A REALISTIC PATH
TO PROGRESS
The path from 'we know we need to do something' to 'we are consistently showing up in AI-generated answers' is not a single project.
It is a programme of work that builds in phases, and it starts always with an honest understanding of where you are.
Start with assessment, not tools
The first step is not to select a platform or commission a build. It is to establish a baseline. Three things are worth understanding before anything else:
1. Your current AI visibility.
Go into ChatGPT, Perplexity, or a comparable tool and query your product category.
See whether your brand appears, how it is described, and whether that description is accurate and complete. This is your baseline, and it costs nothing to establish.
2. Where your gaps are.
Content quality, data architecture, and team structure are the three areas where gaps most commonly sit.
An assessment should identify which of these is most limiting your visibility, and which improvements would have the highest impact.
3. What a realistic roadmap looks like.
Given your current maturity, your available resources, and your organisation's capacity for change, what can you do in the next 90 days that would generate a measurable improvement?
That is the starting point.
The three-phrase maturity model
Progress follows a predictable arc. The details vary by organisation, but the phases are consistent:
The most important thing about this model is the sequencing. Phase 2 automation and Phase 3 optimisation are meaningless without Phase 1 foundations in place.
Schema generation pipelines that automate the distribution of inconsistent data do not solve the problem - they scale it.
The investment in foundational work at Phase 1 is what makes everything else work.
Quick wins matter - find them early
Large-scale transformation programmes that promise value in 12–18 months rarely sustain momentum. The organisations that build lasting AEO and GEO programmes are the ones that identify one or two concrete, measurable improvements early, and use those wins to build the case for broader investment.
Quick wins might look like:
- Closing the attribute gaps on your top 20 SKUs
- Measuring the change in recommendation frequency
- Implementing schema markup on your highest-traffic product categories and tracking impressions
- Establishing a cross-functional working group with shared KPIs
- Demonstrating a first cycle of the optimisation loop.
None of these require a full programme to be in place. They require focus, a clear metric, and the willingness to report the result.
Build the business case around measurable output
Content operations and data management programmes have historically struggled to demonstrate bottom-line impact. AEO and GEO change this. Visibility in AI-generated answers is measurable, through share of voice by category, recommendation frequency by query, and in more mature implementations, SKU-level attribution for traffic originating from AI channels.
This gives internal champions a genuinely compelling argument. If your DAM programme, PIM implementation, or master data strategy needs investment to advance, the AEO and GEO dimension provides a measurable top-line rationale - not just a cost-avoidance or efficiency case, but a direct contribution to discoverability and revenue.
