AI visibility isn't just a rankings problem anymore. Jason Wade is joined by Tom Gersic (YouEx.ai), Devon Vocke, Awais Haq (Time Technologies), and Babak Akhlaghi to unpack what happens after a company gets found: turning AI-mediated discovery into qualified pipeline, connecting marketing, intake, and CRM data so attribution actually works, and where AI should replace workflow versus just support human judgment. It closes on model economics — local vs. cloud, compliance, API costs — with one thread running through it all: visibility only counts if it turns into revenue.

Bios:

• Jason Wade — Founder, BackTier; host, AI Visibility Podcast. Focuses on AI Visibility/GEO and how entities get cited, included, and recommended by AI systems.
• Tom Gersic — Founder/CEO, YouEx.ai. Ex-Salesforce (12 yrs, VP Product Adoption); builds AI-backed CRM workflows that turn inbound leads into revenue.
• Devon Vocke — Tampa-based digital marketer. Helps companies show up across AI search and discovery, not just traditional rankings.

EvokeStrategy.com

LinkedIn: https://www.linkedin.com/company/evoke-strategy/ 

and https://www.linkedin.com/in/devonvocke/

• Awais Haq — Time Technologies LLC. Connects law firm marketing, intake, and CRM data so firms can see what's actually driving revenue.
• Babak Akhlaghi — Patent attorney, engineer, entrepreneurship-law instructor (University of Maryland). Covers IP, disclosure risk, protecting what founders build.

Being mentioned by AI isn't the finish line. Jason Wade and four guests dig into what turns AI visibility into real pipeline — attribution, CRM workflows, law firm intake, and the model/infrastructure decisions behind it.

Top Quotes

• "There is no single number one anymore. The question is whether you are part of the conversation."
• "AI creates leverage when it moves into workflow, not just when it answers a prompt."
• "Visibility is only one piece of the puzzle. What does it do to ultimately drive pipeline?"

Ranking #1 Is Not the Same Test Anymore

You rank first.

Then someone asks ChatGPT, Gemini, Perplexity, or another AI system the same question — and your competitor gets cited instead.

That is not necessarily a ranking failure. It is a different evaluation system.

Traditional search asks whether your page deserves to rank for a query. AI-generated answers also have to decide whether your company is the right entity, whether your claims are sufficiently corroborated, whether the information is easy to interpret, and whether your brand is reliable enough to cite or recommend inside a synthesized answer.

In this episode, we break down why ranking #1 no longer guarantees visibility when the interface shifts from search results to generated answers.

We cover:

• Why top Google rankings do not guarantee AI citations

• The difference between ranking, citation, inclusion, and selection

• How AI systems evaluate entities rather than individual pages

• Why corroborating sources can outweigh stronger traditional SEO

• How entity ambiguity weakens AI visibility

• Why a lower-ranking competitor may be easier for an AI system to understand and cite

• What companies should measure beyond rankings and traffic

• How AI SEO, GEO, and AEO change the visibility model

The old question was:

“Where do we rank?”

The new question is:

“When the system has to construct an answer, does it understand us, trust the evidence around us, and choose us?”

Jason T Wade is the founder of BackTier and an AI Visibility strategist focused on how companies are discovered, understood, cited, included, and recommended by AI systems.

His work spans AI SEO, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, technical SEO, content strategy, and AI visibility measurement.

BackTier develops systems for measuring and improving visibility across AI-generated search and answer environments.

BackTier: backtier.com
Jason T Wade: jasonwade.com

Something uncomfortable has been happening to brands over the past two years: their identity is being flattened. Ask an AI assistant about a company, and you get a summary — accurate in the broad strokes, stripped of the texture that made the brand distinct in the first place. The voice, the specific values, the thing that made a customer choose you over the identical competitor down the street — all of it gets averaged away into a generic paragraph.

That's the problem the sovereign data movement is responding to. The idea is simple but consequential: instead of letting AI systems scrape and aggregate whatever fragments of your brand exist across the web, you take ownership of the definitive, structured version of your own identity — your history, your positioning, your verified facts — and make that the authoritative source models are meant to pull from.

Practically, this looks like brands building and maintaining their own structured knowledge layer — rich, machine-readable data about who they are — rather than leaving that job entirely to whatever a crawler happens to piece together from old press releases and a five-year-stale About page. It's the difference between letting your reputation be reconstructed by inference and stating it directly, in a form built to be read by the systems doing the reconstructing.

There's a genuine tension worth naming here. Full sovereignty — total control over how you're represented — isn't fully achievable when you don't control the models doing the summarizing. This is an influence strategy, not a guarantee. A brand can produce excellent structured data and still get aggregated into something generic if the underlying system weights other, louder signals more heavily.

But doing nothing is worse. Brands sitting passively while AI systems build their identity out of secondhand fragments are, functionally, letting someone else write their reputation. The sovereign data movement isn't about resisting AI aggregation entirely — that ship has sailed. It's about making sure that when you are aggregated, it's your definition of you doing the shaping, not a statistical average of everyone else's.

We Outrank Them. Why Did the AI Cite Our Competitor?

Traditional SEO says you are winning.

You rank higher.
You have more traffic.
Your domain is stronger.
Your content is better optimized.

Then someone asks ChatGPT, Gemini, Perplexity, or another answer engine a question in your category — and the AI cites your competitor.

This episode explains why.

We break down the difference between ranking in search and being selected inside an AI-generated answer. The systems overlap, but they are not the same. AI models are evaluating whether they can identify the right entity, understand what that entity does, connect it to the question being asked, corroborate the relevant claims, and confidently use it as supporting evidence.

That means a company can outperform a competitor in Google and still lose the AI recommendation layer.

We cover:

• Why search rankings do not guarantee AI citations

• The difference between relevance, authority, and selection

• How entity ambiguity can suppress an otherwise strong brand

• Why third-party corroboration can matter more than another optimized page

• How AI systems assemble evidence across multiple sources

• Why competitors with weaker SEO can still become easier for machines to cite

• The difference between being discovered, cited, included, and recommended

• What companies should actually measure as AI search becomes more important

The core question is no longer simply:

“Do we rank?”

It is:

“When an AI system has to answer the question, does it understand us well enough — and trust the available evidence enough — to choose us?”

Jason T Wade is the founder of BackTier and an AI Visibility strategist focused on how companies are discovered, understood, cited, included, and recommended by AI systems.

His work spans AI SEO, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, technical SEO, content strategy, and AI visibility measurement.

BackTier develops systems for measuring and improving visibility across AI-generated search and answer environments.

BackTier: backtier.com
Jason T Wade: jasonwade.com

Jason T WadeLinks

ai naming

chat vs. work vs. computer vs. cowork vs... GPT and Claude mess

Episode description

A federal filing describes websites and content intended to produce “GPT framing results.” We examine Clock Tower X, political GEO and the difference between documented intent and unproven influence.

• U.S. Department of Justice: Clock Tower X FARA filings
  https://efile.fara.gov/ords/fara/f?p=1381:200:::NO:RP,200:P200_REG_NUMBER:7649

• Drop Site News investigation
  https://www.dropsitenews.com/p/israel-brad-parscale-ai-chatbots-gaza

• Anthropic research on data poisoning
  https://www.anthropic.com/research/small-samples-poison

• Parscale Strategy
  https://www.parscale.com/

citation network

ai visibility by Jason T Wade, BackTier

Part 2 of 3 - Being Human in the AI Loop | Moderator: Jason T Wade, BackTier