The Real Power of Custom GPTs Isn’t the Tech
▼ Summary
– Google’s 2017 transformer architecture laid the foundation for modern GPTs, which are now integral to SEO and digital marketing work.
– The democratization of technology has lowered barriers, allowing non-coders to build custom GPTs, but strategic application and problem-solving are more critical than technical skills alone.
– Building custom GPTs effectively requires defining the audience, identifying problems, scoping realistically, testing outputs, and aligning with business objectives, not just the technical creation.
– The author’s case study shows that starting with a smaller, focused GPT on accessibility provided valuable lessons in scope and validation before tackling a more complex cultural content GPT.
– SEOs should adopt a product-thinking mindset by validating problems, prototyping, iterating with feedback, and using AI as assistants within structured workflows to enhance strategic decision-making.
The true strength of custom GPTs lies not within their technical capabilities but in the strategic thinking behind their creation. While the underlying transformer architecture introduced by Google in 2017 laid the groundwork for today’s generative AI tools, their real impact emerges from how thoughtfully they are designed to meet specific business objectives and user needs. For SEO professionals and digital marketers, these tools offer powerful ways to enhance keyword research, content ideation, and data analysis, but their lasting value comes from aligning them with clear goals rather than using them as shortcuts.
Democratizing Technology and Raising Expectations
In the past, developing specialized tools required coding skills or reliance on developers, leading to long delays and limited flexibility. Today, technology democratization allows almost anyone with curiosity to experiment with building custom GPTs. This accessibility has raised expectations, users now demand tools that are intuitive, genuinely useful, and efficient. While technical knowledge remains important, it’s the application of that knowledge that truly counts. Marketers must ask whether they are solving real problems and creating workflows that support business priorities.
Why the Building Process Holds the Key
Creating a custom GPT involves more than writing instructions and saving the model. The real work happens before and after, defining the target audience, identifying core challenges, setting realistic scope, testing outputs, and ensuring alignment with broader goals. This mirrors effective marketing principles: understanding user needs and designing solutions that address them directly. For example, international SEO efforts often overlook cultural relevance and digital accessibility. Custom GPTs provide a way to explore these areas without needing programming skills, turning complex challenges into manageable projects.
A Case Study in Adaptation
Initially, the goal was to develop a GPT that generated culturally relevant content ideas for English-speaking markets like the UK, US, Canada, and Australia. Engaging global audiences requires more than translation, it demands linguistic accuracy and contextual awareness, reflecting the broader shift in search toward personalized, context-driven results. However, the project’s scope quickly expanded beyond initial expectations. Rather than abandoning the effort, the approach shifted toward creating a minimum viable product focused on a more consistent challenge: digital accessibility.
The accessibility GPT was designed to identify issues, recommend inclusive language, and support advocacy across teams. It adapted outputs for different roles, helping SEOs, marketers, and project managers apply it within their daily responsibilities. Starting with this project provided valuable lessons in scope management and validation. Because accessibility standards are more uniform than cultural nuances, it became easier to refine prompts and test role-specific outputs. Feedback from colleagues and accessibility experts highlighted inconsistencies and improved the tool’s presentation, reinforcing that accessibility involves how information is shared, not just technical elements like alt text.
Returning to the cultural content GPT afterward brought clearer expectations and a stronger process. The key insight was that value emerges not only from the final product but from the iterative cycle of building, testing, and refining.
Navigating Risks and Challenges
Underestimating time and scope emerged as the primary hurdle, addressed by revisiting plans and starting with smaller, more manageable goals. Platform limitations, including ongoing model updates, AI fatigue, and the risk of hallucinations, required precise prompts and a human-in-the-loop approach. OpenAI acknowledges that hallucinations are mathematically inherent, so treating GPTs as assistants rather than replacements remains essential. Collaboration introduced additional complexity, relying on colleagues’ availability for feedback. Staying flexible and allowing extra time ensured their crucial insights could be incorporated, reinforcing that strategic building focuses on learning and adaptation rather than chasing perfection.
Applying a Product Mindset
SEOs can adopt product thinking to design workflows that are both practical and forward-looking. This involves validating whether a problem truly requires an AI solution, defining specific use cases, and identifying the intended users. Prototyping and testing with clear, role-based instructions, such as having the GPT act as a content strategist or project manager, helps refine outputs. Involving colleagues and subject-matter experts early challenges assumptions and strengthens results. Staying agile in response to platform changes and evolving needs ensures tools remain relevant and effective.
Practical Uses for SEO Professionals
Custom GPTs add value in specific scenarios, especially when integrated into structured workflows with human oversight. Useful applications include analyzing campaign data, assisting with international competitor analysis, supporting content ideation for global audiences, clustering keywords, identifying internal linking opportunities, and drafting documentation. These tools are not meant to replace established platforms or human expertise but to serve as assistants that free up time for deeper strategic thinking.
Adopting Product Thinking in Daily Work
Even without building a GPT, SEOs can apply this mindset by framing challenges strategically, asking who the end user is, what they need, and where their experience falls short. Designing repeatable processes instead of one-off fixes, testing and refining tactics like prototypes, and collaborating across teams with UX, development, and content specialists all contribute to more impactful outcomes. Success metrics should reflect business impact, such as qualified traffic, conversions, or process improvements. Using AI strategically means supporting workflows and highlighting blind spots while maintaining human oversight to ensure accuracy and relevance.
Ultimately, innovation stems not from the technology itself but from how we choose to implement it. We are entering an era of closer human-machine collaboration, presenting SEOs with the opportunity to evolve from tactical executors to strategic thinkers. This means asking sharper questions, testing hypotheses, designing adaptable workflows, and creating solutions that respond to real-world needs, focusing on delivering solutions, not just completing tasks.
(Source: Search Engine Journal)
