AI Chatbot Usage Insights

My biggest takeaways from Ethan Smith on how to win at AEO (i.e. get ChatGPT to recommend your product): 1. Being mentioned most often beats ranking first. In Google, the #1 blue link wins. In ChatGPT, the answer summarizes multiple sources—so appearing in five citations beats ranking #1 in one. Ethan’s strategy: get mentioned on Reddit, YouTube, blogs, and affiliates. Volume of mentions matters more than any single placement. 2. LLM traffic converts 6x better than Google search traffic. Webflow saw this dramatic difference because users who come through AI assistants have built up much more intent through conversation and follow-up questions, making them highly qualified leads. 3. Early-stage startups can win at AEO immediately, unlike with SEO. Traditional SEO requires years of domain authority. But a brand-new Y Combinator company mentioned in a Reddit thread today can show up in ChatGPT tomorrow. The playing field is finally level. 4. The long tail of AEO is 4x bigger than SEO. People ask ChatGPT questions with 25 or more words (vs. 6 in Google). Ethan found gold in queries like “Which meeting transcription tool integrates with Looker via Zapier to BigQuery?”—questions that never existed in search but are perfect for AI. Own these micro-niches. 5. Reddit is proving to be the kingmaker for AI visibility. ChatGPT trusts Reddit because the community polices spam better than any algorithm. Ethan’s exact playbook: make one real account, say who you are and where you work, give genuinely helpful answers. Five good comments can transform your visibility. No automation, no fake accounts—just be helpful. 6. YouTube videos for “boring” B2B terms are a gold mine for AEO. Nobody makes videos about “AI-powered payment processing APIs”—which is exactly why you should. While everyone fights over “best CRM software,” the high-value, zero-competition long tail is wide open in video. 7. Your help center is now a growth channel. All those “Does your product do X?” questions flooding ChatGPT can be answered by help-center pages. Move them from subdomain to subdirectory, cross-link aggressively, and cover every feature question. Ethan calls this the most underutilized opportunity in AEO. 8. January 2025 was the inflection point in AEO growth. That’s when ChatGPT made answers more clickable (maps, shopping cards, citations) and adoption exploded. Webflow went from near zero to 8% of signups from AI. This channel is accelerating faster than any Ethan’s seen in 18 years. 9. The AEO playbook: (1) Find questions from competitor paid search data, (2) set up answer tracking, (3) see who’s showing up as citations, (4) create landing pages answering all follow-up questions, (5) get mentioned offsite via Reddit/YouTube/affiliates, (6) run controlled experiments, (7) build a dedicated team. This exact process is driving real results at scale.

Last week, I shared how Gen AI is moving us from the age of information to the age of intelligence. Technology is changing rapidly and the way customers shop and buy is changing, too. We need to understand how the customer journey is evolving in order to drive customer connection today. That is our bread and butter at HubSpot - we’re deeply curious about customer behavior! So I want to share one important shift we’re seeing and what go-to-market teams can do to adapt. Traditionally, when a customer wants to learn more about your product or service, what have they done? They go to your website and explore. They click on different pages, filter for information that’s relevant to them, and sort through pages to find what they need. But today, even if your website is user-friendly and beautiful, all that clicking is becoming too much work. We now live in the era of ChatGPT, where customers can find exactly what they need without ever having to leave a simple chat box. Plus, they can use natural language to easily have a conversation. It's no surprise that 55% of businesses predict that by 2024, most people will turn to chatbots over search engines for answers (HubSpot Research). That’s why now, when customers land on your website, they don’t want to click, filter, and sort. They want to have an easy, 1:1, helpful conversation. That means as customers consider new products they are moving from clicks to conversations. So, what should you do? It's time to embrace bots. To get started, experiment with a marketing bot for your website. Train your bot on all of your website content and whitepapers so it can quickly answer questions about products, pricing, and case studies—specific to your customer's needs. At HubSpot, we introduced a Gen AI-powered chatbot to our website earlier this year and the results have been promising: 78% of chatters' questions have been fully answered by our bot, and these customers have higher satisfaction scores. Once you have your marketing bot in place, consider adding a support bot. The goal is to answer repetitive questions and connect customers with knowledge base content automatically. A bot will not only free up your support reps to focus on more complex problems, but it will delight your customers to get fast, personalized help. In the age of AI, customers don’t want to convert on your website, they want to converse with you. How has your GTM team experimented with chatbots? What are you learning? #ConversationalAI #HubSpot #HubSpotAI

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

I asked 195 B2B go-to-market leaders about where they're placing their bets for 2026. The top channel bet: AI discovery aka AEO. Wow, things escalated quickly... One company that got a head start on AEO is Webflow, the website building scaleup. Their stats via VP of growth Josh Grant: 1. 10% of signups now come from AI discovery, growing 4x year-on-year. (This is actual LLM-referred traffic, which likely understates things.) 2. 91% of LLM referrals come from ChatGPT alone. 3. ChatGPT traffic converts at 24% (!), 6x higher than Google. 4. For conversions referred by an LLM, two-in-three convert within 7 days. 3 tactics from Webflow you can apply in the next 24 hours (& one to avoid): Avoid: Add an llms.txt file - The Webflow team tried it. They haven’t seen any significant lift. - The takeaway: focus on content optimizations instead. Tactic 1: Automate content refreshing at scale - AI reshuffles answers constantly. Refresh velocity can be the difference between staying on top and missing out. - Webflow built an AI-driven workflow with AirOps to 5x their refresh frequency. Tactic 2: Turn every webinar into 10 pieces of expert content - Webinars can make great source material. Repurposing makes them fresh, structured, and consistently discoverable by both people and AI. - Webflow automates this by transcribing webinars (AirOps), using LLMs to identify themes & soundbites, generating assets & adding an editorial review. Tactic 3: Automate FAQs and schema content for AI discovery - FAQ sections answer long-tail questions and help LLMs get a more granular understanding of the product. ChatGPT can essentially "borrow" your FAQ answers in their repsonses. - Webflow automates this by scraping what people are asking via Reddit & Google (AirOps), generating new FAQs & answers (GPT-5, Claude), pushing updates into the CMS & then tracking any visibility shifts before/after. --- The full story is out NOW in Growth Unhinged: https://lnkd.in/eXP-gnFN Hope you find it useful 🙏 #aeo #marketing #chatgpt

What if I told you that the conversations you have with AI in chat (your prompts, responses, and potentially sensitive context) could be collected and sold for profit without clear user consent? Would you still type those questions? If you work anywhere near AI and care about your data, this should bother you. A recent report by Koi.ai revealed that a browser extension was collecting users’ AI conversations across 10 major AI platforms. With a dedicated “executor” scripts, designed specifically to intercept and capture #AI conversations. Concerns aggravate as below: ➡️ The data harvesting runs continuously in the background, whether the VPN is connected or not. Some major red flags worth noting: 1️⃣ The extension auto-updated to version 5.5.0, with AI harvesting enabled by default (to help you imagine scale of this impact, 8M users' conversations are exposed) 2️⃣ There is no option to disable this behavior, except uninstalling the extension entirely 3️⃣ The extension carries a “Featured” badge, implying platform review and quality standards (clearly a miss) 4️⃣ It is affiliated with a data broker company (biggest giveaway) 5️⃣ The privacy policy explicitly confirms the data flow (precise statements here: https://lnkd.in/gwXq8EKn), yet the Chrome Web Store listing states: “This developer declares that your data is not being sold to third parties, outside approved use cases” High time to #audit what you’ve installed, read the #privacy policies and understand where your #data flows Our convenience is coming at the cost of silent surveillance.

AI-SEO might sound like another buzzword, but it’s becoming real fast. I’m increasingly seeing this across both my test sites and case studies online: Getting your content cited on AI chatbots like ChatGPT, Claude, and Gemini is the next big visibility play. Why? ChatGPT alone has over 400 million weekly users. When your site gets cited as a source on these platforms, you build brand recognition even without the click. But here’s where it gets interesting: The data actually shows that AI-referred traffic gets higher user engagement. According to Kevin Indig’s research, AI-referred sessions average 10.3 minutes compared to 8.1 minutes from Google Search. My guess is that AI users land on your site already primed with context, so they engage longer. So how do you make AI tools want to quote your content? Here’s what I’ve found to be working so far: ✔️ Detailed, data-backed content with unique insights ✔️ Clear data points and statistics ✔️ Original research and case studies ✔️ Well-structured formats with proper HTML and schema Viv, a women's healthcare DTC brand, went all-in on authoritative content backed by clear data points. Result? ChatGPT and Gemini citations drove a 400% organic traffic increase in one month, with AI-driven conversions up 436%. All without a single extra backlink. Most SEOs are fighting yesterday's battle, obsessing over keyword density and backlink profiles. Meanwhile, the future of SEO belongs to the sites AI wants to reference.

You’re in an AI engineer interview. Interviewer: Your RAG chatbot starts giving outdated answers as documents change daily. How would you keep it fresh without reprocessing everything? If your documents change but your embeddings don’t, your system is already outdated. Here’s how you fix that in a production setup: 1. Don’t rebuild - detect change Track updates using timestamps, checksums, or versioning. Only reprocess what actually changed instead of re-indexing everything. 2. Go chunk-level, not document-level If a small section changes, update only those chunks. This keeps updates fast, cheap, and scalable. 3. Event-driven ingestion (real-time freshness) Use Apache Kafka to capture document update events in real time. How it helps: 📍Every document change becomes an event (no missed updates) 📍Consumers automatically trigger parsing + embedding pipelines 📍Decouples your system -> ingestion scales independently from updates Result: your RAG system stays continuously updated, not batch-dependent. 4. Clean your vector store actively Use upserts and deletions to replace outdated embeddings. Otherwise, stale chunks will still show up during retrieval. 5. Make retrieval freshness-aware Store metadata like last_updated or version. Filter or boost recent chunks so the model sees the latest information first. 6. Cache carefully Include document version or timestamp in cache keys. Without this, you’ll serve fast but outdated answers. 7. Add observability (this is where most systems fail silently) Use MLflow to trace your entire pipeline. How it helps: 📍Track which document version and chunks were retrieved per query 📍Monitor when embeddings were last updated 📍Debug issues like stale retrieval or hallucination despite fresh data Result: you don’t just update data, you prove your system is using the latest data. #ai #llm #datascience #rag #chatbot #aiengineering #kafka #mlflow #interview Follow Sneha Vijaykumar for more...😊

A recent issue has emerged where private ChatGPT conversations, once shared, have become publicly searchable on Google. This is a huge red flag for HR. Conversations containing sensitive information, like employee personal details from CVs, confidential business plans, or even legal advice, are now potentially exposed. My key takeaways: ▶️ Data Privacy Nightmare: This isn't just a technical glitch; it's a massive data privacy risk. Imagine employee PII, performance review details, or internal strategy documents showing up in a public search. This could lead to serious breaches and legal repercussions under regulations like GDPR or state privacy laws. ▶️ Policy and Training Gap: The root of the problem is a lack of awareness. Employees are using AI tools without fully understanding the privacy and security implications. This is a clear indicator that your AI policy needs to be robust and your training needs to be a top priority. Do your employees know what they should and shouldn't be putting into AI tools, or sharing from them? ▶️ Mitigation is Key: 🔸Audit Your Tools: Review which AI tools your employees are using and what data they might be processing. 🔸Revise Your Policy: Update your acceptable use policy to explicitly address the use of generative AI, including what types of information are strictly forbidden from being inputted or shared. 🔸Train Your People: Conduct urgent training sessions to raise awareness about the risks of sharing conversations from AI tools. This situation highlights the critical need for a proactive approach to AI governance in HR. It's no longer just about the tech; it's about the people using it and the sensitive data they handle. What's your biggest concern about employees using generative AI?

Here’s how brand & retail C-levels should read OpenAI's insights on how people use ChatGPT (1.1M+ conversations): 1️⃣ Consumers use AI mostly for guidance (28.3%) and writing (28.1%). Shoppers rely on AI for 'everyday support', from tutoring over health advice to drafting text. 🛍️ In retail this translates to AI that helps consumers compare, explain & guide purchases rather than just sell = concierge at scale. ↳ Launch AI assistants as trusted advisors, not transaction engines. Credibility & expertise drive conversion! 2️⃣ Seeking info (21.3%) is the biggest motivation. Almost 1/5 are explicitly searching for specific info (18.3%). 💬 Customers know what they want to know.. Give them deep, accurate answers (e.g. origin of materials, how to style their new shoes, skincare ingredient safety). ↳ Answer Engine Optimization (AEO) will become as important as SEO! Invest in structured knowledge bases + natural-language search to capture this intent. If not, you'll lose terrain already at the Discovery Stage. 3️⃣ Creative ideation + self expression matter more than you think. People are experimenting with AI to create, role-play & brainstorm. 🧑🎨 Expect a bigger demand for co-creation. Soon consumers will want to design their own items, packaging, looks, 1 of 1 personalized recommendations, ... ↳ Pilot AI Brand creativity tools (e.g. sneaker customization, fragrance builder, style boards, ...) to increase engagement and loyalty. 4️⃣ Health, fitness, beauty & self-care (5.7%) have a natural overlap with luxury. The data confirms strong consumer reliance on AI for Beauty & Wellness. Both are strong pockets of growth for the luxury industry today. ↳ Lean into AI-driven beauty, skincare, nutrition & wellness advice. 💡 Merging expert guidance + brand storytelling + AI personalization is the way to win. 5️⃣ Technical help (7.5%) and translation (4.5%) are undercurrents. Today, we already see customers using AI to decode complexity. Whether they use it to translate, calculate or vibe code... ↳ So why not deploy AI to simplify e-commerce? Auto-duty calculators, multilingual product storytelling, aftercare instructions, returns automation, ... 6️⃣ Multimedia creation is still niche (6%), but growing. Image generation is only 4.2% today, yet visual platforms are dominating culture. So we can expect hyper-growth here. Visual AI will explode as fashion, sneakers & luxury are highly image-driven. Early adopters can own this space. ↳ Use AI for campaign content, product visualization or UGC enhancement.. BUT ensure strict brand control to maintain luxury equity. ... WHAT TO REMEMBER ... Consumers are not using AI primarily for fun, but for help, answer & creativity. The edge will come from turning AI into branded experiences, not just a back-office tool. 🚨 Are you a startup that's moving the needle in this space? Leave a comment with what you're doing and I'll hit you up.