Brand-Owned AI: Ambition or Overkill? Rethinking Custom Models in Modern Marketing

By Jason Goth, Chief Technology Officer and partner at Credera

• Building custom AI models is costly and often unnecessary for most marketing teams.
• Alternatives like prompt engineering and data embeddings offer high impact at lower cost.
• Fine-tuning existing LLMs can tailor brand voice without full model development.
• Strategic AI adoption outperforms custom builds in ROI and scalability for marketers.

As generative AI continues to reshape the landscape of modern marketing, a compelling question has emerged for brands: Should we build our own proprietary AI models? The allure is undeniable with the promise of unparalleled control over data, hyper-personalized customer experiences, and an impeccably consistent brand voice. 

Yet, beneath the surface of this ambition lies a crucial question: Is building a custom model a truly smart investment, or an expensive distraction for most marketing organizations?

The Myth of DIY AI Models

The narrative around building custom AI models often overlooks the immense complexities and resource demands involved. Consider the example of DeepSeek, a model widely cited for its relatively low development cost of just under $6 million compared to behemoths like ChatGPT-4 or Google Gemini Ultra. 

While impressive, this figure is often misleading. Even at this “lower” end, such an investment represents:

• Significant Compute Resources: Training large language models (LLMs) requires immense computational power, often involving thousands of GPU hours. Even cloud-based solutions incur substantial ongoing costs for compute and storage.
• Elite Talent: Developing cutting-edge AI models demands a team of PhD-level data scientists, machine learning engineers, and specialized researchers—talent that commands premium salaries and is scarce in the market.
• Extensive Data Curation: The quality and quantity of data are paramount. Sourcing, cleaning, labeling, and managing massive, high-quality datasets for training is a monumental task in itself, adding significant cost and time.
• Operational Overhead: Beyond development, there are ongoing costs for maintenance, updates, security, and integration with existing systems.

For most marketing teams, the idea of replicating such an endeavor, even at DeepSeek’s scale, is simply not feasible or justifiable. The reality is that a $5-7 million (or significantly more) investment in a custom AI model is rarely justifiable for typical marketing use cases. Do brands truly need a custom-built LLM to write better email subject lines, generate tone-matched calls-to-action, or personalize product recommendations? In most cases, the answer is a resounding no.

Marketing automation, personalization, and content generation can be significantly enhanced by AI without the need for ground-up model development. The perceived need for a custom model often stems from a misunderstanding of what’s truly required to achieve specific marketing objectives.

Smart Alternatives to Full Custom Models

The good news for marketing executives is that achieving powerful AI-driven marketing outcomes doesn’t necessitate building a custom model from scratch. Several more accessible and cost-effective alternatives offer similar benefits:

1. Prompt Engineering Excellence

Mastering the art and science of prompt engineering—crafting precise and effective instructions for existing LLMs—can unlock remarkable capabilities. By providing clear guidelines on brand voice, tone, audience, and desired output, marketers can achieve highly customized and on-brand content.

2. Embedding Strategies with Proprietary Data

Rather than retraining an entire model, brands can leverage embedding techniques. This involves creating numerical representations (embeddings) of their proprietary data (e.g., customer interactions, product descriptions, brand guidelines) and feeding them into pre-trained LLMs. This allows the model to draw upon specific brand knowledge and voice without the extensive cost of full retraining.

3. Leveraging Third-Party, Brand Voice-Focused Platforms

The market is rapidly evolving with specialized AI platforms designed specifically for marketing. These tools often come with built-in functionalities for brand voice consistency, content generation, and personalization, offering affordable and plug-and-play solutions that solve common marketing challenges. Consider the innovative marketing automation uses of these platforms. Examples include platforms that specialize in SEO content generation, marketing copywriting, or advanced customer segmentation. 

4. Fine-tuning Existing LLMs

For more specific needs, fine-tuning a smaller, pre-trained LLM on a brand’s unique dataset can deliver impressive results. This process is significantly less resource-intensive than training from scratch and allows the model to adapt to a brand’s specific language, style, and domain expertise.

Checklist for Deciding When Custom AI Makes Strategic Sense:

Before you consider building a custom AI model, ask yourself:

• What problem are we truly trying to solve? Define the specific marketing challenge with clarity.
• Can it be done with existing tools? Explore the full potential of prompt engineering, embedding strategies, fine-tuning, and specialized third-party marketing AI platforms.
• What’s the actual ROI? Quantify the potential benefits against the substantial investment required for custom development, including ongoing maintenance and talent acquisition.

Ultimately, the generative AI revolution offers unprecedented opportunities for marketers. However, the path to leveraging this power effectively does not necessarily lead through building a custom AI model. For the vast majority of brands, the focus should be on strategic adoption and optimization of existing AI capabilities. This is key for AI for marketing.

By adopting a pragmatic and strategic approach, marketing leaders can harness the transformative power of AI to drive personalization, efficiency, and creativity without falling victim to the allure of unnecessary and costly custom development.

Jason Goth is chief technology officer and partner at Credera. With over 25 years of experience, Jason plays an important role helping clients deliver complex technology solutions, modernize legacy environments, and adopt emerging technology. Jason works across all of Credera’s technology teams to align capabilities with market need and ensures that Credera’s resources deliver innovative solutions with high quality and velocity. Jason’s additional responsibilities at Credera include driving awareness of Credera’s capabilities, approach, and results.