Abdul Hadi

Accurate, relevant recommendations may boost confidence in buying and change which brands people consider.

In one post, Abdul Hadi links trust in AI recommendations to purchase decisions and explains how those recommendations can affect a shopper’s consideration set. The post presents these as research findings but does not name the studies.

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The post attributes this to more specific AI queries, trust in recommendations, and the ease of choosing from a short list. The conversion figures lack named sources; ask for them before using these as benchmarks.

The post compares a stated 1.8% average conversion rate for Google organic search with figures for ChatGPT, Perplexity, and Claude, and gives a B2B SaaS figure of 18.7%. Abdul Hadi uses these comparisons to argue that AI search deserves attention even if it sends less traffic.

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AI can help with answers, summaries, and recommendations, while Google remains useful for live information, shopping details, images and video, niche sources, and links people can check. The position is more balanced than a simple claim that AI is taking over search.

A comparison post names live updates, product prices and availability, visual search, smaller websites, and source verification as areas where Google has an advantage. Its conclusion: use AI and Google together.

Across the three posts, the emphasis shifts from why AI recommendations can affect buying decisions to the claimed conversion opportunity and the limits of what AI search can replace.

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Abdul Hadi contrasts Google organic search with AI search engines and lists conversion rates: Google organic 1.8 percent; ChatGPT 14.2 to 16.0 percent; Perplexity 10.5 to 12.4 percent; Claude 5.0 to 16.8 percent.

• The post also gives B2B SaaS at 18.7 percent, professional services at 21.3 percent, and e-commerce at 9.8 percent.
• It explains the gap through trust transfer, intent precision, and reduced decision fatigue, noting that Google searches average 2.8 words while AI searches average 12.3 words, and that Google shows more than ten links while AI shows two or three curated recommendations.
• It closes by asking whether a brand appears in AI search.
• Ask for the sources behind the figures before reusing them.

For brands planning a campaign

For a B2B SaaS brief, this gives a ready-made argument for GEO and AI-search work: target high-intent AI answers, not only Google rankings. The conversion gap can help justify budget, but validate the rates before making them central to a client proposal.

It bridges AI visibility and real purchase behavior.

Abdul Hadi summarises research-style findings.

• Three claims stand out: when people trust an AI system's recommendations, they are more likely to follow those suggestions and buy; AI tools including ChatGPT can change a shopper's consideration set because users transfer trust to suggested products; and accurate, personalised AI recommendations increase confidence and purchase intention.
• The post closes by saying, in effect, that readers are still ignoring this shift.
• Ask for the studies behind the claims before using them in a client proposal.
• The post makes a short persuasive argument built on trust rather than channel mechanics.

For brands planning a campaign

A GEO or AI-search campaign can shift the client conversation from whether AI mentions a brand to whether it appears in the AI-generated consideration set. That supports monitoring and improving how a brand appears in chatbot recommendations, especially for considered B2B purchases.

Read it after the conversion posts for practical boundaries: AI answers, Google verifies.

Abdul Hadi lists five areas where Google remains ahead: live updates such as sports scores and stock tickers; shopping and e-commerce product, price, and availability information; images and videos from Google Images, YouTube, and TikTok; niche websites such as local news, blogs, and smaller video platforms; and context or verification because Google links to sources while AI sometimes guesses without showing where information comes from.

• The conclusion is that AI is strong for answers, summaries, and recommendations, while Google wins for real-time, visual, and source-verified content.
• The post recommends using AI and Google together.

For brands planning a campaign

For a B2B SaaS AI-visibility brief, this post supports a dual approach: use AI search for answer and recommendation surfaces, but keep Google and source-verified content strong for real-time, visual, and trust-building queries. It can set realistic expectations and avoid pitching AI search as a full replacement for search.

The comparison post gives average conversion of 1.8% for Google organic against roughly 10-17% for AI assistants, and industry figures such as 18.7% for B2B SaaS and 21.3% for professional services. Three explanations are offered: users transfer trust to a brand an AI recommends, AI queries run longer and are more specific than Google searches, and AI presents two or three options instead of ten links.

The conversion percentages and the claim that AI recommendations speed purchase decisions by 73% lack cited studies or dates. Ask for the sources before using them to guide a decision.

For brands planning a campaign

A B2B SaaS team assessing AI search should read this as a case for funding AI-search (GEO) content and tracking AI referrals as a separate conversion path from organic search. The numbers are asserted rather than sourced, so test them against the brand's own referral and conversion data instead of using them as planning benchmarks.

Examples: Source 1

Three linked findings are used: trust in the AI system predicts whether people act on its suggestions; AI recommendations can reshape the shortlist of brands a shopper considers; and personalized, accurate recommendations raise purchase intention. Accuracy and relevance build trust, which can translate into sales.

Research findings condensed into three numbered points, ending with a direct challenge to readers to stop ignoring AI influence.

For brands planning a campaign

This points to content built to be the source an AI system repeats: specific, accurate and consistent enough for the assistant to cite at the consideration stage. Brands should monitor whether they appear in AI answers before the purchase decision, not just in click-through reports.

Examples: Source 1

The conclusion: AI is strong on answers, summaries and recommendations, while Google remains stronger for real-time, visual and verifiable results. The two should be used together.

For brands planning a campaign

A useful counterweight to a pure AI-search pitch. It argues for maintaining conventional search visibility, product and feed data while adding AI-search work, rather than moving budget wholesale from one channel to the other.

Examples: Source 1

Each makes one argument and ends with a question or call to share. Subjects alternate between explanatory summaries of research on AI and buying behaviour and head-to-head comparisons of search channels.

Numbered lists with one claim per point, short sentences and hashtags such as #AISearch, #DigitalMarketing and #B2BMarketing.

For brands planning a campaign

The format suits a compact, shareable argument about search behaviour for marketers and business readers. The posts read as educational commentary rather than product content, so a brand partnership would work best when the brand can supply the data or product detail behind the claim.

Examples: Source 1 · Source 2 · Source 3

Clwyd Probert connects AI discovery with category creation and community-led marketing for small businesses, often linking to HubSpot resources. Abdul Hadi focuses on AI search conversion claims and buyer psychology.

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Daksh Jain focuses on reusable prompt lists and tool tutorials. Abdul Hadi examines why AI recommendations influence buying and how AI search compares with Google.

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Richard Newton works through concrete ecommerce page audits and collection-page examples, and pitches services for Sprite. Abdul Hadi focuses on AI-search conversion arguments and channel comparisons.

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Where Abdul Hadi argues with conversion numbers, Probert reframes the shift itself: Google's ten blue links, with click-through falling from 28.5 percent at position one to 2.5 percent at position ten, versus two or three AI mentions and no second page to rank on.

• The post claims AI discovery rewards the quality of an answer rather than a backlink history, and names Hampton as a business that won customers through ChatGPT queries about leadership challenges.
• A second post takes the strategy further: HubSpot built Inbound Marketing as a movement it never trademarked, and Probert sets out a category-creation sequence — name the inefficient market, codify a methodology, publish the thinking, build community — citing a London HubSpot user group as producing more qualified leads than paid ads.
• Choose Probert when the client needs the strategic case for rethinking search plus a community or category component beyond the conversion analysis in those posts.

The tradeoff is measurement and operational specificity.

• Probert's answer-engine case rests on the click-through shift and one named customer example, not on the AI-versus-organic conversion comparison and industry breakdowns that make Abdul Hadi's posts useful for internal education.
• Neither creator publishes sourced, dated figures, so buyers should validate the claims against their own referral data.
• Probert's community and category work also takes longer to appear in a referral report than a metrics and prompt piece would.
• Both Probert posts route readers to a linked resource, so agree in writing what is sponsored, what the client may reuse, and what happens after the click.

Examples: Source 1 · Source 2

One post is a library of twenty ChatGPT prompts for SEO and content marketing, covering featured-snippet targeting, competitor analysis, topical maps, content briefs, search-intent mapping, FAQ schema and content gap analysis, each written for readers to paste.

• The other lists eight free Google tools — Search Console, Analytics 4, PageSpeed Insights, Keyword Planner, Business Profile, Tag Manager, Merchant Center and Trends — with what each shows and why a beginner should master it.
• Jain fits a brief where GEO is already accepted as a priority and the gap is a repeatable prompt and tool set the team can adopt immediately, alongside Abdul Hadi's case for why AI answers matter.

These posts offer inputs to do the work, not the argument for doing it.

• They offer no AI-referral conversion comparison, consideration-set reasoning, or positioning of AI answers against conventional search, so the measurement framework and case for budget have to come from elsewhere.
• The format limits depth: twenty prompts and eight tools cover a lot of ground, but none is worked through on a real site.
• If the client's success measure is a change in AI-referral sessions or conversion rate, Jain's posts get the team started but do not define what to track or how to distinguish the client's own numbers from the industry percentages Abdul Hadi repeats.

Examples: Source 1 · Source 2

One post argues that perfect product markup is wasted when the page around it cannot answer a shopper's obvious question: structured data can state the product, price and stock, but it cannot explain who the product is for or answer the concern a buyer has before clicking Buy, a point made sharper by Google listing Discover as somewhere retailer-hosted product videos can appear.

• Newton describes Sprite looking for buyer-ready questions competitors have left unanswered and turning them into an organic revenue channel over 90 days.
• The second post reads Reformation's plan to double its store fleet as a collection-page problem: store discovery leads to category searches such as linen dresses, sustainable occasionwear and wedding guest dresses, and a strong collection page has to make the category unmistakable, match shoppers' words, and give search engines context.
• Both posts end with a self-run audit prompt a team can act on today.

This is a different buyer task.

• Newton's method fixes what product and collection pages say and how they are marked up; it does not answer whether an assistant names the brand when asked a category question, the centre of the AI-search brief Abdul Hadi's posts address.
• It also assumes a catalogue of pages to work through, so a SaaS vendor with a handful of product pages gets less from it.
• Both posts carry a direct offer — drop your website and get a free specific read, or a link in the comments — so the writing doubles as lead generation; establish whether that diagnostic is part of the engagement or the start of a paid one.
• Ask for evidence behind the 90-day revenue-channel claim; the posts do not publish supporting figures.

Examples: Source 1 · Source 2