Trade volumes are steadily growing, while trade compliance is becoming more complex. A volatile tariff environment and rising protectionism — through both tariff and non-tariff barriers — are driving this change. This has resulted in AI playing an increasingly important role across trade compliance and logistics.
Logistics automation has been the focus of most importers, exporters, freight forwarders, and customs brokers, driven by the desire to leverage technology to digitally transform their trade compliance functions.
Traditionally, AI in the customs and trade space was built by software developers and data scientists who, while technically skilled, often lacked deep domain knowledge of customs regulations and supply chain complexity. The tools they created reflected that gap—technically sound, but disconnected from the day-to-day realities of compliance teams. These solutions were designed top-down, with IT departments leading the charge and little input from the operators actually navigating trade flows.
That’s starting to change. No-code AI platforms are now making data science accessible to a broader audience, not just those with technical backgrounds. The people who live the complexity — compliance officers, logistics managers, and customs brokers — are now shaping the tools themselves. Instead of relying on dev teams to guess their needs, they can build, configure, and adapt AI tools to fit real-world processes. This shift democratizes AI and puts problem-solving power in the hands of those who actually understand the problems.
Common Challenges and Limitations of IT-led AI platforms
Considering that strong IT skills were necessary to develop AI code, it is unsurprising that the development of AI-powered logtech solutions was the forte of developers and data scientists, whose academic qualifications and expertise were primarily in IT, rather than the trade compliance and logistics industries.
This meant that most logtech software lacked the industry perspective and insights, knowledge of which was a prerequisite to developing solutions that addressed the underlying operational problems and procedural intricacies faced by most importers, exporters, freight forwarders, and customs brokers. Traditional IT-led systems often failed to determine the critical factors influencing operational efficiency.
As a result thereof, logistics automation using traditional AI posed some common challenges and limitations, which only became apparent to users at post-deployment. These limitations also affected the workforce, as manual interventions and staffing adjustments were often required to compensate for inflexible systems.
Amongst the more critical ones were:
1. Inadequate industry expertise and understanding of procedural minutiae
Because IT experts drove logistics automation, the products developed, though technologically sound, often did not take into account specific aspects of trade compliance and logistics.
2. Limited ability to customise or alter workflows
Due to the complexity of the AI code used, it was difficult to make changes to the software to incorporate unique industry-specific requirements. Traditional platforms often lacked features that allowed users to easily customize workflows to their specific needs.
3. Delays in implementation or changes
Since these were not operator-first platforms, additional requirements or modifications identified at a later stage would necessitate the involvement of IT personnel, who, with their existing workload and limited business understanding, would require more time to handle the change requests, leading to delays.
4. Lack of scalability
As IT was needed to handle every change requested, the product’s scalability across geographies and verticals was restricted, adding to additional costs and time, resulting in increased cost due to IT-dependent scalability.
Rise of No-Code AI
As users become cognizant of the limitations of traditional AI tools, they are turning towards no-code AI for trade compliance and logistics automation.
It is estimated that by 2025, 70% of new applications will be developed using low-code or no-code AI technologies, up from less than 20% in 2020.
In essence, no-code AI enables users to create applications and automate business processes without writing code, thus democratizing software creation and adaptation to business requirements.
No-code AI facilitates user-led innovation through the creation of operator-first platforms by using elements such as drag-and-drop interfaces and visual components. No code tools enable users to perform tasks such as building applications, automating workflows, and analyzing data without programming expertise.
Ergo, even users with no coding knowledge can create the applications they need.
Advantages of No-Code AI
No-code AI helps businesses overcome the limitations of traditional AI tools and has the following advantages:
- Accelerating speed of development and deployment: As the reliance on IT is eliminated, supply chain players are equipped with the ability to make necessary changes.
- Incorporation of industry-specific expertise: With user-led innovation, information and insights that are unique to the industry are incorporated, creating operator-first platforms.
- Lower costs: Since changes are managed by supply chain professionals who actually use the tool, rather than expensive IT staff, expenses are lower.
The key benefits of no-code AI for logistics and supply chain operations include more efficient processes, reduced costs, and simplified workflows, leading to improved operational efficiency.
No-code AI has thus ushered in a new era of user-led innovation in the field of logistics automation, replacing the IT-led model.
Frontline users such as supply chain operators, compliance leads, and analysts are leading the development of operator-first platforms. These platforms offer seamless integration with existing enterprise systems, further enhancing their value by enabling real-time data flow and operational insights.
KlearNow’s Configurable AI Empowers Supply Chain Operators
For businesses looking to boost user-led innovation, KlearNow’s configurable no-code AI is the ideal choice. The platform allows users to build and deploy AI models tailored to their business needs.
KlearNow enables logistics automation through operator-first platforms, empowering supply business users to adapt faster, act faster, and scale smarter without waiting for a development team. In this process, the process of software development and enhancement is rendered faster and smoother, with greater industry relevance. KlearNow also functions as a comprehensive system for logistics automation, integrating advanced AI models to streamline operations.
To gain the no-code AI advantage, contact KlearNow and book your free demo today.
Frequently Asked Questions (FAQs)
1. How does warehouse automation benefit from no-code logistics automation software?
Warehouse automation increasingly relies on logistics automation software powered by artificial intelligence to streamline everyday tasks like material handling and order fulfillment. No-code platforms enable companies to easily configure these tools without developer input, improving access for frontline users and allowing faster adjustments to real-world warehouse operations—leading to reduced operating costs and greater customer satisfaction.
2. Why is artificial intelligence more effective today than in earlier logistics systems?
Earlier programs were developed by IT teams with limited knowledge of trade and logistics. Today, artificial intelligence tools are more effective because operator-first platforms give direct access to the people who perform repetitive tasks and manage human resources daily. This shift allows the AI to better reflect actual workflows, improving outcomes and making automation support far more relevant.
3. Can no-code platforms help companies reduce operating costs and improve support?
Yes. By removing the dependency on IT for every system change, companies can make quick updates themselves using no-code tools. This leads to lower development costs, faster deployment, and improved support for logistics operations—all while maintaining or improving customer satisfaction and gaining a competitive advantage in the market.
4. What kind of repetitive tasks in logistics and compliance are ideal for machine learning?
Tasks like data entry, document classification, customs form processing, and scheduling in material handling are perfect candidates for machine learning automation. With no-code tools, these can be automated directly by logistics and compliance professionals, eliminating bottlenecks and freeing up human resources to focus on strategic activities.
5. How are companies using no-code AI to gain a competitive edge in the logistics market?
Leading companies are adopting no-code AI to streamline logistics automation, reduce costs, and respond faster to market shifts. With greater access to AI tools, compliance teams and warehouse staff can automate everyday tasks, adapt systems to local requirements, and enhance customer satisfaction — giving them a true competitive advantage without needing to code.
