The supply chain industry's investment frenzy in artificial intelligence is following the classic pattern of past hype cycles—excitement is outpacing results. Eric Rempel, Chief Innovation Officer at Redwood, warned attendees at the FreightWaves AI Supply Chain Symposium in Chicago on Wednesday that the sector is approaching Gartner's Peak of Inflated Expectations, with the Trough of Disillusionment close behind. Rempel made the comments during a conversation with FreightWaves founder and CEO Craig Fuller, who noted the volume of capital—both public and private—flowing into AI, but also rising skepticism from parts of the venture capital world.
The Hype Cycle Warning
Rempel did not dispute the hype. "I think if you just reference it as one of those Gartner hype cycle curves that Gartner plays out here, we're on the way up to the Peak of Inflated Expectations, especially in supply chain, and we're about to go into the Trough of Disillusionment," he said. The core issue, according to Rempel, is the gap between demos and real-world deployment. "There are a lot of AI demos better than anything I've ever seen in my entire life. You can put an AI demo together, you can build something wonderful, you can do it for the enterprise, you can do the show. It's unbelievable." But supply chain doesn't run on clean demos. "Everything goes wrong all the time," he said. "If our customers could do it themselves, they wouldn't need us."
People, Process, and the Labor Impact
Getting value out of AI is no longer a technology problem—it is an organizational one. "What it takes is not just a technology lens to get it valuable within your organization, but a people and process lens," Rempel said. That message is complicated by headlines about AI-driven layoffs, which Rempel pushed back on directly. Redwood has gone the opposite direction: "We've only been hiring because of it." He argued that many layoffs in the news trace back to pandemic-era overhiring, not AI replacement. "A lot of organizations over-hired in COVID, there was a lot of bloat and waste, and they've done RIFs, but those RIFs are for things we just completely didn't need," Rempel said. "It's a great way for a value sheet to say, 'Hey, we're producing results and look at what we can do as an organization.' But I don't think that's the end-all, be-all."
The Spot Market for AI Compute
Fuller drew on a Bloomberg opinion piece to frame the labor shift in freight terms, comparing spot market to contract rates to AI and its pricing. Instead of supply and demand of assets, it is the supply and demand of available compute and tokens. Rempel sees the same pattern forming in AI pricing. Flat-rate subscriptions are giving way to usage-based costs as enterprises scale up. "The AI in the subsidized era was all contract market, bigger plans, $20 or $200 a month, you get all this compute," Rempel said. "That's very quickly going away, especially at the enterprise level. It's becoming a spot market, and organizations are rethinking how they staff."
That shift is colliding with organizations that move far slower than the technology. "Compute power and capability are shifting every three to six months in experiments, but people take longer to change," Rempel said. "Instituting change management within organizations is a three-to-five-year journey."
Change Management and Early Impressions
Fuller shared a change management anecdote about a four-hour-long morning process to build a newsletter. The challenge was managing resistance to using AI to speed it up, even after seeing dramatically improved output. When the worker had tried the technology three years earlier, they found it lacking and never went back despite the changes in capabilities. Early first impressions soured the willingness to adopt.
What This Means for Your Procurement Team
For procurement and supply chain leaders, the implication is clear: the current hype cycle demands rigorous evaluation. AI investments should focus on organizational readiness and change management—not just the technology. The shift from contract to spot-market pricing for AI compute will affect budgeting and vendor negotiations. Expect a 3-5 year journey for true adoption; early demos should not be mistaken for production readiness. As Redwood's experience shows, hiring for AI can increase, not reduce, headcount—but only if the process is managed correctly.
| Phase | Description | Timeline |
|---|---|---|
| Peak of Inflated Expectations | Rapid investment, high excitement, many demos | Now |
| Trough of Disillusionment | Reality check, failed deployments, skepticism | Upcoming (2026-2027) |
| Slope of Enlightenment | Practical use cases, organizational adoption | 2027-2029 |
| Plateau of Productivity | Widespread value, stable pricing | 2029+ |
Key Data Points
- Hype cycle stage: Approaching Peak of Inflated Expectations, soon to enter Trough of Disillusionment (per Rempel)
- AI pricing shift: From flat-rate subscriptions to usage-based spot market at enterprise level
- Organizational change timeline: 3-5 years for effective change management
- Compute capability change cycle: Every 3-6 months
Outlook
Rempel's warning serves as a counterpoint to the frenzy. The road to AI value in supply chain will be longer and more people-intensive than the current investment surge suggests. Leaders should prepare for a period of disillusionment, prioritize change management, and avoid mistaking demos for deployable solutions.
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