How to Use Conversational AI to Improve Customer Journeys

I've tested over 20 AI agent frameworks in the past 2 years. Building with them, breaking them, trying to make them work in real scenarios. Here's the brutal truth: 99% of them fail when real customers show up. Most are impressive in demos but struggle with actual conversations. Then I came across Parlant in the conversational AI space. And it's genuinely different. Here's what caught my attention: 1. The Engineering behind it: 40,000 lines of optimized code backed by 30,000 lines of tests. That tells you how much real-world complexity they've actually solved. 2. It works out of the box: You get a managed conversational agent in about 3 minutes that handles conversations better than most frameworks I've tried. 3. Conversation Modeling Approach: Instead of rigid flowcharts or unreliable system prompts, they use something called "Conversation Modeling." Here's how it actually works: 1. Contextual Guidelines: ↳ Every behavior is defined as a specific guideline. ↳ Condition: "Customer wants to return an item" ↳ Action: "Get order number and item name, then help them return it" 2. Controlled Tool Usage: ↳ Tools are tied to specific guidelines ↳ No random LLM decisions about when to call APIs ↳ Your tools only run when the guideline conditions are met. 3. Utterances Feature: ↳ Checks for pre-approved response templates first ↳ Uses those templates when available ↳ Automatically fills in dynamic data (like flight info or account numbers) ↳ Only falls back to generation when no template exists What I Really Like: It scales with your needs. You can add more behavioral nuance as you grow without breaking existing functionality. What's even better? It works with ALL major LLM providers - OpenAI, Gemini, Llama 3, Anthropic, and more. For anyone building conversational AI, especially in regulated industries, this approach makes sense. Your agents can now be both conversational AND compliant. AI Agent that actually does what you tell it to do. If you’re serious about building customer support agents and tired of flaky behavior, try Parlant.

Every delightful customer interaction begins with the marketer, and it can only be as powerful as the #CRM and #metadata underpinning it. With agents supporting them at every step of the customer journey creation process, marketers and #customerengagement teams can now create superior experiences shaped by intelligent and emotionally resonant conversations. At a cognitive level, the human brain no longer perceives AI as a “chatbot.” It perceives a relationship. This emotional shift fundamentally changes how consumers relate to brands, fostering deeper loyalty and trust. When customers interact with agents in a way that feels natural, their engagement deepens. The implications go far beyond engagement. Every AI-driven interaction generates a wealth of contextual data, far richer than what brands could ever collect from a single web form or survey. In one conversation, an agent can gather insights about a customer’s preferences, behaviors, and intent, building a more complete, dynamic customer profile. This continuous intelligence loop allows brands to maximize the value of every interaction. Let’s bring this to life with an example... Imagine Melanie, one of your many potential customers. She’s been thinking about joining Posh Fitness, a popular gym chain in her city. Instead of filling out a form, she decides to engage with the agent on their website. As they chat, it quickly feels more like a friendly exchange than a transaction. Melanie shares her fitness goals, whether she wants to lose weight, gain muscle, or improve flexibility, and the agent listens closely, asking the right questions to understand her needs and intent. The agent gathers valuable insights through this conversation that a simple web form could never capture. Melanie mentions her dietary restrictions, her preference for a supportive personal trainer style, and that she loves outdoor workouts but needs a flexible schedule due to her busy life. In just a few minutes, the agent collects a wealth of data about Melanie: her goals, preferences, and availability—all essential to crafting a personalized experience. And because the conversation feels human-like and emotionally resonant, it creates an immediate connection to Posh Fitness. By collecting this richer data early in the relationship, Posh Fitness can offer tailored recommendations and build Melanie’s loyalty well before she signs up. This isn’t just about closing a sale. It’s about building trust and delivering personalized experiences that evoke emotions and feel deeply human. Brands that will thrive in the era of #Agentic #AI are those that recognize the shift from transactional interactions to relationship-driven engagement. This isn’t just about personalization; it’s about creating experiences and dialogues that feel alive—where AI and marketers co-create journeys that adapt in real time, amplifying the impact of every customer moment.

This is a gem of a case study about how to apply AI across a business. Singapore Airlines is partnering with OpenAI to apply AI to its business in the following ways, reports A'bidah Zaid Shirbeeni in MARKETING-INTERACTIVE: 1. Personalize the airline’s virtual assistant to intuitively plan personalized travel and offer customers self-service options. Business Benefits:  ✅ Self-service delivers higher revenue impact than the flight recommendation chatbot ✅ Intuition (read: ChatGPT’s new memory) and personalization promote customer engagement 2. Create an internal AI assistant to guide employees on operations and automate routine tasks. Business Benefits:  ✅ Faster decision-making when time is critical ✅ The assistant applies learnings from past issue resolutions and support solves to answer current questions 3. Integrate ChatGPT with operations tools to crunch out complex workflows such as scheduling flight crews while referencing applicable regulatory guidelines. Business Benefits:  ✅ Optimizes planning ✅ Streamlines operations WHY THIS MATTERS: Singapore Airlines’ idea of an “AI-first customer journey” shifts the lens from thinking about AI-first companies toward using LLMs to build better customer experiences. That’s a powerful shift. This is applied AI at its finest - to build better customer experiences. What ideas spring to mind when you think about AI-first customer experiences at your company? ✨ Conversational AI imperatives from Chatbot Europe: https://lnkd.in/edxvM8d3 #ai #cx #ux #chatbot #appliedai #marketing Image credit: MARKETING-INTERACTIVE

🎯 "The best salespeople ask good questions." But what if AI could help you ask the PERFECT questions at scale? Tomorrow on the GTM AI Podcast, I have Lihong Hicken joining me, the CEO of TheySaid | World's 1st AI Survey shared a brilliant framework for using AI surveys to both book meetings AND deeply understand your customers: Here's her fascinating approach: 1. Start with the Money Question "I ask sales teams: What's the ONE question that tells you if this is a good lead or not?" Real example she shared: ❌ Don't ask: "Are you interested in outsource development?" ✅ Instead ask: "How satisfied are you with your current outsource development agency?" 2. Let AI Go Deeper The AI then: - Explores pain points - Uncovers specific challenges - Discusses potential solutions - Books meetings with qualified prospects 3. Train AI Like Your Best SDR "You don't want a general AI chatting with your customer. You want it to be YOUR employee." She programs the AI to: - Use company messaging - Focus on specific value props - Route enterprise vs. SMB leads differently - Book directly into the right rep's calendar 🎯 The brilliant part? While booking meetings, you're simultaneously gathering intelligence about: - Customer satisfaction - Common pain points - Buying triggers - Product gaps She is thinking outside the box and leverage her own AI survey tech to do things that could not have been done before, like: Pipeline Generation & Sales: - Using AI surveys as an outbound prospecting tool - Converting research requests into sales opportunities - Qualifying leads through AI-driven conversations Customer Intelligence: - Getting honest feedback through AI conversations vs. human interactions - Capturing customer sentiment at different journey stages Win/Loss Analysis: - Moving from annual to continuous feedback collection - Reducing analysis costs (from $20-50K to $150) Upsell Strategy: - Moving from "sitting duck" to proactive upsell approach - Identifying expansion opportunities through AI conversations Customer Experience: - Creating engaging survey experiences vs. traditional methods - Using AI for continuous customer pulse checks Product Development: - Using customer feedback for feature prioritization - Understanding pricing expectations Market Research: - Reducing research costs and timeline - Getting both quantitative and qualitative insights Operations Efficiency: - Automating feedback collection - Reducing need for large BDR teams So excited to show this to you all and I may or may not have an example survey for you to test the tech out on yourself! Drops tomorrow ;)

I recently saw an AI demo that didn't just feel impressive but felt inevitable. It's a crystal clear preview of how AI agents will revolutionize customer experiences forever. The shift from passive "Q&A" chatbots to proactive, multimodal AI agents will transform digital commerce journeys, especially in high-involvement sectors like electronics, automotive, and home improvement. As Joseph Michael says it right, "This is next-level customer service that understands text, speech, images, and even live video." Traditional customer service chatbots have plateaued. They handle basic queries well enough—but they're nowhere near ready for what customers increasingly demand: proactive, personalized, multimodal interactions. As Patrick Marlow (doing the demo in this video) puts it beautifully, here in this video, you will see: ✅ A customer points their camera at their backyard plants. The AI instantly identifies each plant, recommending precise care products tailored specifically for those plants. ✅ The customer casually requests landscaping services. The AI schedules an appointment instantly. ✅ When price negotiations occur, a human seamlessly steps in—no awkward handoffs or "please wait while I transfer you." Here's why this matters to your business: 📌 Customer expectations have evolved beyond simple query resolution. They now expect tailored, interactive journeys. 📌 Static chatbots and scripted interactions no longer differentiate your brand; they commoditize it. 📌 Proactive multimodal AI experiences drive deeper engagement, accelerate purchase decisions, and dramatically boost brand preference. At Swirl®, we're already building specialized multimodal AI agents designed precisely for this next generation of customer experiences with a key focus on discovery, search, and purchase. If you're still relying on traditional chatbots, you're already behind. The future isn't chatbots answering questions; it's AI agents proactively curating personalized customer journeys. Is your business ready for this shift? Let's talk... #ArtificialIntelligence #CX #Ecommerce #AIagents