BY HANS BERGGREN, AGNETA CHRISTERSDOTTER, AND JENS DREMO
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The supply chain has always been about coordination. Between demand and production. Between inventory and delivery. Between companies and operations that, in practice, depend on each other’s decisions, but are often driven by different incentives, systems, and time horizons.
That is also why the supply chain is changing at a slightly slower pace than many other sectors and industries. Not because the technology is lacking, but because the reality is complex. Many players with long and complex relationships. Add legal structures and business-critical dependencies to that, and you get a world where everyday stability is often valued higher than experimentation. At the same time, something is happening now in our world of supply chains. Not in the form of a dramatic revolution, but as a cautious curiosity about how technology can be used in entirely new ways. As the degree of digitized flows in supply chains really starts to take off, we can begin using predictive AI as a way to better understand the pace the market is already setting.
Forecasts aren't the problem—uncertainty is the problem
Forecasts are a part of nearly every supply chain. They are often manual, sometimes system-supported, and almost always involve significant margins of error. This is particularly true in industries with long lead times and complex dependencies, such as the automotive industry, where forecasting has been in use for over 30 years and the volume of shared data is enormous. The problem lies in the consequences of the uncertainty surrounding these forecasts, which manifest as excessively high safety stock levels, capital tied up unnecessarily, last-minute changes leading to emergency production scrambles, or, in the worst-case scenario, product write-offs and lost delivery capacity.
This is where predictive AI really starts to get interesting. Not because it eliminates uncertainty in the supply chain—that will always be there—but because, in certain areas, it can reduce that uncertainty. This is especially true when, as in the automotive industry, the volume of data is vast. When the range of uncertainty in the decision-making process shrinks, even marginally, it changes how people plan, order, and produce. For certain items and flows, that improvement alone may be enough to create a calmer, more synchronized behavior throughout the entire chain. We have seen this clearly in our work with the supplier side of the automotive industry. When forecast signals are analyzed over time, in combination with historical patterns, a more stable picture of demand emerges than the one often communicated in traditional forecast files. The effect isn’t dramatic at every data point, but it’s sufficient to influence inventory strategies, purchasing decisions, and production planning.
Synchronization is just as important as optimization
For many years, supply chain development has been about optimization: lower costs, shorter lead times, higher service levels. These factors remain important, but as complexity increases, synchronization becomes just as crucial. A synchronized supply chain isn’t about everyone doing the same thing, but about everyone moving in step. It’s about decisions being made based on a shared understanding of reality. That changes do not come as surprises, but as signals that are picked up in time. Here, the metaphor of the orchestra is useful. In an orchestra, not everyone plays the same instrument, but everyone follows the same beat. The conductor sets the tempo—and in the supply chain, it is in practice the market, in combination with driving parties that can influence the tempo, that acts as the conductor. Demand, economic conditions, customer behavior, and external disruptions set the rhythm. The question is how well organizations and networks manage to follow it. In this context, predictive AI can be seen as a way to hear the beat more clearly, rather than trying to rewrite the sheet music.
Trust, Data, and Human Decisions
A recurring issue in the use of AI in the supply chain is trust. Not in the technology itself, but in the decisions it produces. Many organizations are more forgiving of human errors than of technical ones. An inaccurate forecast made by a planner can often be explained and accepted. An AI-based suggestion that deviates from the norm, however, raises concerns, even if it is statistically better. Therefore, the introduction of AI is at least as much a cultural journey as it is a technical one. Transparency in how the models reason, the ability for human oversight, and clear “learning loops” are crucial. In practice, predictive AI works best when it doesn’t replace decisions, but challenges them—and when the organization allows itself to learn from the outcome. This has been a central principle in PipeChain’s work: AI-based forecasts are sound recommendations, not a magic crystal ball. It is still humans who take responsibility for the decision, but with a broader and more consistent foundation. We believe in automation with control and regulation functions that ensure the best possible results and value creation over time.
Small steps, real problems
Another important lesson is that value creation rarely stems from large-scale, overarching AI initiatives. Value is created when the technology is applied to concrete, everyday problems and a cultural shift begins. Our own AI initiatives are often employee-driven and focused on solving the specific challenges our customers face. This might involve interpreting incoming PDF orders that would otherwise require manual processing. Or automating the compilation of customs documents. Or gradually improving forecast accuracy to reduce the risk of overstock or stockouts. The common thread is that the technology isn’t introduced as something abstract, but as a way to reduce friction in existing workflows. That’s also where acceptance grows: when people realize that work actually becomes easier, calmer, and more predictable.
From hype to everyday life
AI in the supply chain is neither a passing trend nor a one-size-fits-all solution. The real change lies somewhere in between. We believe that the organizations that will succeed in the future are those that combine realism with curiosity: those that dare to use technology where it is useful, but do not expect miracles. Those that invest in data quality, relationships, and working methods—not just in models.
Ultimately, it’s about building supply chains that can better handle fluctuations—ones that respond more quickly, but not erratically. And ones that, as the market shifts pace, manage to keep up without losing their balance. That’s where a synchronized supply chain begins to take shape. Not perfectly. But in step.
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Hans Berggren is the CEO of PipeChain
Agneta Christersdotter is the CEO of PipeChain Networks
Jens Dremo is the CEO of PipeChain SCM Tyringe
