2023 was an incredible year in the development of artificial intelligence (AI). With the massive adoption of technologies like ChatGPT, millions of people are now uncovering new ways to use AI to create content, including email, video animation, and even code.

Since its first debut to the public in 2022, generative AI has dominated headlines and conversations about its potential impact on nearly every aspect of business and life.

OpenAI reported reaching 100 million individual users in just two months within the first 60 days of releasing ChatGPT, with continued mass adoption as it ascended into corporate accounts.

Corporate interest in generative AI and machine learning (ML) reached unimaginable levels of adoption based on the mere promise of AI. A survey by Deloitte Insights revealed more than 80% of business leaders believe generative AI will increase efficiencies in their business.

But much of what you see of AI is demonstrated in its potential form. That’s because AI continues to learn. And what it needs to learn is data. And so next year, abundant, clean, and accurate data will be key to making the most out of AI, especially in retail.

Data is the lifeblood that fuels AI, enabling it to build product inventory models correctly, predict fulfillment and labor requirements, and create shipping strategies that ensure a positive customer experience.

However, retail data is often poorly structured, incomplete, and resides in multiple systems, making it tricky and expensive to locate and consolidate. Retailers can train and test AI models by re-creating the exact conditions in which an order was sourced if they have access to the correct data, such as when an order was placed, the item’s location, how much labor will be needed at a particular store, and how long it takes to process the order from multiple locations.

However, the cost of finding all this data can account for 80% of an AI project budget. And in many cases, organizations still don’t have the right data even after much effort. So, the project fails before it’s launched.

To successfully use AI/ML, retailers need to know how to ask the right questions and access clean data with signals that are relevant to those questions. When working with customers, we need to focus on helping them use AI technology to improve inventory availability and order management, which drives faster inventory turns, lower delivery costs, quicker and more accurate deliveries, and more efficient fulfillment operations.

Modern event-based retail order management systems (OMS) are a part of that process to help retail professionals access and leverage the correct data for AI/ML to reduce project failures and drive growth.

Modern inventory data and order management technology can capture time series data and other contextual data, such as general order history, inventory positions at a specific point in time, fulfillment rules, and attributes for locations, products, and customers. Instead of the normal process of purging or condensing data, the information is stored for future analysis.

In addition, the highly flexible and composable nature of best-of-breed OMS solutions improves inputs to extend AI/ML models. This enables users to capture and tag any additional data when needed. For example, OMS can make it easier to extract information by tagging data that contains signals.

OMS makes relevant and enabling data possible by adding custom attributes to orders, returns, locations, products, shipments, and inventory positions. It can also capture point-of-sale transactions. This provides retailers with a complete picture of offline demand, which is critical to supporting AI/ML use cases in the future.

With an OMS, AI models are more agile and flexible, allowing them to evolve to meet changing demands while enabling workflows and user interfaces to leverage different model outputs. This will help retailers find the right data to enable generative AI to achieve its fullest potential in retail, improving profits and customer loyalty.