Depuis quelques mois certains sites expertisés confrontent les valeurs de l’expertise préalable ou de la valorisation algorithmique avec des estimations produites via des outils IA grand public. La démarche est légitime : comprendre, comparer, questionner. Ces outils donnent parfois un ordre de grandeur proche parfois pas.
The real question is understanding what scope those estimates are built on and what that scope leaves out.
Why general-purpose AI cannot value your industrial assets
An insurance valuation doesn’t rest on a general estimate of an asset type. It rests on this specific site, this specific piece of equipment in this specific configuration with these technical specifications and these operating conditions. Information that general-purpose AI does not have, because no one has provided it and it doesn't exist in any public database.
This is not a processing power limitation. It is a structural one: these tools operate on a universe of generic data. Your assets are not generic.
The implication follows directly: if your declared values are built on an incomplete scope, you are carrying an underinsurance or overinsurance risk that you will only discover when a claim arises.
Ce que nos experts ont appris à faire dans ces situations, c’est expliquer ce périmètre. Montrer ce que la valorisation intègre que l’IA ne voit pas. Et quelque part, cette pression nouvelle est utile : elle pousse à mieux justifier, à documenter plus précisément, à rendre les hypothèses lisibles pour les assureurs et les courtiers.
Why the algorithm cannot value assets on its own ?
Algorithmic valuation models are powerful. They process volumes of data no single expert could absorb, detect patterns invisible at the scale of a multi-site portfolio and produce comparable estimates across hundreds of industrial sites simultaneously.
But they only work if what you feed them is reliable.
Anasse, who leads our algorithmic models at SENOEE, is clear on this: : "What isn't in the data, the algorithm cannot perceive. It will only ever know its own universe."
Si cet univers est incomplet ou construit sur un périmètre qui ne correspond pas à la réalité du site le modèle produira quand même une réponse, cohérente avec ce qu’il a appris, mais déconnectée de la valeur de remplacement à neuf réelle. Il n’a pas conscience de ce qu’il ignore. Dans notre métier, cela peut représenter plusieurs dizaines de millions d’euros de valeur assurée.
The rule our data teams and experts share comes down to four words: Cheat in, Cheat out."
The sophistication of the model does not correct weak input data. It amplifies it.
The field expert: a data source, not an adjustment variable
For high-value equipment (production lines, technical installations, and specialized assets that concentrate the bulk of a site's insured values) the relevant data exists in no public database.
Hugo, Head of the insurance appraisal.division, puts it plainly: "It is the human who sources the data, who provides information to the algorithm. For all high-value equipment, we go in with a great deal of precision, research, documentation, and shared experience."
Two pieces of equipment with the same reference can carry very different replacement cost values depending on their age, technical modifications, and production environment. The expert engineer who has walked the site sees this. The AI model does not.
Dans beaucoup de débats sur l’IA, l’humain est positionné en aval : celui qui vérifie, qui valide, qui corrige en dernier recours. Dans la valorisation des actifs industriels assurés, c’est structurellement l’inverse. The expert intervenes upstream in the chain collecting, qualifying, and structuring the field data that feeds the models. Without that input, the algorithmic reference framework has nothing to learn from.
What "justifiable" means to your underwriter
Anasse raises a point that matters to insurers and brokers : "These are environments where it is very hard to say: 'here is a value, and that is the definitive one.' You have to be able to stand behind it, justify it, and audit it."
That is precisely what field data makes possible and what no algorithm alone can guarantee.
At Senoee, one rule is non-negotiable: we never release an algorithm that cannot be explained. Every algorithmic decision must be open to inspection, audited, and traced step by step. When a site deviates significantly from comparable assets in replacement cost value, in building-to-equipment ratio, in consistency with the declared production process the model must be able to explain why.
"When you have sites that deviate by 30% from the rest of the portfolio, we can justify it we can get into the detail,"Anasse specifies. It is this level of traceability that enables the Risk Manager to hold a solid position with their underwriter and the broker to build a compelling renewal submission for insurers.
This requirement for interpretability sits within an increasingly robust regulatory framework. Since theEuropean AI Act came into force in 2024, algorithmic systems used in high-risk domains have been subject to growing traceability and auditability obligations. Senoee built its models with this logic before regulation made it mandatory.
Insurance appraisal and algorithmic valuation: a value chain, not a hierarchy
orithmic valuation does not run in parallel withinsurance appraisal.It is its direct extension.
The data collected in the field structured and standardised against a shared reference framework becomes the raw material for the models.
Hugo translates this through the Pareto rule that structures his team's work:
"In industry, 20% of equipment accounts for 80% of a site's value. Those are the ones we treat with the greatest care sourcing them, justifying them, building the value. For the rest, the algorithm helps us scale the work."
The algorithm handles what is repetitive and comparable. The expert focuses their attention on what is strategic, atypical and carries significant weight on insured values. The model improves with each cycle: every new piece of field data refined by experts sharpens the accuracy of subsequent valuations. This is what Anasse calls a living reference framework : not a one-off deliverable, but a system that gains in precision as field expertise feeds it.
Control operates on two levels. "We will not deliver a result to a client if we spot an anomaly in the output. We trace back to the input data to find the source of the problem." Data verified on the way in, results audited on the way out: it is this double check that makes valuations objective at renewal.
What the insurance appraiser's role is becoming in the age of human-guided AI
The question was never "will the expert disappear?" It was "what does their role become?"
Hugo answers without ambiguity: "I still see the expert as exactly that collecting rich data. A field engineer who comes in to challenge AI and feed our algorithms."
Anasse converges on the same conviction: "The idea is not to replace the expert. You always need someone to exercise judgment, someone who feeds the system. The goal is to help them push their analysis further, to challenge more assumptions."
The more precise the models become, the more they depend on quality data to keep improving. As the algorithm scales, it does not reduce the need for insurance appraisal it raises the bar.
What Senoee's teams observe day in, day out does not rest on a theoretical conviction about AI. It is the outcome of a model tested, adjusted, and refined across thousands of industrial sites in nearly a hundred countries. Precision emerges from the dialogue between field expertise and algorithmic intelligence not from either one alone.
In your organisation, who is currently responsible for the data quality that feeds your valuations?
