Artificial intelligence is gradually transforming industrial companies. From technical support and maintenance to quality, compliance, and product expertise, use cases are multiplying and the potential gains are significant.
But in the industrial sector, the question is no longer just how to deploy AI.
The real question now is: can we trust it?
At Ask for the moon, we see that reliability is currently one of the top concerns for business leaders and CIOs. In an industrial environment, an incorrect answer can have consequences far beyond a simple waste of time: a diagnostic error, a poor product recommendation, regulatory non-compliance, or a business interruption can quickly lead to significant costs, operational delays, penalties, or lost revenue.
Why reliability has become the main challenge for industrial AI
Generative AI models like ChatGPT, Copilot, and Gemini have widely democratized access to artificial intelligence. They are capable of producing answers on a vast range of topics in seconds.
However, these models have a well-known limitation: hallucinations.
A hallucination occurs when an AI generates a response that sounds credible but is factually incorrect. It might invent a product reference, cite a non-existent procedure, or incorrectly interpret technical documentation.
In many consumer use cases, this type of error remains inconsequential.
In the industrial sector, the situation is different.
Employees rely on information daily to make technical decisions, follow procedures, respond to customers, and ensure operational compliance. An incorrect answer can therefore generate significant costs or create operational risks.
Furthermore, according to several industry studies, the costs of poor quality can account for between 5% and 30% of an industrial company's revenue. The reliability of information is therefore a major economic issue.
Not all AIs are equal when it comes to reliability
Many companies are now discovering that the quality of a response depends on much more than the power of the model being used.
An AI assistant may be highly effective at drafting an email or summarizing a document, yet struggle when it comes to answering a complex business question.
In the industrial sector, reliability relies above all on the quality of the knowledge utilized, an understanding of the business context, and the ability to recognize situations where the available information is insufficient.
This is precisely what distinguishes a general-purpose AI from an AI designed for critical industrial use cases.
The 5 criteria for reliable industrial AI
Reliable knowledge sources
The quality of an answer depends directly on the quality of the information used.
A reliable AI must be based on validated procedures, technical standards, regulations, product documentation, or company-approved feedback.
Without control over sources, it becomes difficult to guarantee the relevance of answers.
Understanding the business context
Industrial environments use specific vocabulary, internal acronyms, and frameworks unique to each organization.
A reliable AI must be able to understand this context to provide answers adapted to operational realities on the ground.
An AI that limits hallucinations
The goal is not to answer at any cost.
A reliable AI prioritizes accuracy over completeness. When it does not have sufficiently reliable information, it must be able to acknowledge this and state that it does not know, rather than producing an uncertain or erroneous answer.
Human validation when necessary
Subject matter experts play an essential role in the quality of the knowledge used by the AI.
They must be able to enrich, correct, and validate information to continuously improve the relevance of the answers.
Appropriate governance and security
Reliability also requires data control.
Companies must maintain control over the documents used, access to information, the models deployed, and data hosting conditions.
What our benchmark shows about the reliability of AI solutions
To concretely measure the ability of different solutions to answer complex industrial questions, we conducted a comparative benchmark covering 12 real-world questions derived from industrial use cases.
Several AI assistants on the market were evaluated based on various criteria: accuracy, consistency, business relevance, response reliability, and the ability to avoid hallucinations.
One of the key takeaways from this study is that AI performance does not depend solely on the model being used.
The quality of the knowledge utilized, content governance, and the ability to recognize limitations play a decisive role in the reliability of the answers.
Solutions capable of leveraging validated business knowledge generally achieve better results on complex technical questions than general-purpose assistants.
To discover the full analysis, check out our comparative benchmark of AI solutions for the industry.
How Ask for the moon ensures reliable answers
At Ask for the moon, reliability is not just an extra feature.
It is the very foundation of the platform.
Governance by your business experts
Experts select the sources used by the assistant and define the reference content.
The generated answers are therefore based on knowledge approved by the company rather than on unverified information.
An AI that knows how to say "I don't know"
In an industrial environment, a wrong answer is often more problematic than no answer at all.
When no reliable information is available, Ask prioritizes transparency and acknowledges its limitations rather than producing a potentially erroneous response.
Leveraging field expertise
A large portion of industrial knowledge remains undocumented.
It resides in the experience of technicians, engineers, and subject matter experts.
When an answer cannot be found in existing documentation, Ask identifies the most relevant colleagues to assist the user. Validated exchanges then permanently enrich the company's knowledge base.
This approach helps secure expertise that is often difficult to pass on and particularly valuable as many experts approach retirement.
Agnostic architecture
Ask for the moon adopts a model-agnostic approach.
This agnosticism allows for the selection of the most suitable technologies for every need, while maintaining the control, validation, and governance mechanisms required for industrial use.
A sovereign approach
Companies retain full control over their data, access, and AI governance.
This flexibility meets the security, compliance, and sovereignty requirements of even the most demanding industrial organizations.
Reliability: the essential condition for AI adoption
In the industrial sector, the success of an AI project does not depend solely on the performance of the model used.
It relies above all on the trust users place in the generated answers.
The companies that derive the most value from AI will be those that successfully combine technological performance, business expertise, and knowledge governance.
This is precisely the approach developed by Ask for the moon to help industrial players accelerate access to expertise while maintaining a high level of reliability.
Would you like to evaluate the reliability of an AI solution for your company?
Check out our comparative benchmark AI solutions for the industry or speak with our team to discover how Ask for the moon can secure your most critical AI use cases.
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