to create a list of potential suppliers, checking their finances, customer ratings, and other pertinent information.
7. Implement dynamic pricing and promotions
Dynamic pricing lets you modify product prices to match current customer demand, market conditions, and other factors.
It ties in with AI-powered demand forecasting, allowing businesses to make real-time pricing adjustments based on predicted demand fluctuations and inventory levels.
From demand forecasting to dynamic pricing, every element is aligned to anticipate and meet customer needs. This interconnectedness ensures that the entire supply chain moves in unison to deliver the best possible customer experience.
Nick DeGiacomo, CEO of Bucephalus
With this strategy, you can attract 82% of US shoppers who like comparing prices online, enabling them to find the best possible deals.
In terms of planning product promotions, knowing your product portfolio performance and real-time inventory availability has plenty of positives.
Print-on-demand wall art fulfillment company, merchOne, makes use of this information when setting up promotions for their ecommerce partners.
CEO Philipp Muehlbauer shared that their live production data helps “to apply discounts or premiums depending on the current production capacity and speed, to manage expectations and consumer demand.”
AI can also analyze your entire data set to optimize promotional strategies.
Even if you haven't previously implemented a specific promotion, like “buy one, get one” on product A, AI can learn from similar promotions run on similar products.
Machine learning can identify patterns and apply these valuable insights to product A so you can see for yourself if it’s a profitable move for you.
Challenges of AI Inventory Management Implementation
Is AI’s brainpower enough to level up your inventory game? Yes, if you implement it according to plan and prepare ahead to address its limitations, which are:
Data dependency and quality
Talking to Supply Chain Dive, Innovation at GS1 Senior Director Bob Czechowicz shared, “Unless you can feel confident that you have quality and consistent data, you’re probably going to struggle to solve challenges.”
AI is only as smart as the data fed to it.
If your data is incomplete, outdated, or outright wrong, AI’s output suffers as well.
Cleaning your data is the first step. But you’ll also need to establish and maintain standardized data practices to minimize errors and maintain high-quality data in the long run.
Data security and privacy
80% of data experts agree: AI is making data security more challenging.
Relying on AI inventory management software may expose your company to vulnerabilities. 74% of cyberattacks are traced back to software supply chain partners.
Consider using role-based access controls (RBAC) to restrict access to inventory management systems and secure backups in case of data loss or corruption.
It’s also important to adhere to data protection regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), as these frameworks can help you safeguard your data and maintain its quality.
Initial cost and complexity
Implementing AI for ecommerce inventory management can be a hefty investment. Among the expenses are:
- AI software subscriptions
- Integration with existing systems
- Manpower for implementation and maintenance
- Staff proficiency training
AI requires expertise, whether in-house or outsourced. So it helps to have a trained team before deploying any AI system to avoid misinterpreting data and botching inventory forecasts.
Once your team is all set, create a solid base for scaling up by testing and ironing out your initial strategic plans.
Success with AI in inventory management isn't just about the technology – it's about implementation strategy. The key is to start slow with a pilot project and then iterate from there to build a full-fledged product.
Hardik Chawla, Senior Product Manager - Technical at Amazon SCOT
Our Top Picks For Inventory Management Software
Inventory management systems are quickly adopting AI to enhance their tools. The availability of third-party integrations is also expanding.
We’ve tried and tested different platforms to help you find one that offers the best value to your business.
Check out our list of the Top 30 inventory management tools to see which ones can help you implement AI effectively:
The Future of AI in Inventory Management
AI’s capabilities are only going to get better.
Improved real-time inventory tracking and demand forecasting can drive more predictable supply chains in the future.
AI-powered platforms will continue to automate tasks that were once laborious and error-prone, including data sharing and shipment coordination.
If you’re a fan of trailblazing technologies, Chawla said we’re likely to see the following applications in the not-too-distant future:
- Blockchain-integrated inventory tracking: This could revolutionize how we manage and verify inventory across complex supply chains through real-time, tamper-proof tracking from production to delivery.
- Visual stock auditing: Using AI-powered cameras, warehouse shelves could be continuously scanned, identifying receive defects, product conditions, and quantities in stock.
- Natural language inventory queries: Picture warehouse workers or executives asking complex inventory questions in natural language. “What's our current stock level of red sneakers in size 9, and how does it compare to last year's holiday season?” Large language models (LLMs) could provide a detailed, contextualized response instantly.
Final Thoughts
94% of companies plan to use AI in their operations in 2024, and for good reason. Despite the challenges associated with their implementation, the cons aren’t enough to outweigh the pros.
Modernizing inventory management to the point where you can enjoy superior inventory visibility and better decision-making was once a pipe dream in the supply chain industry—but now it’s a reality.
If after reading this, you’ve become more eager to adopt AI for your ecommerce operations, here are other aspects of your business where you can implement this.
For those looking for a more AI-driven inventory management tool, we have compiled a list of the 20 best ecommerce options and a checklist of what features to look for.
In case you're looking for other ways to improve your inventory management, here's a guide.
In the world of ecommerce, things move fast, and you have to keep up with them. Subscribe to our newsletter with the latest insights for ecommerce managers from leading experts in ecomm.
AI Inventory Management FAQs
OK, we’ve made it to the end, but you still have a few questions. If we guessed correctly, the following should answer them. (fingers crossed)
How does AI improve inventory accuracy and reduce stockouts?
AI uses advanced machine learning algorithms to sift through massive amounts of data in real-time.
Analyzing multiple factors, such as historical sales, seasonal trends, supplier lead times and service levels to determine each SKU’s optimal reorder points and quantities.
Best of all, it can incorporate adaptive learning and simulation to predict the most likely outcomes.
Can inventory management be fully automated with AI?
AI can do a lot of heavy lifting, yes, but still requires human intuition.
Managing supplier relationships, negotiating terms, and ethical decision-making often requires complex reasoning that AI lacks.
Plus, AI still has its limitations.
For instance, supply chain data scientist Anish Anand noted that Generative AI, in particular, could capture noise instead of true demand signals, and provide inaccurate forecasts due to poor data quality.
Therefore, a combination of machine and human intervention is still best.
What is the role of AI in supply chain management beyond inventory?
Researchers expect the AI adoption rate to skyrocket across supply chains.
Beyond AI inventory management, businesses can use emerging technologies to design and maintain high-performance cloud systems. AI-driven predictive analytics help minimize downtime by identifying possible vulnerabilities in cloud infrastructure before they arise.
This proactive monitoring is handy in risk management as well. AI algorithms analyze transactional data to locate patterns that indicate fraudulent activities, equipment failures, and maintenance requirements.
Logistics also benefits from AI-powered forecasting. Using machine learning, logistics service providers can gain unprecedented insights into future transport demand, enabling proactive decision-making.
Data & AI Solutions Architect Manuel Fontenla of Ontruck AI Tech shared, “By carefully adapting and customizing technologies, freight transport providers can gain profound insights into demand patterns. This can enable them to predict seasonal demand with remarkable accuracy and optimally size their fleets to minimize costs.”