Course lesson · Senior-Leader
Building the Business Case
A GEO business case states the problem with evidence, the cost of a bounded programme, an upside estimate built from your own data with every assumption labelled, and the risks, then asks for a staged commitment with a clear decision point. Because AI visibility has no reliable industry benchmarks yet, honesty about uncertainty is what makes the case credible. This lesson walks through each part with a worked, illustrative model.
In this lesson
- The structure of a GEO business case
- Costing people, tools and content
- Building an upside estimate with labelled assumptions
- Presenting risks and a staged plan
- Exercise and quiz
What goes into a business case?
| Section | Question it answers |
|---|---|
| Problem and evidence | What's happening, and how do we know? |
| Proposal | What will we do, for how long? |
| Cost | What will it take in money and time? |
| Upside | What could we gain, under what assumptions? |
| Risks | What could go wrong, and what if we do nothing? |
| Decision and milestones | What are we asking for, and when do we review? |
Keep it to one or two pages. Leaders read short, specific cases.
How do you present the evidence?
Use your own baseline: a manual check or tracked prompt set (see GEO 101). Illustrative:
- "In 40 discovery prompts across ChatGPT and Perplexity, we were recommended in 7; our two main competitors in 22 and 18."
- "Three answers quoted our 2023 pricing."
- "GA4 shows AI assistant sessions growing month on month from a small base."
- "Sales report prospects mentioning ChatGPT in discovery calls."
State sample sizes. A small sample is fine if you say so.
How do you cost the programme?
| Cost type | Examples | How to estimate |
|---|---|---|
| People | GEO/SEO lead time, content writers, engineering hours | Hours × loaded rate, or share of salary |
| Tools | Visibility tracking, log analysis | Vendor quotes |
| Content | New pages, updates, research | Pieces × cost per piece |
| Off-page | Review programme, PR | Programme budget |
| Opportunity cost | Work you'll pause | Name it explicitly |
Present a pilot cost (for example one quarter) and a full-year cost separately.
How do you estimate the upside honestly?
Build a simple model from your own funnel, with every input labelled as data or assumption. Illustrative model, not real data:
| Input | Value | Type |
|---|---|---|
| Monthly demo requests | 400 | Data (CRM) |
| Share self-reporting an AI assistant | 5% | Data (form, 1 quarter) |
| AI-sourced demos per month | 20 | Calculated |
| Demo-to-close rate | 15% | Data (CRM) |
| Average first-year deal value | €12,000 | Data |
| Increase in AI-sourced demos if recommendation rate improves | +25% / +50% / +100% | Assumption (low/mid/high) |
Annual upside = 20 × uplift × 12 × 15% × €12,000:
| Scenario | Extra AI demos/month | Extra annual first-year revenue |
|---|---|---|
| Low (+25%) | 5 | €108,000 |
| Mid (+50%) | 10 | €216,000 |
| High (+100%) | 20 | €432,000 |
The uplift is the weakest link, so say so: "We don't know how visibility translates to demos; the pilot will test this."
What risks should you include?
| Risk | Mitigation |
|---|---|
| Visibility doesn't improve | Pilot with a decision point; test cheap changes first |
| Improvement doesn't reach pipeline | Track self-reported attribution and AI referral key events |
| Engines change behaviour | Report with control groups and ranges |
| Doing nothing | Competitors keep defining the category in AI answers |
| Reputation damage from low-quality content | Quality standards; no mass thin pages (Google) |
How do you structure the ask?
Ask for a stage, not a forever programme:
- Pilot (one quarter): baseline, fix access, improve 5–10 priority pages, set up attribution.
- Decision point: review visibility, accuracy and early outcomes against stated thresholds.
- Scale (if justified): budget for the next two quarters.
Illustrative threshold: "Continue if unbranded recommendation rate on priority prompts rises meaningfully against control topics, or AI-sourced demos increase."
A quick example
Illustrative. A CMO rejects a first draft promising "3× AI traffic". The rewrite shows a pilot costing one-third of a content marketer's time plus a tool subscription, a three-scenario upside table with the uplift labelled as an assumption, and a review date. It's approved because it's honest and bounded.
Exercise
Write a business case covering cost, a labelled upside estimate and risk. Use the structure above, your own funnel data where possible and low, mid and high scenarios. Mark every assumption clearly, and end with a pilot ask and a decision date.
Quiz
- What makes a GEO business case credible?
a) A guaranteed ROI b) Evidence, labelled assumptions and a staged ask c) Industry hype Answer: b
- In the upside model, which input is usually the weakest?
a) Monthly demo requests b) The assumed uplift from better visibility c) Deal value Answer: b
- What should a pilot include?
a) A decision point with thresholds b) No measurement c) A five-year commitment Answer: a
- Why present low, mid and high scenarios?
a) To show uncertainty honestly b) To confuse leaders c) It's legally required Answer: a
- Which is a mitigation for "improvement doesn't reach pipeline"?
a) Self-reported attribution and AI referral tracking b) Stop measuring c) Publish more thin pages Answer: a
Next: Lesson 3: Budgeting tools, people and content
Sources
- Google Search Central, Creating helpful content
- Google Analytics Help, About key events
Questions
How do I estimate GEO's upside without benchmarks?
Build a simple model from your own data, label every assumption, and show low, mid and high scenarios.
Should I promise a specific ROI?
No. Present a range with explicit assumptions and a plan to test them early.
What's a good first ask?
A bounded pilot, often one quarter, with clear measures and a decision point.