I just watched a great episode from The Grow and Convert Marketing Show that breaks down the exact question many of us in marketing have been asking: what should we actually do to increase our AI visibility? The episode cuts through the noise and fearmongering from some of the AI visibility tools and gives a clear, practical framework you can use today. Here’s the short, friendly recap I’d share with a colleague—what I learned, what to avoid, and a simple plan you can implement this week.

Why marketers are suddenly anxious about AI visibility

First, the context: a bunch of CMOs, founders, and marketing leads are opening up AI visibility dashboards, seeing competitors “winning,” and getting understandably nervous. The common pattern is this: an SEO/AI tool runs a bunch of prompts, tallies how often your brand is mentioned in AI overviews, spits out a single percentage or “share of voice,” and then you look bad on paper.

That panic is often misplaced. The episode makes a core point I agree with: these tools tend to prioritize quantity over quality. They measure how frequently your brand appears across a wide net of prompts—but they don’t judge whether those prompts actually matter to your business. In other words, a high visibility score that’s driven by irrelevant, top-of-funnel, or non-buying-intent queries isn’t valuable.

The common traps: irrelevant prompts and false comparisons

Two client examples from the episode illustrate this well:

• A B2B software client saw a competitor showing up in AI overviews for a bunch of queries like “things to do in Illinois,” “most visited cities,” and city-specific travel guides. That competitor publishes consumer-facing content, so they naturally appeared in those AI prompts. But our B2B client sells software to vacation-related businesses—not consumer travel guides—so those AI mentions are largely meaningless.
• A look at SEMrush’s AI brand performance demo for Warby Parker showed a “share of voice” percentage and dozens of specific queries. Some prompts made total sense (e.g., “Which retailers have the best customer reviews for eyewear?”) and mattered to Warby Parker. Other prompts—like “Who offers in-app virtual try-on for glasses?”—might be irrelevant or of very low commercial value.

Both examples show the same problem: tools give a big-picture metric without filtering for intent or business relevance. That metric can make leaders panic even when the brand is doing the right things for its customers.

Two rules that will save you from unnecessary panic

If you remember only two things from this article (and the episode), let them be these:

1. Intent matters. Not all prompts are equal. A mention in a “what are fun things to do in Springfield” overview is not the same as ranking in an AI overview for “best property management software for vacation rentals.” Pick queries aligned to buying intent.
2. SEO fundamentals still matter most. From the data referenced in the episode and our own observations, there’s a strong correlation between ranking in Google search results and showing up in AI overviews (including ChatGPT and Google’s AI responses). So prioritize the core things that make you rank well on Google.

How AI overviews actually decide what to show

The episode summarizes this neatly into two inputs that influence LLM answers like ChatGPT and Google AI overviews:

• Training data: The LLM’s broad knowledge built from public datasets, books, podcasts, and web content. Getting into that training data is a long-term brand effort—centuries of marketing activities add up here.
• Live web search: Many LLMs “search the web” when they don’t have enough internal information, and they use Google or other web sources. That makes your presence in current Google search results a very direct lever to influence AI answers.

Practically: focusing on appearing in top Google results (your domain or reputable third-party pages that mention you) is the most tangible way to influence whether AI mentions your brand.

A simple, practical framework you can implement today

Stop staring at a single “AI visibility” percentage and start controlling what matters. Here’s a step-by-step playbook I’d use right now if I were advising a marketing team with limited budget:

1. Pick 5–10 core topics (not a thousand prompts). These should be the queries that directly indicate buying intent and align with your product. Examples: “prescription glasses online,” “equipment rental software,” “best content marketing agency for SaaS.” Keep them tight and product-focused.
2. Map intent for each topic. Decide whether each topic is TOFU (top of funnel), MOFU, or BOFU (bottom of funnel) and what a successful outcome looks like: visits, demo signups, trial starts, or direct conversions.
3. Audit your current rankings. For those 5–10 topics, track where your pages currently appear in Google. Do this monthly. You can use a paid tool or a simple spreadsheet with manual checks.
4. Fix and optimize your pages. Update content, clarify intent, add conversion opportunities, and ensure pages answer the user’s question better than competitors. This is classic SEO content work—do it well.
5. Earn placements on other relevant pages/lists. If other sites produce “best of” lists or roundups for these topics, get on them. Traditional PR outreach and relationship building still work here—email editors, share case studies, provide data, and be helpful.
6. Monitor AI overviews for those topics, not your overall percentage. If you want, use an AI-tracking tool and focus reporting on those 5–10 queries rather than a single share-of-voice metric.
7. Be wary of short-term “hacks.” Commenting across many Reddit threads, paying for placement, or other manipulative tactics might give transient wins. They’re not a substitute for sustainable SEO and product-driven marketing.

Examples of good vs. bad AI visibility efforts

From the episode, here’s how to classify potential activities:

• Good: Updating core product pages and buying-intent content. This improves organic rankings and is likely to increase AI mentions in meaningful ways.
• Good: Earning placements on authoritative lists that already rank well. That amplifies signals without being spammy.
• Less useful: Trying to rank for dozens of irrelevant prompts the tool suggests. This wastes effort on topics that won’t convert.
• Risky: Paying for placements that violate policies or trying to game AI algorithms with low-quality tactics. Short-term gains can become long-term penalties.

What about expensive AI visibility tools?

There are helpful tools out there that can automate monitoring and give you a big dashboard. But if you can’t justify the budget, you don’t need them to make progress. The hosts suggested a pragmatic alternative:

• Pick your 5–10 priorities, build a simple spreadsheet, and check them periodically.
• Have your team discuss status and actions every month. This focuses your efforts on topics that matter.

If you do decide to invest in a tool later, you’ll have clarity on which queries you care about and can ask the tool to monitor those specifically—rather than relying on whatever list it auto-generates.

Final takeaways — what I’m telling my team

If you’re feeling that uneasiness after opening an AI visibility report, here’s the friend-to-friend advice I’d give:

• Don’t panic over a single share-of-voice number. It’s easy to misinterpret. Ask: which prompts contribute to that number, and do those prompts matter?
• Pick a handful of meaningful queries and own them. Monitoring and optimizing 5–10 buying-intent topics is far more productive than chasing hundreds of irrelevant prompts.
• Double down on SEO basics. Strong organic ranking signals are the most reliable way to influence AI outputs today. Create great content, earn links, and fix UX/conversion issues.
• Use PR and list placements strategically. Getting on trusted lists that already appear in search results is a sensible, scalable tactic to increase the chances AI tools reference you.
• Avoid reliance on hacky short-term tactics. They might work momentarily, but long-term brand and product strength wins.

“Do the basics. If you’re not doing the basics right now, then you’re going to have a lot harder time showing up in AI.” — Summarized from The Grow and Convert Marketing Show

How to get started this week (quick checklist)

1. Pick 5–10 product-related topics with clear buying intent.
2. Search each topic on Google and note the top 3–5 results.
3. Audit your content for those topics—are you answering the searcher’s question? Is there a clear conversion path?
4. Update or create the highest-value pages first (optimize for intent and conversions).
5. Identify 3 external sites/lists where your brand should appear and start outreach.
6. Set a monthly review to check rankings and AI overview presence for your chosen topics.

Closing thought

AI visibility is real and worth thinking about, but it’s not a magic replacement for SEO or product-driven growth. Focus on the queries that drive value, do the SEO fundamentals well, and use PR/list placements to complement your efforts. If you do that, the AI mentions will follow—without the panic and without wasting resources on irrelevant metrics.

If you want to dive deeper, follow The Grow and Convert Marketing Show for more breakdowns like the one I summarized—it’s a great resource for practical, no-nonsense marketing advice.

I recently sat down with Mukund Mohan on his Seattle Side and the East Side podcast to walk through how I think about content, SEO, AI, and affiliate marketing in 2025. I wanted to capture that conversation here—what I’ve learned since my first job out of college at Kissmetrics, what I’d do differently if I were starting today, and the concrete playbook I use now to help B2B companies grow traffic, leads, and revenue.

Table of Contents

• Two-minute backstory (so you know where I’m coming from)
• What would I do differently if I were hired into a startup in 2025?
• How I use AI in content operations (without letting it ruin the brand)
• B2B affiliate marketing—what it is, why it matters, and how I think about it
• When should you start an affiliate program?
• Practical steps to launch an affiliate program that scales
• Wrap-up: diversify, humanize, and build durable systems
• Final thought

Two-minute backstory (so you know where I’m coming from)

My career began when Neil Patel hired me into a marketing role at Kissmetrics. I was an entry-level content marketer—writing blog posts and support documentation, shipping content, building a knowledge base. That blog became legendary. People loved the content so much they used to say they loved the Kissmetrics blog more than the product itself.

Since then I’ve led content teams across startups, founded my own company, and co-founded Stone Press—a B2B affiliate SEO company that scaled aggressively. We drove millions in revenue through SEO, inbound, conversion optimization, and building high-performing teams. We grew to a meaningful business, but platform shifts and core changes in Google’s ranking algorithm hit us hard and forced a pivot. Now I help clients stand up or revitalize their B2B content programs, bringing lessons from both the golden SEO era and the messy, AI-driven present.

What would I do differently if I were hired into a startup in 2025?

If I were starting today in a VC-backed analytics or SaaS company, my role would look familiar but with sharper structure and modern tooling. I’d still be focused on content: blog posts, landing pages, and support articles. But the workflows would leverage AI in tactical ways—especially for speed and iteration—while maintaining a human-first editorial lens.

The core, timeless playbook I still believe in is: write content that impacts people, ship consistently, and distribute through multiple channels. That means building personal brands around the work, maintaining an engaged email list, and ensuring content distribution isn’t dependent on a single platform.

Where 2025 is different is in tooling and distribution risk. AI can accelerate content ops—for ideation, outlines, first drafts, and even keyword research. But it’s also saturating channels with low-quality content. That makes authentic, human insight more visible. If you can create genuinely human content—insightful, narrow, opinionated—you stand out more now than ever because AI content has homogenized a lot of the signal.

Editorial vs SEO-first: When to choose which

There’s a temptation to swing between editorial magazine-style content and pure SEO-driven growth. My experience is that both approaches are valuable—and they can complement each other when done intentionally.

• Editorial-first: Great for brand building, thought leadership, and multi-channel distribution (email, social, podcasts). You ship consistently and build a following.
• SEO-first: Great for predictable, compounding traffic. Focused on bottom-of-funnel pages, evergreen informational content, and systematic updates.

In the past I leaned heavily into editorial and then swung too far into SEO because it delivered consistent ROI. My regret was not doubling down on SEO in one of my projects when it would’ve produced far larger returns. Today I typically architect a hybrid: establish a tight SEO foundation for bottom-of-funnel pages, then layer editorial content and multichannel distribution on top to diversify acquisition.

“Content that impacts people, shipping really consistently. Multi-channel distribution. Build personal brands and a tight email list—then tie everything into a flywheel.”

How I use AI in content operations (without letting it ruin the brand)

AI is a tool, not a replacement. I use it to accelerate research, generate outlines, and scale repetitious tasks (like support article first drafts). But I push back on purely AI-generated outputs being published as-is. The role AI plays is to free up human time for judgment, angle, and unique insight.

Some practical ways I use AI:

1. Keyword discovery and clustering at scale to inform a content plan.
2. Drafting outlines and iterating headlines rapidly so writers can focus on insight and voice.
3. Creating summarized research and citation lists for complex topics to speed the subject-matter expert’s drafting.
4. Automating repetitive support content generation, then reviewing and humanizing it before publishing.

That said, there’s a lot of low-effort AI content out there. You can spot it: a certain flatness in voice, generic examples, and predictable structure. If humans can create authentic content—even something simple like “hello, here’s our human perspective”—that authenticity stands out. The bar for “human” is lower now, because AI-created content often lacks personality.

B2B affiliate marketing—what it is, why it matters, and how I think about it

Affiliate marketing in B2B is underrated. It absolutely works for SaaS companies serving SMBs and mid-market customers. Enterprise is trickier because volumes are lower and buying cycles are longer, but for many SaaS categories affiliate is a significant channel.

The important shift is that affiliate programs do more than just drive direct commissions. They influence who gets mentioned across the web, which affects SEO signals, social buzz, and even the data LLMs learn from. The web is an ecosystem: publishers, review sites, listicles, and micro-influencers all create the signal that search engines and large language models consume. If your competitors are actively courting those publishers with affiliate incentives and you aren’t, you’re losing share of voice.

“If the number three company in the category is willing to pay and number one and two don’t, publishers will feature number three. That mention ripples into search, social, and LLM training data.”

How affiliate programs change discovery

Think of it this way: publishers and content creators monetize by recommending tools. If they can earn an affiliate commission for recommending Product C, they will—especially if Products A and B don’t have a competitive program. As those articles and listicles propagate, they feed into search signals and the datasets used by LLMs. Over time that visibility compounds, which is why affiliate programs are strategic, not just tactical.

Who to recruit as affiliates and how to reach them

I use a two-pronged approach: inbound + targeted outreach.

• Inbound funnel: Maintain a clean signup flow and manage it actively. You’ll get high-volume, low-quality inbound, but occasionally a “golden goose” will sign up. Treat inbound as a lead channel and screen it.
• Targeted outreach: Cold outreach to niche publishers, micro-influencers, and up-and-coming creators. Start small—don’t try to land the top-tier publications immediately. Work your way up the ladder.

The sweet spot is publishers who are hungry, have category-aligned audiences, and don’t already have lucrative deals with incumbents. Offer fair commissions, provide strong creative assets, and build relationships. Micro-influencers and niche YouTube channels are often easier wins than the big review sites early on.

When should you start an affiliate program?

My older view was: build SEO and product-market fit first, then bolt on affiliates. Now I often recommend launching an affiliate program earlier—especially if you’re entering an established category where mentions matter. If search and LLM outputs already favor incumbents, you need every lever available to flip mentions and shape narrative.

A practical sequencing I like:

1. Get your SEO foundation in place: homepage, pricing page, competitor/alternatives pages, key vs/roundup posts.
2. Run modest bottom-of-funnel paid ads if it makes sense to buy initial conversions and test messaging.
3. Launch an affiliate program early enough to start getting mentions from niche publishers and micro-influencers.
4. Layer on editorial and multi-channel distribution to build brand and email lists.

For SEO foundation, a small company only needs 15–30 high-quality pages to start: landing pages, pricing, comparison pages, a handful of evergreen posts. Those pages should be architected for conversion and updated regularly—ideally quarterly. Freshness matters now more than it used to.

Pricing pages and the unforgiving nature of discovery

One concrete example I shared on the podcast: I’ve audited companies where the pricing page had a noindex tag. That tag tells search engines to ignore the page entirely, meaning potential customers can’t easily find pricing via search. If users can’t discover pricing, conversions and organic visibility suffer. That shows how small technical issues can cripple discovery—especially when everything else seems “hot” and working around them.

Practical steps to launch an affiliate program that scales

Here’s a checklist I use when helping clients stand up affiliate programs:

1. Decide pricing and commission structure aligned to LTV and margins.
2. Choose an affiliate platform that integrates with your tracking and payout needs.
3. Create dedicated affiliate landing pages and tracking links (UTMs) so you can attribute properly.
4. Build an affiliate resource hub: creatives, copy snippets, demo videos, comparison charts, and onboarding guides.
5. Start outreach to niche publishers and micro-influencers with tailored offers and co-marketing ideas.
6. Monitor performance, optimize commissions and creative, and slowly trade up to higher-authority publishers.
7. Integrate affiliates into broader BD and PR outreach—affiliate relationships often open doors for partnerships.

Wrap-up: diversify, humanize, and build durable systems

If there’s one through-line to everything I’ve learned, it’s this: don’t be hostage to one channel or one platform. Build a content engine that combines a solid SEO foundation, human editorial voice, smart use of AI to accelerate operations, and strategic affiliate programs to earn mentions across the web.

AI is a force multiplier when used correctly—but it’s not a substitute for real insight. Affiliate marketing is no longer just a growth add-on; in many categories it’s a strategic lever that shapes discovery and long-term visibility. And finally, update your core pages often, watch the technical fundamentals (like whether the pricing page is indexable), and keep your distribution channels diversified so one platform’s disruption won’t sink your growth.

If you’re a marketer, founder, or growth leader, focus on the foundation: homepage, pricing, key landing pages, and a compact list of priority posts. Then add affiliates early if you need to shape category conversation. Use AI to speed up the work, but keep humans in the driver’s seat for voice and angle. Do that, and you’ll build content that actually moves the business.

Final thought

I loved talking with Mukund about this—if you want to dive deeper on any of the tactics above (keyword clustering, affiliate compensation math, onboarding affiliates, or how to structure quarterly content refreshes), I’m happy to share templates and examples. Start small, iterate, and always ask: will this content or program still be driving value in six months? If not, rethink it.

If you’re in sales and you haven’t heard of LinkedIn X-Ray search, buckle up—because you’re about to add a high-precision tool to your prospecting arsenal.

LinkedIn X-Ray search is the practice of using Google (or any search engine) to bypass LinkedIn’s limitations and uncover publicly available profiles—without needing Sales Navigator, InMail credits, or a 1st-degree connection. It works by combining advanced search operators with LinkedIn’s public profile structure to zero in on your ideal prospects.

Why Sales Teams Should Care

Let’s be honest: LinkedIn’s native search is limited, especially if you’re on the free plan. Even with Sales Navigator, filters can be clunky, and LinkedIn isn’t always generous with visibility outside your network. That’s where X-Ray search shines.

With a few smart keywords and operators, you can:

• Find decision-makers at target accounts
• Filter profiles by job title, geography, or tech stack
• Discover hidden talent or past roles that align with your ICP
• Avoid recruiter and job listing noise

A Working Example

Say you’re looking for Content Marketing Strategists in the US at Fortune 1000 companies. Here’s a sample search string you can drop into Google:

site:linkedin.com/in ("content marketing strategist" OR "content lead") ("United States" OR USA) ("Fortune 500" OR "Fortune 1000") -intitle:job -intitle:hiring

Boom. You now have access to dozens (if not hundreds) of profile pages with the right role, right market, and a filter to exclude job listings and recruiter spam.

Pro Tips for X-Ray Mastery

• Use Boolean logic: Combine keywords using AND, OR, - (exclude) and parentheses.
• Stick to site:linkedin.com/in: This narrows results to actual LinkedIn profiles, not groups or jobs.
• Add filters: Use "location", "past company", or "current company" in quotes to refine further.
• Exclude the fluff: Add -intitle:job -intitle:hiring -site:linkedin.com/jobs to avoid noise.
• Use intitle and intext for any mentions on the profile

When to Use It

• Pre-call research
• Target account list building
• Persona validation for ABM
• Prospecting for outbound email or LinkedIn DMs

One Warning

LinkedIn can throttle access or obfuscate some data due to privacy settings. So don’t expect 100% coverage. But for scrappy sellers or SDRs looking for an edge—it’s a goldmine.