Table of Contents

· The Difficulty of Assessing Risks in Complex International Markets
· The Impact of Policy and Economic Shocks
· Traditional Risk Assessment and Scenario Planning Approaches
· How AI Transforms Risk Assessment and Scenario Planning
∘ Simulating Policy Impacts
∘ Identifying Hidden Risks
∘ Dynamic Risk Monitoring and Adaptive Planning
∘ Addressing Cultural and Needs Diversity
· How AI Tools Can Transform Fever-Tree’s Risk Assessment and Mitigation
· Choosing the Right AI Tools for Risk Assessment and Scenario Planning
∘ Open-Source Tools and Frameworks for Custom Solutions
∘. Python-Based Tools
∘ R-Based Tools
∘ Specialized Risk Frameworks
∘ Enterprise-Level Solutions
∘ Solutions for Small and Niche Businesses

Fever-Tree, the UK-based premium mixer brand best known for its tonic waters (which are absolutely delicious, by the way), manages one of the world’s toughest supply chains. A key ingredient in tonic water is quinine, which is primarily sourced from cinchona bark. Currently, the company sources most of its quinine from the conflict-prone eastern Democratic Republic of the Congo (DRC), which accounts for an estimated 55% of the global supply.

Sourcing from the Eastern DRC areas exposes Fever-Tree to a unique set of risks, including, among others, political instability and conflict; supply chain disruptions (due to violence, strikes, or infrastructure breakdown); ethical and reputational risks (such as association with conflict minerals or human rights abuses); regulatory and compliance risks (e.g., import/export bans and sanctions); and price volatility and currency fluctuations. Truly, it is one of the most difficult markets in the world to access safely, reliably, and responsibly, and as a former researcher of political violence in the Eastern DRC, I can hardly imagine the fraught context and complexity behind managing private sector activity there.

A few years ago, predictably navigating such a complex environment would seem to be nearly impossible — but leveraging new AI tools, companies like Fever-Tree can:

• Proactively model and predict conflict, allowing it to adjust sourcing or logistics in advance
• Initiate and consistently update resilience scoring for each supplier or route, factoring in new data as situations evolve
• Improve scenario planning by running thousands of “what-if” scenarios and recommend optimal contingency plans.
• And, it can even empower AI-powered logistics platforms to reroute shipments in real-time based on changing security or infrastructure conditions.

Companies like Fever-Tree sit atop a heap of uncertainty, and not just in the DRC. When geopolitical tensions rise, when policies shift rapidly, and as supply chains grow more intricate, organizations need more sophisticated tools to navigate uncertainty. Artificial intelligence offers promising solutions for businesses seeking to better understand, predict, and mitigate risks in this volatile environment-particularly those affected by policy shocks like recent tariff regimes. This article examines how AI technologies can transform risk assessment capabilities for businesses operating in international markets.

The Difficulty of Assessing Risks in Complex International Markets

Many businesses operating globally frequently fail to properly address the multifaceted challenges of international markets and struggle with persistent pain points that limit their ability to effectively anticipate and mitigate risks. Even for the businesses that have implemented specialized software solutions and tailored risk management platforms to systematize their risk management, these tools often lack the predictive capabilities and processing power needed for getting a handle on truly complex international risk scenarios.

Companies with complex international supply chains often lack visibility beyond first-tier suppliers, creating significant blind spots when it comes to their ability to assess the risks originating deeper within their supply networks. This limited visibility becomes particularly problematic when geopolitical tensions or trade policy changes affect suppliers several tiers removed from direct relationships.

Cultural differences and varying regulatory environments compound these challenges, requiring businesses to monitor and interpret diverse information streams across multiple languages, legal frameworks, and business norms. The sheer volume of data required to effectively monitor global operations can easily overwhelm traditional analytical approaches, leaving critical insights buried and inaccessible.

Perhaps most frustratingly, many businesses find themselves in a constantly reactive posture, unable to proactively manage threats effectively. By the time a threat becomes obvious, the deployment window for the optimal response has often closed, forcing firms to implement suboptimal solutions under pressure. Being primarily reactive as an organization is particularly dangerous in today’s interconnected global economy, where political, economic, and natural disaster shocks and disruptions can cascade rapidly through supply chains and markets.

The Impact of Policy and Economic Shocks

Recent years have demonstrated how vulnerable businesses are to major policy and economic shocks and highlighted the limitations of traditional risk assessment approaches in preparing for and responding to these events.

The COVID-19 pandemic provided a perfect demonstration of how traditional risk models often fail to account for cascading effects across interconnected global systems. Many businesses discovered that their scenario planning had not adequately prepared them for simultaneous disruptions to supply, demand, workforce availability, and capital markets, highlighting a need for more sophisticated risk assessment capabilities that can model complex, multi-faceted scenarios.

For a more detailed look at how various types of shocks increase risk for businesses, take a look at some of my recent articles on supply chain optimization and resilience, demand forecasting, and dynamic pricing.

Traditional Risk Assessment and Scenario Planning Approaches

Most organizations follow established risk management methodologies or Enterprise Risk Management (ERM) frameworks that follow a cyclical process of identification, assessment, and response based on an organization’s goals and risk appetite. To facilitate these processes, organizations often employ specialized software solutions that offers risk assessment capabilities, compliance management features, and enterprise risk management functionality.

The traditional risk management cycle follows three key steps: identification, assessment, and response. Businesses identify risks by examining their internal environment, processes, and policies. Upon pinpointing potential threats, they document these in risk registers and categorize them, assigning scores to each one based on the likelihood of their occurrence and the weight of their potential impact. This prioritization helps organizations allocate limited risk management resources to the most significant threats. The final stage, risk response, then involves selecting appropriate strategies to mitigate, plan for, transfer or remove, or accept each identified risk.

While many commonly used software solutions help systematize risk management, they often lack the predictive capabilities and processing power needed for truly complex international risk scenarios. Traditional scenario planning typically involves developing a limited number of plausible future scenarios based on key uncertainties, then evaluating strategies against these scenarios. This approach is inherently constrained by human cognitive limitations and often fails to account for the full range of possible outcomes or complex interactions between variables. When dealing with international markets, where thousands of interconnected factors might influence outcomes, traditional approaches simply cannot process the necessary complexity.

How AI Transforms Risk Assessment and Scenario Planning

Artificial intelligence offers transformative capabilities for risk assessment and scenario planning in complex international contexts. By leveraging advanced algorithms, machine learning, and big data analytics, AI models can help businesses overcome many limitations of traditional approaches.

Simulating Policy Impacts

One of AI’s most powerful applications in risk assessment is simulating the financial and operational impacts of various policy scenarios. For businesses concerned about tariff policies, AI can nearly instantly analyze and provide answers to questions like “What if country X imposes retaliatory tariffs?” or “What if tariff Y increases by 10%?” by simultaneously processing thousands of variables and interdependencies.

Even more excitingly, AI-enabled tools can allow leaders to go down each possible scenario rabbit hole and change the input variables to provide more color. Simulations can model direct cost implications, potential changes in customer behavior, competitor pricing responses, currency exchange rate effects, and supplier economics adjustments in a single integrated and cohesive analysis.

These simulations can be updated in near-real time as conditions change, providing decision-makers with continuously relevant insights rather than point-in-time analyses that quickly become outdated.

Identifying Hidden Risks

AI can analyze vast amounts of unstructured data from a diverse array of sources to identify emerging risks before they become obvious. This becomes especially useful when it comes to analyzing interconnected dependencies within value chains and flagging risks that might otherwise remain invisible. This early warning capability provides businesses with the lead time needed to implement effective mitigation strategies rather than being primarily reactive in the face of crisis.

For instance, a tariff on a seemingly minor raw material could impact a supplier several tiers down the supply chain, eventually causing significant disruption to production capabilities. Companies can leverage AI to map and monitor global supply chains, identify vulnerabilities, and predict potential disruptions such as delivery delays, price movements, and supply constraints, giving businesses valuable time to prepare and adapt.

Dynamic Risk Monitoring and Adaptive Planning

Unlike traditional risk assessment approaches that often provide static analyses, AI systems can continuously monitor conditions and update assessments in real-time. This dynamic approach is particularly valuable in today’s fast-moving global environment where trade policies, economic conditions, and geopolitical situations can change rapidly. Machine learning models improve over time, learning from outcomes and refining their predictions and recommendations based on new data and patterns. This creates a virtuous cycle where risk management capabilities continually evolve and strengthen rather than becoming outdated as conditions change.

AI-powered platforms can track thousands of risk factors simultaneously, from shipping delays and weather events to regulatory changes and social media sentiment that might indicate brewing political instability. When significant changes occur or risk thresholds are crossed, these systems can alert decision-makers and suggest potential responses based on predefined scenarios and business objectives.

For more information on the use of AI tools for stress testing and identifying mitigation and contingency strategies in real-time, take a look at some of my recent articles on supply chain optimization and resilience, demand forecasting, and dynamic pricing.

Addressing Cultural and Needs Diversity

When operating across different jurisdictions, it is crucial to develop culturally aware policies and conduct localized risk assessments that take into account the specific norms, values, and beliefs of the local population. Cultural differences can significantly influence how risks are perceived, the likelihood of certain events occurring, and the effectiveness of risk mitigation strategies. AI can assist in this process by analyzing cultural data, such as communication patterns, social behaviors, and historical responses to risk, to inform the development of culturally sensitive risk frameworks.

Such tools can also play a vital role in identifying and mitigating bias in risk assessment models, detecting potentially discriminatory elements that might lead to biased outputs and ensuring that risk assessments are fair and account for the diverse needs and perspectives of individuals and communities in different cultural contexts. Incorporating cultural and needs diversity into risk assessment through the use of AI can lead to more accurate risk evaluations, improved stakeholder engagement, and ultimately, more successful and sustainable international business operations.

How AI Tools Can Transform Fever-Tree’s Risk Assessment and Mitigation

For companies like Fever-Tree, sourcing from high-risk regions like the DRC, AI-powered risk management tools have transitioned from being simply a competitive advantage into a true necessity. By providing real-time visibility, predictive insights, and dynamic scenario modeling, AI enables business leaders to anticipate, plan for, and mitigate the complex risks inherent in global supply chains. This empowers Fever-Tree to continue delivering premium products while safeguarding its brand, bottom line, and ethical commitments.

AI and advanced analytics solutions can enhance the speed, quality, and reliability of risk management for companies like Fever Tree when operating in volatile regions like the DRC by providing:

1. Real-Time Supply Chain Visibility

AI-powered platforms can ingest and analyze data from multiple sources — supplier reports, satellite imagery, news feeds, social media, and logistics partners — to provide a live map of Fever-Tree’s supply chain. For example:

• Supplier Risk Scoring: AI models can automatically score suppliers based on recent incidents (e.g., strikes, violence, weather disruptions) and flag emerging risks.
• Geospatial Analysis: Satellite data can track infrastructure status (e.g., road closures, port congestion) and environmental risks (e.g., flooding, drought).

2. Scenario Modeling and Simulation

AI can help Fever-Tree simulate the impact of various disruptions:

• Conflict Escalation: What if a key region in the DRC becomes inaccessible due to conflict? AI models can simulate the impact on inventory, costs, and delivery timelines.
• Regulatory Shocks: What if new sanctions are imposed? AI can model alternate sourcing scenarios, cost implications, and lead times.
• Price Volatility: Machine learning models can forecast price movements for quinine based on historical data, weather patterns, and geopolitical signals.

3. Early Warning Systems

Natural Language Processing (NLP) and machine learning can scan global news, NGO reports, and social media for early signs of instability or supply chain disruption in the DRC. Fever-Tree can receive alerts days or weeks before traditional channels would flag an issue, allowing for proactive mitigation.

4. Supplier Network Optimization

AI can analyze Fever-Tree’s supplier network to identify:

• Hidden dependencies: For example, if several suppliers rely on the same transport route or port, creating a single point of failure.
• Diversification opportunities: AI can suggest alternative suppliers or regions that minimize risk, factoring in cost, quality, and ethical considerations.

5. Ethical and Compliance Risk Monitoring

AI tools can help ensure that Fever-Tree’s sourcing complies with international standards (e.g., Modern Slavery Act, conflict minerals regulations) by:

• Automating document and contract analysis to check for compliance clauses.
• Screening supplier backgrounds for links to unethical practices or sanctioned entities.

Choosing the Right AI Tools for Risk Assessment and Scenario Planning

Several AI-powered solutions are currently available to help businesses enhance their risk assessment and scenario planning capabilities, offering a variety of approaches to managing international business risks, particularly those related to policy uncertainty and economic volatility.

Any organization looking to adopt AI into their business and manage the risks associated should consider using frameworks like those provided by the National Institute of Standards and Technology (NIST), which provide guidance for organizations implementing AI-powered risk assessment tools responsibly.

Open-Source Tools and Frameworks for Custom Solutions

Organizations looking to build their own risk assessment capabilities can leverage several open-source tools and frameworks, but there can be significant barriers to entry involved in implementing these tools. Most organizations interested in building and implementing their own tools should consider a phased approach, starting with simpler tools that address specific risk areas and gradually building more sophisticated capabilities as they demonstrate value. This incremental strategy allows for learning and adaptation while managing implementation risks and resource requirements.

Here are some open-source tools and frameworks that companies can leverage to build their own risk solutions. For a more detailed look at these tools, check out my previous article on using these tools to improve dynamic pricing.

Python-Based Tools

- Pandas: Data manipulation library for risk data analysis (Beginner-Intermediate difficulty)

- Scikit-learn: Machine learning library for developing risk prediction models (Intermediate)

- PyTorch/TensorFlow: Deep learning frameworks for advanced risk modeling (Advanced)

- NetworkX: Network analysis library for supply chain risk mapping (Intermediate)

R-Based Tools

- R Shiny: Interactive visualization framework for risk dashboards (Intermediate)

- forecast: Time series analysis package for risk trend modeling (Intermediate)

- mcmc: Markov Chain Monte Carlo simulations for scenario planning (Advanced)

Specialized Risk Frameworks

- OpenRisk: Open framework for financial risk analysis (Intermediate)

- CORAS: Risk assessment methodology and tools for security analysis (Intermediate)

- Monte Carlo Simulation Libraries: Various libraries for probabilistic risk modeling (Intermediate-Advanced)

Enterprise-Level Solutions

Large multinational corporations typically require comprehensive, integrated risk management platforms that can handle complex global operations. These organizations can benefit from custom-developed AI models tailored to their specific industry dynamics and risk profiles. With larger datasets and dedicated data science resources, enterprises can leverage advanced deep learning and neural network approaches to gain competitive advantages in risk management.

Solutions for Small and Niche Businesses

Smaller businesses, specialized manufacturers, and smaller exporters face different challenges with more constrained resources for risk management. For these organizations, focused or industry-specific solutions tackling relevant regulations, supply chain challenges, and market dynamics often deliver better results than general-purpose tools.

Businesses should prioritize solutions with shorter implementation times and faster time-to-value. API-based solutions and modular platforms allow smaller businesses to start with basic risk assessment capabilities addressing their most pressing concerns (such as tariff exposure or supply chain vulnerabilities) and gradually expand as needs evolve and resources permit. Cloud-based risk management platforms with subscription models will also offer smaller businesses access to sophisticated capabilities without large upfront investments.

AI-Powered Risk and Scenario Planning in Complex Markets was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

Table of Contents

· Introductory Anecdote: The Vietnamese Furniture Manufacturer
· The International Pricing Puzzle
· How Shocks Disrupt Pricing Models
· Limitations of Legacy Pricing Approaches
· The AI-Powered Pricing Revolution
· Real-Time Cost Fluctuation Analysis
· Optimized Pricing Recommendations and Automation
· Application: The Vietnamese Furniture Manufacturer
· AI Pricing Solutions for Today’s Market
· Building Your Own AI-Powered Dynamic Pricing Engine: Open-Source Options
· Enterprise Solutions
· Solutions for Small and Niche Businesses

Key Question: How can Artificial Intelligence (AI) tools improve dynamic pricing strategies for companies operating in international markets and reduce uncertainty in an ever more complex and fast-changing landscape?

Introductory Anecdote: The Vietnamese Furniture Manufacturer

Imagine that you are the CEO of a mid-sized furniture manufacturer based near Ho Chi Minh City, Vietnam. For years, your company has thrived serving the growing domestic Vietnamese market, though it also exports some quality wooden furniture to the US and Europe. You largely employed a cost-plus pricing strategy that you make manual adjustments to based on quarterly reviews of competitor catalogs and raw material costs.

Now in early May 2025, your business is feeling the shockwaves of the geopolitical moment. Intensifying US-China trade friction and new tariff regimes mean that shiploads of Chinese furniture, originally destined for American ports, have suddenly been diverted to European and Southeast Asian markets, including Vietnam. Almost overnight, your main markets have been flooded with aggressively priced alternatives. Your traditional tools are useless for getting a hold on the situation, and sales are plummeting as retailers switched to cheaper Chinese suppliers. Your cost-plus prices, factoring in quality materials and ethical labor, simply can’t compete on sticker price alone. The panic begins to set in. How will you survive this onslaught?

Optimally pricing a product, tool, or service gets infinitely more complicated as a business expands internationally. Fluctuating exchange rates can erode profit margins or make products unaffordable overnight. Diverse regulatory landscapes impose varying rules on pricing, tariffs, and taxation, demanding constant vigilance and adaptation. Cultural nuances shape consumer perceptions of value and price sensitivity, requiring tailored approaches that resonate with local preferences. Intricate global supply chains introduce complexities in logistics, shipping, and customs duties, all of which impact the final price and profitability.

Traditional pricing models are growing increasingly inadequate amid rapid policy shifts, economic volatility, and supply chain disruptions. Using outdated models and tools can make pricing a reactive rather than proactive endeavor, based on lagging data or gut feelings rather than real-time intelligence. In the face of today’s wild market swings and with the impact of constant policy changes so difficult to forecast, many companies are growing increasingly frustrated by their inability to respond effectively to competitor movements, accurately factor in fluctuating input costs across borders, and the sheer complexity of managing potentially thousands of SKUs (shorthand for stock-keeping units, or individual products) in dozens of markets.

AI-powered dynamic pricing tools can rewrite this story for many firms, providing a significant competitive edge and empowering businesses to navigate the complexities of the global marketplace with greater confidence and resilience. This article explores how artificial intelligence is revolutionizing dynamic pricing capabilities for businesses operating internationally, providing essential insights for business leaders seeking to maintain competitiveness and profitability in uncertain times.

The International Pricing Puzzle

Businesses operating internationally face numerous challenges when it comes to establishing effective pricing strategies across borders, particularly if they’re using outdated, static models. Many businesses use models or strategies that struggle to capture important metrics like:

- Cultural Value Perception: Price sensitivity can vary dramatically between markets, and what seems fair in one country may appear exorbitant or suspiciously cheap in another.

- Exchange Rate Volatility: Currency fluctuations can directly impact profit margins because the value of revenues earned in foreign currencies can be unpredictable when converted back to a business’s home or main trade-denominated currency. This can lead to a “pass-through issue,” as businesses have to determine how much of an exchange rate gain or loss should be transferred to customers. This may, in turn, lead to further price fragmentation if businesses employ different strategies across their markets.

- Regulatory Compliance: Operating across multiple international markets requires complying with complex and often highly variable sets of regulations concerning pricing, import duties, value-added taxes (VAT), and other taxes or levies. In some markets, a lack of knowledge of the stability of existing regulatory frameworks (particularly in politically volatile or highly corrupt countries with rent-capturing leadership structures) can make investing in entering a market difficult. Anti-dumping laws, aiming to prevent the sale of goods at unfairly low prices to protect local industries, can add another layer of regulatory complexity for businesses setting global pricing policies.

- Supply Chain Complexities: Variable cost factors, such as international shipping, insurance, warehousing in foreign markets, customs duties, and handling charges can lead to “export price escalation,” leading the final retail price in a foreign market to be considerably higher than the price in the country of origin.

- Competitive Landscape Monitoring: The challenge of tracking competitor pricing strategies across international markets has grown increasingly difficult in today’s digital marketplace given diverse competitive landscapes, varying numbers of competitors, and the difficulty in obtaining timely and accurate pricing information from all relevant players.

- Inflation Differentials: Countries experience varying rates of inflation and exhibit different abilities to manage these rates. In markets with inflation management issues, businesses may have to take measures to adapt product components or packaging, change sourcing strategies, shorten credit terms, or even exit particular markets if inflation or pricing controls become too costly.

- Transfer Pricing Management: Transfer pricing involves setting prices for sales transactions between entities within the same company across different countries, often for raw materials, components, finished goods, or services. Transfer pricing decisions require balancing the interests of various stakeholders like parent companies, governments, and joint-venture partners while also complying with complex tax regulations.

How Shocks Disrupt Pricing Models

Policy shocks, such as the unpredictable nature of the Trump administration’s tariff regime, can create a climate of uncertainty that leads businesses to struggle to anticipate future cost increases and the potential reactions of competitors. This uncertainty makes accurate forecasting exceptionally challenging and can make it difficult to formulate stable pricing strategies under traditional price models.

The imposition of tariffs directly increases the cost of imported goods, and companies that find substitutes or replacements to avoid or mitigate the impact of tariffs may still see increased costs on raw materials and finished goods. This becomes even more difficult to track when factoring in supply chain balancing efforts meant to diversify the country of origin of goods, specific tariff rates imposed and their variance on different goods and countries of origin, the availability of domestic substitutes, and the responsiveness of consumers to price changes. Consumer behavior may also shift as tariffed goods become more expensive, with changing demand elasticity rendering historical sales data unreliable for forecasting future sales volumes at different price points.

Similarly, economic shocks like that provided by the COVID-19 pandemic can cause widespread disruptions to global supply chains, directly impacting production costs and the ability of businesses to meet demand. The pandemic also triggered dramatic and often unpredictable shifts in consumer demand patterns, with lockdowns, changes in consumer priorities, and economic uncertainty leading to surges in demand for some goods and sharp declines for others. These abrupt changes made historical demand data largely irrelevant for forecasting future sales and pricing strategies and demonstrated the limits of traditional, more static models.

Image credit: BairesDev

Limitations of Legacy Pricing Approaches

Despite the increasing complexity and volatility of international markets, many companies continue to rely on traditional pricing processes, established pricing models, and rudimentary tools for setting and adjusting prices. Typical traditional, more static models include:

- Cost-Plus Pricing: Calculated by taking total production and logistics costs (including known tariffs, shipping) and adding a fixed margin. While straightforward, it ignores market demand, competitor actions, and perceived value, and struggles to adapt quickly to volatile input costs.

- Competitive Pricing: This involves benchmarking against competitors in specific local markets. It’s a very reactive strategy that can be too slow to shift as gathering comprehensive, real-time competitor data across multiple international markets manually is resource-intensive and can run into unforeseen delays.

- Value-Based Pricing: This type of pricing is based on the perceived value to the customer in a specific market. It requires understanding customer value across diverse international markets, which can be challenging, and can be hard to quantify and adjust dynamically.

- Market Penetration and Price Skimming: Under these approaches, a company offers lower initial prices that potentially impact initial profit margins, or launch at high prices that may alienate potential customers.

- Geographical Pricing: This approach involves setting different prices for the same product in different locations, considering local purchasing power, taxes, and costs.

Very often, these strategies are managed with spreadsheets, internal databases, and basic enterprise resource planning (ERP) modules. With these tools, forecasting can really only rely on historical sales data and relatively static market assumptions. While the traditional toolkit may still work well enough much of the time for many businesses, it doesn’t really give business owners insight into how to actually optimize their pricing, and certainly falls short when facing major shocks and complex international operations.

The AI-Powered Pricing Revolution

Dynamic pricing provides a powerful alternative to static models, and AI tools can be powerful catalysts that enable crucial agility in pricing. AI can analyze vast datasets, identify hidden patterns, predict future trends, and recommend optimal pricing adjustments with a speed and precision that surpasses traditional human capabilities. By leveraging advanced analytical capabilities, machine learning algorithms, and real-time data processing, AI tools offer businesses the potential to transform their dynamic pricing strategies in international markets and provide a significant competitive edge.

For business leaders, moving from reactive adjustments to proactive, data-driven strategies likely means leveraging AI tools. Here are some ways AI is transforming international pricing:

Real-Time Cost Fluctuation Analysis

AI tools can integrate directly with supply chain and financial systems, tracking input costs — raw materials, labor, logistics, warehousing, and critically, tariffs and duties — across different jurisdictions almost instantaneously. They can monitor exchange rate fluctuations and assess their impact on landed costs in each market to provide an accurate, up-to-the-minute understanding of true costs — an essential feature when tariffs or shipping rates change unexpectedly. For deeper insight into AI applications for real-time analysis, check out my previous articles in The AI Edge on demand forecasting and supply chain optimization.

Optimized Pricing Recommendations and Automation

This is where AI truly shines, by analyzing real-time cost data alongside other critical factors such as competitor pricing, inventory levels, demand elasticity models, seasonality, and promotional performance to generate optimized pricing recommendations. Based on this multi-factor analysis, AI engines can recommend optimal price points for each SKU in each market. These recommendations can be reviewed by pricing teams to allow businesses to respond to market dynamics in minutes, not days or weeks.

Here’s a more detailed look at each set of factors that can inform optimized recommendations:

- Competitive Intelligence and Price Tracking: AI models scrape and analyze competitor prices across multiple online channels and markets. Beyond simple price tracking, AI can analyze competitors’ product assortments, promotional activities, and pricing patterns to reveal their underlying strategies.

- Demand Sensing and Forecasting: AI algorithms analyze sales data, website traffic, seasonality, macroeconomic indicators, news and consumer sentiment, and relevant external factors like weather events to predict demand with far greater accuracy than traditional methods.

- Hyper-Personalization and Segmentation: AI identifies granular customer segments based on behavior, purchase history, and demographics, allowing for tailored pricing strategies or promotions that maximize conversion and lifetime value while navigating ethical considerations around fairness.

- Price Elasticity Modeling: AI models determine how sensitive demand is to price changes for specific products in specific markets or customer segments.

- Inventory Levels: AI models can factor current stock levels and supply chain lead times to optimize pricing for inventory turnover or margin maximization.

- Scenario Planning: AI tools can simulate the potential impact of various events — a new tariff regime, a competitor’s strategic move, a supply chain disruption — on costs, demand, and profitability, enabling proactive contingency planning.

- Business Rules and Goals: You set the strategic parameters — e.g., prioritize market share gain in Region A, maximize margins on Product Line B, clear excess inventory of Product C — and the model will calibrate recommendations accordingly.

- Market Entry/Exit Analysis: By analyzing demand, competition, and cost structures, AI can help identify promising new international markets or signal when market conditions suggest deprioritizing a specific region.

These insights enable businesses to rapidly adjust prices in response to changing conditions while balancing profit maximization with customer satisfaction — crucial for remaining competitive in ambitious international markets.

Application: The Vietnamese Furniture Manufacturer

Let’s return now to our fictional Vietnamese furniture manufacturing firm and imagine how AI-powered dynamic pricing could have rewritten the story. Instead of quarterly reviews, an AI tool could have:

1. Detected the Threat Early. By monitoring online marketplaces and competitor websites in Vietnam 24/7, AI tools would have flagged the influx of new, low-priced Chinese competitors weeks earlier than the company’s manual checks.

2. Enabled Rapid Response. As it analyzed the exact price points and product types of new competitor firms, an AI tool could recommend targeted price adjustments on specific furniture lines most vulnerable to competition. While this might mean accepting temporarily lower margins on some goods to retain key retail partners, it could still maintain higher prices on unique, less substitutable items and could even increase its margin where possible to offset losses. Such tools might also be able to identify whether some of the Chinese competitors’ offers were comparable in quality or simply cheaper, providing an opportunity to drive a new sales or marketing strategy centered around our fictional company’s competitive advantage.

3. Optimized Costs and Margins. Simultaneously, an AI tool could track the company’s fluctuating wood, finishing, and shipping costs, ensuring that any price adjustments maintained acceptable minimum margins and could highlight where cost savings were urgently needed or could be improved.

4. Identified Pivot Opportunities. Seeing intense pressure in the domestic market, an AI-enabled model could analyze demand, competition, and cost-to-serve data for other potential export markets (perhaps Australia, Japan, or new ASEAN trade partners) where the influx of diverted Chinese goods was less severe. This would allow the company to consider making a strategic pivot to reduce its reliance on a now hyper-competitive home market.

Rather than simply slashing prices across-the-board and crippling profitability, a well-built and trained AI tool could enable this Vietnamese firm to make surgical, data-driven decisions to defend market share where necessary, protect margins where possible, and identify escape routes to less turbulent waters. Such tools transform pricing from a defensive reaction into a strategic lever for navigating uncertainty.

AI Pricing Solutions for Today’s Market

There are numerous open-source tools and models that can be adapted to individual company needs, as well as several companies offering AI-powered tools and platforms to help businesses implement dynamic international pricing. I have allocated some of these tools and firms below.

Building Your Own AI-Powered Dynamic Pricing Engine: Open-Source Options

For businesses with technical capability or an interest in investing in it, leveraging open-source tools and machine learning/deep learning frameworks can provide a customizable and cost-effective path to developing a proprietary dynamic pricing engine. This approach requires data science and engineering expertise but offers maximum flexibility and control. Here are some open-source tools and frameworks companies can use to construct a dynamic pricing system tailored to their specific needs:

- Python Scientific Stack (NumPy, Pandas, Scikit-learn): This collection of libraries can be leveraged by businesses to process, manipulate, and analyze diverse datasets. Used in conjunction, they can support large, multi-dimensional arrays and matrices and offer a wide array of ML algorithms that run regression models, clustering algorithms, and time series analysis tools.

- TensorFlow and PyTorch: These powerful open-source deep learning frameworks can be used to build sophisticated models such as custom neural networks for demand forecasting, reinforcement learning for optimal pricing policies, and natural language processing (NLP) models.

- Prophet (by Facebook/Meta): Prophet is an open-source forecasting procedure designed for time series data that often has seasonal effects and holiday impacts. It can be leveraged for demand forecasting at the SKU level or for broader market trends. It allows for the inclusion of regressors, so factors like promotions or competitor pricing changes can be incorporated into forecasts.

- Statsmodels: This Python module provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. Businesses can use it to build rigorous models of how price changes impact sales volume.

- Scrapy and BeautifulSoup (Python libraries): Useful for gathering competitive intelligence data and price tracking, these web-scraping and parsing tools allow businesses to build custom spiders to crawl competitor websites, online marketplaces, and industry news sites to gather real-time pricing information, product assortments, and promotional activities.

- OpenAI Gym (or Gymnasium) & Ray RLlib: For businesses venturing into Reinforcement Learning for dynamic pricing optimization, these tools allow businesses to create a custom environment that simulates their market, including customer behavior, competitor actions, and cost structures that can train automated models that dapt to changing market conditions and optimize pricing in real-time.

- Apache Superset or Metabase: While not ML models themselves, these open-source business intelligence and data visualization tools monitor the performance of dynamic pricing models and allow for more human oversight. Businesses can create dashboards that visualize pricing recommendations, actual price changes, impact on sales and margins, and key performance indicators (KPIs).

Enterprise Solutions

Large manufacturers, distributors, or retailers with complex global operations, large product catalogs, multiple sales channels, and significant B2B dealings would likely benefit most from comprehensive platforms that integrate into complex systems. For large organizations with complex international operations requiring scalability and extensive features, here are some tools to consider:

- Vendavo: Specialized in AI-driven price optimization for B2B organizations

- PROS: Provides AI-powered SaaS solutions for pricing across various industries

- Pricefx: Offers a comprehensive suite of AI-based price management software

- DynamicPricing.ai: Provides dynamic pricing automation specifically for e-commerce and DTC brands, integrating with platforms like Shopify and Magento.

- Revionics: Focuses on AI-powered retail price optimization

- Retalon: Provides AI-powered retail pricing software for unified commerce

- Competera: Offers an AI-powered pricing platform for omnichannel retailers

- Zilliant: Focuses on B2B price optimization and sales guidance, helping companies improve margin and revenue performance.

Solutions for Small and Niche Businesses

Smaller manufacturers exporting specialized goods or firms importing niche products may find enterprise platforms overly complex and expensive. Those prioritizing ease of use and affordability might derive more value from more focused tools such as:

- PriceLabs: Primarily serves hospitality but applicable to related sectors

- Peak.AI (Starter Edition): Suitable for smaller manufacturers

- Impact Analytics (SMB Solutions): Tailored offerings for small/medium businesses

- SYMSON (Core Plan): Viable option with competitor analysis capabilities

- Prisync: Cost-effective e-commerce solution for competitor price tracking

- Price2Spy: Invaluable for understanding the competitive landscape in target export markets

- Tgndata (Business Plan): Scalable option with price intelligence features

Navigating International Market Uncertainty with AI-Powered Dynamic Pricing was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

Key Question: How can Artificial Intelligence (AI) tools improve demand forecasting for companies operating in international markets and reduce uncertainty in an ever more complex and fast-changing landscape?

In the early 20th century, gauging the demand for a product in a foreign market was an expensive, time-intensive, and data-poor endeavor. For a business in New York seeking to sell its goods in London in 1930, gathering even basic insights would likely involve costly overseas trips for market research, reliance on potentially slow and unreliable postal communication for feedback (4–6 weeks for most mail crossing the Atlantic), or expensive transatlantic telephone calls. In the end, most companies would rely on past sales trends, expert opinions, prevailing economic conditions, and the hunches of salespeople.

These days, a product trending at the right moment on TikTok or Instagram can boost the revenue of a 100-year-old company like Stanley from $74 million in 2019 to $750 million in 2023 (a 913% increase). Or, as in the case of ultra-low price Chinese retailers Shein and Temu, President Trump’s closure of the de minimis provision has quite suddenly and radically altered their competitiveness and could lead to a reduction in demand for their products.

While demand forecasting methods have transformed pretty significantly over the past century, their limitations when confronted with the volatility inherent in global operations remain clear due to data, technological, human bias, and external forecasting limitations. AI offers a powerful suite of tools and techniques that can revolutionize demand forecasting, offering businesses the opportunity to gain more insight into and improve resource allocation, risk mitigation, and sustained growth.

Why It’s Difficult to Forecast Demand in International Markets

Accurately forecasting demand across international markets has become increasingly challenging due to the growing complexity of the international system. Companies face significant hurdles when attempting to predict customer needs across diverse geographies, each with its own unique market dynamics, consumer behaviors, and regulatory environments, including:

Data Quality Issues: Organizations frequently struggle with datasets that are incomplete, inaccurate, or outdated, particularly when entering emerging markets or launching new products. Even basic inconsistencies in data collection methods or formats across international operations can compromise forecast accuracy.

Unpredictable External Factors: Supply chain disruptions, economic downturns, and geopolitical tensions can trigger rapid shifts in consumer behavior. Seemingly predictable elements like seasonal variations can exhibit irregular patterns (an increasing phenomenon driven by climate change) across different cultures and geographical locations.

I’ve seen a great example of this over the past few months as I’ve traveled throughout Southeast Asia, where it’s been quite the process hopping around to stay ahead of monsoons and heat waves that seem surprisingly localized. As I’ve moved from country to country to stay ahead of sub-optimal weather patterns, I’ve seen winter, spring, and summer all a short flight away from one another, providing a window into the simultaneous seasonal variation that manufacturers and sellers have to account for even across limited geographic scopes.

Technological and Human Limitations: Smaller enterprises often rely on poorly fit-for-purpose and overly basic tools like spreadsheets that lack sophisticated analytical capabilities. Human bias further complicates matters, as decision-makers may overemphasize past experiences or gut feelings over data-driven insights. The complexity of global supply chains, with multiple tiers of suppliers across various countries, each with distinct regulations and operational challenges, adds another layer of difficulty that requires tremendous insight to see through effectively.

Navigating Economic and Policy Shocks: The opacity obstructing organizations navigating the fallout and new realities (which seem to change on a daily or weekly basis) of President Trump’s tariff levies demonstrates how quickly demand patterns can shift and the difficult nature of understanding where the equilibrium will fall after a shock. Traditional forecasting models using pre-tariff data will probably fail to predict the future decrease in consumer demand due to these price hikes and the emergence of more competitive alternatives from companies less affected by tariffs.

Traditional Approaches and Their Limitations

Companies often approach demand forecasting by analyzing historical sales data to identify patterns and trends. They implement Sales and Operations Planning (S&OP) processes that develop unified forecasts to inform production, inventory, and sales strategies. Common methodologies that drive these forecasts include time series analysis techniques like moving averages and exponential smoothing, regression analysis to identify relationships between demand and other variables, and qualitative forecasting when historical data is limited. The tools used by companies can range from basic spreadsheets to sophisticated statistical software packages like SAS or SPSS, as well as demand forecasting modules within Enterprise Resource Planning (ERP) systems.

Despite their widespread use, these traditional methods and tools often struggle when confronted with international market complexities. They are not primed to handle sudden market shifts and unexpected events, and will operate under the assumption that past trends will persist even though this is rarely the case in dynamic global environments. Perhaps most crucially, these types of systems typically cannot integrate external data sources that might provide early warnings of demand changes or dynamically adjust as new data becomes available.

These forecasting tools often lack the granularity needed to accurately predict demand across diverse international markets with varying consumer preferences and economic conditions. The processes are frequently time-consuming, prone to human error, and challenged by data silos and poor interdepartmental communication. The result is often low forecast accuracy, leading to overstocking or stockouts — both costly outcomes that create significant operational problems.

Advantages Provided by AI in Demand Forecasting

AI offers a transformative approach to demand forecasting for companies navigating international market complexities and potential policy or economic shocks. A few key areas that AI tools can immediately add value to include:

• Improved accuracy and reduced errors by identifying complex patterns and correlations that human analysts might miss.
• Real-time data analysis allowing continuous market monitoring and dynamic forecast adjustments.
• Enhanced scenario planning for modeling different potential outcomes based on various policy changes, economic conditions, and competitor actions.
• Streamlined processes through automation, freeing human analysts for more strategic tasks.
• Greater personalization and granularity by analyzing customer-level data for tailored regional or segment-specific predictions.
• Supply chain optimization by integrating demand forecasts with supply chain data.
• Anomaly detection to flag unusual patterns that might indicate potential disruptions or opportunities.

Let’s take a deeper look at these capabilities and how they can transform the industry from two key perspectives: integrating external signals, and improving elasticity modeling.

Integrating External Signals

One of AI’s most significant advantages is its ability to incorporate external signals — essentially, data from outside a company’s internal system — into forecasting models. Unlike traditional methods that primarily rely on historical sales data, machine learning models can integrate diverse non-traditional data for more comprehensive insights.

For example, AI algorithms can analyze sentiment in news articles and social media discussions related to international trade policies and announcements. By gauging whether the overall tone is positive or negative and identifying key themes, AI can provide insights into how these developments might influence market confidence and consumer demand. For instance, negative sentiment surrounding willingness to pay for Shein or Temu products due to potential tariff increases could signal an anticipated demand decline for affected goods.

Some of the most interesting applications come from these systems’ ability to monitor competitor pricing changes across different markets in real-time, allowing businesses to anticipate shifts in market share and adjust forecasts accordingly. The tools can similarly analyze vast amounts of online data to identify consumer behavior shifts by monitoring website traffic, search trends, and social media activity to detect emerging preferences.

Beyond these examples, AI can integrate macroeconomic indicators, geopolitical events, and even weather patterns to provide a truly holistic view of demand influences in international markets. This enhanced foresight allows businesses to react proactively, adjusting inventory levels, production schedules, and resource allocation effectively.

Improved Elasticity Modeling

Demand elasticity measures the willingness of consumers to pay for a product or good relative to the cost. Water has a relatively low elasticity; no matter the cost, people need water to survive and would likely pay hefty sums in a severe shortage. Airplane tickets, on the other hand, have a greater elasticity, with the willingness of customers to pay for tickets at higher prices being significantly reduced.

When events like tariffs cause price increases, understanding how demand for specific products will respond becomes crucial so that companies can reevaluate their price models, input purchases, and production schedules. AI and machine learning algorithms can analyze historical sales data, past pricing changes, competitor strategies, and other relevant factors to develop more accurate price elasticity models across different international markets.

This capability allows businesses to predict with greater precision how tariff-induced price increases might affect product demand. For example, AI might reveal that demand for a particular product is highly elastic in a specific market, meaning even a small price increase could significantly reduce sales volume. This insight enables more strategic pricing decisions, including potentially absorbing some tariff costs or focusing on value-added features to justify higher prices.

AI-Powered Forecasting Solutions

A growing array of AI-powered demand forecasting tools designed specifically for businesses operating internationally are becoming available, both in the form of enterprise products and open source models. Typical use cases for these tools include specialized AI forecasting platforms, modern supply chain management software, predictive analytics platforms, and external data integration tools.

Businesses can build and deploy their own sophisticated forecasting models that can process large international datasets, identify subtle trends, and generate more accurate predictions than traditional statistical methods. They can also help in understanding the drivers of demand in different markets.

If you’re a business looking to invest in new demand forecasting tools that can simplify visibility into complex international markets, the key functionalities and capabilities that you’ll want in new products or models is advanced time series forecasting, regression analysis, machine learning algorithms, anomaly detection, and causal modeling. Depending on your technical capabilities to integrate open-source software into your workflow and whether you plan to create and integrate your own models from open-source toolkits or purchase an existing enterprise solution, these tools can be used at varying degrees of cost and can provide varying levels of utility.

Open-source AI and ML platforms and models make the source code freely available for anyone to view, modify, and distribute under specific open-source licenses and are highly flexible so that any business can tailor the code for their own purposes. Some examples in the context of demand forecasting include:

• Machine learning frameworks (TensorFlow, PyTorch, scikit-learn): These provide the foundational libraries to build custom forecasting models tailored to the nuances of international markets.
• Time Series Libraries (e.g., Prophet by Meta, statsmodels): these offer specialized tools and models for time series analysis, crucial for understanding historical demand patterns.
• General-Purpose ML Models (from libraries like scikit-learn): These can be used to build regression or classification models incorporating various international demand drivers.
• While not solely for forecasting, Open Source LLMs (LLaMA, Gemma, Mistral etc.) could potentially be used in conjunction with forecasting tools to analyze unstructured text data from international sources (e.g., news articles about economic changes, social media sentiment in different countries) to identify potential impacts on demand. This would likely involve using the LLM for tasks like sentiment analysis or topic modeling on translated or multilingual text data.

An easier route would likely be to purchase proprietary platforms that you can integrate into your own stack with resources that can help you grow your models. As these models are managed by companies rather than communities, they often offer more support, more consistent feature releases, and sometimes better security. However, these tools are usually less flexible and more costly due to licensing fees. Companies making proprietary platforms designed for building and deploying ML models at scale include:

• Google Cloud AI Platform
• Amazon SageMaker
• Microsoft Azure Machine Learning
• IBM Watson Studio

Here are some examples of AI-powered demand forecasting tools that leverage AI and machine learning to help businesses predict future demand with greater accuracy. I have purposefully left out those companies that were included in last week’s supply chain article, but many of those firms offer solutions relevant to demand forecasting as well.

• Crest AI
• Cube
• Datup
• Netstock
• Prediko
• Vena

Conclusion

Overall, the value that AI can provide to supplement knowledge and decision-making, particularly when compared to more traditional demand forecasting methods, is clear in the growing turbulence imbuing the international trade landscape. AI-powered solutions can thoroughly address the limitations of traditional forecasting methods through enhanced data integration capabilities, advanced machine learning algorithms, real-time monitoring, external data integration, elasticity modeling, scenario planning, and automation. The introduction of these frameworks and the many open-source and proprietary tools that can be implemented cost-effectively can quickly make a dent in your business without the need to shell out for expensive enterprise systems or consultants.

If you’d like to learn more about how to incorporate these tools into your tech stack or learn more about how your business can directly benefit from AI and ML algorithms, don’t hesitate to reach out to me at reaipolitique@substack.com.

Enhancing Demand Forecasting in International Markets with AI Tools was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article is the second edition of The AI Edge, a larger series detailing specific ways that AI tools can support companies and organizations impacted by international market turbulence to better manage global exposure.

Key Question: How can AI tools improve supply chain optimization and resilience for organizations working in international markets and who are vulnerable to policy, economic, natural disaster, and other kinds of major shocks?

The shock to the system dealt by the COVID-19 pandemic provided a clear example of the impact that competing shocks driven by a natural disaster, frantic policy responses, and an economic slowdown can have on supply chains. As the world shut down in the face of uncertainty, factory shutdowns, port congestion, labor shortages, and wild swings in consumer demand created unprecedented disruption. Tech companies faced critical semiconductor shortages as retailers struggled with unusual inventory mismatches and manufacturers found themselves hit by severe workforce availability issues. My own research in Southwestern Uganda found that 88% of businesses in the region closed down for more than 12 months, with the prices of staple goods often increasing 200% or more due to supply chain pressures and the need to replace grain supplies locked away in Ukraine’s ports due to conflict with Russia.

Today, we are in another period of incredible turbulence. The threat of massive tariffs by the U.S. could directly increase the cost of imported materials and exported goods for many companies, forcing difficult decisions: absorb costs and lose money, raise prices and risk competitiveness, or undertake complex and expensive supplier diversification. Attacks on shipping lanes by Yemen’s Houthis or cyberattacks on port IoT infrastructure could sever critical transport links, disable key suppliers, or cut off access to essential raw materials at any moment. Unstable supply forecasts for key goods like cocoa in the Ivory Coast and key minerals for the green revolution held by rebel forces in the Democratic Republic of the Congo are driving a mass scramble for alternatives.

For leaders managing complex international supply chains, it can feel a bit like navigating a never-ending storm. A lack of visibility and transparency makes predicting and understanding how to address bottlenecks and disruptions exceedingly difficult; unanticipated material shortages often lead to halted production and increased costs; and the outbreak of war, the incidence of natural disasters, and sudden regulatory changes can suddenly make crucial national or regional operations near impossible. Volatile freight prices (and growing insurance costs) erode margins, and managing endless backup suppliers presents a constant a hassle, particularly for industries competing for the same limited resources.

As the world gets more complicated, technology is increasingly more effective for disentangling confusion. AI tools can help organizations improve the resilience and flexibility of their supply chains through offering insights in real-time monitoring, supplier diversification, route optimization, and inventory management.

This article is organized into 4 sections:

1. Overview of supply chain shocks
2. AI forecasting and real-time crisis mitigation use cases
3. Specific tools that support the use cases
4. Three real-life case studies of businesses benefitting from AI-enabled supply chain tools

Supply Chain Shocks

Disruptive ‘shocks’ in recent years have clearly underscored the vulnerability of global supply chains. Shocks are most simply explained as unexpected changes to a given environment that drive profound impact. In the case of supply chains, shocks can impact the flow of goods and materials within a supply chain and often lead to delays, shortages, and price increases.

Policy and regulatory shocks like sudden tariff changes or trade restrictions, export/import bans, or major regulatory changes. These types of shocks can be positive or negative, but they tend to breed uncertainty which can hinder long-term planning and investment.

Geopolitical disruptions can include armed conflicts and territorial disputes, consistent terrorism or piracy, regional instability that limits access to markets, diplomatic tensions affecting trade relations, or blockades or closures of strategic transportation corridors.

Economic shocks affect the ability of companies and countries to engage in trade, whether through sudden or severe exchange rate volatility, financial crises, significant inflation or deflation, or commodity price spikes or collapses. Market shocks such as strategic moves by competitors, supplier bankruptcies, or rapid demand surges or collapses can also be classified as economic shocks.

Natural disasters and environmental events such as earthquakes, volcanic eruptions, hurricanes, floods, droughts, extreme temperature events, and wildfires can disrupt production and transportation networks. Climate change is expected to drive increased severe weather events and long-term impacts that may fundamentally change natural resource allocation.

Technological disruptions and infrastructure failures such as port congestion or closures, power grid failures, energy shortages, transportation infrastructure collapse, or telecommunications network disruptions can all lead to massive delays. Cyberattacks on logistics systems and infrastructure are increasingly worrying.

How AI Can Improve Shock Forecasting and Mitigate Crises in Real-Time?

For business leaders trying to understand how to plan for multiple potential futures, or how to keep all the important variables straight in the midst of crisis, AI tools add tremendous value by offering to vastly simplify complexity. Such tools can assist with pattern identification and future event prediction that often outperforms human-driven models to give leaders significantly more insight into the impact their decisions will have downstream.

Here are a few particularly interesting use cases:

Scenario Planning and Simulation

Build a Digital Twin: You can create virtual replicas of your entire supply chain to simulate various shock scenarios and test mitigation strategies without real-world consequences. For example, you could task an agent with simulating various disruption scenarios (e.g., a supplier shutdown, a blocked shipping lane, a sudden tariff) and test the effectiveness of different mitigation strategies before implementing them. This allows for data-driven contingency planning.

Run Monte Carlo Simulations: Monte Carlo Simulations leverage probabilistic modeling to understand the range of potential outcomes across thousands of possible disruption scenarios. They’re useful because they offer insights into a range of potential outcomes, not just a single prediction. Unlike game theory, which tries to identify the most rational outcome of a given set of decisions by multiple actors, Monte Carlo Simulations are best for estimating the possible outcomes of uncertain events and identifying the most likely ones.

Perform Stress Testing: You can ask AI agents to change certain variables or apply pressures you suspect you might face. By subjecting your supply chain model to extreme conditions, you can identify hidden vulnerabilities and critical failure points before they’re exposed by actual events.

Forecast Shocks

Get Real-Time Updates Coupled With Predictive Analytics: Advanced AI tools (using Natural Language Processing) can continuously scan global news, social media, and government announcements for early signals of policy changes or geopolitical tensions. Machine learning algorithms will simultaneously analyze historical data, real-time logistics information, weather patterns, economic indicators, and social media sentiment to forecast potential disruptions from supplier delays and port congestion to demand fluctuations and price volatility. While human analysts are still needed alongside these tools at this stage, the tools can provide incredible insights by aggregating a broader spectrum of information and making connections that most human models struggle to make.

Demand Sensing: AI algorithms detect subtle shifts in consumer behavior that may indicate upcoming demand volatility, allowing for proactive inventory adjustments.

Perform Supplier Risk Assessments: Machine learning models can analyze global supplier databases, considering factors like location, cost, lead times, geopolitical risk, and potential tariff impacts evaluate a supplier’s financial health, geopolitical exposure, and operational resilience. Based on the available information and the forecasted risk, AI tools can then identify and vet alternative suppliers before disruption hits.

Run Pattern Recognition and Set Up Anomaly Flagging Protocols: AI systems can analyze historical disruption data to identify early indicators of potential shocks, giving your organization valuable lead time to prepare contingency plans. It can also identify unusual patterns in supply chain data that human analysts might miss, and can notify you about potential problems before they escalate.

Note: As with other uses cases for AI, AI forecasting tools are known to sometimes hallucinate, so it is important to consider AI-driven forecasts as a tool to support analysis, not as the gospel truth.

Mitigating Real-Time Crises with AI

Implement Dynamic Resource Allocation: AI tools and agents can be tasked with automatically rebalancing inventories and recalculating optimal inventory levels for critical components — for example, by balancing the cost of holding stock against the risk of tariff-induced shortages. They can also be tasked with or automatically reallocate inventory and supplies to prioritize customers and markets based on strategic importance during shortage situations.

Dynamic Route Optimization: AI models can recalculate optimal shipping routes and logistics strategies and instantly reroute shipments when established routes are compromised or due to changing port regulations, shipping costs, and tariff zones.

Automate Key Crisis Management Tasks: Workflow automation tools can be tasked with executing pre-defined response protocols without human intervention, reducing reaction time and ensuring emotions don’t compromise initial crisis response.

Enhance Visibility and Collaboration: AI-powered platforms can integrate data across previously siloed systems to provide a unified, real-time view of the entire supply chain. This visibility facilitates faster decision-making and better collaboration with partners.

The Growing Ecosystem of Sector-Specific AI-Powered Tools

If you’re looking to implement AI tools to manage your supply chain, there are a few different routes you can take. If you have the technical inclination and tools, or you have a limited budget and want to test out some potentially useful models, there are plenty of existing AI time series forecasting tools that can provide predictive analytics capabilities by analyzing historical data and incorporating real-time information like logistics updates, weather, or economic indicators to forecast potential disruptions, so long as the business inputs have been correctly managed and applied.

When it comes to scenario planning and real-time crisis management, these tools can provide the predictive intelligence needed by larger simulation, optimization, and automation systems, but can’t really act as a full-scope solution on their own. In some use cases above like creating digital twins, much of the complex optimization and automation functions, and generating broad risk assessments, these forecasting tools should be considered as crucial input providers or components within a larger system, but would need to be integrated into existing company systems or separate end-to-end enterprise solutions.

Some models that can be directly implemented using your own data or the data you purchase include:

1. NeuralProphet — Initially associated with Meta/Facebook, now community/Stanford-driven
2. Greykite — Developed and open-sourced by LinkedIn
3. Darts — Open-source library, primary maintainer is Unit8
4. Statsmodels — Community-driven open-source project
5. forecast / fable — Open-source packages primarily developed by Rob Hyndman and collaborators. Comes with a forecasting principles and practices textbook.
6. Sktime — Community-driven open-source project, affiliated with The Alan Turing Institute
7. StatsForecast — Open-source library developed by Nixtla
8. Vertex AI Forecasting — Cloud service by Google

If you have a vision for integrating all your data and analytics capabilities into one framework, or you want to run more complicated scenario models and automate real-time crisis management, it would likely be simplest to buy an enterprise solution from an AI-enabled supply chain solutions company.

Some examples of companies providing AI-enabled solutions allowing companies to plug in these time-series forecasting tools into a holistic platform include:

1. Kinaxis — scenario planning and concurrent planning
2. SAP IBP — integrated business planning with AI features
3. Resilinc — supply chain mapping and risk monitoring
4. Project44 — real-time visibility and tracking
5. Blue Yonder — demand forecasting and optimization
6. Everstream Analytics — risk analytics and visibility
7. Interos — multi-tier supply chain resilience monitoring

Businesses That Have Used AI to Reduce Exposure to and Risk From Shocks

The following are case studies I have taken directly from the websites of the companies providing integrated supply chain solutions with AI-enablement and summarized. While each of the examples involve larger companies, they do provide interesting insight into how these tools can drive impact at scale and can be readily adapted by smaller businesses to solve specific pain points.

1. Project44: Saving Blue Diamond $1M Annually in Transportation Costs

Blue Diamond, a large retailer of almond-based products, was manually managing $300 million supply chain and logistics functions using a disjointed collection of documents, spreadsheets, and processes. The cumbersome nature of the system relied on time-intensive and error-prone manual processes that made running scenarios or simulations and gaining accurate or timely visibility into critical order information extremely difficult. Sometimes, it would take days for a customer service representatives to collect the information needed to respond to a customer. To solve the problem, Project44 and partner firms built a unified platform that managed all the company’s information and brought in outside data necessary to effectively forecast demand, supply, and logistical bottlenecks.

As a result, Blue Diamond experienced:
- $1M in annual transportation expense savings due to improved visibility and lead times
- 95% reduction in scenario planning time — down from six hours to 20 minutes
- 99% fill rates for customer orders, compared to an industry average of 89%
- 10% improvement in forecast accuracy
- 20% reduction in supply on hand — down from 75 to 60 days of inventory

2. Kinaxis: Improving Supply Chain Disruption Response Times

Konica Minolta, a large manufacturer providing a diverse range of products and services serving approximately two million users across 150 countries, found itself concerned about its ability to produce and ship products from its factories in China during the COVID-19 pandemic. It leveraged Kinaxis’ Maestro tool to simulate the length of time that the shutdown could be in place, what effect it would have on the sales plan, and how long it could manage under shutdown conditions. Though challenges continued following the COVID-19 pandemic with global logistics disruptions and semiconductor shortages, Konica Minolta continued to use the Maestro simulation functionality to get very close to the actual state of material procurement and shipping delays that were caused by port congestion and were able to use this in estimating results and discussing countermeasures.

3. Everstream Analytics: Critical Supply Secured Prior to COVID-19 Shutdown

Everstream’s platform was instrumental in warning an automotive manufacturer of an impending shipping disruption of key product inputs in the run-up to COVID-19 shutdowns in China. The system sent an automated alert to a Tier-1 automotive manufacturer customer sourcing from Wuhan, China, noting an incident at a key factory in Wuhan and making the company aware of expected limited air cargo capacity coming out of China in the near future. Based on the automated alert powered by AI, the company made an unscheduled maximum quantity one-time buy and shipped its cargo through the Russian rail system. They were able to secure key supplies ahead of a very serious disruption which may have otherwise shuttered production and gave them a huge competitive advantage over other automotive companies who had not known about the issue or had not reacted in time.

Conclusion

Whether due to Donald Trump’s tariff crusade, COVID-19, the Ever Given running aground in the Suez Canal, the Ukraine War, massive labor strikes, or droughts and wildfires, businesses of all sizes have felt the impact of sudden policy, economic, and natural disaster shocks in recent years. The introduction of AI tools, whether for forecasting or for full-scale automated supply chain and crisis management, have introduced a new opportunity for businesses of any size to hedge against risk to their supply chains and to respond quickly as threats materialize.

Building Resilient, Anti-Fragile Supply Chains Using AI was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

This article is part of a larger series detailing specific ways that AI tools can support companies and organizations impacted by international market turbulence to better manage global exposure. You can find other articles in the series here.

Key Question: How can AI tools improve supply chain optimization and resilience for organizations working in international markets and who are vulnerable to policy, economic, natural disaster, and other kinds of major shocks?

The shock to the system dealt by the COVID-19 pandemic provided a clear example of the impact that competing shocks driven by a natural disaster, frantic policy responses, and an economic slowdown can have on supply chains. As the world shut down in the face of uncertainty, factory shutdowns, port congestion, labor shortages, and wild swings in consumer demand created unprecedented disruption. Tech companies faced critical semiconductor shortages as retailers struggled with unusual inventory mismatches and manufacturers found themselves hit by severe workforce availability issues. My own research in Southwestern Uganda found that 88% of businesses in the region closed down for more than 12 months, with the prices of staple goods often increasing 200% or more due to supply chain pressures and the need to replace grain supplies locked away in Ukraine’s ports due to conflict with Russia.

Today, we are in another period of incredible turbulence. The threat of massive tariffs by the U.S. could directly increase the cost of imported materials and exported goods for many companies, forcing difficult decisions: absorb costs and lose money, raise prices and risk competitiveness, or undertake complex and expensive supplier diversification. Attacks on shipping lanes by Yemen’s Houthis or cyberattacks on port IoT infrastructure could sever critical transport links, disable key suppliers, or cut off access to essential raw materials at any moment. Unstable supply forecasts for key goods like cocoa in the Ivory Coast and key minerals for the green revolution held by rebel forces in the Democratic Republic of the Congo are driving a mass scramble for alternatives.

For leaders managing complex international supply chains, it can feel a bit like navigating a never-ending storm. A lack of visibility and transparency makes predicting and understanding how to address bottlenecks and disruptions exceedingly difficult; unanticipated material shortages often lead to production halting and increased costs; and the outbreak of war, the incidence of natural disasters, and sudden regulatory changes can suddenly make crucial national or regional operations near impossible. Volatile freight prices (and growing insurance costs) erode margins, and managing endless backup suppliers presents a constant a hassle, particularly for industries competing for the same limited resources.

As the world gets more complicated, technology is increasingly moew effective for disentangling confusion. AI tools can help organizations improve the resilience and flexibility of their supply chains through offering insights in real-time monitoring, supplier diversification, route optimization, and inventory management.

This article is organized into 4 sections:

1. Overview of supply chain shocks
2. AI forecasting and real-time crisis mitigation use cases
3. Specific tools that support the use cases
4. Three real-life case studies of businesses benefitting from AI-enabled supply chain tools

Supply Chain Shocks

Disruptive ‘shocks’ in recent years have clearly underscored the vulnerability of global supply chains. Shocks are most simply explained as unexpected changes to a given environment that drive profound impact. In the case of supply chains, shocks can impact the flow of goods and materials within a supply chain and often lead to delays, shortages, and price increases.

Policy and regulatory shocks like sudden tariff changes or trade restrictions, export/import bans, or major regulatory changes. These types of shocks can be positive or negative, but they tend to breed uncertainty which can hinder long-term planning and investment.

Geopolitical disruptions can include armed conflicts and territorial disputes, consistent terrorism or piracy, regional instability that limits access to markets, diplomatic tensions affecting trade relations, or blockades or closures of strategic transportation corridors.

Economic shocks affect the ability of companies and countries to engage in trade, whether through sudden or severe exchange rate volatility, financial crises, significant inflation or deflation, or commodity price spikes or collapses. Market shocks such as strategic moves by competitors, supplier bankruptcies, or rapid demand surges or collapses can also be classified as economic shocks.

Natural disasters and environmental events such as earthquakes, volcanic eruptions, hurricanes, floods, droughts, extreme temperature events, and wildfires can disrupt production and transportation networks. Climate change is expected to drive increased severe weather events and long-term impacts that may fundamentally change natural resource allocation.

Technological disruptions and infrastructure failures such as port congestion or closures, power grid failures, energy shortages, transportation infrastructure collapse, or telecommunications network disruptions can all lead to massive delays. Cyberattacks on logistics systems and infrastructure are increasingly worrying.

How AI Can Improve Shock Forecasting and Mitigate Crises in Real-Time?

For business leaders trying to understand how to plan for multiple potential futures, or how to keep all the important variables straight in the midst of crisis, AI tools add tremendous value by offering to vastly simplify complexity. Even without relying in full on AI tools to provide accurate analysis, AI excels at identifying patterns and predicting future events based on vast datasets that human-driven models often struggle to capture in full, giving leaders the chance to navigate complex situations and solve problems with significantly more insight into the impact their decisions will have downstream.

Here are a few particularly interesting use cases:

Scenario Planning and Simulation

Build a Digital Twin: You can create virtual replicas of your entire supply chain to simulate various shock scenarios and test mitigation strategies without real-world consequences. For example, you could task an agent with simulating various disruption scenarios (e.g., a supplier shutdown, a blocked shipping lane, a sudden tariff) and test the effectiveness of different mitigation strategies _before_ implementing them. This allows for data-driven contingency planning.

Run Monte Carlo Simulations: Monte Carlo Simulations leverage probabilistic modeling to understand the range of potential outcomes across thousands of possible disruption scenarios. They’re useful because they offer insights into a range of potential outcomes, not just a single prediction. Unlike game theory, which tries to identify the most rational outcome of a given set of decisions by multiple actors, Monte Carlo Simulations are best for estimating the possible outcomes of uncertain events and identifying the most likely ones.

Perform Stress Testing: You can ask AI agents to change certain variables or apply pressures you suspect you might face. By subjecting your supply chain model to extreme conditions, you can identify hidden vulnerabilities and critical failure points before they’re exposed by actual events.

Forecast Shocks

Get Real-time Updates Coupled With Predictive Analytics: Advanced AI tools (using Natural Language Processing) can continuously scan global news, social media, and government announcements for early signals of policy changes or geopolitical tensions. Machine learning algorithms will simultaneously analyze historical data, real-time logistics information, weather patterns, economic indicators, and social media sentiment to forecast potential disruptions from supplier delays and port congestion to demand fluctuations and price volatility. While human analysts are still needed alongside these tools at this stage, the tools can provide incredible insights by aggregating a broader spectrum of information and making connections that most human models struggle to make.

Demand Sensing: AI algorithms detect subtle shifts in consumer behavior that may indicate upcoming demand volatility, allowing for proactive inventory adjustments.

Perform Supplier Risk Assessments: Machine learning models can analyze global supplier databases, considering factors like location, cost, lead times, geopolitical risk, and potential tariff impacts evaluate a supplier’s financial health, geopolitical exposure, and operational resilience. Based on the available information and the forecasted risk, AI tools can then identify and vet alternative suppliers before disruption hits.

Run Pattern Recognition and Set Up Anomaly Flagging Protocols: AI systems can analyze historical disruption data to identify early indicators of potential shocks, giving your organization valuable lead time to prepare contingency plans. It can also identify unusual patterns in supply chain data that human analysts might miss, and can notify you about potential problems before they escalate.

Note: As with other uses cases for AI, AI forecasting tools are known to sometimes hallucinate, so it is important to consider AI-driven forecasts as a tool to support analysis, not as the gospel truth.

Mitigating Real-Time Crises with AI

Implement Dynamic Resource Allocation: AI tools and agents can be tasked with automatically rebalancing inventories and recalculating optimal inventory levels for critical components — for example, by balancing the cost of holding stock against the risk of tariff-induced shortages. They can also be tasked with or automatically reallocate inventory and supplies to prioritize customers and markets based on strategic importance during shortage situations.

Dynamic Route Optimization: AI models can recalculate optimal shipping routes and logistics strategies and instantly reroute shipments when established routes are compromised or due to changing port regulations, shipping costs, and tariff zones.

Automate Key Crisis Management Tasks: Workflow automation tools can be tasked with executing pre-defined response protocols without human intervention, reducing reaction time and ensuring emotions don’t compromise initial crisis response.

Enhance Visibility and Collaboration: AI-powered platforms can integrate data across previously siloed systems to provide a unified, real-time view of the entire supply chain. This visibility facilitates faster decision-making and better collaboration with partners.

The Growing Ecosystem of Sector-Specific AI-Powered Tools

If you’re looking to implement AI tools to manage your supply chain, there are a few different routes you can take. If you have the technical inclination and tools, or you have a limited budget and want to test out some potentially useful models, there are plenty of existing AI time series forecasting tools that can provide predictive analytics capabilities by analyzing historical data and incorporating real-time information like logistics updates, weather, or economic indicators to forecast potential disruptions, so long as the business inputs have been correctly managed and applied.

When it comes to scenario planning and real-time crisis management, these tools can provide the predictive intelligence needed by larger simulation, optimization, and automation systems, but can’t really act as a full-scope solution on their own. In some use cases above like creating digital twins, much of the complex optimization and automation functions, and generating broad risk assessments, these forecasting tools should be considered as crucial input providers or components within a larger system, but would need to be integrated into existing company systems or separate end-to-end enterprise solutions.

Some models that can be directly implemented using your own data or the data you purchase include:

1. NeuralProphet — Initially associated with Meta/Facebook, now community/Stanford-driven
2. Greykite — Developed and open-sourced by LinkedIn
3. Darts — Open-source library, primary maintainer is Unit8
4. Statsmodels — Community-driven open-source project
5. forecast / fable — Open-source packages primarily developed by Rob Hyndman and collaborators. Comes with a forecasting principles and practices textbook.
6. Sktime — Community-driven open-source project, affiliated with The Alan Turing Institute
7. StatsForecast — Open-source library developed by Nixtla
8. Vertex AI Forecasting — Cloud service by Google

If you have a vision for integrating all your data and analytics capabilities into one framework, or you want to run more complicated scenario models and automate real-time crisis management, it would likely be simplest to buy an enterprise solution from an AI-enabled supply chain solutions company.

Some examples of companies providing AI-enabled solutions allowing companies to plug in these time-series forecasting tools into a holistic platform include:

1. Kinaxis — scenario planning and concurrent planning
2. SAP IBP — integrated business planning with AI features
3. Resilinc — supply chain mapping and risk monitoring
4. Project44 — real-time visibility and tracking
5. Blue Yonder — demand forecasting and optimization
6. Everstream Analytics — risk analytics and visibility
7. Interos — multi-tier supply chain resilience monitoring

Businesses That Have Used AI to Reduce Exposure to and Risk From Shocks

The following are case studies I have taken directly from the websites of the companies providing integrated supply chain solutions with AI-enablement and summarized. While there are enterprise-level solutions, they do provide interesting insight into how these tools can drive impact at scale.

1. Project44: Saving Blue Diamond $1M Annually in Transportation Costs

Blue Diamond, a large retailer of almond-based products, was manually managing $300 million supply chain and logistics functions using a disjointed collection of documents, spreadsheets, and processes. The cumbersome nature of the system relied on time-intensive and error-prone manual processes that made running scenarios or simulations and gaining accurate or timely visibility into critical order information extremely difficult. Sometimes, it would take days for a customer service representatives to collect the information needed to respond to a customer. To solve the problem, Project44 and partner firms built a unified platform that managed all the company’s information and brought in outside data necessary to effectively forecast demand, supply, and logistical bottlenecks.

As a result, Blue Diamond experienced:
- $1M in annual transportation expense savings due to improved visibility and lead times
- 95% reduction in scenario planning time — down from six hours to 20 minutes
- 99% fill rates for customer orders, compared to an industry average of 89%
- 10% improvement in forecast accuracy
- 20% reduction in supply on hand — down from 75 to 60 days of inventory

2. Kinaxis: Improving Supply Chain Disruption Response Times

Konica Minolta, a large manufacturer providing a diverse range of products and services serving approximately two million users across 150 countries, found itself concerned about its ability to produce and ship products from its factories in China during the COVID-19 pandemic. It leveraged Kinaxis’ Maestro tool to simulate the length of time that the shutdown could be in place, what effect it would have on the sales plan, and how long it could manage under shutdown conditions. Though challenges continued following the COVID-19 pandemic with global logistics disruptions and semiconductor shortages, Konica Minolta continued to use the Maestro simulation functionality to get very close to the actual state of material procurement and shipping delays that were caused by port congestion and were able to use this in estimating results and discussing countermeasures.

3. Everstream Analytics: Critical Supply Secured Prior to COVID-19 Shutdown

Everstream’s platform was instrumental in warning an automotive manufacturer of an impending shipping disruption of key product inputs in the run-up to COVID-19 shutdowns in China. The system sent an automated alert to a Tier-1 automotive manufacturer customer sourcing from Wuhan, China, noting an incident at a key factory in Wuhan and making the company aware of expected limited air cargo capacity coming out of China in the near future. Based on the automated alert powered by AI, the company made an unscheduled maximum quantity one-time buy and shipped its cargo through the Russian rail system. They were able to secure key supplies ahead of a very serious disruption which may have otherwise shuttered production and gave them a huge competitive advantage over other automotive companies who had not known about the issue or had not reacted in time.

Conclusion

Whether due to Donald Trump’s tariff crusade, COVID-19, the Ever Given running aground in the Suez Canal, the Ukraine War, massive labor strikes, or droughts and wildfires, businesses of all sizes have felt the impact of sudden policy, economic, and natural disaster shocks in recent years. The introduction of AI tools, whether for forecasting or for full-scale automated supply chain and crisis management, have introduced a new opportunity for businesses of any size to hedge against risk to their supply chains and to respond quickly as threats materialize.

Summary: This article provides an overview of the value of implementing Artificial Intelligence (AI) into organizational risk frameworks to better forecast, plan for, and be alerted to changes to individual company environments in the context of increased tariff-driven economic uncertainty. This article will be followed by deeper dive articles into five key areas in which businesses can leverage AI to mitigate the impact of tariff and policy uncertainty.

Yesterday, staring out over the spectacularly blue water of the Indian Ocean from a 14th-floor rooftop in Colombo, Sri Lanka, I watched dozens of cargo ships move almost imperceptibly slowly along the horizon. As I looked on, I started to wonder how many of the items in those containers were making their way to the United States, or would be made into products that would eventually make their way to those shores. And of those items, I wondered how many of those items making their way to the U.S. suddenly cost significantly more than when they were shipped — or, at least, will cost significantly more once they pass customs and enter the country. How many of the containers I see will sit in stasis at the docks as U.S. companies cancel their import contracts, unable or unwilling to sell their goods at new price points?

The global volatility that has rocked the international trade landscape in recent weeks has proven particularly complicated and difficult for businesses that import or export products to navigate. Pundits have tried and failed to forecast the administration’s next moves while businesses struggle to understand their exposure in a new era of Trump’s protectionist trade policy. As has become abundantly clear in recent weeks, lobbying and strategic positioning are proving less sufficient than they once did, and the typically accepted rational economic frameworks can’t eliminate the inherent uncertainty of doing business in a highly globalized world.

The new era highlights a critical challenge: how can businesses maintain efficiency and plan strategically when faced with unpredictable, external policy changes they cannot control?

Successfully navigating an ever-changing web of regulations and shifts, forecasting the impact of potential new policies and political realities after elections, or gauging the best response to sudden geopolitical shifts and economic fluctuations is a tall order at any time. But in an era of data abundance and the limitations of human-built and analyzed models, the value of AI emerges ever more clearly as a powerful toolkit to make sense of an uncertain world.

AI excels in its ability to process vast amounts of diverse data, identify patterns invisible to the human eye, and model complex scenarios quickly. A properly built and utilized AI tool can equip businesses to move from a reactive posture in times of crisis towards proactive adaptation, allowing them to enhance resilience and drive efficiency even amidst policy storms.

In this series specifically focused on the impact of economic shocks driven by the current Trump tariff regime, I will detail specific use cases of AI that can support companies and organizations who can no longer rely on their usual frameworks for navigating the uncertainty ahead. In upcoming articles, I will dive into the specific ways in which businesses can leverage AI to mitigate the impact of tariff and policy uncertainty in five key areas:

1. AI-Powered Supply Chain Optimization & Resilience
2. Enhanced Demand Forecasting
3. Dynamic Pricing Strategies
4. Sophisticated Risk Assessment & Scenario Planning
5. Automated Compliance & Regulatory Monitoring

The Ripple Effects of Uncertainty

The imposition of new or unexpectedly severe policies, such as tariffs, can create significant operational hurdles for businesses, such as:

1. Supply Chain Disruptions: Tariffs on specific goods or countries can instantly make established supply chains less economically viable, forcing the rapid development and deployment of alternative suppliers or routes, often at higher costs or lower quality.
2. Cost Volatility: Input costs can surge overnight, impacting margins, production planning, and budgeting accuracy. Predicting the final landed cost becomes a major challenge for businesses — and in the current state, some remain unable to get even a quote for crucial inputs as each link in the supply chain struggles to re-orient.
3. Pricing Dilemmas: Businesses must decide whether to absorb increased costs (effecting profitability), pass them onto consumers (possibly leading to reduced demand), or build complex hybrid strategies.
4. Increased Demand Forecasting Complexity: Tariffs can alter consumer purchasing power, shift demand towards non-tariffed goods, or trigger retaliatory measures impacting export markets. Traditional forecasting models are not well-placed to handle and adjust to these sudden shocks.
5. Compliance Burden: Navigating changing tariff codes, country-of-origin rules, and documentation requirements increases administrative overhead and compliance risks.

These challenges demonstrate how external policy shifts introduce layers of uncertainty that traditional planning methods (relying on historical data and static assumptions) often fail to adequately address.

Why Traditional Methods Are Falling Short

Companies, academics, and consultants are struggling to identify the immediate impact that the new tariff regime will have on individual businesses, industries, and entire demographic groups alike. Part of the difficulty in simplifying complexity in the current environment is due to the innate limitations of current human-driven models:

• Slow Reaction Time: Manual analysis of potential policy impacts is time-consuming. By the time a strategy is formulated, the situation may easily have changed again.
• Limited Scope: Human analysts can only process a finite amount of information and model a limited number of scenarios based on their individual knowledge, training, and experience.
• Bias and Intuition: Decisions can be overly influenced by past experiences or gut feelings, which may not apply in novel situations.
• Static Planning: Annual or quarterly plans become quickly outdated when faced with sudden, significant policy shifts.

The utilization of AI as an analytical tool can drive deeper insights on top of human-driven models, allowing analysts and executives to access other approaches and interpretations of the available data to can drive more holistic decision making.

Enter AI: Transforming Uncertainty into Actionable Intelligence

AI can be leveraged and transformed into more effective and better-fit tools for navigating the current global uncertainty by providing deeper analyses that human analysts and executives can leverage to gain alternative insights into murky conditions. AI tools can provide early warnings of potential policy changes or disruptions, run complex models tailored to your firm to answer questions like “what if country X imposes retaliatory tariffs?” and “what if tariff Y is lifted?,” or dynamically recalculate optimal shipping routes and logistics strategies based on changing port regulations, shipping costs, and tariff zones.

In upcoming articles, I will dive into five specific ways in which businesses can leverage AI to mitigate the impact of tariff and policy uncertainty and be better prepared for current and future market turbulence, including:

1. AI-powered supply chain optimization and resilience, with applications in real-time monitoring, supplier diversification, route optimization, and inventory management.
2. Enhanced demand forecasting, accomplished through using enhanced elasticity modeling and better integrating external factors from non-traditional data sources side-by-side alongside internal company data.
3. Dynamic pricing strategies using real-time cost fluctuation analysis and optimized price recommendations.
4. Sophisticated risk assessment and scenario planning applications, such as via simulating policy impact and identifying hidden risks that human models are less likely to catch.
5. Automated compliance and regulatory monitoring via intelligent document processing and proactive alerting.

Putting AI into Practice

While businesses cannot control government policies, they can control how they prepare for and react to them. By leveraging AI, companies can move from a position of vulnerability to one of informed agility. AI provides the analytical power and predictive capabilities needed to understand complex dynamics, anticipate potential disruptions, and make smarter, faster decisions, ultimately driving greater efficiency and resilience in an unpredictable world.

As always, implementing AI into your work stream and your business’ forecasting and analysis capabilities isn’t something as easy flipping a switch. It requires clear objectives, good data, the right tools and partners, and building an adaptable and scalable framework that iterates as you do. With the right approach, input data, and people, AI can significantly assist businesses to minimize their exposure to changes outside their control and transform uncertainty from an overwhelming obstacle into a manageable variable.

Taming the Tariff Storm: How AI Can Help Businesses Navigate Uncertainty was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

Would you rather fight 100 duck-sized horses or one horse-sized duck?

The depicted scenario represents a classic case of asymmetric warfare, starring three unequally endowed actors who will be forced to take different approaches to battle with one another based on their relative strengths and weaknesses. Given that the human in this contest is likely to have the least power to bring to bear in the situation, it is vital to formulate a strategy that gives them the greatest chance of defying the odds.

Winning in cases of asymmetric capability is entirely a question of choosing the optimal opponent and employing good strategy. Hannibal was outnumbered 10:1 against Rome at the Battle of Cannae in 216 B.C. but tricked and defeated the Roman army in their home territory through his strategy. When Cisco — now a Fortune 100 firm — was just a startup that couldn’t really compete head-to-head with major firms, it chose to focus on overlooked technical niches that eventually allowed it to capture two-thirds of the router market while incumbents like IBM and Microsoft lost out on their more narrowly focused applications. By choosing the weaker or less strategically capable and adaptable opponent and playing to human strengths, anyone who finds themself in this dangerous situation can work out the option with the highest chance of success.

By approaching this conundrum through the lens of asymmetric warfare and analyzing a variety of strategic parameters based on the physical and psychological characteristics of the three parties in conflict, a clear answer emerges — it would be best to fight a horse-sized duck.

Physiological Considerations

Taking on 100 duck-sized horses would be a dangerous task, clearly illustrated by the horse’s most formidable weapon: the kick. The average horse weighs in at 1371.28 pounds, compared to just 4.41 pounds for a duck. However, ducks are much less dense than horses, being made of lightweight materials that allow them to fly more easily, so a duck-sized horse would be significantly more dense than a normal duck. Assuming that a horse the size of a duck would be four times as dense as the average duck-sized horse, it would weigh approximately 17.64 pounds, about 78 times lighter than a normal horse.

The impact of a horse’s kick is commonly compared to that of a small automobile moving at 20 miles per hour. Given the standard weight for a 2023 Ford Focus of 3,320 pounds, a kick from our duck-sized horses would be roughly equivalent to an object weighing 42.5 pounds hitting you at 20 miles per hour. This is most closely approximate to being hit by an oversized cinder block travelling the same speed — and don’t forget the little iron horseshoes.

By contrast, the most dangerous weapon a giant duck wields could probably be expected to be its massive beak — but an instrument that large might not actually be capable of doing that much damage. In an interview on this pressing question, the department chair of Wildlife, Fish, and Conservation Biology at the University of California, Dr. John Eadie, explained that the beak of a 1,000-pound duck would contain between fifty and seventy eight-inch crusher blades that could theoretically do some pretty serious damage. However, as Cornell University ornithologist Dr. Kevin McGowan counters, the force of any duck bite is spread out over a large surface area given the relatively flat shape of the bill, meaning that the bite itself wouldn’t likely cause much injury. While it would be wise to remain extremely cautious of a massive duck’s beak, I believe it would be significantly easier to elude such a large and presumably slow-moving weapon compared to the hooves of 100 sprightly horses.

Flight would not likely be a tool in a horse-sized duck’s arsenal, as a duck so large would probably not be able to get off the ground. At 1,000 pounds, says Dr. Eadie, the required ratio of body weight to wing area would require wings measuring 416 square feet. There is a reason animals rarely reach such large sizes in nature — the ability to feed and internally regulate such a massive frame is simply too difficult. Thus, however large the duck’s wings might be, it would almost certainly be confined to earth or tire itself out just trying to take flight.

Still, the wings of such a duck would be incredibly heavy and strong, and surviving a beating from them would be a tall order — that is, if such a large bird was even able to support them. While the strength needed to propel a bird (one that actually can fly) through the air has been likened to a gymnast holding the iron cross position for 24 hours, the wings of a bird that can’t fly would exhibit greatly atrophied muscles and probably could not be moved by the bird with any particular effectiveness.

Horses have evolved to run far and fast, but in a protracted battle, I would expect to have much greater endurance. Mammals with four legs are not as efficient runners as humans over great distance and time. In Born to Run, author Christopher McDougall illustrates the manner through which Kalahari bushmen hunt by literally running their prey to death. He juxtaposes the natural rhythm of a four-legged mammal opening and closing its legs, moving its own diaphragm, with the upright manner in which people run wherein the ability to breathe is independent of the movement of their legs. Over long distance, this makes animals with four legs more likely to need breaks — the average antelope in McDougall’s book could only run about 8 or 9 minutes in a row without pausing. A distance runner myself, I would seemingly hold an advantage over the horses if I were able to evade them long enough to tire them out (in the book, it took the author a few hours to run a sickly antelope to the point of cardiac arrest).

A normal duck has incredible flight endurance and birds generally have a far more efficient respiratory system than mammals, meaning that a horse-sized duck should easily outlast a person in the cardio arena under normal circumstances. However, given that a duck as massive as the one in our scenario would constantly lose large amounts of heat as a result of its high surface area to body volume ratio, it would likely not be too difficult to tire it out and bring it to the point of collapse. So long as any fight takes place in a strategically advantageous environment for humans where the duck’s body heat loss rate ends up being a greater detriment to the it than its superior cardio endurance is an asset, the duck would remain a far easier opponent to combat.

Psychosocial Considerations

An obvious difficulty when fighting one hundred duck-sized horses is managing the sheer number of attackers, especially when all are organized and operating as a unit. However, from our knowledge of equine social structures, it seems unlikely a person would be forced to fight all of them simultaneously. Horse herd size is typically capped at around twenty-five horses and are generally organized under a clear hierarchy, with two main leadership roles: a lead mare, who typically dominates herd movements and operations, and between one and five stallions. The mare controls the direction of the herd and mitigates intra-herd aggression, while the stallions are charged with protecting the herd from predators. Crucially, horses intuitively prefer to flee rather than to fight and generally have to feel attacked in order to become aggressive. Under the parameters in our scenario, a given person would end up fighting four individual herds with the majority of the horses in each herd taking up protective stances but not attacking unless they felt threatened. This decreases the number of attackers to between four and twenty males. While this is a significantly more manageable number, it’s is still a significant number of individual attackers to deal with.

Most experts say that ducks are generally not aggressive, except with regard to competing males during the mating season, and not particularly intelligent, either. In a study referenced by Dr. Eadie, half of a duck’s brain was removed surgically with no discernible change in its behavior. Much like in the biblical tale of David versus Goliath, a human would be smaller, more nimble, and more quick-thinking than a massive duck attacker. Strategically, this presents a significant number of opportunities to exploit the duck’s weaknesses and gaps, so long as the person can stay out of harm’s way in the meantime, and would introduce a significantly greater tactical advantage compared with contending against a guerilla-style attack by the 20-odd duck-sized horses.

Strategic Considerations

The case of an individual fighting 100 duck-sized horses could reasonably equate to a modern-day case of a small state taking on an insurgent group utilizing guerilla tactics. Small states typically have less well-funded militaries and cannot fight using the same tactics that insurgents can, often struggling mightily to combat dispersed and tactically complex opponents who can play by a different set of rules among a sympathetic populace. An individual forced to fight 100 duck-sized horses would find themselves in an even worse situation, holding back the tide of an enemy much greater than themselves and likely without the funding and tools needed to do so. As these horses would be well-armed, hard to pin down, and willing to protect one another as part of the herd, it would be wise to avoid waging war against a guerilla army of duck sized horses.

The case of a person fighting a horse-sized duck would then be akin to a small state fighting a larger state. In a military contest, a small state fighting a larger state would typically find itself at a major disadvantage, should it choose to fight a more well-endowed enemy head-on; it would quickly be overwhelmed by the size of the larger entity’s army and the resources at its disposal. Large state militaries, on the other hand, tend to be less agile and present a large, unified target to attack. Throughout history, there are many notable examples of small states who beat back large armies using irregular, guerilla-style tactics, often winning not through military might alone but by draining significant resources and making continuing the fight less palatable than accepting defeat.

Given the horse-sized duck’s obvious physical limitations — it would be slow-moving with an unwieldy beak and wings — and psychosocial limitations — it would not be particularly intelligent or particularly aggressive until provoked — it would be significantly easier to take on the single horse-sized duck. A person would have ample time to devise a strategy against the duck and plenty of rest in between attempts as the duck would likely tire from launching its own attacks rather quickly.

Conclusion

Given the above scenarios, there is one clear best option — to fight a single horse-sized duck. Any human would have a greater physical, psychosocial, and strategic advantage over the duck than they would over an army of horses. While watching a group of people playing with mini horses on a ranch somewhere would undoubtedly be delightful, fighting a group of these animals would be a very different story.

Would you rather fight 100 duck-sized horses or one horse-sized duck? was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

The business and security implications of Kenya and Rwanda’s adoption of genetically modified crops and products

Genetically Modified Foods

In 2022, with over four million people facing food insecurity during Kenya’s worst drought in 40 years, the Kenyan government lifted its decade-long ban on genetically modified (GM) crops and organisms (GMOs) in the hopes that the use of such crops would improve yields.[1],[2],[3] Rwanda followed suit in February 2024 and allowed GM crops to be cultivated on its territory for the first time, embracing the need to find disease-resistant alternatives to the traditional Casava crops being ravaged by the Brown Streak and Mosaic diseases. [4],[5],[6]

Climate change is driving higher temperatures, reduced and inconsistent water supplies, increased extreme weather events, biodiversity loss, ecosystem degradation, and reduced arable land (40% of Africa’s future crop production is already estimated to be lost as a result).[7],[8] Many countries facing increasing food insecurity due to climate change are also seeing increased instances of conflict as climate pressures compound existing tensions between groups over land use and natural resource access.[9] And on top of the expected collision of climate change pressures and rapid population growth in the coming decades, the external shocks of COVID-19 pandemic and Russia’s war in Ukraine have exacerbated current food insecurity across the continent.

Kenya and Rwanda are not alone amongst African nations seeing growing food insecurity. A total of 868 million people on the African continent were found to be either moderately or severely food-insecure in 2022.[10] That was 60% of the continent’s population at the time, and with Africa growing so quickly that it’s expected to be home to more than 25% of the world’s population by 2050, it’s likely that the proportion will continue to grow without significant and immediate investment into improving food security. With rampant instability across many climate-affected countries and overpopulation pressures creeping up, significant changes in agricultural productivity and scale will be required. Genetically modified organisms and products could provide a crucial part of the complex set of solutions needed to keep Africa’s population fed.

Genetically Modified Foods

The significance of the current raft of challenges to keep Africa’s population fed has led scientists, farmers, and political leaders to consider whether GM foods could offer part of the solution to Africa’s food security crisis. GM foods are those whose composition has been altered to produce a particular characteristic — for plant-based food products, this often includes nutritional, consumption or crop yield benefits including increased disease resistance, cold resistance, drought tolerance, herbicide resistance, and nutrition availability.[11],[12] Though there is ample evidence that the African continent has the capacity to be food self-sufficient — 60% of the world’s untilled arable land sits in Africa — the continent accounts for just 4% of global agricultural output, and weak infrastructure and a lack of investment in a robust intra-African food trade has led to the continent importing approximately USD 50 billion worth of food annually. [13],[14],[15] As the gap between production potential and hungry people looks set to persist in the near term, some leaders are exploring how biotechnology can improve sustainable agriculture production, particularly for resource-poor and small-scale farmers.[16]

Cultivating GMOs often allows for decreased chemical pesticide usage and a corresponding reduction of agricultural waste runoff, offering a reprieve from the health impacts those chemicals can have.[17] GMOs can also be made more weather and drought-tolerant, allowing for continued or increased yields in harsh environments where non-GM plants can no longer survive.[18] This is especially important in Africa, as it may ameliorate the flow of migrants leaving increasingly inhospitable environments due to extreme weather and reduced arable land, and may even help reduce the incidence of conflict.[19] Many people in Africa often rely on one main staple crop such as rice or cassava which can lead to high malnutrition rates due to a lack of dietary quality and diversity. GM foods can be altered to provide increased nutritional value to reduce the level of malnutrition some experience.[20],[21]

Why Aren’t GMOs More Widespread in Africa?

Despite the opportunity for increased food security opportunities that GM crops can offer, only 12 African countries allow for the production or importation of GMOs and have built biosafety or GMO regulatory frameworks.[22] This is often because consumers and politicians have displayed widely varying interest in and acceptance of GM products across Africa, prompting a 2002 Economist article entitled “Better Dead than GM-Fed?”[23],[24],[25] Those who argue against GMOs give reasons ranging from fears over food and environmental safety, to negative social attitudes toward GMOs, to economic and trade impacts.[26] GM crops tend to be herbicide-resistant, allowing farmers to use herbicides indiscriminately on their crops without the crops themselves dying. While this generally leads to higher yields on less land, it can lead to an overuse of lab-made chemicals that may have unclear long-term impacts on health, biodiversity, and ecosystems, and could increase corporate control over agriculture.[27]

Many African countries question whether GM crops are really needed and therefore whether the risks outweigh the rewards. In Zambia, for example, 61 percent of farmers who applied ecosystem-based adaptation practices under natural resource conservation or sustainable organic agricultural frameworks reported surplus yields, with sales of surplus crops growing from 25.9 percent to 69 percent.[28] And in Burkina Faso, farmers have reclaimed 200,000 to 300,000 hectares of degraded lands using indigenous rehabilitation methods and have increased cereal production by an estimated 80,000 to 120,000 tonnes.[29] These success stories should undoubtedly be promoted and expanded where possible, but where these strategies are not seeing success, it would be wise for policymakers to explore the technical solutions provided by GMOs.

Security Implications

The impact of climate change, which will continue driving increased drought incidence and desertification, will further damage the ability of African nations to meet their citizens’ food needs. There is a clear and demonstrable link between the growth of violent extremism in Africa and the environmental degradation that effects food security.[30] Food and water insecurity, malnutrition, rapid population growth, and weak governance, particularly along borders, have driven an explosion of grievances which have sent many individuals into the arms of violent extremist groups (VEGs).[31] In many cases, individuals consider joining a criminal or extremist group to be their best option to ensure personal and familial survival.

Migration and displacement rates have also risen in recent years, with individuals moving to cities and refugee camps or making treks across dangerous terrain for thousands of miles as previously arable land loses its productive capacity. In the Sahel, home to the world’s fastest growing terror group, Jama’at Nusrat al-Islam wal-Muslimin (JNIM), climate change is affecting where and when rain falls, where food and fodder can be grown, and where people can live. With governments in the region being unable to provide adequately for the needs of its citizens in more hard-hit areas, these individuals are turning to other groups who say that they can. African farmers are particularly vulnerable to climate pressures as the continent’s agriculture sector tends to be overly reliant on rain-fed agriculture and because most farmers have limited access to adaptive technologies.[32] On a continent where the livelihoods of approximately 80% of Africa’s working age population depend upon agriculture, governments will find increasing their population’s resilience to food security shocks and improving their access to adaptation strategies and technologies to be of extreme importance for maintaining stability.[33]

Business Implications

Many African countries will likely rejoin the debate over whether to allow their citizens to grow GMOs after watching Kenya and Rwanda do so, recognizing the need for major shifts to national agricultural strategies. Governments across the continent understand the crossroads at which they find themselves and the dire need to quickly boost domestic resiliency and improve their nations’ food security. They are thus looking to invest in solutions that boost domestic resiliency and improve their nations’ food security. Businesses should look at ongoing GMO trials in African markets and begin building a strategy to identify the best entry points and timelines for the various potential markets for their products.

There have already been some successful introductions of GMOs in select African markets. South Africa has a very limited amount of arable land and is expected to be hit hard by rising temperatures and decreased rainfall due to climate change. To combat this, it introduced Bt maize, a strain of maize genetically engineered with the Bacillus thuringiensis (Bt). This strain now makes up more than 80% of the maize planted in South Africa and has reportedly significantly reduced pesticide usage and limited crop damage. Bt maize also has improved food safety as it is less susceptible to fungal attack, reducing myco-toxin levels for consumers.[34]

The market for GMOs in Africa is estimated to reach USD 871 million by 2025. While this figure across the continent represents a paltry sum compared to market in the United States alone, where the value of the GMO market is expected to exceed USD 70 billion in 2030, the adoption of GM food and product regulatory frameworks by Kenya and Rwanda could change all that.[35] Organizations like the Syngenta Foundation are working to introduce GM crops resistant to pests like fall army worm which cost African economies billion of dollars a year in crop losses.[36] If farmers are convinced to grow plants with adaptations like these and consumers are educated to accept them, the opportunities for growth in this space could be significant.

There are also some development and policy elements to consider for countries allowing in GMOs, and businesses may be able to carve out a niche by catering most effectively to meeting development goals. The involvement of organizations like Monsanto, which operates a business model that tends to aggressively discourage competition, has been viewed critically by many observers and could prove to be an unsustainable or even disruptive business model for small-holder African farmers. This model would not be one to emulate in Africa, particularly as it could result in large firms and landholders offering to buy the land of small-scale farmers to build mechanized agriculture on a large scale. This would lead to more crowded cities and likely to worse health and development indicators. GMO agribusinesses also have to be wary of investing in countries whose primary markets do not allow GM foods or products to be sold in their countries. Losing out on markets in countries with these types of restrictions in Africa and the European Union has been a reason some countries who would likely benefit from the crops have banned them.

Instead, companies can reduce the risk of entering new markets with a different type of producer at a different scale of farming and still win huge contracts by working with aid agencies and major international donors. Many agencies and donors have made it clear that they are interested in investing in GMO projects that build capacity and increase food security, marking an opportunity for implementing partners and GM seed producers to work together to make it more financially feasible for major firms with expensive products to offer. If these seeds prove to make life better for farmers, their yields will increase in terms of both output and consistency, allowing them the capital to buy directly from companies in the future, either at full price or at a government-subsidized rate. Africa imports approximately USD 50 billion worth of food annually; businesses that can demonstrate the ability to effectively contribute to closing this gap will surely be able to work with governments to drive mutually beneficial opportunities.

Of course, the moves by Kenya and Rwanda could fail to create domestic acceptance for GMOs or could cause significant disruption to trade with major partners. The European Union’s (EU) strict regulations on GMOs, for example, have historically caused its trading partners in Africa to carefully consider their investment into GMOs to maintain access to EU markets.[37] Many countries in Africa have not yet built the necessary biosafety frameworks required to regulate biotechnology products, leaving further doubts amongst some consumers.[38] Investing in GMOs without buy-in from citizens could, therefore, miss the mark in terms of expected impact, and countries that choose to add GMOs to their agricultural arsenal will need to educate their citizenry.

While genetically modified organisms are certainly not a cure-all for Africa’s food woes, there can be no doubt that they could act as a useful and important tool for African countries being ravaged by climate change. They can improve food and physical security for these nations and could provide an opportunity for significant foreign investment. Businesses and governments should include GMOs in the conversation as they work together to drive solutions to food insecurity across the continent.

[1] https://www.reuters.com/world/africa/kenya-lifts-ban-genetically-modified-crops-response-drought-2022-10-04/

[2] https://www.reuters.com/world/africa/kenyan-court-throws-out-challenge-seeking-block-gm-crops-2023-10-12/#:~:text=In%20October%202022%2C%20Kenya%20lifted,worst%20drought%20in%2040%20years.

[3] https://theconversation.com/kenya-has-lifted-its-ban-on-genetically-modified-crops-the-risks-and-opportunities-192636

[4] https://www.reuters.com/world/africa/kenyan-court-throws-out-challenge-seeking-block-gm-crops-2023-10-12/#:~:text=In%20October%202022%2C%20Kenya%20lifted,crops%20since%20lifting%20the%20ban.

[5] https://www.newtimes.co.rw/article/14852/news/law/rwanda-finally-passes-law-on-gmos-what-next

[6] https://www.theeastafrican.co.ke/tea/news/east-africa/kenyan-court-stops-gmo-imports-4036516

[7] https://www.un.org/africarenewal/magazine/special-edition-agriculture-2014/despite-climate-change-africa-can-feed-africa

[8] https://www.nature.com/articles/d44148-021-00082-5

[9] https://www.brookings.edu/articles/climate-migration-deepening-our-solutions/

[10] Africa — Regional Overview of Food Security and Nutrition 2023 (FAO; AUC; United Nations Economic Commission for Africa (ECA); WFP;, 2023), https://doi.org/10.4060/cc8743en.

[11] Eric Worlanyo Deffor, “Consumer Acceptance of Genetically Modified Foods in the Greater Accra Region of Ghana,” Journal of Biosafety & Health Education 02, no. 02 (2014), https://doi.org/10.4172/2332-0893.1000118.

[12] Sheetal Ann Lamichhane, “Genetically Modified Foods-Solution for Food Security,” International Journal of Genetic Engineering and Biotechnology Volume 5, no. Number 1 (2014): 43–48.

[13] https://www.worldbank.org/en/news/press-release/2012/10/24/africa-can-feed-itself-earn-billions-avoid-food-crises-unblocking-regional-food-trade

[14] https://african.business/2019/11/energy-resources/gm-foods-the-battle-for-africa

[15] https://agra.org/news/daniel-njiwa-africas-journey-to-self-sufficiency-the-power-of-intra-africa-trade/#:~:text=This%20shift%20away%20from%20heavy,Africa's%20vulnerability%20to%20external%20disruptions.

[16] Sheetal Ann Lamichhane, “Genetically Modified Foods-Solution for Food Security.”

[17] Francis Dizon et al., “Genetically Modified (GM) Foods and Ethical Eating,” Journal of Food Science 81, no. 2 (February 2016), https://doi.org/10.1111/1750-3841.13191.

[18] Dizon et al.

[19] https://www.brookings.edu/articles/climate-migration-deepening-our-solutions/

[20] Dizon et al., “Genetically Modified (GM) Foods and Ethical Eating.”

[21] University of South Africa et al., “South African Consumers’ Knowledge of Genetically Modified (GM) Food Products and Influences That Affect the Purchasing Decision of GM Foods,” African Journal of Food, Agriculture, Nutrition and Development 23, no. 122 (July 26, 2023): 23873–97, https://doi.org/10.18697/ajfand.122.21270.

[22] https://allafrica.com/stories/202402270020.html

[23] https://www.economist.com/science-and-technology/2002/09/19/better-dead-than-gm-fed

[24] University of South Africa et al., “South African Consumers’ Knowledge of Genetically Modified (GM) Food Products and Influences That Affect the Purchasing Decision of GM Foods.”

[25] Deffor, “Consumer Acceptance of Genetically Modified Foods in the Greater Accra Region of Ghana.”

[26] https://theconversation.com/kenya-has-lifted-its-ban-on-genetically-modified-crops-the-risks-and-opportunities-192636

[27] https://theconversation.com/whats-the-latest-on-gmos-and-gene-edited-foods-and-what-are-the-concerns-an-expert-explains-204275

[28] https://www.un.org/africarenewal/magazine/special-edition-agriculture-2014/despite-climate-change-africa-can-feed-africa

[29] https://www.un.org/africarenewal/magazine/special-edition-agriculture-2014/despite-climate-change-africa-can-feed-africa

[30] UNDP, “Dynamics Of Violent Extremism In Africa: Conflict Ecosystems, Political Ecology and the Spread of the Proto-State.” 9.

[31] Muggah and Cabrera, “The Sahel Is Engulfed by Violence. Climate Change, Food Insecurity and Extremists Are Largely to Blame.”

[32] Laura Pereira, “Climate Change Impacts on Agriculture across Africa,” in Oxford Research Encyclopedia of Environmental Science, by Laura Pereira (Oxford University Press, 2017), https://doi.org/10.1093/acrefore/9780199389414.013.292.

[33] https://www.sofalapartners.com/our-thinking/rethinking-african-agricultural-reform-zwp3z

[34] Muzhinji and Ntuli, “Genetically Modified Organisms and Food Security in Southern Africa.”

[35] https://www.gminsights.com/industry-analysis/genetically-modified-feed-market

[36] https://african.business/2019/11/energy-resources/gm-foods-the-battle-for-africa

[37] https://theconversation.com/genetically-modified-crops-may-be-a-solution-to-hunger-why-there-is-scepticism-in-africa-207364

[38] Muzhinji and Ntuli, “Genetically Modified Organisms and Food Security in Southern Africa.”

The End of “Better Dead than GM-Fed” in Africa? was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.

In 2022, as consumer price inflation hit 21-year high at 37%, protestors took to the streets of Accra, vandalizing images of Ghana’s President and calling for his removal and that of Ghana’s financial policymakers. Businesses closed their doors for days in protest over the government’s inability to keep costs down for consumers in markets and at fuel pumps, and for over a year, protests decrying economic conditions rocked the nation and its leadership. Ghana, until recently described as an “island of peace” in the chaotic neighborhood of West Africa and as “Africa’s shining star” by the World Bank, was yet another promising developing economy brought to a standstill by the economic crisis brought on by COVID-19.

Ghana’s protests resulted from a severe economic downturn over the course of 2022 that led to it defaulting on both domestic and external debts. The crisis arose largely due to a combination of external shocks such as the COVID-19 pandemic and the war in Ukraine which exacerbated existing vulnerabilities. This resulted in acute financing pressures, a massive depreciation of its currency (the cedi), declining international reserves, slowing economic activity, and high inflation.[1] The Cedi lost more than 50% of its value in 2022, and Ghana’s Finance Minister reported that interest payments were taking up an average of 70% — 100% of government revenues.[2] Its debt burden was assessed as unsustainable and on 19 December it entered into a sovereign default on USD 60 billion on both domestic and external debt. Ghana requested IMF support and on 17 May 2023 was approved for a USD 3 billion loan to be allocated over the course of 3 years.[3] On 14 March 2024, Ghana began conversations with overseas bondholders after reaching a deal in principle to restructure USD 5.4 billion of its loans on 12 January 2024. [4],[5]

Ghana’s predicament, and how it restructures its debt in order to repay both its original creditors and the IMF, will be watched with great interest by African nations who sit on the geopolitical fence. The United States has historically been known as Ghana’s leading source of trade and investment and remains its largest bilateral aid and development partner. China has now taken over the leading trade position, becoming both Ghana’s largest source of imports and its largest export market in 2023. This shift is reminiscent of a broader trend across the continent, where the U.S. provides the most official development assistance while China has become the continent’s leading bilateral trade partner and infrastructure provider.[6] But while China has made a rapid ascent in terms of influence and economic might in Africa, questions remain for many African nations as to who they should align with in the expected coming geopolitical struggle between the East and the West.

The Ghanaian case and how it is handled by China, particularly in the wake of Zambia’s recent negotiation difficulties, will offer important strategic insight to other African nations who haven’t yet chosen a side between the US and China. As a relatively advanced economy within its geopolitical neighborhood, Ghana’s decision-making and policy proscriptions are often emulated by other African nations hoping to reach similar levels of prosperity. If Ghana is unable to reach an understanding with China as to how to best restructure its debt and suffers further economic backsliding because of China’s focus on recouping its entire investment, it may give other African nations pause as to whether and how they want to engage with the Asian power going forward.

China’s Dilemma as a Creditor

Of Ghana’s USD 60 billion default, only USD 1.9 billion is owed to China.[7] While the amount is a fairly small percentage of the total (around 3.3%), China has recently demonstrated in negotiations with the Zambian government that it prefers not to offer concessions on loans; rather, it wants maturity extensions.[8] Rather than agreeing to a haircut on the total value to be repaid by Zambia, China and some of the other creditors pushed for maturity extensions averaging twelve years and dropped the coupon rate to just 1%. In the initial negotiations, the government made it very clear that it wanted to put together a simple debt restructuring program and did not want to implement “state-contingent debt instruments” which would link payouts to variables such as economic growth or commodity prices.[9] This statement was likely targeted at Chinese creditors.

According to the IMF, about USD 619 million of the debt Ghana owes to China is collateralized, backed by the nation’s oil, cocoa, and bauxite reserves.[10] If Ghana is unable to pay its outstanding debts to independent power producers and to Chinese lenders, the country could be forced to offer China a portion of such assets.[11] According to an Africa Programme Officer at the Natural Resource Governance Institute, should Ghana be unable to supply enough of the requisite commodities, China could go even further and withhold tax revenue from Chinese-financed projects to recoup its loan.[12] The effects of such a seizure would provide little relief for Ghana and would take away much of the revenues the country will need to get back on track, likely digging deeper the hole the country finds itself trying to climb out of.

China has long marketed its bilateral debt offerings as being largely ‘no strings attached.’ While this never meant countries wouldn’t have to repay their debts, why, after two decades of building relationships across the continent, would China appear unwilling to negotiate and risk the image it has worked so hard to cultivate as being a better partner to Africa than the West?

The answer may lie in the country’s newfound economic position. China has entered a profound economic slump with record levels of debt (288% of GDP in 2023) piling up, a crashing property sector, a 60% decline in new construction, and a currency that hit a 16-year low a few months ago.[13] In Africa, funding offered by Chinese creditors has tumbled recently, from a total investment of GBP 175 billion between 2000 and 2021 to just over one billion in 2022.[14] This is just 12% of the annual average of the previous two decades.

Part of the hole in China’s economy has come from its role as a major bilateral creditor via its Belt and Road Initiative (BRI) which was launched in 2013. It has spent at least USD 1 trillion on the project in the decade since,[15] and expected that debt service payments on the loans it offered would make up a significant source of its government revenue.[16] A lot of the funds China might’ve used to prop up its economy in its current downturn is thus tied up on development projects it won’t likely see repayments on anytime soon. In general, China rarely offers debt forgiveness because it worries that if it appears too soft on defaults it may open the door to increasing bilateral defaults and massive losses on its expected investment returns. In its current predicament, being as cash strapped as it is, it might make sense for China to offer a haircut on the amount to be repaid in exchange for faster repayment terms, but it seems Beijing is holding firm on waiting for its money back in full, even if only at a slight premium.

The West’s Opportunity

The US and China are unequivocally locked in a strategic rivalry in Africa, with some experts referring to the current state of affairs as “the new Cold War.”[17] Generally speaking, the African continent has rapidly risen as a key geopolitical battleground between democratic and autocratic states in recent years. Tensions between African nations and the West have risen consistently as autocratic-leaning African leaders, particularly those who do not want external moralizing impacting their internal affairs, increasingly see Western authorities and their constant promotion of democracy as a threat to power consolidation. Countries like China and Russia, by contrast, promise financing and little interference in exchange for resources, lucrative contracts, and loyalty on the international stage, a much more palatable offering to autocrats — or to those looking to become one.

The recent experiences of Ghana, Zambia, and Sri Lanka, where debt restructuring has proven to be quite difficult, may cause some developing nations to rethink whether China’s stated ‘no strings attached’ loans actually offer the freedom they were expected to. Though Ghana was just Africa’s eighth largest economy in 2021, it became China’s largest trading partner in Africa.[18] What kind of message does it send to other African nations if China won’t forgive a relatively small amount of debt from its largest African trading partner?

China’s cash flow problem opens the door for Western actors to offer better terms to nations looking for investment or for debt forgiveness. The change in Chinese engagement levels in Africa, along with how the tone and tenor of negotiations in places like Ghana play out, will give the US and other Western nations an opportunity to fill in the void and return African nations who may have been courted by Russia and China to a Western sphere of influence. The perception that many creditors have that large investments in developing countries carry high risk (and thus promotes a problematic credit rating) locks out countries like Ghana from international credit markets, forcing them to choose from a more limited pool of creditors.[19] The Ghanaian case thus provides the US and the West with an opportunity to demonstrate its value as a partner that can weather all storms, one that would be willing to take a loss where China is not. In any case, the majority of Ghana’s debt is held by members of the Paris Club of creditors who are generally open to alternative routes of debt forgiveness than strict repayment, which also makes China appear to be the sticking point should it continue to push back on the terms offered.[20]

In just a quarter century, Africa will be the most populous continent and is expected to reach certain development indicators on par with much of the rest of the world, making it a key future market and a likely source of future power. Through the prism of current debt restructuring talks in Ghana and Zambia, the U.S. must not squander the opportunity to demonstrate strategic value to developing nations and promote itself as a more forgiving lender to those in tough situations.

The United States should take advantage of recent uncertainty over how China will negotiate with its partners who find themselves in economic crisis and make a push to position itself as being seen to be more willing to compromise on debt than China. The US has historical clout across post-colonial Africa and a large modern African diaspora it can leverage to rebuild its trade and political relations across the continent. It must take advantage of its image (generally speaking) as a champion of development and democracy and demonstrate the opportunity this model poses as a key differentiator against China.

[1] https://www.imf.org/en/Countries/GHA/ghana-qandas

[2] https://www.reuters.com/world/africa/who-holds-ghanas-debt-what-restructuring-is-planned-2022-12-09/

[3] https://www.imf.org/en/Countries/GHA/ghana-qandas

[4] https://www.reuters.com/world/africa/ghana-bondholders-kick-off-formal-talks-13-bln-debt-restructuring-2024-03-14/

[5] https://www.reuters.com/world/africa/ghana-agrees-debt-restructuring-deal-with-official-creditors-ghana-finance-2024-01-12/

[6] https://carnegieendowment.org/2023/03/28/will-africa-shape-u.s.-china-strategic-competition-event-8058

[7] https://www.eiu.com/n/china-and-africas-long-road-to-debt-recovery/

[8] https://www.eiu.com/n/china-and-africas-long-road-to-debt-recovery/

[9] https://www.reuters.com/markets/ghanas-debt-rework-quest-hits-international-bonds-hurdle-2024-04-15/

[10] https://medium.com/the-diplomatic-pouch/analysis-chinas-influence-in-ghana-is-more-powerful-than-ever-what-does-this-mean-c2c534c9c32f

[11] https://www.theafricareport.com/311884/ghana-chinese-loans-take-centre-stage-in-restructure-talks/

[12] https://www.theafricareport.com/311884/ghana-chinese-loans-take-centre-stage-in-restructure-talks/

[13] https://time.com/6835935/china-debt-housing-bubble/

[14] https://blogs.lse.ac.uk/africaatlse/2023/11/16/chinese-investment-in-ghana-is-entering-a-new-stage/#:~:text=In%202022%2C%20funding%20shrank%20to,June%202023%20Half%20Year%20Report.

[15] https://www.cfr.org/backgrounder/chinas-massive-belt-and-road-initiative

[16] https://thediplomat.com/2023/11/a-tale-of-3-nations-debt-restructuring-in-ghana-zambia-and-sri-lanka/

[17] https://www.foreignaffairs.com/africa/why-new-cold-war-will-split-africa

[18] https://medium.com/the-diplomatic-pouch/analysis-chinas-influence-in-ghana-is-more-powerful-than-ever-what-does-this-mean-c2c534c9c32f

[19] https://www.undp.org/sites/g/files/zskgke326/files/2023-05/undp_ghana_policy_brief_on_economic_updates_2_ghanas_debt_and_sdgs.pdf

[20] https://blogs.lse.ac.uk/africaatlse/2023/11/16/chinese-investment-in-ghana-is-entering-a-new-stage/#:~:text=In%202022%2C%20funding%20shrank%20to,June%202023%20Half%20Year%20Report.

Can the U.S. Emerge as the Main Winner in Ghana’s Default Drama? was originally published in reAIpolitique on Medium, where people are continuing the conversation by highlighting and responding to this story.