Artificial Intelligence (AI) is no longer a futuristic trend — it has become a real and accessible tool for businesses. From customer support and marketing to software development and decision support, AI promises significant gains in efficiency, productivity, and competitiveness.
However, adopting AI without clear criteria can introduce legal, technical, ethical, and reputational risks. In this article, we explain the key precautions companies should take when using AI platforms in their operations.
1. Data: the foundation of everything (and the main risk)
AI is only as good as the data it uses.
Data quality and governance
Incomplete, biased, or outdated data leads to incorrect outcomes.
It is essential to define clear rules regarding:
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Who can access data
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Who can modify it
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How it is validated
Data protection and confidentiality
Sensitive data (customers, employees, contracts, source code) should never be entered into public platforms without proper guarantees.
Always verify:
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Where the data is stored
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Whether it is used to train models
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Data retention and deletion policies
GDPR compliance
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There must be a lawful basis for data processing.
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Apply the principle of data minimisation.
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Pay special attention to automated decisions that affect individuals (e.g. HR, scoring, customer profiling).
2. Legal and regulatory framework (AI Act)
The European Union is implementing the AI Act, which classifies AI systems according to risk.
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Low risk: informational chatbots, internal support
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High risk: recruitment, performance evaluation, credit granting, surveillance
For high-risk use cases, companies must ensure:
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Technical documentation
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Mandatory human oversight
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Decision logging and traceability
Ignoring this framework may result in significant fines and reputational damage.
3. Technological dependency and business continuity
Many companies adopt AI without assessing the dependency it creates.
Avoiding vendor lock-in
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Do not base critical processes on a single platform.
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Prefer solutions that are:
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Based on open APIs
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Portable between providers
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Compatible with European cloud or on-premises environments
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Contingency planning
Key questions to consider:
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What happens if the provider changes pricing?
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What if the service becomes unavailable?
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Is there an operational alternative?
4. Information security
AI introduces new attack surfaces.
Main risks
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Prompt injection
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Data leakage through generated responses
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Insecure or vulnerable code generation
Best practices
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Mandatory human review of:
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Generated code
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Public content
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Customer-facing responses
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Role-based access control
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Logging and auditing of AI interactions
5. People, culture, and accountability
Technology does not replace human responsibility.
Employee training
Users should understand:
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What AI does well
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Where it fails
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How to validate outputs
Avoid the common mistake: “The AI said it, so it must be correct.”
Clear accountability
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Final decisions must always be human.
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Define responsibility for:
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Errors
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Legal impacts
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External communication
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6. Ethics, bias, and reputation
Algorithmic bias
AI can reinforce existing biases in data related to:
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Gender
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Age
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Origin
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Socioeconomic profile
Regular testing and monitoring of results is essential.
Transparency with customers
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Inform users when they are interacting with AI.
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Avoid misleading or overly aggressive automation.
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Protect brand reputation.
7. Precautions by business area
Human Resources
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Never fully automated decisions
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Mandatory explainability
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High legal risk
Customer Support
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Limit chatbot autonomy
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Fast escalation to human agents
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Quality and tone monitoring
Marketing and Sales
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Validate content before publishing
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Avoid false promises or unsubstantiated claims
Software Development
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Review generated code
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Validate licensing
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Mandatory security and quality testing
8. Final recommendations
Before scaling AI usage within your company:
✔ Create an internal AI usage policy
✔ Classify use cases by risk level
✔ Start with pilot projects
✔ Ensure human oversight
✔ Review supplier contracts
✔ Prepare for future audits
Conclusion
Artificial Intelligence can be a powerful driver of business competitiveness, but it is not a magic solution. Real value emerges only when AI is used in a responsible, secure, and strategically aligned way.
