We’re past the initial buzz about AI. It’s time to get to work. First, you need to identify the use cases that make sense for AI. Then make sure you have the right data, structured the right way, in the right places to support them. If you fulfill online orders, then it’s critical to consider how data from your distributed order management system (OMS) can be used by AI to drive better business outcomes. But first, let’s clarify what we mean by AI.
The three different types of AI
Predictive AI
Predictive AI uses traditional machine learning methods to optimize business outcomes.
It uses historical data and algorithms to forecast future trends and customer behavior. In ecommerce, predictive AI can be used to:
- Forecast demand
- Personalize shopping experiences
- Optimize inventory and safety stock
This enables businesses to make data-driven decisions that improve sales and efficiency.
Generative AI
Generative AI creates new content, such as text and images, or can be used to generate insights. In ecommerce, it can be used to:
- Create product imagery and ads
- Generate product descriptions
- Augment product attribute data
- Generate personalized marketing campaigns
- Enhance customer experiences by providing tailored recommendations or dynamic visuals
- Discover actionable insights by allowing better interrogation of operational data
This enables teams to produce assets faster, be more efficient, and provide a better customer experience.
Agentic AI
Agentic AI refers to AI systems capable of making decisions and taking actions autonomously to achieve specific goals. The agent “appears like an agent to you”, like a travel agent, or a virtual assistant. When you ask it to do something, it can handle a more complex task or set of sub tasks. In ecommerce, AI agents can be used to:
- Respond to customer service inquiries
- Create personalized promotions
- Manage dynamic pricing
- Optimize ad spend for inventory turns
- Ensure fulfillment SLAs are met
The key here is that AI Agents are able to adapt in real time to optimize outcomes and improve efficiency. But to take advantage of AI, your machine learning models, Gen AI solutions, and AI agents need access to the right data.
How OMS data supports AI use cases
An OMS is a treasure trove of data. Some of this data will need to be accessed live to achieve the right outcomes. Other data could be imported into a Demand Forecasting solution, Customer Data Platform (CDP), or pricing optimization engine. Let’s take a look at some specific data points.
Customer Service
If you plan to deploy AI Agents to support customer service, access to live data is key. Agents will need an up to the minute view of:
- Customer orders and status, including:
- The step in the fulfillment process
- Expected ship date
- Expected delivery date
- Whether it’s expected to arrive on time or if there are any delays
- Accurate inventory availability (including local inventory)
That way they can respond to real-time inquiries, with real time data.
Demand Forecasting
By integrating OMS data with an AI-powered demand forecasting solution, you can take this to the next level. Specifically an OMS can provide:
- Intra-day inventory movements, including both offline (consumed from Point of Sale) and online transactions
- Digital demand signals
What’s a digital demand signal?
A modern, event based OMS captures an event every time a customer checks whether an item is in stock or not. Which means you can look at the ratio of stock availability checks to orders⸺and it turns out, this can have huge predictive value. Why? The ratio can range from 3:1 for a more commoditized item, to 100:1 or more for more expensive items, with a lot of variability in between. And they can change over time. Which means they provide a unique opportunity to further improve your demand forecast accuracy. And this more accurate demand forecast can be used to further optimize order sourcing logic to maximize inventory turns, and inform your replenishment strategy.
Personalized Promotions
Want to tailor promotional messages to each customer? Then you’ll want OMS data in your CDP, and access to live inventory availability. Think about how your promotional messages could be different if an AI Agent could see:
- Online order history
- On time delivery rate, both overall and for each order (so you can rectify a bad experience)
- Return rate
- Local inventory availability
Not only could you improve conversions, but also make amends for poor past experiences to ensure future purchases.
Fulfillment SLA Optimization
This is where access to live data is essential. To prevent fulfillment SLAs being missed by taking corrective action, an AI Agent will need access to the following real-time stats:
- Current status of an order
- How long an order has been in each status
- Expected ship date
- Expected delivery date
- Whether it’s expected to arrive on time or if there are any delays
- Alternate inventory availability
- Average order processing time at a location
- Current number of orders in process at a location
- Current fulfillment capacity by location
That way AI Agents can detect potential delays early, and take corrective action so you can keep promises to your customers.
Pricing Optimization
AI opens up new opportunities around dynamic pricing to increase conversions. Once again, OMS data is an important input. Specifically, inputs to dynamic pricing may include:
- Intra-day inventory movements, including both offline (consumed from Point of Sale) and online transactions
Ad Spend Optimization
Return on Advertising Spend (ROAS) is the typical metric used to measure ad performance. However, with the advent of AI comes new opportunities. No longer are marketers restricted to ad campaigns shot months before products come to market. They can now generate new ones on the fly. Which means, you’ll want your ad platform or CDP to have access to:
- Real time stock availability
- Sell-through rate (for both online and offline demand)
- Target sell through date
Why? So you can stop wasting ad spend for items that are out of stock. And, even better, optimize ads to hit target sell-through dates and increase physical inventory turns. That way you can avoid expensive pull backs of inventory from stores, and/or minimize markdowns.
Key Performance Indicators (KPIs) to measure AI success
How do you measure the success of your AI initiatives? Let’s take a look at the KPIs you can look at for each use case.
- Customer Service
- Call volume
- Call resolution time
- First call resolution
- Average call handling time
- Customer Satisfaction Score (CSAT)
- Demand Forecasting
- Demand forecast accuracy
- Inventory turns
- Inventory carrying costs
- Average delivery distance of an order
- Delivery costs
- Personalized Promotions
- Click through rate
- Conversion rate
- Fulfillment SLA Optimization
- Order fill rate
- On-time-in-full rate
- Perfect order rate
- Average order processing time
- Net Promoter Score
- Pricing Optimization
- Conversion rate
- Inventory turns
- Ad Spend Optimization
- Inventory turns
- Markdown rate
OMS data is critical to AI success
AI has the power to transform business operations. Unlock growth, efficiency, and improve customer experiences—but it all starts with the right data. Your AI solutions need access to the right information, in the right place, at the right time. And for some use cases, especially Customer Service and Fulfillment SLA Optimization, real-time data is essential.
By integrating OMS data with AI systems, businesses can enhance decision-making, optimize operations, and deliver superior customer experiences.
Ready to harness the full potential of AI? Start by unlocking the power of your OMS data today.
For more information on how Fluent Order Management is uniquely positioned to support your AI strategy, contact us today.
