ROI Calculation & Business Case: Justifying AEO Investment to Your CFO

Updated January 17, 2026

Your sales team just lost another deal. The prospect told them they "asked ChatGPT for recommendations" and received a shortlist of three vendors. Your company was not on it.

This is not a hypothetical scenario. Research from Magenta Associates found that 66% of B2B decision-makers now use AI tools like ChatGPT, Copilot, and Perplexity to research suppliers, and 90% of these buyers trust the recommendations these systems provide. When your brand is invisible to these platforms, you are not just missing marketing metrics. You are losing qualified pipeline before your sales conversations even begin.

The CFO's objection is predictable: "Why do we need this when we already pay for SEO?" The answer is mathematical. AI search represents a structural shift in buyer behavior, and the conversion economics are fundamentally different. While AI referrals currently represent just 1.08% of total website traffic, Ahrefs' analysis of 30-day traffic patterns revealed that AI search visitors converted to signups at 23 times the rate of traditional organic search visitors. That means even with lower volume, your effective customer acquisition cost drops significantly while revenue per visit increases.

To secure budget approval, you must shift the conversation from "being found" to "revenue impact." This guide provides the exact formulas, benchmarks, and business case framework you need to prove that Answer Engine Optimization is the most efficient capital allocation for protecting your future pipeline.

The financial risk of AI invisibility

Invisibility in AI search is not a branding problem. It is a market share problem with a specific dollar value attached.

Gartner predicts that traditional search engine volume will drop 25% by 2026 as AI chatbots and virtual agents replace direct queries. Alan Antin, Vice President Analyst at Gartner, stated that "Generative AI solutions are becoming substitute answer engines, replacing user queries that previously may have been executed in traditional search engines." Meanwhile, AI Overviews appeared in 13.14% of queries by March 2025, up from 6.49% in January 2025. That is a 102% surge in just two months.

Think of this shift as silent churn of your inbound pipeline. If 30% of your target buyers now use AI platforms for initial research and you have a 0% citation rate, you are effectively invisible to 30% of your addressable market. That percentage is growing approximately 1% month-over-month based on current AI referral traffic growth patterns.

The competitive displacement risk is even more acute. Industry analysis shows that just five brands capture 80% of top AI-generated responses for any given B2B category. If your competitors establish authority with AI engines first, they build compounding advantages. Each citation reinforces their entity authority in the semantic graph, making them harder to displace over time.

Calculate your current exposure using this simple formula:

Monthly Revenue at Risk = (Monthly Target Pipeline) × (% Buyers Using AI) × (Competitor Citation Rate - Your Citation Rate) × (Average Deal Size) × (Close Rate)

For a B2B SaaS company targeting $2M in monthly new pipeline where competitors have a 40% citation rate and you have 0%, this represents approximately $240,000 in annual revenue exposure, assuming conservative 30% AI adoption among buyers and a 25% close rate.

The cost of doing nothing is not zero. It is the compounding loss of market share as AI platforms become the default research layer for B2B buyers.

How to calculate AEO ROI

To justify Answer Engine Optimization investment, you need to move beyond directional arguments about "emerging channels" and present specific return-on-investment projections. The formula below provides the structure for these calculations.

Primary ROI formula

ROI = [(Projected Value - AEO Cost) / AEO Cost] × 100

The critical variable is Projected Value, which depends on six measurable inputs:

Projected Value = Search Volume × AI Usage % × Citation Rate × Click/Read Rate × Conversion Rate × LTV

Let me walk through each variable with specific benchmarks and data sources.

Search Volume: Start with your existing keyword research from tools like Ahrefs or SEMrush. Export monthly search volume for your target question-based and informational queries. If "best CRM for accountants" receives 2,400 monthly searches and you are targeting 50 similar queries, your baseline search volume might be 120,000 monthly queries.

AI Usage %: This is the percentage of searches occurring on AI platforms versus traditional search engines. Current data shows 13.14% of queries trigger AI Overviews, but Magenta's research indicates 66% of B2B buyers now use AI tools for research. For conservative business case projections, use 15-20% as your starting multiplier, adjusting upward for tech-savvy industries.

Citation Rate: The percentage of target queries where your brand is cited in AI-generated answers. Industry benchmarks show that 30%+ is considered competitive, 20-26% indicates you are losing share to competitors, and 0% means you are invisible. The only way to measure your current citation rate is through a comprehensive AI Visibility Audit testing your target queries across ChatGPT, Claude, Perplexity, Google AI Overviews, and Copilot.

Click/Read Rate: The percentage of users who engage with your brand after seeing your citation. This is the most difficult metric to estimate because the data is emerging. Pew Research found that only 1% of searches lead to users clicking a link within an AI Overview, and separate analysis revealed click-through rates drop from 15% to 8% when an AI Overview is present. However, brands cited in AI Overviews earn 35% more organic clicks than those not cited. Use 1-2% for conservative business case projections.

Conversion Rate: The percentage of visitors who convert to leads or customers. This is where AEO economics become compelling. Microsoft Clarity's analysis of over 1,200 publisher and news websites found that site visitors from LLMs converted to signups at 1.66%, compared to 0.15% from search. Ahrefs reported that AI referral traffic drove 12.1% of their signups while accounting for only 0.5% of traffic, representing a 23x conversion advantage. Use 3-5x your current organic conversion rate for business case projections to maintain credibility.

LTV (Lifetime Value): Your average customer lifetime value in dollars. Use your existing finance-approved customer LTV figure.

AEO Cost: Total investment in Answer Engine Optimization activities, including content creation, technical optimization, audits, and agency fees. Discovered Labs' pricing starts at €5,495 per month for 20+ optimized articles, comprehensive tracking, and Reddit authority building.

Estimating AI search volume and citation rates

The baseline for your ROI calculation comes from understanding where you currently stand. Without an AI Visibility Audit, you are essentially guessing at the denominator in your market share calculation.

An effective audit tests 75-100 high-intent buyer queries across all major AI platforms and measures two critical metrics: whether your brand is mentioned in the generative answer portion, and whether you appear in the citations list. Industry data shows that some companies achieve 20-30% mention rates within 6-8 weeks of focused optimization, while others remain at 0% because their content lacks the structure, verifiability, and entity clarity that LLMs require for citations.

To estimate your addressable AI search volume, take your traditional search volume from keyword research tools and apply the AI usage multiplier. If you have 120,000 monthly searches for your target query set and 20% of your audience uses AI platforms, your AI search volume is approximately 24,000 monthly queries.

Your citation rate projection should follow a realistic growth curve. Typical optimization timelines show initial citations appearing within a few weeks to a few months, with faster results for websites that already have established SEO foundations including discoverable content and authoritative backlinks. A conservative projection might assume 8-15% citation rate by week 3-4, growing to 22-35% by month 2, and reaching 40-50% by month 3-4.

Calculating projected pipeline value

Now apply the formula to a real scenario. Assume you are a B2B technology company with these inputs:

• Search Volume: 120,000 monthly queries across your target set
• AI Usage %: 20% (conservative for tech-savvy buyers)
• Citation Rate: 0% today, projected 35% at month 3
• Click/Read Rate: 1.5% (conservative estimate)
• Conversion Rate: 8% (assuming 2% baseline organic conversion × 4x AI traffic advantage)
• LTV: $45,000 (average customer lifetime value)
• AEO Cost: €5,495/month ($6,100 USD equivalent) = $18,300 for 3-month engagement

Before AEO (Current State):
120,000 × 20% × 0% × 1.5% × 8% × $45,000 = $0

After AEO (Month 3 Projection):
120,000 × 20% × 35% × 1.5% × 8% × $45,000 = $45,360 in monthly pipeline value

90-Day ROI Calculation:
[($45,360 × 3 months) - $18,300] / $18,300 × 100 = 642% ROI

This is a conservative projection using the lower end of conversion rate improvements. If you achieve the 23x conversion advantage that Ahrefs documented, the pipeline value increases proportionally.

The compounding effect is equally important. Unlike paid advertising where visibility stops when spending stops, content optimized for AI citations continues generating value. Each piece of content can be cited for multiple queries, creating increasing returns as your topical authority grows in the semantic graph.

Forecasting AEO cost per lead (CPL)

CFOs think in terms of efficiency metrics. To win budget approval, translate your AEO investment into cost per lead and compare it directly to your existing channels.

Start with your projected lead volume. Using the formula from the previous section, calculate how many leads you expect from AI-referred traffic:

Monthly AI-Referred Leads = (Search Volume × AI Usage % × Citation Rate × Click/Read Rate × Conversion Rate)

Using the same B2B tech example:
120,000 × 20% × 35% × 1.5% × 8% = 100 leads per month at month 3

With a $6,100 monthly investment, your cost per lead is $61.

Now compare this to your current channel CPL benchmarks. For B2B SaaS and technology companies, typical cost per lead ranges are:

• Paid search: $150-$300 per MQL
• Content syndication: $100-$200 per MQL
• Traditional SEO: $80-$150 per MQL (when properly attributed)
• Paid social: $200-$400 per MQL

At $61 per lead with a 4x higher conversion rate than traditional organic traffic, your effective cost per SQL (sales-qualified lead) drops even further. If only 25% of traditional organic MQLs become SQLs but 60% of AI-referred MQLs become SQLs (due to higher buyer intent), the comparison becomes:

• Traditional SEO: $120 CPL ÷ 25% SQL rate = $480 per SQL
• AEO: $61 CPL ÷ 60% SQL rate = $102 per SQL

The efficiency advantage compounds at every stage of the funnel because AI users are further along in their decision-making journey, having already researched and narrowed their choices before engaging with your content.

One critical factor in CPL forecasting is content volume. While traditional SEO agencies might produce 8-12 blog posts per month, Answer Engine Optimization requires higher frequency. Discovered Labs' packages start at 20 pieces of content per month because citation probability increases with topical coverage density. Each piece of content is a shot on target, and collectively they build the topical authority that AI models look for when deciding what to cite.

The long-term CPL trajectory is equally important. Paid channels have relatively flat efficiency curves. You pay $200 per lead in month 1 and month 12. Answer Engine Optimization has a declining CPL curve because content compounds. The 100 pieces you publish in months 1-5 continue generating citations and traffic in months 6-12, while your monthly cost remains constant. This is the "compounding interest" model of content investment.

AEO vs. traditional SEO: A financial comparison

Your CFO's natural question is "Why can't our existing SEO agency just do this?" The answer lies in the fundamental difference between optimization goals and the resulting financial outcomes.

| Metric | Traditional SEO | AEO (Answer Engine Optimization) | Financial Impact |
|---|---|---|
| Primary Goal | Rank in top 10 search results | Be cited in AI-generated answers | AEO: Lower CAC long-term due to higher conversion rates |
| Core Unit of Success | Keyword ranking position | Citation frequency across AI platforms | AEO: 23x higher conversion rate potential |
| Key KPI | Rankings, impressions, organic traffic | Brand mentions, citation rate, share of answers | AEO: 50-60% reduction in customer acquisition costs |
| Conversion Rate | 1-3% for B2B | 4-12% for AI-referred traffic (3-5x higher) | AEO: Higher revenue per visit |
| Time to Value | 6-12 months | 3-4 months for measurable citation growth | AEO: 50% faster payback period |
| Compounding Effect | Backlinks, domain authority | Entity authority, semantic graph positioning | AEO: First movers harder to displace |

The distinction matters financially because the cost structures are different. Traditional SEO focuses on link building, technical site optimization, and keyword-targeted content. The content your current agency produces is optimized for keyword density, readability scores, and backlink acquisition. This is why it ranks well in Google but does not get cited by ChatGPT.

AI models prioritize different signals. Answer engines look for clarity and structure, verifiable expertise, third-party validation, entity relationships, and up-to-date information with timestamps. Content must be structured in 200-400 word blocks with tables and FAQs for optimal RAG (retrieval-augmented generation) processing. This requires a fundamentally different content methodology.

The CITABLE framework that Discovered Labs developed addresses these requirements:

• Clear entity and structure (2-3 sentence BLUF opening)
• Intent architecture (answer main plus adjacent questions)
• Third-party validation (reviews, community mentions, news citations)
• Answer grounding (verifiable facts with sources)
• Block-structured for RAG (200-400 word sections, tables, FAQs, ordered lists)
• Latest and consistent (timestamps plus unified facts everywhere)
• Entity graph and schema (explicit relationships in copy)

Your existing SEO agency is optimizing for a different algorithm. Asking them to pivot to AEO is like asking a direct mail specialist to run your paid search campaigns. They might figure it out eventually, but you will pay for their learning curve with lost market share while competitors establish citation dominance.

The traffic volume versus traffic quality trade-off also affects financial projections. Traditional SEO generates higher absolute traffic volume today because 13.14% of queries trigger AI Overviews versus 86%+ still showing traditional results. However, Ahrefs' data proves that AI traffic quality is dramatically higher. Traffic from AI assistants generated 12% of Ahrefs' signups while accounting for only 0.5% of traffic. This is the definition of quality over quantity.

When building your financial comparison for the CFO, emphasize that you are not replacing SEO with AEO. You are adding a high-efficiency channel with better conversion economics to diversify away from dependence on a single algorithm that Gartner predicts will decline 25% by 2026.

The CFO-ready AEO business case template

A business case that wins CFO approval has five essential sections. Each section addresses a specific financial concern and provides the data your finance team needs to evaluate the investment.

Executive summary and problem statement

Start with the one-minute pitch that frames invisibility as a quantifiable revenue leak.

Problem Statement Template:

"We are invisible to [X%] of B2B buyers who now use AI platforms for vendor research. Industry research shows that 89% of B2B buyers have adopted generative AI for vendor research, naming it one of their top sources of self-guided information in every phase of the buying process. An AI Visibility Audit testing 75 high-intent queries across ChatGPT, Claude, Perplexity, and Google AI Overviews revealed we have a 0% citation rate while our top 3 competitors average 42%. This gap represents approximately $[X] in annual pipeline at risk."

Solution Overview Template:

"We propose a 90-120 day Answer Engine Optimization sprint to close this visibility gap. The initiative will produce 60-80 pieces of AI-optimized content using a proven framework, establish third-party validation signals across Reddit and review platforms, and implement technical optimizations for entity clarity. Target outcome: achieve 30-40% citation rate for priority queries, generating an estimated 100-150 AI-referred MQLs per month with 3-5x higher conversion rates than traditional organic traffic."

The Ask Template:

"Investment: $18,300-$25,000 for 3-4 month engagement with month-to-month terms and no long-term lock-in. Partner: Discovered Labs, a specialized AEO agency with proprietary technology for citation tracking and a structured content methodology purpose-built for LLM retrieval."

Expected Return Template:

"Conservative projection: 287% ROI in first quarter based on 30% citation rate, 1.5% click/read rate, and 4x conversion advantage. Moderate projection: 415% ROI at 35% citation rate with 5x conversion lift. Payback period: 90-120 days. Year 1 pipeline impact: $450,000-$650,000 in incremental qualified opportunities."

Financial projections and payback period

This section provides the mathematical proof. Use the ROI formula from earlier with three scenarios: conservative, moderate, and aggressive.

Conservative Scenario (70% confidence):

• Citation Rate: 20% by month 3
• Conversion Lift: 3x vs. organic search
• Monthly Pipeline Value: $28,000
• 90-Day ROI: 287%
• Payback Period: 4 months

Moderate Scenario (50% confidence):

• Citation Rate: 30% by month 3
• Conversion Lift: 4x vs. organic search
• Monthly Pipeline Value: $42,000
• 90-Day ROI: 415%
• Payback Period: 3 months

Aggressive Scenario (20% confidence):

• Citation Rate: 40% by month 3
• Conversion Lift: 5x vs. organic search
• Monthly Pipeline Value: $56,000
• 90-Day ROI: 642%
• Payback Period: 2.5 months

Present all three scenarios to show you have thought through the downside case. Most CFOs will mentally anchor to the conservative projection and view any upside as a bonus.

Include a sensitivity analysis showing what happens if key variables change:

• If citation rate is only 15% instead of 20%, ROI drops to 215% but still exceeds most marketing channels
• If conversion lift is 2x instead of 3x, ROI is 191% with a 5-month payback
• If AI usage % is 15% instead of 20%, the model still generates 215% ROI

This demonstrates that the business case holds even if multiple assumptions prove too optimistic.

Your payback period calculation should account for the typical 3-4 month optimization timeline. Initial citations appear within a few weeks to a few months, with faster results for websites that already have established SEO foundations. Full optimization takes longer because AI models need to observe your content across multiple queries, validate your information against third-party sources, and build confidence in your entity authority.

The compounding value after payback is equally important. In month 7-12, your cost remains constant at roughly $6,000-$7,000 per month, but pipeline value continues increasing as older content gets cited for new queries and your citation rate improves from 30% to 45%+. This is when the CAC efficiency advantage becomes dramatic.

Mitigating investment risk with performance milestones

The CFO's unstated fear is that AEO is an experimental channel where you pay for "learning" rather than results. The way to address this fear is through a structured milestone approach with clear exit criteria.

Month 1 Milestones (Days 1-30):

• Week 1-2: AI Visibility Audit delivered showing current 0% citation rate, competitive benchmarking (competitors at 40%+ citation rate), and 8-10 "quick win" queries where you are close to breaking through
• Week 2-4: Daily content production begins using CITABLE framework, targeting 20-25 pieces in month 1
• Week 3-4: Initial citation signals appear in 8-12% of tested queries

Success Criteria: If citation rate is still 0% after 4 weeks with no upward trend, this indicates a fundamental issue with content quality, technical implementation, or query selection. At this point, you should have a "kill or fix" conversation with your agency.

Month 2 Milestones (Days 31-60):

• Citation rate grows to 22-30% of priority buyer queries
• Competitive gap narrows from 40% vs. 0% to 35% vs. 25%
• First measurable increases in AI-referred traffic appear in analytics (tagged UTM parameters, traffic source analysis)
• 40-60 new AI-referred MQLs with 2-3x higher conversion-to-SQL rate vs. organic search

Success Criteria: If citation rate has not reached at least 18-20% by day 60, conduct a strategic review. Potential issues include overly competitive queries (pivot to adjacent long-tail), conflicting information across third-party sources (fix consistency), or insufficient topical coverage (increase content volume).

Month 3-4 Milestones (Days 61-120):

• Citation rate reaches 35-45% of target queries
• Company now appears alongside top 2-3 competitors in AI recommendations for key use cases
• AI-referred MQLs increase to 80-120 per month
• SQL conversion rate is 40-50% vs. 20-25% for traditional organic search
• Projected pipeline impact is $120,000-$180,000 based on average deal size and close rates

Success Criteria: If you have not achieved at least 30% citation rate and measurable pipeline impact by day 120, the engagement should be reconsidered. However, industry data shows that most focused AEO implementations see 20-30% mention rates within 6-8 weeks, making this a reasonable expectation.

The milestone structure also creates natural decision points. After month 1, you have proof of concept (citations are appearing). After month 2, you have early pipeline data (MQLs are converting better). After month 3-4, you have full ROI validation (payback achieved, ready to scale).

Discovered Labs' month-to-month contract terms align with this milestone structure. You are not locked into a 12-month commitment based on promises. You pay for one month at a time and evaluate results weekly through citation tracking reports. If the milestones are not hit, you can exit the engagement with 30 days' notice.

This risk mitigation structure is the answer to the CFO's "experimental channel" objection. You are not asking for a blank check. You are proposing a time-boxed pilot with clear success metrics and multiple decision points where you can adjust or exit if results do not materialize.

How Discovered Labs supports your business case

The business case I have outlined requires two things your CFO will demand: baseline data to justify the investment and a credible partner to execute the strategy. Discovered Labs provides both.

The AI Visibility Audit is what transforms your business case from theoretical to quantified. We test 75-100 high-intent buyer queries across ChatGPT, Claude, Perplexity, Google AI Overviews, and Copilot, measuring your current citation rate (likely 0%) and competitive positioning (showing which 3-5 competitors dominate and what percentage of queries they capture). This audit gives you the specific numbers you need for the "Problem Statement" section of your CFO presentation.

The audit also identifies your fastest path to initial wins. Not all queries are equally difficult to capture. Some queries have weak competitive coverage or align perfectly with your existing content, requiring only optimization rather than net-new creation. We prioritize these "quick win" queries for month 1 to generate proof points faster.

Our proprietary CITABLE framework addresses the execution risk. CFOs want to know there is a structured methodology, not just "create more content and hope AI cites it." The framework ensures every piece of content is optimized for LLM retrieval through clear entity structure, verifiable expertise, third-party validation, and proper schema markup.

The technology advantage is equally important. While most agencies rely on generic SEO tools, we have built internal technology that tracks where your content gets cited, measuring metrics like share of voice in AI results and citation frequency across platforms. This gives you the weekly performance data you need to prove progress to your CFO and adjust strategy when needed.

For companies that want to test the approach before committing to ongoing engagement, the AEO Sprint provides a low-risk entry point. This is a 14-day intensive engagement delivering 10 optimized articles ready to publish, a comprehensive AI Visibility Audit across all major engines, schema structure for LLMs, a 30-day action plan, and content gap analysis. The fixed-price sprint ($4,995) lets you prove the methodology works and generate initial citation signals before proposing a larger ongoing program.

We also provide Reddit marketing services as a critical component of third-party validation. AI models trust external sources more than your own website. When prospects research vendors, LLMs pull from Reddit discussions, G2 reviews, industry forums, and news mentions to validate claims. Our dedicated account infrastructure of aged, high-karma accounts allows us to build authentic presence in target subreddits where your buyers conduct research, creating the third-party signals that increase citation probability.

The combination of audit-first data, proprietary methodology, internal tracking technology, and flexible engagement terms gives you everything you need to build a defensible business case that satisfies CFO scrutiny while limiting downside risk.

Frequently asked questions about AEO investment

How is Answer Engine Optimization different from the SEO we already pay for?

Traditional SEO optimizes for ranking in search engines like Google using keywords, backlinks, and technical site factors. Success is measured through rankings and impressions. Answer Engine Optimization optimizes for being cited in AI-generated answers across platforms like ChatGPT, Perplexity, and Google AI Overviews. Success is measured through citation frequency and brand mentions. The critical financial difference is that AI traffic converts at 23 times the rate of organic search, which means even with lower volume, you achieve lower customer acquisition costs and higher revenue per visit.

What is the typical payback period for AEO investment?

Based on industry benchmarks from B2B implementations, payback typically occurs in 90-120 days (3-4 months). Results appear within a few weeks to a few months, with faster outcomes for websites that already have established SEO foundations including discoverable content and authoritative backlinks. Initial citations appear by week 3-4, measurable pipeline impact by month 2, and full ROI validation by month 3-4.

How do you measure ROI when AI citations do not always drive direct traffic?

The goal of AEO is not necessarily instant traffic or direct conversions. The primary value is brand discovery and presence within AI-generated responses that influence buyer perception and consideration. We measure three layers of impact: direct traffic (users who click through from AI platforms, tracked via UTM parameters), influenced traffic (users who see citations, then search your brand directly, measured through branded search volume increase), and citation reach (the total number of times your brand is mentioned to prospects, measured through share of voice across target queries). Brands cited in AI Overviews earn 35% more organic clicks than those not cited, proving the halo effect of visibility.

This feels experimental. How do we ensure we are not throwing money away?

Three safety mechanisms address this concern. First, work with vendors offering month-to-month terms with clear performance milestones. You can exit the engagement with 30 days' notice if KPIs are not met. Second, start with a time-boxed 90-120 day pilot to prove ROI before committing to a larger program. Third, require an initial AI Visibility Audit that provides hard data on your current 0% citation rate and competitor benchmarks, so you are investing based on proven gaps rather than assumptions.

Why should we invest when AI search is still only 1% of traffic?

AI referral traffic is growing approximately 1% month-over-month, and AI Overviews surged from 6.49% of queries in January 2025 to 13.14% by March 2025, a 102% increase in two months. This trajectory suggests AI Overviews will become the dominant search feature within 12-18 months. More importantly, competitors establishing authority with AI engines today are building compounding advantages. Research shows just five brands capture 80% of top AI-generated responses for any given B2B category. Waiting means becoming invisible while competitors own the category.

Key terminology

Answer Engine Optimization (AEO): The process of structuring your brand's content and information so AI-powered answer engines like ChatGPT, Perplexity AI, and Microsoft Copilot can discover, understand, and cite it when answering user questions.

Citation Rate: The percentage of target queries for which your brand appears as a source in AI-generated answers. If your brand is cited in 30 out of 100 priority queries, your citation rate is 30%. Industry benchmarks indicate 30%+ is competitive while 0% means you are invisible to AI-assisted buyers.

AI Share of Voice: Your percentage of total citations relative to competitors in AI responses for your target query set. If there are 100 total citations across 4 competitors and you have 30, your AI Share of Voice is 30%.

LLM (Large Language Model): The AI systems that power platforms like ChatGPT, Claude, and Perplexity, which generate answers by retrieving and synthesizing information from trained data and real-time web searches.

CITABLE Framework: Discovered Labs' proprietary methodology for creating content optimized for AI citation, focusing on clarity, intent architecture, third-party validation, answer grounding, block structure, consistency, and entity relationships.

Cost Per Lead (CPL): Total marketing investment divided by number of leads generated. For AEO, calculate as monthly investment divided by AI-referred MQLs to compare efficiency against other channels.

Ready to build your CFO-ready business case? Book an AI Visibility Audit to get the baseline data you need. We will test your target queries across all major AI platforms, benchmark your 0% citation rate against competitors, identify quick-win opportunities, and provide the specific numbers required for your financial projections. With month-to-month terms and no long-term commitment, you can prove ROI in 90-120 days before scaling investment.