AI has made SEO teams ambitious about what they can automate. Tasks that previously required engineering support can now be solved with the help of Claude or ChatGPT.

That’s exciting, but it also creates a new problem: thinking you can automate everything. In modern language, that often comes down to one question: Should we build or buy this new tool?

This build-versus-buy dilemma has never been simple, and AI has made it even more complicated. The challenge goes beyond cost. It involves security, maintenance, data access, internal capabilities, workflow fit, and whether a custom solution will remain maintainable, reliable, and useful six months from now.

How AI lowers the barrier to building

AI has lowered the barrier to experimentation. Even without technical knowledge, you can now create a custom GPT, build a workflow, connect data sources, or create an internal AI assistant.

But that doesn’t mean the same person can build and maintain a tool that will remain reliable over the next few years.

In most cases, AI can help SEO teams analyze data, identify patterns, summarize information, and recommend actions. It can save a lot of time, and teams that ignore AI are clearly falling behind.

But, at least for now, AI isn’t doing truly creative work in the same way humans do. It works from existing patterns and predicts likely outputs. That may change in the future.

AI also comes with hidden costs. Internally built tools are often treated as free because the invoice usually doesn’t sit with the SEO team. But that doesn’t mean token usage, API calls, infrastructure, engineering time, security reviews, and maintenance don’t cost money.

We are already seeing this effect. Reuters has described it as “corporate AI sticker shock,” with companies struggling to forecast usage-based AI costs. TechCrunch also reported that Uber introduced AI spending caps after blowing through its annual AI budget in four months.

Today, marketing teams aren’t the heaviest AI users, especially compared with engineering teams. But that can change quickly.

And when usage grows, the bills will grow too. That will naturally make companies ask which AI tools and AI-powered workflows create value and which ones only consume budget.

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Start by defining what you need

Before deciding whether to build or buy, SEO teams need to define what they really need.

Different ways to use AI and automation

Many teams group these solutions together, but they vary significantly in cost, complexity, and maintenance requirements.

• A custom tool: A more complex internal system that usually needs engineering support. It is often more about automation, but it can have an artificial intelligence aspect.
• A custom workflow: A repeatable process built with different tools, such as a custom GPT, Claude project, spreadsheet, reporting template, and so on. It often includes automation, for example, a scheduled task in an AI tool, and it usually has an artificial intelligence layer.
• A custom layer on top of SaaS: Using data from existing tools and shaping it into your own reporting, prioritization, or recommendation workflow.
• A true AI agent: A system that can take more autonomous actions. For example, it can scan your Slack and follow up with people you are still waiting on.

These aren’t the same, but people often label them incorrectly. Calling everything an “AI agent” creates confusion and can lead to wrong estimates about cost and complexity.

Look for repetitive, context-rich tasks

We’re still experimenting. Most of what our team has built focuses on daily tasks that require a lot of manual work.

For example, we’ve created a custom GPT that evaluates whether our content matches our personas and their pain points. The goal is not to replace the human copywriter or reviewer. It is to determine whether a piece remains generic and whether a few additions can make it more relevant.

We are also using AI for translations, monthly reporting, and a weekly summary that combines meeting notes, Slack, and Jira, and helps me see whether I have missed adding a task to Jira or where I still need to follow up.

One of our latest workflows transforms recorded internal meetings into organized landing page briefs.

These types of tasks are good candidates for AI-powered custom workflows because they rely on internal context, repeatable processes, and company-specific knowledge.

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Not everything should be built

One example from our team was a prompt tracking tool that my colleague vibe-coded. It worked well as a starting point. But the data presentation was not perfect, and it was hard to create a trend graph without additional manual steps.

Soon, it became a maintenance burden because every external change in any of the LLM tools required fixes, for which we needed engineering help.

The real issue was reliability. For AI visibility and prompt tracking, we needed consistent data in one place, presented in a way we could analyze over time. That is why we moved to a specialized platform like Peec AI instead of continuing to maintain our own version.

That experiment was still valuable. It helped us understand the problem, the complexity, and the features we actually needed from an external vendor.

And this is one of my pieces of advice: whether you want to build a tool internally or buy one, always test what is already available on the market. Only then will you really understand what you actually need. You may think you need 10 features, only to realize you use only three.

For business-critical tools such as rank and AI visibility tracking, and website crawling, small SEO teams without dedicated technical support should usually be careful about building from scratch. If the data is fundamental to decision-making, reliability should be your main decision factor.

Use AI where your data already lives

Buy the crawler, rank tracker, or AI visibility platform. Then focus your internal efforts on connecting data from these tools to custom information, such as your GA and GSC accounts or even CRM data. Once connected, create reports that combine all these sources and enable you to analyze everything in one place.

MCP connections are also worth considering. The Model Context Protocol is an open standard for connecting AI applications to external systems, data sources, tools, and workflows. With MCP servers, you can analyze data from your primary tools directly using AI, taking your current workflows to the next level.

This doesn’t mean you’re required to learn how to code. But they need to know enough to ask the right questions.

If a tool connects to an internal knowledge base, customer data, or proprietary research, you should be aware that this could pose a security risk. And it might turn out that it is better for the company to dedicate an engineer to support you rather than risk exposing sensitive information.

You should also understand what the final cost will be for your company when you decide to go with a custom tool. Custom tools aren’t free just because the invoice doesn’t sit with SEO. Engineering time, security reviews, AI tokens, and API usage are all part of the cost.

Before asking leadership for a tool, SEO teams should be able to explain the workflow problem, the expected value, the cost of buying compared with the estimated cost of building, and what might happen if nothing is done.

The best requests don’t start with: “We need this tool.”

They start with: “Here is the problem, here is why it matters, here is what we’ve tested, and here is the best way we think we can solve it.”

How to prioritize what to build first

There’s no single prioritization matrix that will work for every situation.

A website crawler, a content evaluation tool, a report builder, or a competitive intelligence system can’t be judged by the same criteria.

If you are in a situation where you think you need more than one tool, start by mapping your current workflow and what your ideal situation looks like.

Once you do that, the patterns will be clear. Often, your strongest priorities will fall into two groups.

The first are tools that can support revenue creation. SEO teams are usually part of the marketing organization, and marketing is expected to bring visibility or leads. If a tool can help identify content opportunities, improve conversion rates, increase AI visibility, or surface gaps versus competitors, it can be seen as a priority.

The second group is workflows and tools that can help you minimize repetitive manual work. This category may not create revenue, but it will give your team time back to focus on more strategic work.

Don’t forget that quick wins also matter. Stakeholders don’t want to wait three months before seeing results. A smaller project that can bring value in three weeks will help you build trust and make it easier to get support for bigger initiatives.

Cross-team value should also be part of your decision.

SEO problems are often not just problems for your team. Competitive intelligence, for example, matters to PPC, ABM, content, product marketing, and sales, too. If several teams share the same pain, the business case becomes stronger.

So don’t be afraid to act as a cross-team synchronization layer when needed. Talk to the same teams you have already worked with, and try to understand their workflows and pain points, and where your needs overlap.

And remember, the best tool is not always the most ambitious one. Starting with something small is often the smartest move.

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Good decisions start with proper scoping

AI has made it easier to build, but that doesn’t mean you don’t need to think about what really needs to be built.

Before deciding whether to build, buy, or customize, take the time to properly scope the work.

• Understand the problem, the value you expect, who will use the solution, and who will maintain it after launch.
• Talk to your team and other teams. Determine whether this is only an SEO problem or a wider business problem.
• Don’t build because AI makes it possible. Don’t buy because a demo looks impressive.

Without proper scoping, you can end up with an expensive SaaS tool that doesn’t fit your workflow or an internal tool your team can’t maintain.

Always think first. Dedicate enough time to scope properly. Then decide whether to build, buy, or customize.