Who Controls the Data, Controls the Margin
The Recycler Live & Trade Days 2026 continues to expand its program with the participation of Antonio Sánchez Navarro, CEO of Nubeprint, who will bring a data- and AI-driven perspective. The event, taking place from May 20 to 22 in Dortmund, will gather industry professionals to discuss strategic decisions in an increasingly demanding environment.
For years, the business relied on hardware, contracts, and consumables supply. This model worked while the market was growing, as increasing volumes absorbed inefficiencies and protected margins. However, today we operate in a mature market, where margin pressure is constant and driven by structural change.
The contested installed base — printers not directly managed by manufacturers — is shrinking. As OEMs integrate services, platforms, and closed ecosystems, the competitive space for independent players is narrowing. The market is not disappearing, but it is being redistributed, especially to the disadvantage of those who do not control information.
When the Product No Longer Differentiates
Hardware no longer provides a sustainable competitive advantage. Quality across manufacturers is largely homogeneous, and technical differences rarely influence customer decisions.
Faced with this reality, many players have turned to price competition, leading to continuous margin erosion.
If the product no longer differentiates and price destroys profitability, competitive advantage must be found elsewhere: data. Every printed page generates information, and every connected fleet produces strategic data.
The key question is no longer whether data exists, but who can structure it, analyze it, and turn it into decisions before others do.
The Silent Shift in the Sector
Some manufacturers understood long ago that data is a strategic asset. As a result, they have developed proprietary platforms and closed ecosystems that allow them to anticipate demand and optimize operations.
Meanwhile, much of the channel continues to operate reactively:
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Forecasting based on historical data
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Stock held “just in case”
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Production after orders are received
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Avoidable technical interventions
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Immobilized capital
This model is not a mistake, but a legacy of a different stage. However, in a market that does not grow for everyone, inaction leads to a gradual loss of margin.
The solution is not to replace the current model, but to complement it with a data-driven intelligence layer that improves operational precision.
Management by Intuition vs. Management by Certainty
Two management approaches now coexist:
Intuition-driven companies:
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Purchase based on historical patterns
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Produce using estimated forecasts
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Maintain high stock levels
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Compete on price
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React late to the market
Certainty-driven companies:
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Anticipate demand with precision
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Align production with real consumption
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Optimize inventory
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Reduce immobilized capital
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Minimize technical issues
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Protect margins
The difference between these models is not technological, but structural.
From Business Intelligence to Artificial Intelligence
The sector has experienced a similar situation before with Business Intelligence. Many companies implemented tools without changing how decisions were made, resulting in attractive dashboards with little real impact.
A similar pattern is emerging with Artificial Intelligence. While its potential is undeniable, many projects fail or are scaled back due to issues such as:
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Lack of measurable return
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Underestimated costs
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Poor or unstructured data
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Unrealistic expectations
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Misalignment with business needs
The common mistake is starting with technology (“we want AI”) instead of starting with the problem (“which decision do we want to improve”).
Data-driven operational intelligence is not an experiment, but a strategic layer built on existing data.
The Key Question for the Sector
In the coming years, the redistribution of margin and market share will not depend on hardware, but on who can:
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Anticipate demand
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Reduce immobilized capital
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Optimize production
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Minimize unnecessary interventions
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Control information
The real question is not whether to invest in data, but how much margin companies are willing to lose while waiting.
Dortmund: A Necessary Debate
In Dortmund, key topics will be addressed, including:
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How to avoid common mistakes in AI projects
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Identifying real barriers (cultural and strategic)
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Setting realistic expectations
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Turning data into competitive advantage
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Understanding the role of closed ecosystems