Turning Economic Headwinds Into AI Tailwinds: Why Smart Companies Are Investing in AI Now, Not Later

The global economy is shifting. In the face of protectionist trade policies, new tariffs, and rising costs, business processes face disruption while margins come under tightening pressure. 

These new trade restrictions and tariffs are more than an accounting headache. They change how goods move, how companies price, and how supply chains operate. Naturally, many businesses react by slowing spending and playing defense.

But for forward-thinking leaders, this is an opportunity. Historically in periods of economic friction, opportunities for reinvention emerge. This moment is no different. What is different is the technology we now have. Specifically AI, which can not only help companies survive the storm but position them for long-term success.

What we’re seeing today from leading companies is a shift toward proactive investment, particularly in AI. They’re not waiting for conditions to improve. Instead, they’re building the systems that will give them an edge now and an even bigger advantage when the economy rebounds.

AI Optimizes an Enterprise’s Core Costs

Large enterprises consistently face five major cost drivers that heavily influence strategic and operational decisions: 

• Cost of goods sold (COGS) — including raw materials, manufacturing, and supply chain costs — remains a core expense for product-centric firms. 
• Real estate and energy consumption across factories, offices, and warehouses adds significant fixed costs, especially at global scale.
• Customer acquisition and retention — spanning marketing, sales, and support — represent substantial investments to drive and sustain growth. 
• Technology and infrastructure spending has surged with digital transformation to cover cloud services, cybersecurity, and enterprise software.  
• Labor costs — including salaries, benefits, and workforce overhead — continue to be a major outlay, particularly in knowledge- and service-based industries.

Smart companies recognize they can use AI, automation, and data to contain or optimize these core costs now. Then when markets recover, they’ll emerge ahead of competitiors thanks to leaner operations, faster decision-making, and superior customer experiences.

AI Introduces New Cost-Saving Strategies, Too

To manage rising complexity and global scale, leading enterprises are now adopting AI-driven cost-saving strategies rooted in their technology and data: 

• Agentic AI and automation reduce operational costs across customer service, supply chain processes, billing, and invoicing through autonomous, streamlined workflows that eliminate repetitive tasks and enable leaner operations.
• Predictive analytics and machine learning (ML) transform energy and resource management and COGS management, allowing companies to optimize inventory and supply chains, forecast demand more accurately, and reduce waste.
• Personalized, AI-driven marketing and customer service improve customer segmentation, acquisition, and retention.
• AI-driven procurement uses natural language processing (NLP) and data mining to identify cost-saving opportunities in contracts, consolidate vendor spend, and flag risky suppliers.
• Workforce planning and optimization uses AI to forecast staffing needs, shift demand, and attrition risks, thus helping HR and operations optimize headcount and reduce overtime and underutilization.

The common thread: Companies that embed artificial intelligence into their core operations shift from cost-cutting to cost-smart strategies that balance efficiency, resilience, and growth. 

5 Real-Life Examples of Companies Optimizing Costs With AI

TELUS

At TELUS, AI is reshaping how pricing decisions get made. Teams moved from manual models to AI-powered systems that suggest optimal prices in real-time. The result is faster decisions, reduced risk, and improved margins, even as market conditions fluctuate.

In addition, TELUS and TELUS Digital partnered to drive measurable business outcomes by embedding AI into sales, customer intelligence, and strategic planning. Tools like a PSO (professional sales organization) copilot combined with a Gen AI agent improved win rates and customer targeting, reducing manual effort and shortening deal cycles. By automating insights from over 100K call transcripts and leveraging forecasting models, the program enables faster, more accurate decisions, cutting analysis time by up to 80% and significantly lowering operational costs.

Convoy

Convoy, a leading digital freight network, uses an AI-powered platform that, according to a case study by Redress Compliance, works by analyzing shipment details, carrier availability, and lane preferences to match loads with the most suitable carriers. It also considers factors like route efficiency, pricing, and load type to optimize load distribution and route planning. This approach has cut booking times by 50%, lowered transport costs by 25%, and reduced emissions by 35%.

Schneider Electric

​Schneider Electric is revolutionizing industrial energy management with predictive AI. The company analyzes data from production and utility systems to forecast consumption patterns and identify inefficiencies. Its predictive energy platform employs ML models to optimize energy usage in plant utility systems, leading to significant reductions in both energy consumption and carbon emissions. For instance, a semiconductor plant achieved $1 million in annual energy savings and reduced 10,000 tons of CO₂ emissions per site, with a return on investment realized in under six months.

Walmart

Walmart uses AI not only to negotiate with thousands of suppliers at scale, but to reroute logistics in real time as global conditions shift. AI helps Walmart maintain continuity and keep costs low, even as the global trade map gets redrawn.

Iberostar Hotels & Resorts

Iberostar Hotels & Resorts is leveraging AI to enhance sustainability and customer experience across its global properties. By implementing AI technology in its kitchens, Iberostar has significantly reduced food waste, saving approximately 1.5 million meals and cutting over 2,500 tons of CO₂ emissions annually. The AI system uses smart cameras and scales to monitor food preparation, providing chefs with real-time data to optimize inventory and minimize waste. These efficiency improvements ensure fresher meals and more personalized service while contributing to environmental goals and enhancing guest experience.

These aren’t moonshots. They’re practical, proven, and already paying off.

A Simple Playbook to Start Now

1. Understand your biggest cost drivers

To get the most ROI from AI, you need to know where it will deliver the biggest impact:

• Start by tracing where you’re bleeding cost, time, or quality.
• Look at your top five cost centers (e.g., COGS, logistics, customer service, procurement, labor).
• Ask yourself, “Where are we manually intensive? Error-prone? Slow? Repetitive?”

2. Map core business processes from end to end

Visually break down key workflows:

• Order-to-cash
• Procure-to-pay
• Plan-to-produce
• Engage-to-serve (customer experience)

From there, interview frontline teams to uncover bottlenecks, repetitive tasks, and unmet data needs that are prime opportunities for AI and automation. At each step of the operational process, ask:

• Is this decision rule-based or repeatable?
• Is this task taking humans too long?
• Could data or pattern recognition make this faster or smarter?

Target the steps with:

• Lots of human intervention
• Data that’s being underutilized
• Errors or rework
• Long turnaround times

3. Benchmark against industry use cases

Look at what leading companies in your industry are doing with AI:

• Telecom: Network optimization, predictive maintenance, customer churn prediction, dynamic pricing, personalized service delivery
• Logistics: Load optimization, demand forecasting, real-time rerouting
• Retail: Dynamic pricing, personalized marketing, fraud detection
• Manufacturing: Predictive maintenance, energy optimization
• Healthcare: Patient triage, claims automation, diagnostics

4. Score opportunities with a simple matrix

Once you’ve collected ideas, score them across three axes: business impact, technical feasibility, and time to value.

‍High impact + high/medium feasibility + fast ROI = priority ideas.

At Willowtree, we cap off our Agentic AI Use Case Workshops with a two-week framework that coalesces teams around priorities. Our ROI-centric approach aligns impact and feasibility, resulting in clear and actionable roadmaps.

5. Support with change enablement

To create real enterprise value through AI, everyone should embrace it at their current level of understanding. Training should be role-specific, outcome-driven, and continuous:

• Give leaders executive-level AI training so they have the knowledge to drive AI strategy, manage risk, and make informed investment decisions.
• Enable frontline teams with hands-on AI training that enhances workflows, complements human judgment, and builds confidence in using tools like copilots and automation platforms.
• Upskill technical teams on end-to-end AI development and tooling while encouraging cross-functional collaboration to accelerate solution delivery. 

Invest in AI With Confidence

While today’s economic conditions are challenging, they also create the urgency — and opportunity — for meaningful transformation. AI isn’t just a tool. It’s a strategic advantage that builds resilience now and positions you to lead when the market turns.

As an end-to-end provider of Data & AI solutions, WillowTree helps forward-thinking enterprises with everything from strategy and governance to data readiness. We handle the execution side too, like deploying AI agents, establishing production frameworks, and conducting ongoing monitoring.

Learn more by exploring our Data & AI services.