Best Practices to Integrate AI Into Your Order Management System

Best Practices to Integrate AI Into Your Order Management System

Woman working with AI algorithms on computer

Artificial intelligence (AI) and machine learning (ML) are paving the way to completely transform order management. AI and ML enable vast business opportunities, from easing processes to enhancing customer experiences.

But what makes it even more interesting? The true potential of AI-based order management systems (OMS) lies in the ability to integrate data across various platforms for real-time inventory management and predictive analytics. But where do you begin? How can AI and ML-based algorithms provide holistic insights when trained on a massive data set? In this article, we’ll dive into the best practices to integrate AI and ML with your order management solution.

Assess your Current OMS

There are several factors that will give you an idea of how many resources you need to embrace AI-based systems and whether your business could handle it or not:

  • Understand your business architecture and current OMS capabilities.
  • Run a comprehensive analysis of your order fulfillment cycle and identify areas, pain points, and gaps in the supply chain that could be improved with the power of AI.
  • Define customer expectations.
  • Analyze your operational maturity to gauge the stability of your business, consistency in processes, and optimal allocation and use of resources.
  • Identify which AI features such as predictive analysis, automation, or inventory optimization will reap maximum benefits for which areas of your order management.

Accordingly, you can customize a solution that aligns the power of AI with your unique business needs to yield the desired results.

Set Clear Objectives

Hire experts who are well-versed with OMS, ML techniques, AI algorithms, and data analysis as they will help you set clear objectives for the project and monitor relevant KPIs. These experts will collaborate with your teams to ensure seamless implementation and operation of your AI-based OMS.

Data Collection

Since AI heavily relies on data, make sure to maintain data quality and security throughout order management processes. We recommend doing the following:

  • Gather customer information, historical order data, market trends, inventory records, etc.
  • Conduct data cleaning and preprocessing methods to eliminate duplicate entries, errors, and inconsistencies.
  • Take proper data security, privacy, and governance measures to safeguard customers’ personal information.
  • Make sure to maintain utmost data integrity, accuracy, and compliance when preparing data to input into your AI models.

Machine learning workflow for OMS

Choosing AI Algorithms

Choosing an optimal tech stack is important when building a scalable and reliable AI-based OMS. It involves selecting a set of applications or software you wish to leverage to fulfill a specific purpose. This comprises of right programming languages, frontend/backend tools, frameworks, API integration, database, cloud platforms, machines, control robots, etc.

Most importantly, choose suitable AI algorithms and models that will fill the gaps in your OMS and align well with your defined project goals. These include ML algorithms, NLP models, and deep learning architectures.

When choosing algorithms, make sure to consider factors like ease of use, scalability, security, development teams’ skills/knowledge, data complexity, computational requirements, and model interpretability.

Testing and Validating

Next comes training the predictive AI models with the prepared data. Divide the prepared data into sets of training and validation to check their accuracy and performance.

Consistently analyze data and customer feedback to detect opportunities for improvements. Identify false negatives/positives, accuracy of predictions, and modify AI models based on real-time feedback. Based on the validation results, you can make further amendments.

User Training

Employees may feel skeptical about AI. To help them overcome this unfamiliarity, implement user training via detailed, clear documentation to introduce them to AI-based OMS, and communicate the value proposition of AI-based OMS.

Involve users in the development processes such as feedback meetings, and user testing to promote their interest. Address any misconceptions through strategic change management initiatives.

Further, you may collaborate with data scientists, AI experts, and domain experts to overcome any change management challenges.

Conclusion

We hope that this detailed guide on how AI and ML can propel your OMS capabilities gave you valuable insights into how you can implement it, where to start, and what the future holds for your OMS. Leveraging AI and ML systems for inventory management not only reduces your sales cycle time but also gives you a competitive edge over others. Moreover, it also saves on operational costs.

Soon, using AI will be the key factor to succeed in digital commerce. It is up to you to decide when will you take the first step towards AI to stay on top of your game.

Ignitiv, in partnership with Kibo, can help you leverage the power of AI and ML. Ignitiv is a boutique full-service agency that combines strategy, digital marketing, technology and customer analytics expertise to help craft integrated digital experiences that deliver more customers, more revenues and more profitability. Contact us today to discuss your specific needs and explore how we can help you implement a customized solution.

This article is part of a series – check out part 1: How AI/ML is Redefining Order Management and part 2: Benefits of Integrating AI and Machine Learning Into Your OMS.

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.