Generative Engine Optimization: The Shift in SEO Strategies

What Changed and Why It Matters

The shift from traditional search rankings to AI-generated responses has fundamentally altered how brands pursue online visibility. By 2026, Generative Engine Optimization (GEO) has moved from an experimental concept to the dominant framework, with UK enterprises now measuring success by whether they appear as cited sources inside outputs from ChatGPT, Google AIO, and DeepSeek rather than by their position in blue-link results.

Industry sentiment tracked a sharp arc between 2024 and 2026. Early skepticism gave way to near-universal acceptance that ranking on a traditional results page is no longer sufficient on its own. AI visibility has become the primary KPI for forward-thinking organizations, and the pressure to earn cited-source status inside generative responses is reshaping budgets, workflows, and tool choices across agencies and in-house teams alike.

The problem is that most established platforms were not built for this environment. Legacy SEO tools such as Ahrefs and Semrush are designed around crawlable link graphs and keyword rankings, making them poorly suited to map the non-linear, probabilistic nature of generative AI. That capability gap is widening. Compounding the issue, pricing for these platforms has continued to climb even as their ability to surface accurate AI visibility data remains limited, placing real strain on agency budgets and client relationships.

For UK SEO professionals and site owners, the practical consequence is clear: the measurement frameworks and toolsets that worked reliably through 2023 now leave meaningful blind spots in any visibility strategy.

Key Confirmed Details from the Three-Tool Comparison

A structured test of three AI visibility checkers, BuildSOM, Profound, and Semrush, used 75 prompts across UK and Hong Kong markets to measure real-world performance differences. The setup monitored 3 domains with 30 prompts in the UK and 45 in Hong Kong, running queries across ChatGPT, DeepSeek, Google AIO, and Google AI Mode on standard annual billing plans.

Cost Differences Are Substantial

Monthly pricing varies sharply between tools. BuildSOM comes in at USD 229, Profound at USD 665 (which doubles for two-region coverage), and Semrush at USD 1,030 once you factor in the required base SEO plan alongside per-domain and per-prompt add-ons. For teams already using dedicated SEO platforms with built-in toolsets, the Semrush pricing structure in particular warrants careful budget review before committing.

Accuracy, Model Coverage, and Data Retention

BuildSOM simulates native language environments to produce human-like results for non-English markets. Profound and Semrush run non-English prompts through English-centric setups, which the test found produces misleading data for regional campaigns. On model coverage, BuildSOM supports ChatGPT, Gemini, Google AIO, Google AI Mode, DeepSeek, and Perplexity. Both Profound and Semrush lack Google AI Mode and DeepSeek support. Data retention also differs: BuildSOM stores results for 360 days, Profound retains data indefinitely, and Semrush limits history to 60 days.

When a tool processes non-English prompts through an English-centric setup, the resulting data does not reflect how generative engines actually respond to users in those markets. For agencies reporting AI visibility to international clients, that distinction is not a minor technical footnote, it is the difference between actionable insight and misleading reporting. (Hyogi Park, MOCOBIN)

Who Is Affected and the Main Implications

The shift toward generative engine optimisation (GEO) is not creating equal disruption across the industry. Three groups are feeling the pressure most directly: UK SEO agencies locked into legacy tool contracts, multinational site owners targeting non-English audiences, and budget-conscious marketers who cannot justify enterprise pricing.

UK agencies are caught in a difficult position. Long-term contracts with legacy platforms mean they are paying for tools that increasingly return inaccurate AI-sourced data and cannot track the non-linear response patterns that characterise AI-generated search results. Their clients absorb the downstream consequences.

For site owners targeting Chinese-speaking audiences across the UK and Hong Kong, the challenge is more specific. Accurate tracking across English, Cantonese, Mandarin, and Simplified Chinese requires multilingual coverage that most legacy platforms were not built to handle. The gap between what these tools promise and what they actually deliver in non-English contexts is a practical operational problem, not just a theoretical one.

Cost is a compounding factor for smaller operations. Platforms like Profound and Semrush use per-region pricing models, which means brands targeting multiple regions face costs that can double or triple quickly. Marketers who need comprehensive coverage of Google AIO, DeepSeek, and ChatGPT for effective GEO strategies often find that the tools capable of delivering that coverage sit well outside their budgets.

Practical Response and Next Steps

Before committing to annual contracts, SEO professionals should test AI visibility tools using domain-specific prompts in their actual target languages. The differences between platforms are meaningful enough to warrant hands-on evaluation rather than relying on feature lists alone.

BuildSOM currently offers a free account with no credit card requirement, which makes it a practical starting point for testing real domains and prompts across target languages and regions. By contrast, Profound requires a credit card to begin, and Semrush offers no free trial for its AI visibility features. That gap in accessibility matters when budgets are under scrutiny.

What to Audit Before Deciding

• Verify whether non-English prompts run in native language environments, not translated interfaces.
• Check AI model coverage against your specific requirements, particularly for Google AIO, DeepSeek, and ChatGPT.
• Run a minimum of 15 prompts per language per region to establish a meaningful visibility baseline and identify citation opportunities.

Longer-Term Strategic Considerations

Beyond tool selection, the underlying content strategy needs to shift. As covered in the context of the broader AEO and AI search transition, prioritizing structured and authoritative data tends to perform better for AI citation than traditional keyword-focused tactics. This is the core of GEO-focused content work.

Avoid over-reliance on any single platform. Evaluate scalability, compatibility with existing SEO workflows, and long-term data retention policies before switching tools. These factors carry real operational weight once a platform is embedded in regular reporting cycles.

Signals To Watch

For SEO professionals and site owners tracking the GEO landscape, a handful of concrete indicators will reveal how quickly the market is maturing. The most direct signal is enterprise adoption rate among UK businesses. As more organisations shift budget and strategy toward generative engine optimisation, competitive pressure on holdouts will intensify, making adoption pace a reliable proxy for overall market confidence.

Pricing behaviour in legacy SEO platforms is worth watching closely. If established tools begin raising subscription costs without meaningfully improving multilingual accuracy or AI model coverage, agency churn becomes a realistic outcome. That churn, in turn, opens space for new entrants to challenge incumbents on both price and capability.

Coverage and Accuracy in Non-English Markets

A specific technical concern is whether AI visibility tools can sustain reliable data beyond 60-day windows, particularly in non-English regions. Tools that simulate native language environments accurately will hold a structural advantage. Those that continue producing misleading multilingual outputs risk losing credibility with international clients. Understanding on-page SEO fundamentals remains relevant here, since content signals that perform well in traditional search often interact differently with generative engines across language contexts.

Finally, monitor which AI engines are gaining market share beyond the current dominant models. As new engines attract users, tools that cover only a narrow set of models will face growing gaps in their reporting, creating both a risk for clients and an opportunity for platforms willing to expand their coverage quickly.