When Google introduced the transformer architecture in its 2017 paper “Attention Is All You Need,” few realized how much it would help transform digital work. Transformer architecture laid the foundations for today’s GPTs, which are now part of our daily work in SEO and digital marketing.
Search engines have used machine learning for decades, but it was the rise of generative AI that made many of us actively explore AI. AI platforms and tools like custom GPTs are already influencing how we research keywords, generate content ideas, and analyze data.
The real value, however, is not in using these tools to cut corners. It lies in designing them intentionally, aligning them with business goals, and ensuring they serve users’ needs.
This article is not a tutorial on how to build GPTs. I share why the build process itself matters, what I have learned so far, and how SEOs can use this product mindset to think more strategically in the age of AI.
From Barriers To Democratization
Not long ago, building tools without coding experience meant relying on developers, dealing with long lead times, and waiting for vendors to release new features. That has changed slightly. The democratization of technology has lowered the entry barriers, making it possible for anyone with curiosity to experiment with building tools like custom GPTs. At the same time, expectations have necessarily risen, as we expect tools to be intuitive, efficient, and genuinely useful.
This is a reason why technical skills still matter. But they’re not enough on their own. What matters more, in my opinion, is how we apply them. Are we solving a real problem? Are we creating workflows that align with business needs?
The strategic questions SEOs should be asking are no longer just “Can I build this?,” but:
- Should I build this?
- What problem am I solving, and for whom?
- What’s the ultimate goal?
Why The Build Process Matters
Building a custom GPT is straightforward. Anyone can add a few instructions and click “save.” What really matters is what happens before and after: defining the audience, identifying the problem, scoping the work realistically, testing and refining outputs, and aligning them with business objectives.
In many ways, this is what good marketing has always been about: understanding the audience, defining their needs, and designing solutions that meet them.
As an international SEO, I’ve often seen cultural relevance and digital accessibility treated as afterthoughts. OpenAI offered me a way to explore whether AI could help address these challenges, especially since the tool is accessible to those of us without any coding expertise.
What began as a single project to improve cultural relevance in global SEO soon evolved into two separate GPTs when I realized the scope was larger than I could manage at the time.
That change wasn’t a failure, but a part of the process that led me toward a better solution.
Case Study: 2 GPTs, 1 Lesson
The Initial Idea
My initial idea was to build a custom GPT that could generate content ideas tailored to the UK, US, Canada, and Australia, taking both linguistic and cultural nuances into account.
As an international SEO, I know it is hard to engage global audiences who expect personalized experiences. Translation alone is not enough. Content must be linguistically accurate and contextually relevant.
This mirrors the wider shift in search itself. Users now expect personalized, context-driven results, and search engines are moving in that same direction.
A Change In Direction
As I began building, I quickly realized that the scope was bigger than expected. Capturing cultural nuance across four different markets while also learning how to build and refine GPTs required more time than I could commit at that moment.
Rather than leaving the project, I reframed it as a minimum viable product. I revisited the scope and shifted focus to another important challenge, but with a more consistent requirement – digital accessibility.
The accessibility GPT was designed to flag issues, suggest inclusive phrasing, and support internal advocacy. It adapted outputs to different roles, so SEOs, marketers, and project managers could each use it in relevant ways in their day-to-day work.
This wasn’t giving up on the content project. It was a deliberate choice to learn from one use case and apply those lessons to the next.
The Outcome
Working on the accessibility GPT first helped me think more carefully about scope and validation, which paid off.
As accessibility requirements are more consistent than cultural nuance, it was easier to refine prompts and test role-specific outputs, ensuring an inclusive, non-judgmental tone.
I shared the prototype with other SEOs and accessibility advocates. Their feedback was invaluable. Although their feedback was generally positive, they pointed out inconsistencies I hadn’t seen, including how I described the prompt in the GPT store.
After all, accessibility is not just about alt text or color contrast. It’s about how information is presented.
Once the accessibility GPT was running, I went back to the cultural content GPT, better prepared, with clearer expectations and a stronger process.
The key takeaway here is that the value lies not only in the finished product, but in the process of building, testing, and refining.
Risks And Challenges Along The Way
Not every risk became an issue, but the process brought its share of challenges.
The biggest was underestimating time and scope, which I solved by revisiting the plan and starting smaller. There were also platform limitations – ongoing model development, AI fatigue, and hallucinations. OpenAI itself has admitted that hallucinations are mathematically unavoidable. The best response is to be precise with prompts, keep instructions detailed, and always maintain a human-in-the-loop approach. GPTs should be seen as assistants, not replacements.
Collaboration added another layer of complexity. Feedback loops depended on colleagues’ availability, so I had to stay flexible and allow extra time. Their input, however, was crucial – I couldn’t have made progress without them. As none of the these are under my control, I could only keep on top of developments and do my best to handle all of them.
These challenges reinforced an important truth: Building strategically isn’t about chasing perfection, but about learning, adapting, and improving with each iteration.
Applying Product Thinking
The process I followed was similar to how product managers approach new products. SEOs can adopt the same mindset to design workflows that are both practical and strategic.
Validate The Problem
Not every issue needs AI – and not every issue needs solving. Identify and prioritize what really matters at that time and confirm whether a custom GPT, or any other tool, is the right way to address it.
Define The Use Case
Who will use the GPT, and how? A wide reach may sound appealing, but value comes from meeting specific needs. Otherwise, success can quickly fade away.
My GPTs are designed to support SEOs, marketers, and project managers in different scenarios of their daily work.
Prototype And Test
There is real value in starting small. With GPTs, I needed to write clear, specific instructions, then review the outputs and refine.
For instance, instead of asking the accessibility GPT for general ideas on making a form accessible, I instructed it to act as an SEO briefing developers on fixes or as a project manager assigning tasks.
For the content GPT, I instructed the GPT to act as a UK/ U.S. content strategist, developing inclusive, culturally relevant ideas for specific publications in British English/Standard American.
Iterate With Feedback
Bring colleagues and subject-matter experts into the process early. Their insights challenge assumptions, highlight inconsistencies, and make outputs more robust.
Keep On Top Of Developments
AI platforms evolve quickly, and processes also need to adapt to different scenarios. Product thinking means staying agile, adapting to change, and reassessing whether the tools we build still serve their purpose.
The roll-out of the failed GPT-5 reminded me how volatile the landscape can be.
Practical Applications For SEOs
Why build GPTs when there are already so many excellent SEO tools available? For me, it was partly curiosity and partly a way to test what I could achieve with my existing skills before suggesting a collaboration for a different product.
Custom GPTs can add real value in specific situations, especially with a human-in-the-loop approach. Some of the most useful applications I have found include:
- Analyzing campaign data to support decision-making.
- Assisting with competitor analysis across global markets.
- Supporting content ideation for international audiences.
- Clustering keywords or highlighting internal linking opportunities.
- Drafting documentation or briefs.
The point is not to replace established tools or human expertise, but to use them as assistants within structured workflows. They can free up time for deeper thinking, while still requiring careful direction and review.
How SEOs Can Apply Product Thinking
Even if you never build a GPT, you can apply the same mindset in your day-to-day work. Here are a few suggestions:
- Frame challenges strategically: Ask who the end user is, what they need, and what is broken in their experience. Don’t start with tactics without context.
- Design repeatable processes: Build workflows that scale and evolve over time, instead of one-off fixes.
- Test and learn: Treat tactics like prototypes. Run experiments, refine based on results. If A/B testing isn’t possible, as it often happens, at least be open to making any necessary adjustments where necessary.
- Collaborate across teams: SEO does not exist in isolation. Work with UX, development, and content teams early. The key is to find ways to add value to their work.
- Redefine success metrics: Qualified traffic, conversions, and internal process improvements matter in AI times. Success should reflect actual business impact.
- Use AI strategically: Quick wins are tempting, but GPTs and other tools are best used to support structured workflows and highlight blind spots. Keep a human-in-the-loop approach to ensure outputs are accurate and relevant to your business needs.
Final Thought
The real innovation is not in the technology itself, but in how we choose to apply it.
We are now in the fifth industrial revolution, a time when humans and machines collaborate more closely than ever.
For SEOs, the opportunity is to move beyond tactical execution and start thinking like product strategists. That means asking sharper questions, testing hypotheses, designing smarter workflows, and creating solutions that adapt to real-world constraints.
It is about providing solutions, not just executing tasks.
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