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Tariff Wars – Not a Winter but Maybe a Frost. Negotiate Uncertainty and Navigate AI for a Business Spring

Being Human for a Better Tomorrow in the Age of AI

  • April 10, 2025
  • 42nd edition
Acceleration Tariff Wars Not a Winter but Maybe a Frost. Negotiate Uncertainty and Navigate AI for a Business Spring

After 33 editions spanning work, life, and public go in the next 8 editions on Accelerated Innovation (AI with AI) we explored how AI can drive new business, business models (Services as a A Sofware) and called out 39 AI disruptors across Financial Services, Pharma, Healthcare, Enterprise Software, Physical AI and Retail and World of Art.

Enterprise AI adoption for efficiency and innovation was exploding like life evolution in the Cambrian era when tariff wars suddenly introduced a chilling effect reminiscent of an Ice Age climate shift. The tariff hikes, retaliations, negotiations and sentiments threatened a deep AI Winter from business demand destruction, caution and conservatism in AI adoption for enterprises and increase cost, impose capital constraints and innovation at AI technology companies.

Yesterday’s 90 day pause on tariffs (excluding China, Mexico, and Canada) offers a respite and raises hopes that governments worldwide will find common ground to protect citizen and business interests.

While a harsh AI winter may be averted, we still face a period of frost and uncertainty as negotiations unfold and businesses assess potential outcomes. In this period, navigating AI is as critical as negotiating uncertainty itself.

Impact of Tariff: Understanding the Challenge

Tariffs serve legitimate purposes in protecting domestic industries, but sudden implementation disrupts established business models and supply chains. When imposed abruptly, they challenge fundamental assumptions about demand, cost, and fulfillment that underpin business planning and viability.

Inflation, Supply Chain Risks for Businesses and Consumers

New tariffs effectively function as a tax on imported goods, pushing up prices for businesses and consumers alike. Analysts widely agree this fuels higher inflation, forcing central banks to maintain elevated interest rates.

A March 2025 CNBC Fed survey found GDP growth forecasts falling from 2.4% to 1.7%, with tariffs now seen as the top threat to the U.S. economy, replacing inflation as the primary concern.

Consumer confidence has declined alongside rising prices, prompting fears of demand reduction and increasing recession odds.

Beyond cost implications, tariffs create significant supply chain complications. Many companies attempted to beat tariff deadlines by front-loading imports—ordering extra components before implementation—leading to port congestion and inventory imbalances.

Companies are now scrambling to reconfigure supply chains, seeking alternative suppliers in countries with lower tariff exposure. These pivots require time and investment: qualifying new vendors is neither quick nor free, and alternatives often come with higher costs or reduced efficiency.

Businesses face difficult choices: absorb higher costs (sacrificing margins) or pass them to customers (risking demand erosion).

Funding Challenges for AI Startups and Tech Companies

The tariff situation casts a shadow over the funding environment for AI ventures. Venture capital and private equity thrive on stable economic conditions and predictable policy environments. The current volatility dampens investor enthusiasm, particularly for early-stage ventures.

While established technology companies like Google and Meta have substantial resources, they too rely on capital markets for major projects and face stock price pressure in uncertain environments.

Overall, the cost of capital for AI initiatives has increased: inflation-fighting interest rate policies make debt more expensive, while equity investors have become more cautious about valuations in an unpredictable trade landscape.

Faced with uncertainty, companies adopt defensive spending postures. Both households and enterprises tighten budgets, creating a challenging environment for significant technology purchases and experimental initiatives.

Probable Cutbacks in AI Adoption & Tech Budgets

One probable immediate consequence of corporate caution is the scaling back of AI-related investments.

When confronting higher input costs and uncertain outlooks, companies often categorize experimental or longer-horizon projects (including AI pilots) as discretionary or postponable.

Price increases driven by tariffs make enterprises more hesitant to explore unproven technologies.

IDC’s March 2025 outlook warned that if tariffs persist, U.S. IT spending growth could decline to 4% from a previously projected 9% baseline—a significant contraction in a sector accustomed to robust growth.

Way Forward for Governments: From Policy Challenge to a Policy Solution

The current economic uncertainty stems from government policy decisions, but the same policymakers have strong incentives to preserve technological leadership.

History shows that periods of protectionism are typically followed by renewed cooperation when economic costs become apparent.

Three factors suggest government policy will eventually adapt:

  • National security and technological sovereignty concerns that initially drove protectionist policies also require thriving domestic tech sectors
  • Consumer price pressures from sustained tariffs create political challenges that demand resolution
  • Global competition in AI remains intense, creating pressure on governments to ensure their domestic industries aren't permanently disadvantaged
Rather than waiting for complete policy reversals, expect targeted exemptions, subsidies, and strategic investments that create pathways for continued AI development even within a modified trade environment.

Way Forward for Business: Actions to Negotiate Uncertainty

The 90-day tariff pause creates a critical window for businesses to prepare for multiple scenarios.

Forward-thinking organizations are using this time to:

  • Develop scenario-based contingency plans that account for different tariff outcomes after the 90-day period
  • Engage with policymakers and industry associations to advocate for their sectors
  • Diversify supply chains while maintaining flexibility to pivot quickly
  • Review and restructure contracts with suppliers and customers to share potential tariff impacts
  • Model financial impacts across various scenarios to prepare leadership and shareholders

Way Forward for Business: Use AI for Price Elasticity and Supply Chain Risk Management

AI capabilities can significantly enhance these uncertainty management efforts.

For instance, companies can leverage AI for sophisticated price elasticity analysis to understand how much of any increased costs can be passed to customers without destroying demand.

Similarly, AI-powered supply chain risk assessment can identify vulnerabilities and suggest mitigation strategies.

The sidebars on AI for Price Elasticity and Supply Chain Risk Assessment (included below) offer practical approaches for implementing these capabilities during this critical planning period.

Holding Off AI is Not an Option: In the Age of AI Winner Takes It All

The internet era demonstrated that network effects often create winner-take-most markets, as seen with search (Google) and social media (Meta). AI appears to be following a similar but even more pronounced pattern.

The economics of AI create particularly steep competitive advantages:

  • Data advantages compound exponentially
  • Talent concentrates where cutting-edge work happens
  • Computing infrastructure benefits from massive scale
  • Regulatory compliance creates high fixed costs that larger players can better absorb

This dynamic means the gap between market leaders and followers widens during challenging periods.

Companies maintaining AI investments during this frost won’t merely gain incremental advantages—they may secure insurmountable leads that define competitive landscapes for the next decade.

Unlike previous technological shifts where companies could catch up in subsequent cycles, AI capabilities may create permanent competitive moats.

Organizations that pause longest may discover they’ve not just fallen behind but become structurally uncompetitive.

Navigate AI for the Business Spring to Follow

In our 38th edition (https://lnkd.in/g5e4frJ6) about “Where’s the money for investing in AI?” we explored finding funding from savings and investing strategically amid volatility, rising costs, and project overruns.

Those principles remain valid. However, tariff wars and their impact raise the stakes considerably.

In Formula 1 racing, overtaking happens on the curves, not the straightaways. Similarly, this period of uncertainty creates opportunities for strategic companies to pull ahead.

The winning businesses in the age of AI should:
  • Accelerate rather than pause AI initiatives
  • Apply AI to the current business model
  • Focus on validated use cases
  • Signal long-term commitment
  • For AI technology companies: Double down on innovation while creating partnerships that demonstrate business value to hesitant customers

We may have avoided a deep AI winter, but even this frost period creates opportunity for those willing to navigate strategically.

When the business spring arrives, the landscape will have permanently changed in favor of those who maintained momentum.

Markets will do what they will. The question is: what will you do?

Welcome your comments, thoughts, and ideas on navigating this period of uncertainty and impact of AI.

Explore, join, and stay tuned for more.

Sidebar 1: AI for Price Elasticity Beyond Traditional Methods

Traditional price elasticity methods, often using linear regression, struggle to capture the complex, non-linear ways customers react to price changes in today’s dynamic markets.

Artificial Intelligence (AI) and Machine Learning (ML) are changing the game. Advanced techniques like Ensemble Models (Random Forests, Gradient Boosting) and Double Machine Learning (DML) deliver far more accurate and nuanced insights.

AI can analyze vast datasets—sales history, competitor actions, seasonality, demographics, even social sentiment—to understand the real drivers of demand.

Crucially, DML helps isolate the true causal impact of price changes, filtering out noise from confounding factors like promotions or economic shifts.

This allows for confident, data-driven pricing decisions, moving beyond simple correlations. AI also enables dynamic, predictive pricing strategies that adapt in near real-time, a stark contrast to static, infrequently updated models.

By leveraging AI, businesses can achieve more precise customer segmentation, tailor pricing effectively, and ultimately optimize revenue and profitability in ways previously impossible.

It’s a shift from reactive adjustments to proactive, intelligent pricing.

Credits: https://arminkakas.medium.com/mastering-price-elasticity-modeling-best-practices-for-2024-a69ea141e18f  

Best wishes

Sidebar 2: AI for Supply Chain Risk Assessment

In today’s volatile global landscape, traditional methods struggle to anticipate and manage complex supply chain risks effectively.

Artificial Intelligence (AI) and Machine Learning (ML) offer powerful new capabilities, transforming SCRM from a reactive to a proactive discipline.

AI/ML excels at analyzing vast, diverse datasets—including unstructured sources like news, social media, and weather reports—to identify hidden patterns, predict potential disruptions, and assess risks with greater accuracy.

Techniques like Random Forest, XGBoost, and Natural Language Processing (NLP) significantly enhance predictive capabilities for events like supplier delays, demand fluctuations, or transportation issues.

These technologies enable real-time monitoring, early warning systems, and optimized mitigation strategies.

Causal ML even allows for ‘what-if’ scenario planning to evaluate interventions.

By integrating AI, businesses gain enhanced visibility, quicker response times, and improved decision-making, ultimately building more resilient and agile supply chains capable of navigating uncertainty.

“AI is a language. Treat it like one: practice, iterate, and mind your grammar prompts, assumptions, and verification.”

II. Questioning / Asking

Good conversations flow from well‑sequenced questions—topical, simple, coherent, cohesive.
LLM Conversation Example 1
Q: What are empirical judgments?

A: Empirical judgments are based on observation, experience, or experimentation.
Q: What are moral judgments?

A: Moral judgments are based on ethical principles and values.
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