How to Build an AI-Driven Order Management System to Streamline Retail Operations

The retail industry is arguably one of the most complex and rapidly growing ones. Customers’ expectations are shifting, multi-channelism is on the rise, and the overall high fulfillment complexity makes it hard to choose the right strategy.

These challenges are often enhanced with stockouts, delayed shipments, manual errors, and disconnected systems. In search of a solution that would help both build a strong retail strategy and reduce the number of downshifts, one should look into order management systems.

Driven by AI, those systems can take care of multiple tasks, like keeping track of stock, handling deliveries, and even post-purchase marketing. But let’s digest it all one step at a time.

This guide will be equally insightful to retailers and product companies that develop software for retailers. It provides an overview of the AI OMS architecture, development roadmap, and ways to overcome the engineering challenges.

The article is authored by two Solutions Architects at MobiDev, Iurii Luchaninov and Serhii Koba, as well as AI Team Leader Anastasiia Molodoria. With years of experience in AI and retail software development, they’ve led projects that bring order management to the next level with intelligent automation and AI at hand.

Without further ado, let’s learn how to build AI order management.

What Is an AI-driven Order Management System?

First of all, an order management system (OMS) helps retailers handle orders from the moment they’re placed by a customer until the order is received by the customer. Traditional systems focus mostly on tracking, but AI for order management takes it further. Starting from predictive analytics and extending all the way to deep personalization. Let’s take a look at all the benefits.

Traditional OMS in Retail Context

A traditional order management system captures an order, checks inventory for it, processes and ships the order, and then handles returns when needed.

Typical features you would expect from an OMS are syncing stock across sales channels, sending orders to the right stores, and managing cancellations. As a result, you can often get rigid rules that require a lot of manual input, usually from your staff.

A non-AI order management system can be slow to react when there are unforeseen changes in sales traffic or when operations get gradually more complex.

4 Ways AI Extends Traditional OMS

AI always comes in handy when retailers need to automate processes and stay proactive when circumstances rapidly change. Therefore, here are the four most common ways AI order management agents can help:

1. Demand forecasting. It allows your OMS to automatically predict order volumes, depending on the product itself, regions, or channels usually associated with sales. This way, your stock is always just right: no stockouts or overstocks.
2. Dynamic order routing. Depending on what’s most cost-efficient, your AI OMS will decide whether to ship from the warehouse, store, or 3PL, which helps avoid unnecessary logistics bottlenecks.
3. Exception handling. These features translate into leveraging AI in order management to flag anomalies. For example, you may get a fraudulent order, a wrong customer address, or experience an unusual spike in orders. All those can be handled with the right set of actions, which your OMS will recommend.
4. Personalization. To bring your customers the best experience, you can link an OMS with your CRM and offer tailored delivery options, promotions, or bundling—all depending on the individual’s specific expectations.

3 Benefits for Software Product Companies

For a software company, the benefits of offering AI-driven OMSs are equally promising. Namely, those include:

1. Differentiation. You get a strong value add for any POS, ERP, e-commerce, or SaaS solution by offering your buyers a powerful OMS with AI built in.
2. Client value. Customer satisfaction grows with faster and more reliable order flows—a benefit any AI OMS has to offer.
3. Faster ROI. You can get to a positive profit stream faster because AI reduces inefficiencies. For example, from the get-go, you can offer fewer manual touches and lower fulfillment costs.

6 Key Use Cases of AI-driven OMS in Retail

An AI-based OMS offers more than just faster tracking your orders. It offers opportunities for waste avoidance, fulfillment speedup, and customer experience improvement. For example, a Finnish retail conglomerate, Kesko, is expecting AI implementation to both reduce waste and drive growth.

Now that you know the benefits of AI in order management, let’s have a look at particular use cases that prove the points made above.

1. Inventory Optimization

AI predicts demand in real-time and maintains a balance of inventory among stores, warehouses, and digital platforms. That means fewer stockouts and less overstock.

In addition, retailers can identify anomalies early, set up automated replenishment, and get enhanced supply-chain visibility, thus boosting ROI and getting a competitive edge in customer expectations fulfillment.

Discover more about AI-powered inventory management.

2. Omnichannel Fulfillment

Orders can be routed to the best source, whether it’s a warehouse, a local store, or a supplier. AI accomplishes this in real-time, so customers get their products faster. As a result, retailers don’t need a real person to monitor new orders live.

In the long run, omnichannel fulfillment allows for breaking data silos, or rather prevents them from ever forming, as the system stays reactive and constantly changes according to the order trends.

Discover how AI can help dissolve retail data silos.

3. Customer Experience Enhancement

Get smarter delivery choices, anticipatory alerts, and fewer surprises. AI makes it easier to personalize how each order’s delivery should be handled and send early notices when there are delays. It all happens in the background, so customers get the best personalization without having to set up their preferences manually.

Learn more about AI and personalization for application experiences.

4. Fraud Detection & Risk Management

AI identifies suspicious patterns in orders, payments, or returns. That minimizes fraud risk and keeps operations safe.

For example, your system might be getting a spike in orders, leading to higher revenue and therefore profit. However, what if all those new orders start being returned? This means someone has launched a fraudulent campaign against you. AI would detect such an anomaly early, based on similar patterns in the historical data, long before a human is able to notice any strange inconsistencies.

Watch a webinar to learn how AI in a POS System helped one of our clients catch a $7K fraudulent tip.

5. Returns Management

Smart reverse logistics save money and get products back on the shelves faster. AI can help with deciding what to resell, repair, and write off, therefore removing the need to make it an ongoing task for staff, or even eliminating the need for a big returns management and logistics team at all.

6. Labor & Resource Planning

By forecasting workload, AI allows for staffing to match the volumes of orders. This avoids teams being overwhelmed when spikes happen or idling during low-season times. With this approach, retailers can plan for brainstorming and creative/strategic meet-ups and avoid them when everyone needs to focus on huge amounts of work that needs to be done.

Build vs. Buy — Choosing the Best Approach for AI Order Management

It’s not straightforward to choose whether to build an order management system using AI from scratch or buy an off-the-shelf one. Both solutions have their pros and cons, and the best way to go depends on your business model, timeline, and long-term plan.

Off-the-Shelf Solutions

Pre-configured OMS solutions and AI add-ons are easy to install quickly. They’re ideal for retailers that need an instant boost in productivity without extensive development. You pay less upfront, and you don’t need an army of AI experts.

The downside is flexibility. These systems may not be attuned to your particular workflows, are difficult to integrate with legacy systems, and are likely to include the same features your competitors already have.

Moreover, a new third-party system most likely will not be able to make predictions based on historical data, as it exists only in your local database . Since the provider has its own data structure, the system will only start collecting records from the moment you connect it to the third-party service.

Custom AI Development

With a bespoke OMS with AI capability, you are in the driver’s seat. You can have it developed to match your processes and include functionality, like custom order routing, personalization, or advanced demand forecasting.

Since the solution is bespoke, it can also boost your competitive edge, albeit at a price. You should get ready for higher expenses, longer timescales, and a need for some AI and machine learning expertise (whether on your team or from a trusted development company).

Hybrid Approaches

A middle option is to add AI modules to your existing OMS. As an example, you might add a forecasting engine, fraud detection model, or delivery optimization APIs.

This option is faster and more secure than attempting to be completely custom-built, and you can incrementally scale. But it won’t be as deep or flexible as a system built from the ground up.

Decision Criteria

In order to make the right decision between those three options, think about:

• Time-to-market – Must you have results now, or can you wait for a build to ramp up?
• Integration complexity – Will the new system fit nicely with your current tech?
• Scalability – Will it handle additional orders, channels, or regions as you grow?
• Long-term vision – Is AI OMS merely an add-on tool, or a core differentiator for your business?

AI Order Management System Architecture: Key 5 Components

Machine learning-driven OMS is not just a single application. It’s a collection of stacked layers that work on data, handle orders, and deliver intelligence where it matters most. A sound architecture makes the system strong, scalable, and easy to scale when retail grows.

Below are the top five components to remember.

1. Data Layer

The sources for data layer components are typically POS systems, ERP records, e-commerce sites, warehouses, logistics, and CRM.

Here are the components:

• Ingestion pipelines for streaming (Kafka, Pulsar), batch jobs (ETL), or APIs
• Data lake/warehouse to hold both historical and real-time records
• Data quality services that verify, deduplicate, and flag anomalies
• Master data management to unify IDs for SKUs, customers, and locations

2. Core OMS Services

These are the components where all the order-related data is processed. They include:

• Order service for lifecycle management: creation, validation, updates, and cancellations
• Inventory service to keep stock in sync across channels
• An allocation service that routes orders to the right warehouse or store, based on AI
• Returns service to manage reverse logistics

All of those are exposed via APIs and are event-driven (event examples could be OrderCreated, InventoryUpdated, etc.)

3. AI/ML Layer

This layer brings the real intelligence to the system and makes your OMS truly AI-driven.

• Forecasting engine to predict demand by SKU, channel, or region
• Optimization engine to make real-time as-a-service routing and allocation decisions
• Anomaly detection for fraud, duplicate orders, or spikes
• Personalization models for delivery options, recommendations, or promotions

These models are integrated as microservices or through model-serving platforms.

4. Integration Layer

To keep it all connected, you, of course, need a seamless integration layer, including:

• Connectors for ERP (NetSuite, SAP, Dynamics), e-commerce (Shopify, Magento), POS, logistics APIs, and CRM
• Event bus/message broker for real-time module-to-module communication
• Idempotent APIs and retry logic to ensure reliability and prevent duplicate processing

5. Monitoring & MLOps

It requires strong monitoring and governance to maintain the system in a stable condition. Therefore, you need:

• Observability with metrics, logs, and traces for every service
• Model monitoring for drift detection, retraining triggers, and A/B testing
• Governance with audit logs, explainability dashboards, and compliance (to GDPR, PCI, etc.)
• Scaling through autoscaling microservices and container orchestration (e.g., Kubernetes).

5-Step Roadmap to Build an AI-driven Order Management System

If you want to build a solid, time-proof, and AI-driven OMS, you need to follow a specific roadmap that allows for developing all the needed modules and avoiding common mistakes. Let’s have a look at what this roadmap can be.

1. Define the foundation

• Map the core OMS workflows. These can include things like order capture, validation, allocation, returns, etc.
• Align with POS, ERP, e-commerce, SaaS, supply chain, and CRM.
• Define a unified data model (make sure to include orders, SKUs, locations, and customers). This will help eliminate fragmentation.

2. Establish the data layer

• Build pipelines that would get data from all the necessary sources (again, POS, ERP, e-commerce, warehouses, and logistics partners).
• Make sure to create default logic for all product and order IDs across all systems.
• Add a quality assurance pipeline that would handle deduplication, standardization, validation, and anomaly detection.
• Implement observability that would ensure freshness, completeness, and accuracy as metrics. Set up alerts for any errors.

3. Add intelligence modules

Now that you have the necessary foundation, you can start leveraging AI in order management by adding specific modules:

• Demand forecasting that will predict order volumes by attributes
• Dynamic order routing to choose the best facilities to send the order from in real-time
• Anomaly detection that will flag fraudulent or problematic orders for your review
• Returns optimization—to recommend the cheapest and fastest reverse logistics path

The best approach is to start with one of those modules and gradually expand to a full suite of AI OMS tools.

4. Architect for scalability

The process is not yet over at stage 3, as you still need to make sure your system is scalable. Otherwise, you risk losing the momentum in the future when you expand. Here’s what you need to take care of:

• Use an API-first approach and always keep an event-driven practice in mind (create events like OrderCreated, Allocated, Shipped, Returned).
• Separate your entire system into specific microservices: order, inventory, forecasting, allocation, returns. This will make it easier to scale each of them.
• Apply model registry, retraining pipelines, drift detection, and automated rollback. All those make your MLOps truly change-proof.
• Ensure system elasticity with autoscaling, retries, circuit breakers, and caching.

5. Differentiate and optimize

Now that your AI order management system is up and running, plan ahead and get a noticeable competitive advantage by:

• Expanding intelligence to customer-facing features, such as personalized delivery promises, proactive delay notifications, and recommendations.
• Continuously tune algorithms based on your KPIs. Those could be fulfillment accuracy, cost per order, stockout rate, and customer satisfaction or NPS.
• Add dashboards for transparency and easier strategic management. These can include decision reasons, model performance, anomaly logs, and similar.

Make sure to evolve iteratively, though. You should treat OMS as a product, not a project.

5 Common Challenges in Building AI-driven OMS and How to Overcome Them

If you want to develop an AI-powered order management system, get ready to face some significant hurdles. They can range from fragmented data to integration problems or cost pressures. Also, consider that the skills you need to sustain advanced ML operations are not something you (or your teammates) can learn overnight. Below are five common challenges, along with the way to solve them.

1. Data Silos and Poor Data Quality

Symptoms: Duplicate SKUs, order ID mismatches between POS/ERP, stale inventory counts, missing product attributes, or inconsistent store codes

Impact: frequent stockouts or overstocks, orders being misrouted, inaccurate delivery estimates calculated, and AI models drifting from noisy inputs

What to implement:

• Unified data model and IDs: Map product, location, customer, and order entities with contracts per source system.
• Ingestion pipelines: Use CDC for ERP/DBs, event streams for POS, and batch loads for history. Apply a schema registry with versioning.
• Quality pipeline (data preprocessing): Deduplicate, standardize, impute, and validate data. Quarantine bad records using rule-based, statistical, and ML-based checks. Depending on the task, different preprocessing techniques may be applied to prepare the data most effectively.
• Master data management: Maintain golden lists of SKUs and locations with governance owners to ensure consistent, accurate references across all systems. This avoids mismatches, duplication, and routing errors downstream.
• Observability: Track freshness, completeness, and accuracy against SLAs with data lineage.

KPIs: Inventory accuracy %, order data completeness %, data freshness lag, and quarantined records resolved per day.

2. Integration Complexity

Symptoms: integrations that break, syncing only by batches, retries are missing, idempotency issues, and timeout storms during traffic peaks.

Impact: Slower order orchestration, missed webhooks, duplicate shipments, and higher operational overhead.

What to implement:

• API-first, event-driven design: Publish OrderCreated, Allocated, Shipped, and Returned events over a message broker
• Reliability patterns: Apply idempotency, retries, and the outbox pattern
• Microservice boundaries: Isolate order, inventory, allocation, forecasting, and returns
• Integration practices: Use verified webhooks, bulk backfills, and polling fallbacks
• Performance tuning: Rely on async processing, circuit breakers, and backpressure

KPIs: Event delivery success %, 95/99p workflow latency, failed/replayed messages, integration MTTR.

3. Explainability and Trust

Symptoms: Black-box allocation or ETA decisions, support agents unable to justify outcomes, retailer resistance

Impact: Low adoption, manual overrides, and compliance risks (e.g., GDPR transparency).

What to implement:

• Interpretable models: Low interpretability is mainly a problem when your system relies entirely on LLMs, since their internal reasoning can’t be fully traced. In contrast, predictive models like gradient-boosted trees allow clearer explanations through feature attributions, making their decisions easier to justify.
• Decision logs: Record inputs, model version, features, confidence scores, and reason codes.
• Business rules overlays: Apply hard constraints (service levels, cold chain, geography) and human-in-the-loop controls.
• Policy guardrails: Run fairness checks, minimize PII exposure, and track consent.

KPIs: % of automated decisions accepted, override rate, time to justify a decision, and audit pass rate.

Additionally, gather feedback from end-users. This way, if the model makes a mistake, you will be the first to know. This information can later be used to fine-tune and train your model better.

4. Cost Concerns

Symptoms: Cloud costs spike during season peaks, GPU serving costs rise, infrastructure is over-provisioned, and ROI is hard to predict or hectic

Impact: Projects stall, budgets tighten, and leadership grows skeptical.

What to implement:

• Phased rollout: Start with forecasting or allocation in select SKUs/regions. Run A/B tests to prove value.
• Model right-sizing: Use classical or boosted tree models before deep learning. You don’t always need deep learning models, though. If you have limited historical data (e.g., under a year), classical or boosted tree models are often more accurate and resource-efficient. The right model choice should depend on the task, as well as the volume and quality of your data.
• Efficient serving: Batch forecasts, autoscale real-time allocators, and cache stable features.
• FinOps discipline: Tag costs by component, set budgets and alerts, use spot instances for training, and apply model distillation.

KPIs: Cost per order, infrastructure cost per 1,000 predictions, payback period, and contribution margin lift.

5. Talent Gap

Symptoms: Small teams with limited ML/MLOps experience, slow experimentation, brittle notebooks.

Impact: Delays, technical debt, and unstable production models.

What to implement:

• Blended teams: Combine external expertise with that of your own in-house engineers’, and define an enablement plan
• MLOps foundations: Implement a feature store, experiment tracking, model registry, CI/CD for models and data, drift monitoring, and rollback playbooks
• Golden paths: Provide reference pipelines for forecasting, allocation, and anomaly detection
• Runbooks and ownership: Standardize documentation, on-call procedures, and clear ownership

KPIs: Time from experiment to production, % pipelines on CI/CD, mean time to recover from failures, and number of enabled team members.

Build an AI Order Management System with MobiDev

So, are you ready to start using AI for your retail or restaurant order management? All the tips shared above should be quite helpful. However, if you still feel like applying artificial intelligence is too much of a challenge, let MobiDev help.

• Get demand forecasting, smart routing, or an end-to-end AI-driven OMS architecture designed specifically for your workflows, with no unnecessary corner cuts.
• Plan with your own roadmap in mind—let MobiDev help include essential steps for AI OMS implementation without sacrificing what’s already being done.
• Avoid risks and costly mistakes of early AI adoption with the proven track record of expertise that MobiDev will bring to your team.

With your unique business knowledge and MobiDev’s significant order management AI agents development skills, you can get a system that would bring results from day one, get you positive ROI, and ensure a competitive edge like never before.