![\"CRO](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/10\/Feature-image-CRO-Perspectives-Anwar-Aly.jpg\")
<\/figure>\n<\/div>\n\n\nLeader: <\/strong>Anwar Aly<\/p>\n\n\n\n Role:<\/strong> AI-powered CRO Strategist \/ Founder at Zaher.AI<\/p>\n\n\n\n Location:<\/strong> Egypt<\/p>\n\n\n\n Anwar Aly is a seasoned CRO strategist with experience spanning both agency work at Invesp and entrepreneurship with his startup Zaher.AI.<\/p>\n\n\n\n At Invesp, he worked as a Senior CRO Strategist (Part-time), where he applied behavioral psychology, data-driven audits, and structured testing to improve conversion funnels for eCommerce brands. <\/p>\n\n\n\n His role involved diagnosing funnel leaks, running A\/B tests, and guiding clients toward building long-term optimization maturity. <\/p>\n\n\n\n Earlier, as a Conversion Optimization Trainer, he led a one-month program to train CRO interns on fundamentals, AI advancements in experimentation, and Invesp\u2019s structured optimization process.<\/p>\n\n\n\n Building on this foundation, Anwar founded Zaher.AI in 2025, where he now serves as Founder & CEO. Zaher.AI pioneers Generative Engine Optimization (GEO)<\/em>, helping brands \u2014 especially across the Arabic web \u2014 stay visible in AI-driven platforms like ChatGPT, Gemini, Claude, and more. <\/p>\n\n\n\n Being an AI-powered CRO strategist means integrating AI not just as a tool, but as a collaborator in every stage of the optimization lifecycle \u2014 from research and audit to ideation, testing, and continuous learning. <\/p>\n\n\n\n In practice, this means: <\/p>\n\n\n\n I build dedicated AI agents (e.g., custom GPTs) per client that ingest their site data, customer personas, test history, and analytics\u2014allowing me to audit like a senior data analyst and strategize like a full-stack optimization team. <\/p>\n\n\n\n By leveraging AI for structured data parsing, heuristic audits, and even messaging variant generation, I compress workflows from hours to minutes. <\/p>\n\n\n\n I don\u2019t delegate blindly to AI. I guide it with context, prompt frameworks, and examples. Then I use it as a reviewer to catch gaps, biases, or missed opportunities. <\/p>\n\n\n\n For example, in every client engagement, I create a dedicated project-trained GPT that understands their brand voice, customer behavior, and past experiments. It becomes their 24\/7 CRO assistant, guided by my frameworks and logic, which unlocks insights at scale and enables better collaboration between stakeholders.<\/p>\n\n\n\n Traditional CRO relied on manual depth \u2014 slow, meticulous analysis by senior practitioners. AI-CRO, when done right, enables scalable depth, providing mid-level strategists with senior-level reach. It\u2019s no longer about being a technical expert on every platform. It\u2019s about having the right decision mindset and building the right AI workflows to support that mindset.<\/p>\n<\/blockquote>\n\n\n\n AI is pushing CRO beyond surface-level quick wins into the realm of strategic risk mitigation, and that shift is already happening in my work. <\/p>\n\n\n\n Quick wins (like tweaking button colors or headlines) are now commoditized. Anyone can ask ChatGPT for best practices on improving cart conversion. The real differentiator now lies in how deep you go and how dynamically you adapt based on actual behavior and prior test learnings. <\/p>\n\n\n\n Here\u2019s what it looks like in practice: <\/p>\n\n\n\n \u201cWhat learnings from our last 6 landing page experiments can I apply to this new Facebook campaign?\u201d <\/em><\/p>\n\n\n\n It also influenced how I built CROVA, an AI-powered CRO virtual agent. What started as a custom GPT for internal use has become a product others now use to scale their experimentation thinking without needing deep CRO expertise. By embedding test reasoning, historical learnings, and hypothesis mapping, CROVA helps teams make better decisions before they even launch tests, effectively de-risking <\/strong>major product or UX changes. <\/p>\n\n\n\n Faster doesn\u2019t mean sloppier if you approach AI as a reviewer, not just a creator. Here\u2019s my approach to preserving quality while speeding up execution: <\/p>\n\n\n\n I start by doing a full analysis myself \u2014 analytics, heuristics, JTBD, etc. Then I feed those findings into AI (like ChatGPT or CROVA) to get feedback, challenge my assumptions, or reframe them using clearer hypothesis logic. <\/p>\n\n\n\n I provide structured prompt templates that enforce hypothesis frameworks, sample size logic, and instrumentation cues. For example, I might prompt: <\/p>\n\n\n\n \u201cBased on the uplift we\u2019re targeting and historical CVR variance, is the current sample size sufficient to reach 80% power?\u201d <\/em><\/p>\n\n\n\n I always ask AI to reason its answers <\/strong>\u2014 why it suggested a specific test setup or sample size. This prevents blind execution, hallucinations, and improves learning for both me and the client team. <\/p>\n\n\n I use automation tools like Make.com to fetch test logs, analyze variance over time, and check instrumentation tags before any launch, ensuring data integrity is maintained. <\/p>\n\n\n\n I never ship an AI-generated test plan without validating every element. Reports clearly distinguish:<\/p>\n\n\n\n AI doesn\u2019t replace critical thinking \u2014 it amplifies disciplined workflows. The mistake is using AI to skip steps; I use it to strengthen each step.<\/p>\n<\/blockquote>\n\n\n\n AI is absolutely reshaping how we leverage user behavior data, but not in the \u201creplace real users with synthetic ones\u201d way many imagine. Instead, I see it as a force multiplier for amplifying real qualitative data, and not substituting it. <\/p>\n\n\n\n In one project, I was analyzing 7 long-form interviews. Rather than run a traditional affinity mapping, I fed the AI one transcript at a time, tagged user jobs, struggles, and desired outcomes, and then mapped them back to funnel friction points. This allowed me to go from raw data to actionable hypotheses in <1 day, without compromising the integrity of the insight. <\/p>\n\n\n\n Don\u2019t trust AI to \u201cfill the gaps.\u201d That\u2019s where bias creeps in. Always segregate insight tiers: what\u2019s real user input vs. what\u2019s AI inference or extrapolation. I flag those clearly in my client reports to maintain transparency and trust. AI can scale qualitative synthesis, but only when anchored in data integrity and human validation. <\/p>\n\n\n\n Use VWO Copilot for behavioral analysis to automatically scan heatmaps, session recordings, and surveys for recurring friction points and behavioral patterns. You can manually validate these insights and add them to your testing pipeline, allowing you to focus on strategic decisions and experimentation priorities rather than repetitive analysis.<\/p><\/div><\/div><\/div><\/div>\n\n\n Through years of auditing both global and Arab eCommerce brands, a few systemic gaps consistently emerge, especially in MENA: <\/p>\n\n\n\n Many regional brands try to copy design patterns from global players like Zara or ASOS, thinking \u201cif it works for them, it\u2019ll work for us.\u201d <\/p>\n\n\n\n But those brands have brand equity and user trust. Local brands, by contrast, need to earn that trust first, which requires totally different UX flows, persuasion techniques, and social proof strategies.<\/p>\n\n\n\n Despite Arabic accounting for over 50% of searches in MENA, the psychological and persuasive frameworks used are often English-first, then directly translated. That kills performance. Language structure, word order, and emotional triggers differ in Arabic \u2014 meaning true persuasion requires native-first copy, not translation. <\/p>\n\n\n\n Brands often test surface-level elements, like hero headlines, without understanding the underlying jobs-to-be-done, user anxieties, or category-specific friction. Without that strategic framing, they misinterpret results and abandon tests that had potential. <\/p>\n\n\n\n What\u2019s needed is not just running more experiments, it\u2019s shifting the mindset from copying tactics to understanding why those tactics work, and how local context affects them. <\/p>\n\n\n\n Further, most Arab brands are still in the awareness-building stage of CRO. That\u2019s precisely why I launched one of the region\u2019s first Arabic CRO courses: to demystify the practice and show that it\u2019s not just for global tech companies. <\/p>\n\n\n What I\u2019ve seen is this: <\/p>\n\n\n\n So I teach it in layers: <\/p>\n\n\n\n The key is positioning AI as augmentation, not replacement. It gives them speed, structure, and insights, but only if they approach it with the right mindset. <\/p>\n\n\n\n CRO in the region is just getting started, but the hunger is there. When you combine foundational education with powerful tooling, the results are transformational. <\/p>\n\n\n\n There are layers of human understanding that AI just can\u2019t replicate \u2014 at least not yet. <\/p>\n\n\n\n For example, during jobs-to-be-done interviews, I can often pick up on non-verbal cues: when a user pauses before answering, shifts tone when describing a problem, or even changes their posture when talking about a pain point. AI can analyze words, but it can\u2019t feel tension or excitement between the lines. <\/p>\n\n\n\n Empathy, emotional intelligence, cultural nuance \u2014 those are still very human territories.<\/p>\n\n\n\n I once ran a JTBD analysis where the AI flagged a user\u2019s comment as a key moment. They had said they were \u2018happy with the product.\u2019 On paper, yes, it looked like a win. But I was in the interview. I could tell, from tone and body language, that the statement was superficial. <\/p>\n\n\n\n What really mattered was something the user said later, with more energy and detail, about the product\u2019s flexibility. AI completely missed it. <\/p>\n\n\n\n AI can see the surface, but humans can read the weight of meaning. The key is positioning AI as augmentation, not replacement. It gives speed, structure, and insights \u2014 but only if teams approach it with the right mindset.<\/p>\n<\/blockquote>\n\n\n\n The shift must be from tools to decision-making workflows. What matters most now is making platforms strategically smart, not just operationally fast. This should help teams run not just more tests, but the right tests based on past learnings and behavior signals.<\/p>\n\n\n\n What can be prioritized: <\/p>\n\n\n\n A system that reads past experiments and suggests new hypotheses \u2014 not randomly, but based on what\u2019s actually worked<\/em> in similar contexts.<\/p>\n\n\n\n Move beyond static A\/B tests to AI-driven on-site personalization that adapts page structure and content based on real-time behavior signals.<\/p>\n\n\n\n More brands should embed GPT-powered chat on-site to guide, recommend, and remove friction across the funnel. These are not just support bots, they\u2019re conversion assistants.<\/p>\n\n\n\n This is something I already implement for clients \u2014 a GPT trained on their test logs, personas, brand rules, and content. It helps them brainstorm ideas, summarize past learnings, and prepare new landing pages \u2014 instantly. Experimentation platforms should integrate this concept natively.<\/p>\n\n\n\n Here\u2019s how I use AI today:<\/strong><\/p>\n\n\n\n AI can also detect micro-trends that we often miss in test analysis:<\/p>\n\n\n\n It can also answer the \u201cSo what?\u201d<\/em> questions for stakeholders. For example:<\/p>\n\n\n\n \u201cWhy did this variant win?\u201d<\/p>\n\n\n\n \u201cWhich segment should we retarget based on this result?\u201d<\/p>\n\n\n\n AI becomes the real-time analysis partner that helps CROs focus on impact, not just data navigation.<\/p>\n\n\n\n We\u2019re on the edge of two major frontiers:<\/p>\n\n\n\n Imagine a website that doesn\u2019t just A\/B test variants for groups, but adapts its layout and messaging per user in real time. <\/p>\n\n\n\n If a user dwells on ethical sourcing content, the site reconfigures to highlight sustainability benefits. If another user skims product specs, the site prioritizes technical details upfront. <\/p>\n\n\n\n That\u2019s not science fiction \u2014 it\u2019s agent-driven UX personalization, and it\u2019s coming fast. <\/p>\n\n\n\n Soon, users won\u2019t browse manually. They\u2019ll just tell their assistant: <\/p>\n\n\n\n \u201cFind me a black dress for this Friday\u2019s wedding. Budget under $200.\u201d <\/em><\/p>\n\n\n\n And their AI agent will: <\/p>\n\n\n\n This means we\u2019ll need to optimize websites for AI agents, not just humans. Structured data, clarity, and crawlability will directly impact conversion for agents. <\/p>\n\n\n\n The first proof point? <\/p>\n\n\n\n It\u2019s already happening. My new startup, Zaher.AI, focuses on Generative Engine Optimization (GEO) \u2014 optimizing brand visibility in AI tools like ChatGPT, Gemini, and Perplexity.<\/p>\n\n\n\n We\u2019re seeing clients already getting traffic and conversions from LLM-generated recommendations, which is proof that AI is reshaping the discovery layer. And as AI agents take over more of the buyer journey, optimization will shift from users <\/em>to protocols<\/em>. <\/p>\n\n\n\n CRO is evolving into Agent Experience Optimization (AXO) \u2014 and I\u2019m excited to be part of shaping that future.<\/p>\n\n\n\nWhy should you read this interview?<\/strong><\/h2>\n\n\n\n
What being an AI-powered CRO strategist means in practice<\/strong><\/h2>\n\n\n\n
Using AI to amplify capability<\/strong><\/h3>\n\n\n\n
Accelerating execution<\/strong><\/h3>\n\n\n\n
Improving quality through feedback loops<\/strong><\/h3>\n\n\n\n
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How CRO is evolving into a risk-mitigation strategy<\/strong><\/h2>\n\n\n\n
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How to protect test quality while accelerating with AI<\/strong><\/h2>\n\n\n\n
Step 1: Expert first, AI second<\/strong><\/h3>\n\n\n\n
Step 2: Guardrail prompts <\/strong><\/h3>\n\n\n\n
Step 3: AI as validator, not executor <\/strong><\/h3>\n\n\n\n
![\"Expert](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/10\/Expert-First-AI-Second.jpg\")
<\/figure>\n<\/div>\n\n\nStep 4: Instrumentation automation <\/strong><\/h3>\n\n\n\n
Step 5: Transparency and labeling<\/strong><\/h3>\n\n\n\n
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Scaling qualitative insights without losing authenticity<\/strong><\/h2>\n\n\n\n
Here\u2019s how I apply it: <\/strong><\/h3>\n\n\n\n
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A real use case: <\/strong><\/h3>\n\n\n\n
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![\"Scaling](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/10\/Scaling-qualitative-insights-without-losing-authenticity.jpg\")
<\/figure>\n<\/div>\n\n\nCRO adoption challenges in Arab markets<\/strong><\/h2>\n\n\n\n
Blind imitation of global giants <\/strong><\/h3>\n\n\n\n
Underestimation of digital psychology, especially in Arabic <\/strong><\/h3>\n\n\n\n
Tactical execution without strategic structure <\/strong><\/h3>\n\n\n\n
![\"Surface](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/10\/Surface-level-tweaks.jpg\")
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What AI can never replace in CRO<\/strong><\/h2>\n\n\n\n
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The value testing platforms should offer<\/strong><\/h2>\n\n\n\n
Autonomous test synthesizer<\/strong><\/h3>\n\n\n\n
Real-time personalization engine<\/strong><\/h3>\n\n\n\n
AI assistants for users<\/strong><\/h3>\n\n\n\n
Custom GPT companion<\/strong><\/h3>\n\n\n\n
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Future of AI in optimization<\/strong><\/h2>\n\n\n\n
Dynamic, personalized UX in real time <\/strong><\/h3>\n\n\n\n
Agentic shopping <\/strong><\/h3>\n\n\n\n
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![\"Future](\"https:\/\/static.wingify.com\/gcp\/uploads\/sites\/3\/2025\/10\/Future-of-AI-optimization-1.jpg\")
<\/figure>\n\n\n\nWhere AXO fits in the CRO plan:<\/strong><\/h4>\n\n\n\n