Turning AI from Buzzword to Business Value

AI promises transformation across logistics, yet most companies still struggle to capture real returns. Lori Boyer, Head of Content and Host of the Unboxing Logistics Podcast at EasyPost, opened her DELIVER America session with a relatable story: a blender that was too advanced for its purpose. Her point? The same mistake happens with AI.

Businesses chase the flashiest technology without addressing the real problems they’re trying to solve. “Did I need a blender? Yes. Did I need that blender? No,” Boyer joked. The same logic applies to AI — you might need automation, but not every shiny new system will deliver value.

Why 95% of AI Pilots Fail

Drawing on research from MIT, Boyer noted that 95% of AI investments fail to deliver ROI. The study found four main reasons: going too big too fast, chasing shiny demos, obsessing over perfect data, and trying to build custom solutions internally.

“The truth is, AI in logistics is still best at solving narrow, well-defined problems,” she explained. “We need to start small, stay focused, and measure what matters.”

The 90-Day Roadmap to AI ROI

Boyer laid out a simple framework for teams ready to move from experimentation to impact:

• Identify a real problem. Start with a business challenge — not a technology trend.

• Choose one clear goal. Focus on one measurable pain point, such as late deliveries or duplicate invoices.

• Layer AI into existing systems. Work with partners and platforms that already have AI capabilities built in.

• Test, measure, and scale. Run 30–60-day pilots, measure outcomes, and expand what works.

Her mantra: “Start small, learn fast, and let ROI guide the rollout.”

Proven AI Use Cases in Logistics

Boyer highlighted several areas already delivering results:

• Rate shopping: AI tools like EasyPost’s Luma help shippers identify the most cost-effective carrier options, saving up to 20%.

• Invoice auditing: AI-powered validation recovers 1–3% of spend lost to billing errors.

• Route optimisation: Smarter pathfinding can save millions in transportation costs.

• Forecasting: AI-driven forecasting cuts planning errors by up to 50%.

Each example underscored her principle: success comes from narrow, measurable applications — not sweeping digital transformations.

People, Process, and Practicality

Beyond tools, Boyer stressed that AI success depends on people. “AI should augment your workers, not replace them,” she said. “The goal is to make human teams more efficient, not redundant.”

She also warned that successful AI adoption often disrupts existing processes — and that discomfort means progress. “If it’s too easy, it’s probably not doing anything,” she quipped.

Final Takeaway

The road from AI hype to ROI doesn’t require massive investment — just focus and follow-through. As Boyer summed it up: “Start with a problem, partner with experts, assign AI to busywork, and measure what matters. Within 90 days, you’ll know if your AI is truly delivering value.”