AI tools are everywhere — and you’re probably under pressure to pick one. But behind the buzzwords lies a real dilemma: should your business go open-source or stick to a proprietary platform?

It’s not just an IT question. It’s a legal, commercial, and strategic decision — and you’re expected to weigh in.

Here’s a breakdown of the key trade-offs so you can make informed, risk-savvy recommendations that align with your company’s goals.

Strategic control vs convenience: who’s driving?

Proprietary tools (like ChatGPT or Gemini) come ready-to-use with polished interfaces, dedicated support, and shiny feature sets. For time-starved legal teams, they’re tempting — no need to fiddle with settings or patchwork integrations.

But convenience can come at a cost. With proprietary tools:

• You’re locked into one vendor’s roadmap and pricing.
• You may have limited visibility into how the model works.
• Switching later can be painful (and expensive).

Open-source alternatives offer more flexibility and transparency — but usually need more technical firepower to implement. Think of it as the difference between buying a car vs building your own: the DIY route gives you control, but you’ll need capable mechanics on hand.

What to consider: Does your business have the technical resources to support open-source? And is it willing to accept more hands-on work in exchange for autonomy?

IP and copyright: what’s under the hood?

AI models are only as good as the data they’re trained on. And that raises tricky questions:

• What if your tool was trained on copyrighted material?
• Can you safely commercialise outputs created by the AI?
• What happens if someone sues over IP infringement?

Proprietary providers often include liability disclaimers and limited warranties. That means your company could still be on the hook. Open-source models can be even murkier — with little to no protection unless you layer in your own safeguards.

The Generative AI Outlook Report flags this as a live risk: as IP disputes over training data escalate, businesses need clarity on both upstream (training data) and downstream (output ownership) issues .

What to consider: Ask vendors for documentation on training data and IP protections. For open-source, review the licensing terms carefully — some restrict commercial use.

Data privacy and compliance: are your obligations met?

Under UK and EU data protection laws, you’re responsible for how personal data is processed — even by third-party tools.

Proprietary models may process inputs on external servers, often outside the UK or EU. That raises questions around international data transfers, vendor due diligence, and whether outputs can be traced or deleted if needed.

Open-source models can be hosted locally, reducing these risks — but again, that means more internal effort.

The EU AI Act and GDPR both place a premium on transparency and accountability. If you can’t explain how your AI tool handles personal data, that’s a red flag.

What to consider: Map out data flows for both input and output. Ensure you have a legal basis for processing, especially if the tool uses cloud infrastructure. And check if the model allows for things like data deletion or output explainability.

Transparency and explainability: can you trust what it’s telling you?

Business users love a sleek chatbot. But if that bot gives a wrong answer, can you explain why?

Open-source models (when truly open) offer a window into their architecture and training. That’s helpful for assessing bias, making audit trails, and complying with regulatory obligations.

Proprietary models? Not so much. Many function as black boxes, which can be risky in regulated sectors or when decisions affect individuals’ rights.

What to consider: Can you trace how an answer was generated? Is the model auditable? If your business is in a high-risk industry (e.g. financial services, healthcare), these questions aren’t optional.

Long-term value: where’s this heading?

This isn’t just a legal call — it’s a business strategy question. The EU is pushing for open, ethical AI development, and has invested heavily in open-source AI ecosystems . For businesses wanting to align with future regulatory trends, open-source may feel more sustainable.

On the flip side, proprietary models are evolving fast, often outperforming open-source in certain use cases. And they come with less overhead — a serious factor for lean teams.

Hybrid approaches may offer the best of both: using open-source models customised with proprietary data, hosted securely on private infrastructure. It’s not perfect, but it’s gaining traction.

Final thought: don’t go it alone

You don’t need to have all the answers — but you do need to ask the right questions. Involve your IT, security, and procurement colleagues early. Challenge vendors. Push for documentation.

Whether you go open-source or proprietary, your role is to steer the business toward options that are legally sound, commercially savvy, and future-proofed.

That’s the kind of strategic thinking that gets Legal a seat at the table — and keeps you off the fire-fighting treadmill.

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