What does 'Peak of Inflated Expectations' mean for my supply chain AI investment?

According to Gartner's hype cycle model referenced by Redwood's CIO, the industry is near peak enthusiasm where demos look exceptional but deployment reality lags significantly. The Trough of Disillusionment follows, meaning many organizations will soon encounter disappointing ROI. This suggests a critical window to focus on realistic use cases and change management rather than comprehensive transformation promises.

Why is there such a gap between AI demos and actual deployment?

Rempel explained that supply chain operations are inherently chaotic—'everything goes wrong all the time.' Demo environments are controlled; production involves interdependent legacy systems, messy data, and countless edge cases. The challenge is organizational and processual, not just technical. Without proper guardrails, context, and memory built into AI workflows, deploying agents at scale becomes costly and ineffective.

Is AI replacing freight and supply chain workers?

Rempel pushes back on the AI-layoff narrative, noting that Redwood is hiring more due to AI adoption. Many publicized layoffs trace to pandemic-era overhiring bloat rather than AI displacement. However, the labor market is shifting: AI pricing is moving from flat subscriptions to usage-based 'spot market' models, requiring organizations to rethink staffing structures and skill requirements—a three-to-five-year organizational change journey.

How do legacy systems like AS400 fit into an AI-driven supply chain?

Rempel argues that data quality and flow between systems is the real asset, not the interface. AI makes interconnected legacy systems more valuable—not less—because AI can help those systems communicate. However, applying AI indiscriminately at massive scale (billions of transactions monthly) is costly and ineffective. Strategic, targeted applications matter more than wholesale modernization.

What should my organization prioritize: technology or people?

According to Rempel, getting value from AI requires a 'people and process lens,' not just a technology lens. Change management within organizations typically takes three to five years. Early adoption failures often stem from organizational resistance rather than technical inadequacy. Governance, accountability frameworks, and worker retraining are as critical as platform selection.

Technology & Innovation

High Impact

AI Supply Chain Hype Peaking—Reality Gap Widens

Jul 15, 2026

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The freight and supply chain industry is experiencing peak AI hype, with investment and excitement outpacing measurable results, according to Eric Rempel, Chief Innovation Officer at Redwood, speaking at FreightWaves' AI Supply Chain Symposium. The industry is approaching Gartner's Peak of Inflated Expectations with the Trough of Disillusionment imminent—a pattern familiar from prior digital transformation cycles. The core challenge isn't technological capability but organizational readiness. Rempel highlighted a critical gap: AI demonstrations are exceptional, but real-world supply chain operations are messy, unpredictable, and require deep contextual understanding.

Legacy systems, process inertia, and workforce resistance are slowing adoption more than platform limitations. Critically, AI adoption is transitioning from flat-rate subscription models to usage-based "spot market" pricing at enterprise scale, forcing organizations to rethink staffing structures and budgeting approaches. For supply chain professionals, this signals an inflection point. The next 12-24 months will separate genuine AI value creators from vendors riding the hype cycle.

Success requires simultaneous focus on people, process redesign, and governance—not just technology selection. Organizations that treat AI as a tool rather than an organizational change initiative will likely experience disappointment and waste.

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