Recent legal disputes between major rights holders and AI providers have pushed one issue to the front of the board agenda: whether generative AI tools are built, trained, and operated in ways that expose businesses to intellectual property risk. For SMEs in Greater Barcelona, this is not an abstract media story. It is a procurement, compliance, and operating risk question that should be assessed before AI tools are rolled out across teams.
Many companies are already using AI for drafting, research, coding, translation, customer support, and internal knowledge work. The problem is that vendor claims about safety, lawful use, and model governance are often broader than the contractual protections they actually provide. That gap matters when your business depends on branded content, confidential information, software assets, or regulated workflows.
Why this legal dispute matters to business buyers
When music companies or other rights holders accuse an AI provider of using protected material without permission, the business implication is straightforward: the legal and commercial foundations of the tool may be contested. Even if your company is not directly involved in the dispute, your use of the tool can still create downstream risk.
That risk may appear in several forms: uncertain ownership of outputs, weak indemnity terms, challenges around commercial reuse, reputational exposure, or questions about how your own data is handled during prompts, training, fine-tuning, or retrieval. A tool can be useful and still be poorly governed from an enterprise risk perspective.
The main IP and compliance questions SMEs should ask
Before approving a generative AI platform, leaders should ask five practical questions. First, what does the vendor say about training data sources and rights management? Second, who owns the outputs, and are there restrictions on commercial use? Third, does the vendor offer meaningful indemnities, or are protections narrow and conditional? Fourth, can your prompts or uploaded files be reused for model improvement? Fifth, what controls exist for deletion, retention, access, and auditability?
These questions should not be left only to IT. Legal, procurement, security, operations, and business owners all have a stake in the answer. In smaller organisations, the lack of formal review often means tools spread informally before anyone has assessed contractual or governance exposure.
Where companies often underestimate the risk
One common mistake is to focus only on output quality. A tool may generate good text or code while creating hidden obligations in its terms of use. Another mistake is assuming that a well-known brand automatically means low legal risk. Market visibility does not replace due diligence.
Companies also underestimate how quickly shadow AI becomes embedded in daily work. Marketing teams may use AI for campaign assets, HR teams for job descriptions, product teams for documentation, and developers for code suggestions. If those uses happen without policy, vendor review, and approved workflows, the business may lose control over what data is shared and what rights are attached to the outputs.
How to structure an AI vendor audit
A practical audit should review four areas. The first is contractual risk: licensing terms, ownership clauses, indemnities, liability caps, jurisdiction, and termination rights. The second is data governance: prompt handling, training opt-out, retention, subprocessors, and access controls. The third is operating fit: who can use the tool, for which use cases, with what approval rules. The fourth is compliance evidence: policies, certifications where relevant, documentation quality, and escalation paths.
For many SMEs, the right first step is a focused digital audit that maps current tool usage, supplier exposure, and governance gaps. The objective is not to block AI adoption. It is to separate acceptable use from unmanaged risk and to create a basis for confident scaling.
What leaders in Greater Barcelona should do next
For management teams in Greater Barcelona, the immediate priority is visibility. Identify which AI tools are already in use, which teams rely on them, and whether any business-critical content, code, or confidential data has been processed through unapproved platforms. This can usually be done faster than companies expect if ownership is clear and the scope is limited to the most material workflows.
Then classify use cases into three groups: low risk, restricted, and prohibited. Low-risk uses may include generic drafting without sensitive inputs. Restricted uses may include customer-facing content, contract support, or code generation that needs human review and approved tools. Prohibited uses should include any processing that conflicts with confidentiality obligations, regulated data handling, or unresolved IP exposure.
Build policy before scale becomes a problem
If your company wants the productivity gains of generative AI, governance needs to come before broad deployment. That means an approved vendor list, standard review criteria, clear user guidance, and named accountability across legal, IT, and business functions. It also means updating procurement processes so AI-specific IP and data terms are checked before contracts are signed.
The current wave of legal action around AI should be treated as a practical warning for buyers. Businesses do not need to predict the outcome of every dispute. They do need to understand that vendor IP uncertainty can become an operational and contractual issue for customers. The safest position is not to wait for headlines to settle, but to audit tools already in use and put disciplined controls around the next phase of adoption.