During a period of rapid Artificial Intelligence expansion, many companies feel pressured to quickly introduce AI solutions for the factory floor and production lines.
In his presentation, Eng. Karim Zaitov, a digitalization expert at Industrial Cloud Srl, clarifies a fundamental concept: the application of AI in the industrial sector is not a race against time.
It is a structural building process. You do not start with the roof. You start with the foundation.
AI for the Factory: Foundations First, Algorithms Second
Applying AI in production is equivalent to constructing a building:
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First, the foundations are laid.
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Then, the load-bearing walls are raised.
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Next, the internal systems and finishes are installed.
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The roof is completed.
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Only at the very end is the structure enhanced with AI.
Translating this metaphor into the industrial context, four key challenges emerge that determine the success or failure of any industrial AI project.
1. Data: The True Foundation of AI in Production
Artificial Intelligence is built on data. Without structured and reliable data, AI cannot exist. In many industrial SMEs, the current situation remains as follows:
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Paper-based data
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Non-integrated Excel spreadsheets
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Disconnected systems
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Unvalidated information
At this stage, the company is still at the foundation level. Yet, advanced factory AI solutions are often requested, such as automated production management based on marketplace and e-commerce sales. One example is the case study on total production planning based on demand forecasting: companies that still lacked structured data were already seeking intelligent automation. Without digital foundations, however, the application of industrial AI is not sustainable.
2. Processes and Validation: The Load-Bearing Walls of Industrial AI
Collecting data is insufficient. To be effective, data must be:
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Clean
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Validated
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Embedded within clear processes
AI does not create order; it optimizes what is already organized. In a predictive maintenance project for a temperature-controlled freight carrier managing 3,000 containers, the initial step was not the implementation of advanced algorithms.
It was first necessary to:
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Filter data noise
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Correctly structure information flows
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Define clear procedures
Only after this groundwork was established could an industrial AI system for predictive maintenance be successfully applied. Without load-bearing walls, the structure cannot stand.
3. Skills and Data-Driven Culture: The Internal Infrastructure
Internal expertise is another decisive factor in the application of industrial AI. While many companies are currently in the data collection phase, few have progressed to the analysis stage.
Being data-driven involves:
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Making decisions based on objective indicators.
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Constant monitoring of performance metrics.
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Utilizing data to optimize processes.
Without internal personnel capable of interpreting information and collaborating with external consultants, factory AI projects risk failing to produce tangible results.
4. Entrepreneurial Mindset: The Roof of the Structure
The final and most nuanced element is the mindset of the decision-maker. Entrepreneurs who implement AI effectively are typically visionaries who understand and accept that:
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Transformation is gradual.
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Investment is structural, not merely transactional.
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Data may fundamentally challenge intuition.
In the fourth case study, it becomes clear that the primary constraint is cultural rather than technological. Currently, those who implement production AI with a truly strategic vision remain a minority.
The Industrial AI Readiness Index: The Traffic Light Metaphor
To simplify the strategic roadmap, Eng. Karim Zaitov introduces the “Traffic Light” metaphor to evaluate an organization’s readiness for AI integration.
Red Light: Foundation Phase
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Status: Data is either not collected or remains paper-based.
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Infrastructure: Fragmented, non-integrated systems.
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Visibility: Zero real-time visibility into production processes.
Action: This is not the stage for AI. The objective here is fundamental digitalization and the establishment of a basic data architecture.
Yellow Light: Structural Phase
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Status: Data is digitalized but remains siloed (not fully integrated).
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Infrastructure: Systems communicate only partially or inconsistently.
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Focus: Optimization of executive processes, precise industrial costing, and production control.
Action: At this stage, the “load-bearing walls” of the digital enterprise are being raised to support future intelligence.
Green Light: Strategic AI Integration
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Status: Fully integrated information ecosystem.
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Infrastructure: Production, warehouse, logistics, and maintenance are natively interconnected.
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Analysis: Data analysis is already an active part of the workflow.
Action: The concrete prerequisites for high-impact industrial AI applications are met. The architecture is ready for advanced optimization.
Conclusion: Factory AI is an Outcome, Not a Starting Point
Implementing AI in production is not a sprint to see who starts first; it is a structured engineering evolution. Success requires:
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Robust, validated data.
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Transparent, well-defined processes.
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Internal technical competencies.
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A visionary, strategic mindset.
Only by following this hierarchy does AI transform from a buzzword into a definitive accelerator of global competitiveness.
Industrial Cloud Srl è a tua disposizione per capire in quale fase del “semaforo” si trova la tua azienda
Our experts are available to audit your infrastructure and identify your current phase within the “Traffic Light” framework.
