Specifically, the TTC should incorporate a trade agreement framework informed by regular formalized stakeholder input. This framework would focus on (1) avoiding the full range of trade barriers arising from new technologies and (2) tailoring cooperative efforts in a way that allows the United States and the EU to promote their shared values globally and prevents small disagreements from impeding progress. Significantly, a trade agreement foundation for trade and technology cooperation would create huge efficiencies and go a long way toward ensuring US-EU engagement on issues of common interest from administration to administration and from commission to commission.

This recommendation is based on the following elements:

Break down the artificial divide between trade and technology

Let’s start with some basics. One of the original reasons for creating the TTC was to head off unnecessary trade barriers arising from new technologies and diverging efforts to regulate them. Legacy trade barriers coming from deeply entrenched US-EU regulatory divides had proven extraordinarily difficult to bridge. Think, for example, of biotech food, hormone-treated beef, pathogen reduction treatments for poultry (the infamous “chlorine chicken”), and conflicting standards on a wide range of industrial products.

One of the main ideas behind the TTC was to get ahead of the curve and avoid the new-tech version of “chlorine chicken”—that is, to identify and prevent unnecessary trade barriers to products of new technologies from arising in the first place. Another related goal was to ensure that the United States and the EU could globally promote trade rules on products of new technologies that reflect their joint values of transparency, the rule of law, human rights, and high labor standards.

As the TTC has proceeded, however, the tendency in the public narrative has been to create an artificial divide between “technology” and “trade.” This has led to a deeply unhelpful but persistent narrative that the TTC is making good progress on “technology” but is lagging on “trade.” What is probably meant by this is that officials are having useful discussions on initiatives such as an artificial intelligence (AI) roadmap, but that they haven’t resolved long-standing bilateral disagreements over, for instance, bilateral mutual recognition agreements on pharmaceuticals or machinery.

Of course, the officials haven’t: The TTC never set out to provide additional leverage to resolve such deep, long-standing divides, so it makes no sense to judge its success on that basis. (A comprehensive agreement negotiation would do that, but that’s another topic.) It’s not an accident that long-standing disputes such as hormones in beef or pathogen-reduction treatments for poultry have not been on the TTC’s agenda.

To illustrate the problem, treating AI or other new technology product standards as a distinct category from “trade” ignores the fact that having two sets of standards that conflict with each other unnecessarily represents the ultimate trade barrier.

The TTC would benefit from getting back to one of its original goals: avoiding unnecessary bilateral barriers to trade in new technologies, and collaboratively promoting a joint vision of how such products should be regulated on a global basis. This includes early bilateral engagement on proposed legislation—which is where trade barriers often arise.

Sharpen the TTC’s focus on specific common trade goals

Old habits die hard. When the United States and the EU sit down together on trade issues, it’s hard to avoid complaining about what the other side has done or pressing the other side to do something it doesn’t have an interest in doing. This is not to minimize the importance of such discussions: The parties need a forum to complain and advocate.

But the TTC should have a dedicated trade space where the parties can focus on building concretely on the things they can agree on, and to shape and nuance those issues so they are maximally congruent, shedding tangential disagreements. The approach of pressuring the other party to agree reluctantly to the other’s pet initiative simply has not worked in the TTC. (It can work in the context of a comprehensive trade agreement, where there are trade-offs and momentum, but it’s much harder in a cooperative forum such as the TTC).

I can attest from many years as a trade negotiator, especially with the EU, that identifying areas of agreement is not always easy. This is in part because a standard trade negotiation tactic is to turn any proposal, however mutually beneficial, into an “ask” that can be pocketed and used as leverage to get something else. If one side suggests, for instance, that the parties work on compatible standards for a new product produced by both parties, the trade negotiation instinct of the other side will sometimes be to make those discussions contingent on a negotiation it wants that the other does not. This discourages genuine cooperative proposals and attracts proposals on which there is disagreement, hindering cooperation.

The TTC should recognize this dynamic and deliberately avoid it, isolating the areas of agreement and disagreement, and “parking” the latter so the agreed areas can move forward without hesitation or caveats.

To illustrate, the United States and the EU undeniably have a huge common interest in opposing and defending against the increasing impact of a growing number of harmful international trade policies and practices. It is also true, however, that the EU is more hesitant to associate those practices with a particular country, such as China. This difference hobbles cooperation and encourages distancing because cooperation can be interpreted as general broad alignment vis-à-vis China, and the EU doesn’t want to associate itself with all US views on China (or its perception of those views).

One solution to this impediment would be to focus on countering specific harmful trade practices and policies and/or sectors, and perhaps even avoid characterizing the work as China-specific or endemic to any particular system, “nonmarket” or otherwise. In other words, strip out any divisive “ideological” aspect of the issue and move forward on those core elements that are not divisive. This would also sidestep the facile and false binary choice between engagement and disengagement with any particular World Trade Organization (WTO) member. 

Another practical example of this approach: The United States and the EU have significant and long-standing differences between how they define “international standards” in the WTO agreement, and a fight on that issue is tempting whenever they sit down to discuss standards compatibility. But they can and should pragmatically work to avoid future standards conflicts—a huge potential trade barrier—without taking on that ideological disagreement. The same is true for streamlining how products are assessed for conformity with regulations in each of their markets, in which they have a common interest, but widely divergent approaches. 

As a final example, the United States and the EU have a strong common interest in assuring that labor rights are respected around the world, but very different approaches to enforcement—to oversimplify, the United States’ use of trade tools versus the EU’s use of cooperation and persuasion. The same is true of environmental protections and trade. The TTC should avoid taking on this long-standing difference in approach and focus instead on joint practical initiatives that reflect where both sides agree.

To be clear, the TTC has ultimately cooperated well on many issues, but could eliminate much wasted time and energy by deliberately focusing on and shaping the cooperative issues, and not letting the disagreements and complaints slow efforts down.

Establish a durable institutional foundation

US-EU trade fora have, for decades, been a bit ad hoc, designed and developed by particular US administrations or EU commissions, only to be reformulated and reinvented after the next election. That reinvention has several negative effects: It takes significant time and negotiating effort to reinvent procedures. It lacks the durability of an ongoing framework, even though the substance of the subsequent discussions often remains similar. And it lacks a structured mechanism for receiving and incorporating stakeholder input across the broad range of trade issues—input that is critical to anticipating commercially meaningful potential barriers and to identifying and pursuing joint global objectives.

Contrast this process with bilateral meetings that are held under official Trade and Investment Framework Agreements, which have an agreed framework for participation by the agencies, regularly scheduled meetings, and, generally, a regularized formal process for receiving and incorporating stakeholder input. Further, under such framework agreements, regular meetings generally continue from administration to administration, since they are required by the agreement, without a new administration needing to redesign the forum or create a new initiative or set a schedule. This helps avoid the impression that the forum itself solely represents the priorities of any one administration. Policies will evolve from one administration to the next, of course—although targeting core issues of common interest will minimize differences—but the foundational mechanics and logistics, including stakeholder participation, are already in place. The result is both efficiency and legitimacy of the process that brings the parties together.

Indeed, the absence of such an ongoing trade agreement structure between the United States and the EU is notable, given that it is practically a norm with other trading partners.

When US and EU officials meet in Belgium in April, they will be right to celebrate that the TTC has been a significant plus for the transatlantic relationship. At the same time, the TTC could be even stronger and more durable by incorporating a specific trade agreement framework informed by regular formalized stakeholder input. As noted above, two features should take priority going forward: The TTC should aim to avoid future trade barriers arising from new technologies, and it should work to promote US and EU shared values globally and prevent small disagreements from impeding progress. Building on the success of the TTC by adding the permanent structure of a trade agreement framework would help keep the US-EU trade relationship steady and on course, even as the occupants of the White House and the Berlaymont building change over time.

L. Daniel Mullaney is a nonresident senior fellow with the Atlantic Council’s Europe Center and GeoEconomics Center. He served as assistant US trade representative for Europe and the Middle East in the Office of the United States Trade Representative from 2010 to 2023. He was chief negotiator for comprehensive trade agreements with the EU and the United Kingdom, as well as trade lead for the US-EU Trade and Technology Council.

To read the full blog post as it appears on the Atlantic Council website, click here. 

The post The US-EU Trade and Technology Council has Been a Success. Now Build on that Success. appeared first on WITA.

The missing link between AI and supply chain in Biden’s executive orders

The Biden administration has paid considerable attention to both global supply chains and AI. In 2023, President Biden signed two executive orders: one regarding governance and responsibility in AI development and another to improve supply chain resilience. In June 2023, the White House released a progress report to build resilient supply chains for four critical products: semiconductors, large-capacity batteries, critical minerals and materials, and active pharmaceutical ingredients. The development of resilient supply chains is a key component of Bidenomics. For this endeavor, Biden attained $52.7 billion from Congress through the CHIPS and Science Act. Later, in October 2023, the White House released a report summarizing President Biden’s executive order on Safe, Secure, and Trustworthy AI. The order requires developers of powerful AI systems to meet certain safety standards before publicly releasing their solutions. Finally, President Biden announced in November 2023 the establishment of the new White House Council on Supply Chain Resilience to develop new capabilities to monitor existing and emerging risks and to detect and respond to supply chain disruptions in critical sectors with supply chain partners. Although these policies are promising, American policymakers have yet to address the inherent connection between AI development and supply chain resilience.

This trend is not limited to the United States. First proposed in 2021, the EU Parliament reached a provisional agreement with the Council on the EU AI Act in December 2023. The policy provides guidance for high-risk AI systems and breaks down responsibilities throughout the AI supply chain with requirements for importers, distributors, and other supply chain stakeholders.

The benefits of AI-enabled supply chain planning

AI has the potential to revolutionize supply chain operations by improving decision-making and efficiency. According to a 2022 McKinsey survey, respondents reported that the highest cost savings from AI are in supply chain management. Specifically, AI can add value to supply chain planning, including production, inventory management, and product distribution. Companies can also leverage AI-powered tools to process vast amounts of real-time data and improve the accuracy of demand forecasting. With more precise demand forecasts, AI-enabled tools can help firms optimize production and inventory plans across various locations and select the most cost-effective logistics solutions.

Early adopters of AI-enabled supply chain management have reduced logistics costs by 15 percent, improved inventory levels by 35 percent, and enhanced service levels by 65 percent. Adopting AI tools to manage manufacturing operations can be costly, but 70 percent of the respondents from a survey of CEOs of over 150 firms agreed that AI is delivering a “strong ROI.” Despite the potential of AI in supply chains, AI should not decrease employment in supply chain management. Rather, it should create new opportunities to mitigate potential risks associated with adopting new technologies.

The role of AI in mapping supply chains

AI can certainly make internal operations more efficient; this starts with achieving supply chain visibility (i.e., the ability to view and track inventory levels as goods move along the supply chain). Visibility would allow firms to respond to disruptions in real time. A 2021 survey revealed that only 2 percent of companies claimed to have visibility beyond their second-tier suppliers–those who supply materials and parts to their direct suppliers. Without strong visibility, company supply chains are susceptible to disruptions caused by issues such as natural disasters, pandemics, geopolitical issues, trade barriers, and product recalls. Therefore, firms should seek to leverage AI to enhance supply chain visibility.

Mapping the supply chain is a crucial step towards enhancing its resilience, and AI tools can provide substantial assistance in this regard. These tools can gather records like product orders, customs declarations, and freight bookings, which are often represented in various formats and languages. AI algorithms can extract relevant data from both structured and unstructured documents with high precision. AI tools can compile and synthesize this raw data, enabling a firm to map out its different supply chain tiers. For instance, Altana, an AI startup that creates dynamic maps of global supply chains, has developed a generative AI tool that utilizes both public and private data to map a company’s supply chain. This tool is complemented by a large language model (LLM)-informed assistant that responds to employees’ queries posed in plain language. Using document processing systems to capture, analyze, and share documents, such as invoices, bills of lading, and purchase orders, Altana can enhance efficiency and accuracy in logistics and improve communication among supply chain partners.

Using AI to detect changes in demand and supply

AI can also help firms gauge market demand and customer sentiment. Utilizing scanner data collected at point-of-sale locations, along with vast data from customer reviews and blog posts on social media, AI-based tools, such as Google’s Video AI can gather and analyze text, images, and videos. Google Video AI can then develop a real-time, end-to-end supply chain dashboard that can generate alerts for abnormal demand changes due to competition or product issues. The AI can even detect early signs of panic buying using large data sources. The Google Video AI dashboard can then pinpoint the underlying causes for such abnormalities.

In addition to real-time detection of demand changes, AI tools can compile and analyze data on traffic conditions at different supply chain tiers such as ports and warehouses. These tools can detect supply disruptions caused by supply and worker shortages, factory shutdowns, and shipping delays, among other issues. For instance, when ports on the West Coast faced unprecedented delays in September 2021, the US Department of Transportation developed a national transportation supply chain dashboard that tracked three key indicators of goods moving from ports to retail stores: the number of imported containers, US retail inventory levels, and the on-shelf availability of consumer goods. Tracking these indicators in real time offered the ability to detect and react to anomalous patterns as they occurred.

Using AI to design effective responses to supply chain disruption

A supply chain becomes more resilient when it can quickly detect and respond to disruptions, thereby minimizing the impact. According to the supply chain risk management literature, three capabilities are necessary to build resilience: (1) detecting a disruption quickly, (2) designing an effective solution in response to the disruption, and (3) deploying the solution swiftly. Traditionally, firms have ensured supply chain resilience by developing advanced systems to enhance detection, setting up proactive contingency plans, and conducting stress tests for rapid deployment. However, AI and Industry 4.0 technologies, such as sensors, blockchains, and data analytics, can amplify these resilience capabilities manyfold.

With the ability to detect abnormal changes in supply and demand, AI tools can help companies evaluate and compare the effectiveness of different response strategies by conducting simulations. These simulations assess the impact of each possible response on demand and supply as well as the recovery time from disruptions. By analyzing the simulated results and examining the effects of different responses on various supply chain partners, a firm can swiftly develop a well-informed strategy in response to a sudden change. Response strategies may involve modifications to product design, adjusting prices, and switching upstream suppliers. In terms of potential use cases, AI could help a government agency design a supply chain for medical countermeasures to defend against bio-attacks. Retail companies could use AI to simulate and predict the impact of implementing rationing policies in retail stores. More broadly, the goal is to evaluate alternative scenarios to ensure resilience against potential unforeseen disturbances and understand how mitigation strategies affect each part of the supply chain.

AI can help firms respond to crises, but importantly, it can also help companies strengthen supply chains before they are strained. AI can recommend changes to a company’s supply chain policies based on a multitude of factors, such as seasonality and macroeconomic trends. For instance, AI can identify the best supply chain configuration, the optimal number of suppliers (and their locations), and the most favorable terms of the supply chain contracts.

AI implications for employment and public policy

While AI holds immense potential for developing resilient supply chains, the Biden administration can coordinate with the European Union to mitigate various risks arising from AI-enabled global supply chains. Similar to how Biden has pursued responsible AI development in the United States, he should work with European regulators to ensure that the data with which LLMs are trained is sourced ethically and avoids copyright violations. Responsible AI development is key to the stability of supply chains, as AI becomes increasingly engrained in supply chain management.

Even with the help of American and European regulators, firms must manage certain risks through human involvement. AI-enabled supply chains will transform the role of supply chain professionals, eliminating jobs in clerical and data entry, but they will also create new jobs. The data used to train AI and AI-produced insights can be biased. Hence, humans must identify the most relevant data on which to train LLMs and ensure adherence to ethical guidelines. AI also does not always comprehend the contexts and nuances of global supply chains; humans must interpret and examine the appropriateness of AI-generated recommendations. Thus, new personnel, including research scientists, chatbot developers, AI ethics, and bias analysts, are necessary to develop resilient supply chains. Additionally, to manage increasingly complex supply chain operations amidst exceedingly complex geopolitical issues, the role of supply chain managers has never been more critical.

AI promises to disrupt industries, including justice, retail, marketing, transportation, media, and biosciences. Indeed, the World Economic Forum commented that the future of work is changing with machines and AI likely to take on an increasing share of work. The interplay between AI and supply chain management, two critical sectors, is more important than ever to create economic stability and resiliency. However, the future is not as pessimistic as Elon Musk’s claim that AI will take most jobs away–at least not in supply chain management for the foreseeable future.

Maxime C. Cohen is the Scale AI Chair Professor of Retail and Operations Management at McGill University and a Visiting Professor at Yale School of Management.

Christopher S. Tang is a UCLA distinguished professor and the Edward W Carter Chair in business administration.

To read the full article published by Georgetown Journal of International Affairs, click here. 

The post The Role of AI in Developing Resilient Supply Chains appeared first on WITA.