Everyone pitching GEO right now is working from the same deck. Glossy slides. Diagrams of AI search funnels. Bullet points about “generative engine visibility” and “LLM citation signals.” It all sounds plausible in a conference room. What’s harder to find is an honest account of how this work actually gets done – what the day-to-day execution looks like, where it’s straightforward, and where it’s genuinely difficult.
This article is an attempt at that honest account.
What GEO Is Actually Solving For
Before getting into execution, it helps to be specific about the problem. When someone uses a generative AI interface to ask a question – anything from “what’s the best project management tool for a small team” to “what are the risks of taking out a HELOC” – the AI generates an answer. That answer may or may not include your brand. It may describe your brand accurately or not. It may position you competitively against alternatives or frame you unfavorably.
You have limited direct control over any of this. But you have significant indirect influence – through the signals that shape how AI systems understand and represent your brand. Geo services are the structured set of activities that build those signals intentionally rather than leaving them to accumulate (or not) by accident.
The Audit Phase: Knowing Where You Stand Before Moving
Every serious GEO engagement starts with a reality check: how is the brand currently represented in AI-generated responses? This involves systematic querying of major AI interfaces – ChatGPT, Gemini, Perplexity, Claude – with category and comparison questions relevant to the brand. What gets said? What gets omitted? What’s inaccurate?
This audit is often humbling. Brands with strong traditional SEO presences frequently discover their AI representation is thin, outdated, or confused. Products are described incorrectly. Differentiators aren’t captured. Competitors are presented more favorably without apparent justification.
The audit output becomes the optimization brief: here’s the gap between how we’re currently represented and how we should be, and here’s the evidence hierarchy that will close it.
Entity Optimization: Making the Brand Legible to AI Systems
The first execution layer is entity optimization – ensuring AI systems have a clear, consistent, accurate model of what the brand is. This involves several streams of work running in parallel.
Structured data across the brand’s web properties needs to be complete and accurate. Schema markup that clearly identifies the organization, its product categories, its geographic scope, its founding information – all the structured signals that help AI systems categorize the entity correctly. Knowledge graph presence needs to be validated – is the brand’s Knowledge Panel accurate and complete? Are the associated entities (founders, locations, product categories) correctly linked?
Third-party source consistency matters enormously here. If the brand is described differently on its website, on Crunchbase, on LinkedIn, on Wikipedia (if applicable), and on major industry databases, AI systems receive conflicting signals and tend to fall back on the most authoritative or most prevalent description, which may not be the most accurate one.
Content Architecture for AI Extractability
The second execution layer involves restructuring existing content and producing new content in formats that AI systems can efficiently extract from. Generative engine optimization services involve specific content architecture choices: answer-first paragraph structure, explicit FAQ sections with schema markup, declarative factual claims supported by specific evidence, and clear header hierarchies that help AI systems identify which section addresses which question.
This isn’t a wholesale rewrite of existing content. It’s a targeted audit that identifies where existing content has the right information but the wrong structure – and restructures those specific elements to improve extractability while maintaining the content’s human readability.
The Citation Footprint: Building External Evidence
The third layer, and often the most time-intensive, is building the external citation footprint that AI systems use to validate brand credibility. Reviews on authoritative platforms, editorial mentions in credible industry publications, analyst coverage, customer case studies published on respected sites – all of these contribute to how confidently AI systems reference the brand.
This work overlaps significantly with traditional digital PR and link building, but the targeting criteria are different. The focus is on sources that AI training data tends to weigh heavily – established publications with genuine editorial standards, industry databases, review platforms with significant user trust – rather than simply high-domain-authority sites.
Where GEO Work Is Genuinely Difficult
The honest part: certain elements of GEO execution are difficult in ways that the slide decks don’t fully represent. Measurement is genuinely hard. Tracking AI citation frequency across all relevant interfaces, correlating changes in citation patterns to specific optimization actions, and attributing business outcomes to AI visibility improvements – these require custom measurement approaches that don’t yet have standardized tooling.
Timeline expectations are also challenging. Changes to how AI systems represent a brand can take months to materialize – the signals need to accumulate, the training data needs to update, the new patterns need to stabilize. Brands expecting rapid feedback loops will find GEO work frustrating.
The brands that navigate this well combine realistic expectations with disciplined long-horizon investment. The compounding nature of the work – every citation added, every entity signal clarified, every piece of extractable content published – makes the output increasingly valuable over time. Starting is the most important decision. Everything else is execution.