Exporters and logistics companies began investing in technology in the early 2000s, as globalization expanded and once-compact supply chains dispersed, making transport management increasingly complex.
As a consequence, companies initially invested in the latest supply chain software available at the time. However, as technology advanced, they gradually accumulated multiple disparate products. Each of these tools operated at different levels of technological maturity and focused on specific aspects of the broader supply chain. Integrating new services into these environments often requires organizations to re-architect their entire system due to the lack of modularity and flexibility in legacy solutions.
As a result of this bits-and-pieces approach towards developing trade tech stacks, companies today are saddled with multiple logtech systems that are not very well integrated with each other, with each system effectively operating in a silo and not “talking” to one another. These systems are made up of various components that do not communicate effectively, further complicating integration efforts.
Research from McKinsey indicates that the plurality of logtech vendors used by companies has reached an extent where 34% of the logistics service providers (LSPs) have 8 or 9 different technology solutions in their transportation tech stacks.
The problem has amplified since the advent of AI. Rapid advances in the field of AI have driven extensive innovation in the field of logtech. Increasingly sophisticated models are continually being introduced in a short span of time, resulting in suboptimal AI systems interoperability. Today, the inability to easily add new services without major changes or needing to re-architect the entire system every single time is a key limitation of current tech stacks.
Need for Systems Approach to Tech Stacks
With decision points and potential bottlenecks in modern supply chains increasing exponentially, and transport processes becoming more complex, exporters now need a holistic approach to centralized supply chain management. The goal should be to build a comprehensive trade tech ecosystem that addresses end-to-end supply chain planning.
Most companies now follow best practices for API-first development, including designing APIs collaboratively and establishing clear API contracts to ensure consistency and efficiency across the organization.
This involves a change in mindset, moving from deploying point solutions on an ad hoc basis to inculcating a systems approach towards API-first platforms. At its core, this change is a shift from a tactical view to an overarching strategic vision, where the deployment of API-first platforms is a precursor to API platform integration.
APIs enable software applications to communicate with each other, acting as a bridge between legacy systems. This simplifies AI platform integration and makes it more cost-effective. Exporters and logistics service providers must, of necessity, look at the larger picture and evaluate AI-powered API-first platforms in terms of their ability to maximize benefits to the broader supply chain and rationalize overall TCO.
The primary purpose has evolved from solving daily tasks and logistical issues to creating a shared data foundation, through AI platform integration, which will facilitate AI systems interoperability.
Leveraging API-first platforms can resolve challenges related to the lack of AI systems interoperability, where each legacy system functions in isolation, disregarding the requirements of other departments or regions.
The need of the hour is building a centralized supply chain management platform that combines all trade data and empowers management with insights and prescriptive analytics. It is imperative that organizations now incorporate API-first platforms in their tech strategy, prioritizing AI platform integration, with the ultimate objective being AI systems interoperability.
Leverage KlearNow and its API-First Strategy For Your Customs Operations
As one of the pioneers of the logtech industry, KlearNow.AI has been cognizant of the need for AI systems interoperability and has adopted an API-first approach while designing its products, be it KlearCustoms (digital customs clearance product), KlearEngine (for intelligent trade document processing), or KlearHub (global control tower solution).
Through scalable and standardized API’s, KlearNow’s products make AI platform integration easier, eliminating the need for a complete overhaul of existing systems architecture. Its ability to standardize data exchanges and allow other AI tools to plug in ensures seamless AI systems interoperability. Robust error handling and ongoing support are built into the API architecture, ensuring reliable integration and smooth operation within a composable ecosystem.
With its API-first approach, KlearNow.AI is the ideal partner for businesses looking to create a self-sustaining trade tech ecosystem and garner the benefits of an integrated AI platform and AI systems interoperability. Comprehensive documentation and practical examples are provided to help customers integrate and use the platform effectively.
American importers can immediately quantify the expected ROI from using KlearNow.AI’s products using their Importer ROI Calculator, helping them build a robust business case for their technological investment.
Book your free demo today to witness the transformative power of KlearNow’s API-first philosophy.
Frequently Asked Questions (FAQs)
1. Why is API governance important for development teams building client applications?
API governance gives development teams a consistent way to design and manage APIs, ensuring a shared understanding across departments and external customers. With clear API specifications and building blocks, companies reduce integration errors, comply with regulatory requirements, and improve developer experience throughout the software development lifecycle.
2. How can API-first platforms improve the developer experience and avoid breaking changes?
API-first platforms rely on strong API documentation, mock servers, and early design reviews. This approach helps prevent breaking changes, accelerates the software development lifecycle, and ensures that external customers can integrate client applications quickly — often with just a single line of code.
3. How do generative AI, machine learning, and AI agents add value in supply chains?
In trade and logistics, AI applications depend on API-first integration. For example, AI agents can analyze customs data, generative AI can automate trade document classification, and machine learning can optimize lead times by predicting transport bottlenecks. APIs allow companies to customize models, choose the best model for their use case, and even apply object detection to scanned cargo or invoices — all within a consistent, integrated system.
4. How does API documentation and specification support external customers in building applications?
Clear API documentation and standardized specifications enable external customers to build applications faster and more reliably. By providing examples, mock servers, and consistent instructions, companies simplify onboarding and ensure a better developer experience when integrating new client applications into existing supply chains.
5. Why should organizations treat APIs as building blocks for their tech strategy?
APIs are reusable building blocks that bring together disparate legacy systems and new AI applications into a unified ecosystem. With strong API governance and a consistent way of working, these building blocks create interoperability, reduce compliance risks, and ensure that innovations in generative AI, machine learning, and other advanced tools can be scaled across the supply chain without disrupting operations.
