Applying Artificial Intelligence to Drive Growth and Efficiency in Global Value Chains

Accelerated deployment of AI solutions in international trade

Artificial Intelligence (AI) will undoubtably boost opportunities in international trade through the optimisation and automation of existing supply chain operating models. Virtually every economic sector and aspects of trade, especially services, can be impacted by AI, which comprises several underlying technologies, including neural networks, deep learning, natural language processing, computer vision, supervised and unsupervised machine learning, transfer learning, and others[1]. These various types of AI are applied in different ways throughout the industrial world to create targeted solutions provided as descriptive, predictive, prescriptive, and prognostic analytics.

The evolution of AI-based services is already impacting the configuration of global value chains. AI can support firms engaged in trade as they reduce the time, cost, and complexity of identifying and delivering on export opportunities. It can be employed to improve forecasts of future trends, such as consumer demand changes, and to manage risks more effectively in the supply chain. For example, goods can be manufactured based on AI predictions, thereby reducing lags in production times, and improving just in time delivery. Robotics can be used to physically inspect and maintain goods along the supply chain. AI’s strength in analysing data and identifying patterns could be used to immediately ascertain the legitimacy of a trade transaction. As an illustration of the use of AI in customs declaration, A collaborative group of experts, BACUDA (BAnd of CUstoms Data Analysts), has developed a ground-breaking neural network model called DATE (Dual Attentive Tree-aware Embedding) to detect undervalued imports while estimating the additional revenue that could be collected from the inspection of these imports.

Global Value Chains (GVCs) will evolve because of modern trends towards the application of AI in development of smart solutions. For example, the introduction of Industry 4.0 designs based on sensors, IoT and cyber-physical systems which connect machines, equipment, suppliers, and customers. This will include simple machines and self-maintenance at the factory level, complete communications between enterprises along the supply chain, and the ability to manufacture to client specifications, even in small or single batch size[2]. This breakthrough in the technology industry might prove to be highly beneficial to organisations operating in the field of supply chain by providing them a larger scope and stronger foundations for their operations.

Addressing cost and reducing waste in GVCs through AI solutions

AI is used to navigate uncertainty in complex supply chains. With the utilisation of both predictive and prescriptive analyses, AI may bring many benefits, as highlighted by a survey from McKinsey[3] where 63% of executives registered an increase in their revenues and 44% registered a decline in their costs as a direct impact of the introduction of AI within their supply chains. The increase in revenue was mainly attributed to the usage of pricing, demand and sales forecasting, prediction of likelihood to buy and customer-service analytics.

Moreover, the reduction in costs was a direct impact of the optimisation of productivity gains, energy savings, and throughput from manufacturing supply-chain management as well as via lower inventory-carrying cost, lower transportation, and labour costs. While AI will revolutionise the world of trade, at the same time it runs the major risk of making people redundant, especially in low skill segments in the manufacturing fields.

Challenges faced by industries to adopt AI

Many industries have already adopted AI technology in some of their processes, but most of them are not yet fully prepared for the various challenges they face with AI. A survey by Deloitte[4] identified the biggest challenges enterprises are facing in order to integrate AI in their processes. 62% of the respondents ranked highest cybersecurity vulnerabilities, followed by the need for regulations by governments.

Adopters face gaps between their concern and preparedness for AI risks

Source: Deloitte, State of AI in the Enterprise, 3rd Edition, 2020

A majority of businesses fear that reliance on AI can leave them vulnerable. If important processes are solely dependent on AI and something goes wrong, businesses may be heavily affected. This is a feeling felt by 58% of the respondents who stated that AI failures may affect their businesses. Moreover, 57% of the respondents were concerned that AI may use personal data without the consent of persons. The Another significant concern, for 53% of respondents, is over the future of work:  many operations are automated through AI, and this may cause potential job losses.

Reflecting on an AI strategy for Supply Chains

AI’s prominence accelerated in light of the global COVID-19 pandemic. In this connection, AI will form part of every individual’s ‘new normal’ in the long-run, whereby it will include automated, robotic and contactless processes[5]. Border closures and shipment delays as a result of the pandemic have disrupted the supply of goods and services to firms. Global trade has thus led to supply chains becoming more localised as a response to rising trade costs and increasing trade uncertainties. Economies are trying to boost local production to satisfy domestic demand, in turn ensuring fewer disruptions to the supply chain and limiting the movement of goods and people[6]. The post-pandemic supply chain will require customisation of product and service portfolios to serve customers online, also keeping in mind future profitability[7]. Localisation may witness a decline in trade. However, supply chains need to be more resilient with businesses response to the post-pandemic supply chain requiring: i) better estimations of the available inventory along the value chain; ii) optimising production and distribution capacities; iii) identifying where the risks lie and building strategies around it; and iv) diversifying the supply base to regional locations with high profitability[8].

Businesses and organisations must find ways to adapt and respond to unexpected shutdowns, rerouting logistics routes, shifting suppliers and dealing with unanticipated changes in legislation. With the great advances made in data capture and data mining, AI has the potential to give an immediate and compelling competitive advantage to those organisations who are early adopters. Businesses require intelligent software to address industrial pain points for value creation, productivity improvement, insight discovery, risk management, and cost optimisation[9].

The implementation of full AI solutions is, however, daunting and cost-prohibitive, with cost ranging from millions to tens of millions of dollars depending on the size of the organisation. An agile approach would be a cost-effective solution for implementing AI in organisations. By integrating with third-party vendors, organisations can start from where they are, learn what works for them, then scale-up as needed.

Beyond the complexities of adopting the technology and integrating it as a support to existing internal processes, there are questions with regards to the governance framework and ethics of using AI. The regulation of AI around the globe remains a key question that needs to be addressed, as does gaining a better understanding of the impact it can have on jobs, and decisions based biases that can lead to discrimination.

International Economics (IEC) has been involved in several projects analysing and studying supply chains responses to the COVID-19 pandemic, including surveys at different periods of the pandemic looking at the impact of COVID-19 on African businesses and the outlook to COVID-19 in Southern Africa. IEC is also working with the World Bank and UN organisations in the Indian Ocean on recovery strategies, and is currently advising on recovery strategies for Indonesian and Uzbek businesses.

Paul Baker is the founder and CEO of IEC. He is a consultant for various governments in developed and developing countries, an adviser on global corporate strategies to multinationals, and a Visiting Professor at the College of Europe. He is an Expert in the Working Group of the World Economic Forum’s (WEF) Digital Flows Initiatives, an Expert in the WEF/WTO’s TradeTech Working Group on AI, IOT, Blockchain and Digital Identities for trade, and is on the Board of the United Nations Economic and Social Commission for Asia Pacific’s Trade Intelligence Negotiation Adviser.



[1] Meltzer, J. P. (2018). The impact of artificial intelligence on international trade. December 18. Retrieved from Brookings:

[2] Information Technology Partners . (2020, November 21). Industry 4.0 – An Intelligent Introduction. Retrieved from Information Technology Partners (ITP):

[3] McKinsey & Company. (2019). McKinsey & Company. November 22. Retrieved from McKinsey & Company:

[4] Deloitte. (2020). Thriving in the era of pervasive AI. July 14. Retrieved from Deloitte:

[5] Information Week. (2020). Predictions for AI in 2021. December 14. Retrieved from Information Week:

[6] Baker, P. (2021). Top 5 economic takeaways of the coronavirus pandemic and post-COVID-19 recovery solutions. April 9. Retrieved from IEC:

[7] Bergstrom, I. Stewart, P. Gallagher (2020). Looking beyond the horizon, preparing today’s supply chains to thrive in uncertainty, Deloitte Insights. 11 December 2020. Available at:

[8] K. Alicke, X. Azcue, E. Barriball (2020). Supply-chain recovery in coronavirus times—plan for now and the future. McKinsey and Company. March 2020. Available at: ; J. Kilpatrick, L. Barter, C. Alexander (2020). COVID-19: Orchestrating the recovery of supply chains. Deloitte. Available at:–the-recovery-of-organizations-and-supply-chains.html

[9] Chappell, J. (2021). Unlocking the Value of Supply Chain with AI-driven Processes. May 15. Retrieved at


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