How APIs, AI, and machine learning are changing games

Navdeep Singh is the testimony of a supply chain technology expert with over nine years of stability in B2B integration, modernizing EDI and creating secure enterprise systems. Currently he is leading project integration with SoftwareOne, and his main objective is to make the supply chain smarter, more connected and secure with APIs, artificial intelligence, Genai and machine learning.

Business representatives have changed dramatically in recent years with supply chain presentations. There are days when it is considered successful in mailing the purchase order or invoice before requesting it to be entered manually. In the modern world, the argument is similar. Currently, it's not that much about automation, but more intelligent tools that help with real-time connectivity, rapid decision-making, risk management and performance.

Navdeep Singh has played a major role in helping businesses undergo this transformation. He has experienced working with a variety of multinational teams on how technologies such as API, AI, and ML can enhance access, data management and trust concerns by significantly increasing speed and accuracy.

API: How to communicate your supply chain in real time

Among the key elements Singh is working on change is the ease with which businesses can move with static and batch-based transfer of data, using APIs to address real-time communications and edge computing. Traditional systems incorporate large nighttime latency times to transfer files or spreadsheets manually when you want to synchronize data between systems. The API then allows platforms such as SAP Ariba, Oracle, or Coupa, establishing a direct communication channel with vendors, banks and logistics providers when events take place.

According to Singh, opening systems using APIs must be strictly controlled through access control. You need full knowledge of who is connected to your system, what they should not access, and how their activities should be recorded. Otherwise, the likelihood of data leaks or unauthorized access increases rapidly.

The role of AI, genai and machine learning

By helping the systems that APIs connect to, AI and ML helped them become smarter. Singh is involved in numerous projects where AI and its recent model, Genais, have been applied to help AI assist in the decision-making process, detect unusual patterns, and automate much of the manual effort required to review transactions.

For example, machine learning models can detect unexpected activities, such as a sudden increase in purchase orders to new suppliers before people notice. Forecasts can also assist AI tools based on previous order information, seasonality, and supplier performance to suggest improved order timing. Also, when reviewing transactions, Genai is considered normal, can mark anything that requires more specific attention, and the team needs to consider what is really important.

This, according to Singh, is not a substitute for people. This allows them to direct their most important efforts for exceptions, complex decisions, strategies, with routine checks that are automated.

Nevertheless, there is no doubt that AI tools are being used. What information does this train these models? Who can you use that data? And how are the errors intercepted and fixed? According to Singh, unless these questions are answered clearly, technology can lose trust quickly. So, he emphasizes the need for clarity and control in case AI is applied to supply chain systems.

A practical bit of a safer and smarter system

Singh says in his experience there are many practices that an organization can adopt so that safety and effective practices are incorporated into the process of using APIs and AI tools.

1. If it's worth restricting access

There is no need to make all your data available to everyone. When third-party suppliers combine teams through an API or AI platform that reads transaction logs, it is important to be clear about who has access to what and make sure they need to follow this.

2. Encrypt everything

You need to protect your data not only when it's being stored, but also when it's being transferred between systems. It's logs, training data, and backups.

3. Don't leave AI as a mystery

Whenever a Genai or ML model provides recommendations or raises an issue, the user needs to know how it works. The explanation of the most fundamental points of what is observed by the model can be quite distanced in establishing trust early and raising concerns.

4. Don't forget to monitor human beings

Automation is a time-saving technology, but automation is also a problem as business users need to be kept in a loop to minimize errors and prove their confidence in technology. Depending on the outcome, better results will be witnessed by teams checking and adjusting flagged transactions or rules.

5. It's important to review and improve regularly

The system will not remain the same. The API has been changed, the AI model is outdated and new risks are displayed. According to Singh, frequent inspections should be performed to fix performance, test security and update the same as needed. It is not only a good habit, but it also helps to prove all future arrangements.

Looking ahead

It is clear that the use of APIs, AI, and machine learning is restructuring the supply chain. They allow businesses to work faster, detect problems faster, and time-synchronized across boundaries and time zones. Nevertheless, Singh reminds us that a single tool cannot work on its own. They can be truly effective using good data, responsible design, and human judgment.

While Genai also promotes new possibilities for developing and using supply chain tools, Singh emphasizes a practice-oriented methodology that accentuates the central importance of thoughtful design, testing, and trust.

As businesses continue to adapt and digitalize, Singh's work shows that the most successful supply chains are not only fast, they are also reliable, secure and designed.

“Technology doesn't replace decision making, it's rather we need to speed it up. The real difference occurs when APIs and AI are created transparently along with the controls and generated with a prominent consideration of the individuals behind the procedure.”