Integrating AI into SEO workflows will… save you lots of time!

AI has been in SEO for years — here is how to use is to your advantage.

At the risk of sounding irritating there’s a certain irony in the current wave of “AI is going to transform SEO” think pieces. Artificial intelligence has been woven into search for a long time. Google’s RankBrain launched in 2015. BERT followed in 2019. Essentially machine learning has been powering search quality, spam detection, and query understanding for the better part of a decade. The models have changed — the relationship between SEO and AI has not.

What has changed is our side of the equation. For years, AI was something search engines did to us — a black box we reverse engineered, adapted to, and optimised for. But now it’s a tool we can use ourselves. And if you’re not using it yet, you should be!

There is the opporunity to integrate AI into nearly every SEO workflow, and the efficiency gains are huge. Some of my clearest perspective on this has actually come from maternity leave, where I’ve been helping a few friends with their SEO strategy on the side (because the prospect of checking out completely at a time when we are seeing such a big shift felt pretty terrifying!). Without the agency we work with at Bloom & Wild around me, I’ve leaned heavily on AI to work efficiently: developing a couple of content plans, running through site audits between nap “schedules”, doing in an hour what used to take much longer. It’s given me a fresh appreciation for just how much of our work can be done faster without sacrificing quality.

Here’s my practical rundown of where AI is making the biggest difference, and how to start integrating it into your own practice. *

*I’ve experiemented with the paid versions of ChatGPT & Claude over the last couple of years and have found the general output from Claude to be much better - particularly on keyword research and tech audits - so I’m now using Claude exclusively.

1) Keyword Research & Clustering

Keyword research has always been more art than science, but the execution has historically been tedious. Export a seed list, pull search volumes, eyeball intent, manually group related terms, argue with yourself about whether “how to care for roses” and “rose flower arranging” deserve separate pages. Repeat for 2,000 rows.

Try this: Feed your raw keyword export into an AI model with explicit instructions — cluster by semantic intent, flag cannibalisation risks, distinguish informational from transactional from navigational, and surface patterns in the long tail you might have missed. What used to take half a day now takes an hour, and the output is often more thorough than what you’d produce manually, simply because the model can hold the full dataset in view simultaneously.

This frees you to focus on the part that actually requires your expertise: strategic prioritisation. Which clusters map to real business value? Where is there a gap between search volume and competition that represents a genuine opportunity? AI handles the grouping and then you make the call on where to focus.

2) Content Briefs & Outlines

A good content brief is the difference between a piece that ranks and one that’s forgotten. Producing them properly — SERP analysis, competitor coverage, PAA questions, schema considerations, internal linking opportunities — is one of the most resource intensive parts of the job when done at scale.

Try this: Treat brief creation as a structured, repeatable prompt rather than a manual process. Pull the SERP, feed in competitor URLs, cross reff People Also Ask data, and ask the model to synthesise it into a brief with a clear angle and content gaps identified. The key is building prompts that encode your knowledge of the brand — their brand, audience, and competitive position. Generic prompts produce generic briefs; specific inputs produce briefs your competitors can’t replicate.

The time saving here compounds quickly. If you’re producing briefs for a full content calendar, AI can cut that work by 60–70% without reducing quality — often improving it.

3) On-Page Optimisation at Scale

Title tags, meta descriptions, header structure, internal linking, schema markup — on-page work is detail oriented and doesn’t scale naturally with human effort. A site with 10,000 pages is a site where most on-page opportunities go unaddressed simply because there aren’t enough hours.

Try this: Scrape your site (using a website crawler like Screaming Frog) and use AI to audit title tag patterns across large site sections and generate variants that better align with search intent. Ask it to review header structures for pages where primary topics are buried. Use it to surface internal linking opportunities that analytics tools flag but that require contextual reading to evaluate properly.

I hope this much is obvious but AI-generated optimisations need your review before they go live. The model doesn’t know about a brand’s stylistic preferences etc. But the drafting and ideation? Let AI do the heavy lifting.

4) Technical SEO Audits

Much like making a moussaka, Technical SEO has always been a labour of love. Time-consuming, fiddly and a bit of a faff… but worth it when you finish.

It’s also the work that’s hardest to explain to stakeholders, most time consuming to execute, and therefore easiest to deprioritise when a team is stretched. Crawl budget issues, log file analysis, JavaScript rendering problems, structured data inconsistencies — the diagnostics are complex, the fixes contentious, and the business cases required to get developers to act can be a project in itself.

Try this: Feed a crawl export into an AI model and ask it to surface anomalies, prioritise issues by likely impact, and group findings into actionable development tickets. What used to require building elaborate spreadsheet logic or custom scripts can now be structured as a prompt. You still need the expertise to interpret and validate the findings — but the time between raw data and clear diagnosis collapses significantly.

The improvements to the communication layer is key too. Technical recommendations die in dev backlogs because they’re not framed in terms engineering teams respond to. AI is genuinely useful for drafting clearer briefs, better prioritisation rationales, and more persuasive cases for why a fix belongs on the sprint.

5) Competitor Analysis

Competitive intelligence in SEO has always suffered from a sampling problem. You can analyse a competitor’s top pages, track their backlink growth, monitor their content output — but getting a comprehensive view of their strategy requires synthesising a lot of disparate data, and doing it continuously is unrealistic without tooling.

Try this: Feed a competitor’s site structure, ranking pages, anchor text profile, and recent publishing patterns into a model and ask it to surface strategic inferences. What topical areas are they building toward? Where are they underinvested? What’s the pattern in their content velocity? The output is informed hypothesis rather than certainty — but informed hypothesis is exactly what strategy is built on, and it’s much faster to generate than manually piecing the picture together.

6) Reporting & Data Analysis

Stakeholders don’t need more data — they need clearer answers to the questions they’re actually asking: Is this working? Why did traffic drop? What should we do next?

Try this: Use AI to interrogate performance data, identify anomalies, and draft the narrative that connects the numbers to the strategy. The ability to translate a complex traffic trend into a clear, confident explanation is one of the highest value things an SEO practitioner delivers. AI makes the first draft of that translation faster to produce and easier to refine — and it’s particularly useful for spotting patterns in large datasets that you might not have the bandwidth to analyse manually every month.

7) Tracking & Optimising for AI Visibility

This one is newer — and arguably the most important workflow to build now.

As AI-generated answers become a primary surface in search (Google’s AI Overviews, ChatGPT, Perplexity, Gemini), whether your content appears in those answers is becoming as strategically significant as where you rank on page one. It requires a different measurement framework than traditional rank tracking.

Try this: Start by auditing how AI tools currently respond to queries relevant to your brands business. Ask ChatGPT, Perplexity, and Gemini the same questions your target audience would ask. Is your brand mentioned? Are competitor sources being cited? What kinds of content are being pulled — long-form guides, data studies, product pages? This gives you a baseline and surfaces the content types that AI models appear to favour in your niche.

From there, track this systematically. Build a recurring process — monthly at minimum — where you run a consistent set of queries across AI platforms and log what’s being surfaced and cited. Tooling is catching up fast, and several platforms are already building AI visibility tracking into their dashboards (SEMRush has made decent steps here), but a structured manual audit is a solid starting point right now if you don’t have a big budget.

On the optimisation side, the signals that appear to drive AI citation are meaningfully different from traditional ranking signals. Clarity of authorship and expertise, structured and direct answers to specific questions, original data and research, and strong brand mentions across trusted third-party sources all seem to carry weight. Think of it as E-E-A-T applied to a surface where the algorithm is a language model rather than a traditional ranking system.

This area will definitely evolve quickly over the next 12–18 months. The SEOs who build measurement frameworks now — even imperfect ones — will have a genuine head start when the standards solidify.

Where things are headed:

The areas where AI will keep delivering the most obvious time savings are those defined by volume and pattern recognition: large-scale audits, keyword clustering, content gap analysis, structural issue identification. These are tasks where human effort was always the constraint.

The areas where your expertise becomes more valuable, not less, are those requiring genuine judgment in ambiguous situations: reading a competitive landscape and making a strategic bet, deciding when a technical fix justifies the development cost, building content strategy that’s differentiated rather than derivative — and increasingly, figuring out how to show up in AI-generated answers in a world where that’s becoming a primary discovery channel.

The underlying craft of SEO hasn’t been automated. What’s been automated is a lot of the labour that used to stand between insight and execution