“Our data isn’t clean enough.” “We don’t have enough data.” “We need to digitize everything first.”
These objections come up time and again in our exchanges with industrial clients. Ultimately, they reveal a common misconception, that artificial intelligence requires a massive, clean, and perfectly structured database to work.
As a result, this bias slows down many industrial initiatives. Industrial data is rarely perfect; it’s typically partial, noisy, and heterogeneous. Yet, that doesn’t stop AI and data science from delivering value, quite the opposite.
Generate Data Rather Than Wait for It
Letting go of the assumption that AI requires a large set of pre-existing data is a key mindset shift. While this idea may be true in many common cases, where a model is trained on past data to predict output from new input combinations, it doesn’t apply universally.
In fact, in many industrial cases, the goal isn’t prediction but optimization under constraints (space, labor, capacity, machines, inventory, etc.). Rather than relying on historical data, these models use synthetic inputs, modeled to simulate the environment and its constraints. Within this virtually reconstructed environment, reinforcement learning or constrained optimization models can be applied. These approaches identify optimal strategies in scenarios too complex for human decision-making and cognitive capacity, due to the number of variables involved.
Practical Example
• A manufacturer of complex industrial systems was anticipating a significant increase in commercial demand over the coming years and needed to simulate capacity increase and test the resilience of its production system
• Our analysis focused on the factory’s bottleneck work stations, where planning relied heavily on operator know-how rather than structured optimization.
• The scheduling challenge was compounded by multiple production lines sharing key resources and equipment, but operating with different cycle times. The combinatorial complexity of this scheduling made it impossible to manually identify the most efficient allocation of resources.
• Using data science and AI-based planning tools — and more importantly, a pragmatic and business-oriented approach — Avencore simulated millions of scenarios within seconds to determine optimal scheduling strategies — far beyond human capabilities.
• The result: the manufacturer was able to unlock over 20% additional production capacity, without any additional CAPEX.
Imperfect Data Can Still Create Value
Even when data is partial, noisy, or heterogeneous, data science can extract value. It can help reconstruct consistent datasets from incomplete elements. AI models can detect anomalies in past databases, fill in missing data points in time, correct biases, or generate substitute data.
Practical Example
• A defense manufacturer wanted to improve the performance of material flows across its five sites and challenge its existing logistics organization.
• The first step involved reconstructing all past logistics routes using partial data from the ERP system. The large data volume (~2 million movements), the number of possible movement types (40 tracked logistics actions), and the incomplete data (no differentiation between items with the same reference) made it necessary to automate the analysis. A tailored statistical model was developed to trace the path of each unique part over the past 10 years.
• A supervised machine learning model, trained on the reconstructed data, was then used to classify all logistics routes as either normal or erratic.
• The model detected over 15% of logistics routes as unoptimized enabling our clients to reorganize and realize efficiency gains. Once again, the diversity and sheer volume of paths made such analysis beyond the reach of human capability alone.
In short, data science can be fed incomplete data, process it for usability, and extract valuable insights.
Industrial AI: Applied Mathematics and Pragmatism
Industrial AI is not limited to the generative tools currently dominating the headlines. It’s first and foremost about applied mathematics and solving complex problems tailored to each industrial context.
In this sense, AI isn’t a turnkey solution, but a tailored analytical approach. True value comes from finely tuned algorithmic models that can handle complex, often non-reproducible situations. To succeed, this requires accepting uncertainty and favoring targeted experiments: quick proof of concept, value demonstration, and gradual deployment.
“Our solutions are neither LLMs nor chatbots. Unlike generic SaaS platforms, we develop tailor-made models designed to solve the most complex industrial challenges.“
In the end, methodological rigor is key to turning ideas into real value. What matters isn’t the tool itself, but its alignment with real-world complexity.
Conclusion: Industrial AI Deserves a New Perspective
The real value of industrial AI lies not in accumulating perfect data, but in its ability to model, enrich, and make smart use of the data available. Whether by building models with synthetic data to simulate and optimize strategies, or by extracting value from incomplete or noisy datasets, the opportunities are real and available now.
Instead of waiting for ideal conditions that may never come, it’s time to put AI to work where it matters most: at the core of industrial complexity. That’s where it delivers real impact on performance. Get in touch to see how we can help you address your business complexities and leverage the full potential of our AI tools.