How AI/ML is Redefining Order Management

How AI/ML is Redefining Order Management

How AI/ML is Redefining Order Management

AI is rapidly paving its way into digital commerce by significantly reducing repetitive manual tasks, streamlining business workflows, and collecting large sets of data in no time.

Today, order management systems are continuously looking to boost efficiencies and deliver excellent UX by leveraging digitalization and automation. AI systems have evolved to be able to reason, self-learn, organize data, solve problems, and perform human-like tasks as well.

Succeeding in digital commerce requires to first leverage AI for faster order fulfillment, and second use analytics to strike a balance between supply and demand.

An order lifecycle typically consists of the following processes:

  • Inquiry
  • Order entry
  • Quotation request
  • Creating a purchase order and Order Validation
  • Order Processing/Fulfillment
  • Order Distribution and Invoicing
  • Mapping customer experience and managing returns (if any)

As per extensive research by Zippia, only 6% of companies have complete supply chain visibility. Up to 69% do not have full visibility. Another APQC survey of 167 sales order professionals reveals automation saves up to $5 to $15 per sales order and decreases sales order cycle times by more than 46%.


Often businesses do not have real-time inventory visibility to conduct demand predictions, data accuracy, and streamlined order fulfillment. This may further lead to understock, overstock, and holding cost issues. Likewise, businesses may not be able to deliver on time, causing poor customer experiences.

Causes of Stock Out


AI not only offers real-time inventory visibility but also analyzes changing market dynamics and predictive behavior patterns of customers.

In this article, we are going to see why you need AI and ML systems to intelligently transform the face of your Order Management Systems.

Role of AI and Machine Learning in Order Management

AI and ML systems are prevailing in the digital commerce industry due to their robust capabilities such as:

  • Better decision-making
  • Collect, process, and analyze big data
  • Creating smart recommendations
  • Deriving real-time insights
  • Making accurate predictions and forecasts

Regardless of the organization’s size, AI and ML will help you automate tasks, improve data integrity, process data faster, reduce human errors, extract valuable and actionable insights, and increase operation efficiency. This will eventually reduce your operational costs.

What is Artificial Intelligence?

AI is an umbrella concept comprising a set of cutting-edge technologies that mimic cognitive functions similar to human intelligence. It is used to build computers and machines that can reason, learn, analyze, and solve complex problems.

AI systems leverage decision and logic trees to learn, reason, and improve. AI can work with any data – be it unstructured, semi-structured, or structured.

What is Machine Learning?

ML is a sub-category of AI that allows a system or machine to automatically learn from data and experiences to continually improve using algorithms. It analyzes large volumes of data, derives insights, learns, and aids better decision-making.

There are a few things to note about ML:

  • With time, ML algorithms boost performance as they collect more data, identify patterns, and create accurate ML predictive models.
  • ML can only work with semi-structured and structured data.
  • ML systems use statistical models for learning and can self-correct when introduced with new data.

How does AI/ML Differ from Traditional Order Management Methods?

Traditional order management methods often come with a series of pain points, known to drastically (and negatively) impact revenue.

  • Manual Processes: Traditional order management methods heavily relied upon humans, spreadsheets, paper-based systems, and manual data entry. Typically, order data was gathered manually, making it time-consuming and prone to errors. AI reduces manual errors by automating repetitive tasks, allowing for strategic decision-making and more efficient inventory management.
  • Imprecise forecasts: Traditional methods used historical data and lacked real-time visibility and data across inventory at various locations and stages. This made it difficult to predict unforeseen events and respond quickly to changing markets. AI leverages ML predictive models to gather and analyze large data sets in real time to derive actionable insights and generate accurate demand forecasts.
  • Inventory Optimization: Poor analytic abilities of traditional methods led to understock/overstock issues, inflexibility, and ultimately lost revenue and disappointed customers. AI leverages historical data, seasonality, market trends, and many other factors to maintain optimal inventory levels, promote omnichannel engagements, and reduce inventory holding costs as well.
  • Support: Traditionally, support involved a human agent attending to customer queries round-the-clock. Today, AI-based chatbots can intelligently handle common customer queries, without human intervention.

Relevance of Using AI/ML in Order Management

As a company and its data grow, inventory management could quickly become complex as it requires seamless collaboration between procurement, warehouse, distribution, and fulfillment workflows.

To make an OMS more efficient and accurate and deliver immersive customer experiences, businesses must leverage cutting-edge automation technologies. That is where AI and ML come in!

AI and ML systems will help learn behavior patterns, predict fluctuating market demands, and cater to customer expectations strategically. AI can analyze big data, derive valuable insights, create intelligent recommendations, accurately make forecasts and predictions, and help you with informed decision-making.

Without AI, businesses are at risk of underselling, overselling, delayed shipments, inflexible workflows, and frustrated customers.


Ignitiv, in partnership with Kibo, can help you leverage the power of AI and ML and implement them in your order management System with improved scalability and flexibility. Be it complex OMS integrations, composable commerce or other cloud or CX solutions, Ignitiv emerges as a great partner for implementation of custom-made digital commerce solutions that businesses need today to deliver personalized, immersive and efficient omnichannel customer experiences with faster time-to-market and boost in ROI.

Contact us today to know how you can reinvigorate your OMS with AI and ML implementation. Also, check back for our next blog post to learn more about how AI and ML capabilities are changing the face of order management in eCommerce.

Rajib Das headshot
Rajib Das
Founder & CEO, Ignitiv

Rajib Das is a seasoned executive with 25+ years of experience in commerce technology. He is a proven business leader with a track record of bringing new organizations and products around commerce to market leadership, partnering with industry leading platforms such as Kibo, Oracle, Contentful and others. Rajib has expertise in building top performing and efficient go to market teams across strategy, solutions, sales, and delivery. He is a thought leader in Omni-channel commerce.