Data is AI's foundation

GenAI APIs now let any marketer build chatbots, write copy, or generate creative imagery in minutes. But if every competitor can access the same underlying models, the difference between a mediocre martech stack and a market-beating one is what you feed those models.

As Aaron Agius, co-founder and managing director of agency Louder Online, bluntly puts it: “Data is the oxygen of digital marketing in 2025. If you can’t breathe it, you’re dead.”

When everyone has the same tools, what sets you apart?

AI’s algorithms are rapidly commoditizing, and democratizing. High-performance foundation models are available to all, at a price point that remains very manageable.

In that environment, investors and operators agree that unique data creates the moat. A 2025 TechCrunch survey of 20 enterprise-focused VCs underscores this point: More than half said the single biggest edge for an AI company is the rarity or quality of its proprietary data.

Put simply—algorithms can be rented, but the right data must be earned or built.

Why proprietary beats public

Public data can train a model to draft a generic email, but it can’t reveal which of your lapsed loyalty members are primed to repurchase next week. Those insights live in CRM histories, transaction logs, and decades of campaign results—assets only your organization owns.

Of course, having access to that wealth of first-party data is only beneficial if you have the right tools in place to activate it. That’s where ID Graphs and data clean rooms come into play.

Letting AI learn from your brand or agency’s own data

Connecting GenAI models to your owned data set is one way to add value. For example:

• Fine-tuning & embeddings: Large language or vision models are calibrated on your brand manuals, support transcripts, and campaign archives, so outputs stay on-brand and informed.
• Retrieval-augmented generation (RAG): Instead of ‘hallucinating,’ the model pulls facts from your proprietary knowledge base at answer time.
• Feedback loops: Each campaign’s performance data flows back to the model, sharpening predictions for your exact audience over time.

California Management Review describes a three-step progression or evolution for companies who want to take available LLMs and nudge them into bespoke territory:

• Buyer: License an off-the-shelf model like ChatGPT or Microsoft Copilot.
• Booster: Infuse that model with proprietary data.
• Builder: Create domain-specific models anchored in exclusive datasets.

Because today’s largest models are open via API, CMR argues, sustained advantage now comes from moving beyond “buyer” status to become a booster or builder powered by unique data. The payoff is an insight engine competitors can’t replicate.

However, taking an off-the-shelf product like ChatGPT and training it on your proprietary data only gets you as far as the reach of that data. It's also hugely labor intensive. This makes purpose-built AI solutions that come with their own data advantages a tempting proposal.

The Marketing Cloud Advantage

Situated within the Stagwell challenger network, The Marketing Cloud benefits from proprietary audience, creative, and performance data spanning dozens of industries.

Every AI feature—predictive media optimization, dynamic content generation, real-time reputation monitoring—draws on signals no generic platform can access. As martech vendors merge and model access equalizes, that data foundation only grows more valuable.

Learn more about The Marketing Cloud Platform and how it can help your teams drive impact across market research, comms, creative, and media.