Interactive Content Design

Hey Salespeople: Here is a collection of current use cases for AI in sales & CS: ** GenAI in Sales ** --> Draft messaging for personalized email outreach --> Generate post-call summaries with action items; draft call follow ups --> Provide real-time, in-call guidance (case studies; objection handling; technical answers; competitive response) --> Auto-populate and clean up CRM --> Generate & update competitive battlecards --> Draft RFP responses --> Draft proposals & contracts --> Accelerate legal review & red-lining (incl. risk identification) --> Research accounts --> Research market trends --> Generate engagement triggers (press releases; job postings; industry news; social listening; etc.) --> Conduct role-play --> Enable continuous, customized learning --> Generate customized sales collateral --> Conduct win-loss analysis --> Automate outbound prospecting -->Automate inbound response --> Run product demos --> Coordinate & schedule meetings --> Handle initial customer inquiries (chatbot; voice-bot / avatar) --> Generate questions for deal reviews --> Draft account plans ** Predictive AI in Sales ** --> Score leads & contacts --> Score /segment accounts (new logo) --> Automate cross-sell & upsell recommendations --> Optimize pricing & discounting --> Surface deal gaps / identify at-risk prospects --> Optimize sales engagement cadences (touch type; frequency) --> Optimize territory building (account assignment) --> Streamline forecasting (incl. opportunity probabilities; stage; close date) --> Analyze AE performance --> Optimize sales process --> Optimize resource allocation (incl. capacity planning) --> Automate lead assignment --> A/B test sales messaging --> Priortize sales activities ** GenAI in CS ** --> Analyze customer sentiment --> Provide customer support (chatbot; voice-bot / avatar; email-bot) --> Draft proactive success messaging --> Update & expand knowledge base (incl. tutorials, guides, FAQs, etc.) --> Provide multilingual support --> Analyze customer feedback to inform product development, support, and success strategies --> Summarize customer meetings; draft follow-ups --> Develop customer training content and orchestrate customized training --> Provide real-time, in-call guidance to CSMs and support agents --> Create, distribute, and analyze customer surveys --> Update CRM with customer insights --> Generate personalized onboarding --> Automate customer success touch-points --> Generate customer QBR presentations --> Summarize lengthy or complex support tickets --> Create customer success plans --> Generate interactive troubleshooting guides --> Automate renewal reminders --> Analyze and action CSAT & NPS ** Predictive AI in CS ** --> Predict churn; score customer health; detect usage anomalies, decision maker turnover, etc. --> Analyze CSM and support agent performance --> Optimize CS and support resource allocation --> Prioritize support tickets --> Automate & optimize support ticket routing --> Monitor SLA compliance

Fluffy content builds virality. Your story builds legacy. Most coaches don’t see it that way. They stress their brains trying to find something new that makes them stand out. A few hidden tactics. A "secret" from big creators. A new $100 course that's "different." An “unfair advantage” no one else knows. But here’s the truth: Your story is already your unfair advantage. The lessons you learned the hard way. The unique journey only you took. Experiences that shaped you. That’s what connects with people. And when your story aligns with your target buyers—inside the business context of your offer... It cuts through the noise. I struggled to realize this at first. I thought I needed better funnels. Virality. Better pictures etc. A bit of fancy branding. But none of that moved the needle. When I leaned into my experiences and paired them with a clear offer - that’s when buyers engaged in conversations.  They trusted me. They related to me. And they bought from me. Not because I had some secret trick. But because they saw how our stories overlapped. It helped them better relate to my offer. So, admit the failure and how you fixed it. Post your wins and what you learned. Share lessons from your old self. No tricks, gimmicks, or hacks. Just mini, relatable, contextual stories and insights positioned the right way, with the right offer. Your story + your clear offer = your distinguishing advantage. (it's more relatable than you think)

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.

Recently, I’ve seen posts like: 💬 “I built my own recruitment chatbot in minutes!” 💬 “AI handles all my candidate conversations now!” 💬 “It's really easy to build a Whatsapp chatbot with one prompt” While I appreciate the enthusiasm, let’s not oversimplify what it takes to build a truly effective recruitment chatbot. Here’s the reality: deploying a chatbot isn’t as simple as connecting it to an LLM and hoping for the best. Without proper architecture, conversation design, and guardrails, you’re likely to end up with: ❌ Inaccurate or misleading responses ❌ Frustrated candidates stuck in dead-end conversations ❌ Non-compliance with legal and ethical standards Creating a chatbot that genuinely adds value requires: 1️⃣ Conversational AI architecture: Mapping candidate journeys, understanding intents, and designing flows that feel seamless and intuitive. 2️⃣ Conversation design: Crafting dialogues that are clear, empathetic, and aligned with your brand voice and customer/user. This isn’t just scripting out a process map, it’s an art and a science. 3️⃣ Guardrails for LLMs: Ensuring the AI doesn’t “hallucinate” inaccurate answers, at risk of prompt injections or violate candidate trust. This means carefully curated prompts, fallback mechanisms, and automated/constant monitoring. 4️⃣ Governance and compliance: Ensuring your chatbot adheres to legal frameworks (GDPR etc.) and doesn’t perpetuate bias or discrimination. 5️⃣ Iterative learning: Chatbots are never “finished.” They need ongoing testing, feedback loops, and training to stay relevant and accurate. So yes, an off-the-shelf or DIY solution might work for basic FAQs, but if you want a chatbot that handles nuanced candidate queries, assesses fit, or aligns with your employer brand? That takes serious expertise, collaboration, and investment. To those of us who’ve spent years perfecting the craft of conversational AI: our work deserves more credit than a “5-minute chatbot” headline can convey. #ConversationalAI #RecruitmentChatbots #AIinHR #RespectTheCraft #TalentExperience

Travelers ask many questions before booking, and most of them are already answered somewhere in a property’s listing. However, surfacing the right information at the right moment is far from simple, especially when listings are inconsistent or lengthy. In a recent blog, Agoda’s engineering team shares how they tackled this with a conversational AI assistant called the Property AMA Bot. Instead of hardcoding answers or relying solely on generative models, they built a retrieval-augmented system that combines relevance scoring with language generation to provide accurate, grounded responses. Here’s how it works: First, they break down each property’s content into structured “facts” using heuristics and keyword filtering. When a user asks a question, the system retrieves relevant facts using a hybrid of sparse and dense retrieval techniques. Then, these facts are passed into a fine-tuned LLM, which generates a concise answer grounded in the retrieved content. To keep answers factual and safe, they also include fallback rules, so that the bot will refrain from answering if confidence is low or the topic falls outside the known scope. This setup strikes a good balance between traditional Information Retrieval methods and generative models, making the bot both responsive and reliable. This approach is a great example of retrieval-augmented generation in practice, blending engineering pragmatism with the strengths of LLMs to improve real-world user experience. #DataScience #MachineLearning #Analytics #LLMs #ConversationalAI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gVUT97F7

LLMs are optimized for next turn response. This results in poor Human-AI collaboration, as it doesn't help users achieve their goals or clarify intent. A new model CollabLLM is optimized for long-term collaboration. The paper "CollabLLM: From Passive Responders to Active Collaborators" by Stanford University and Microsoft researchers tests this approach to improving outcomes from LLM interaction. (link in comments) 💡 CollabLLM transforms AI from passive responders to active collaborators. Traditional LLMs focus on single-turn responses, often missing user intent and leading to inefficient conversations. CollabLLM introduces a :"Multiturn-aware reward" system, apply reinforcement fine-tuning on these rewards. This enables AI to engage in deeper, more interactive exchanges by actively uncovering user intent and guiding users toward their goals. 🔄 Multiturn-aware rewards optimize long-term collaboration. Unlike standard reinforcement learning that prioritizes immediate responses, CollabLLM uses forward sampling - simulating potential conversations - to estimate the long-term value of interactions. This approach improves interactivity by 46.3% and enhances task performance by 18.5%, making conversations more productive and user-centered. 📊 CollabLLM outperforms traditional models in complex tasks. In document editing, coding assistance, and math problem-solving, CollabLLM increases user satisfaction by 17.6% and reduces time spent by 10.4%. It ensures that AI-generated content aligns with user expectations through dynamic feedback loops. 🤝 Proactive intent discovery leads to better responses. Unlike standard LLMs that assume user needs, CollabLLM asks clarifying questions before responding, leading to more accurate and relevant answers. This results in higher-quality output and a smoother user experience. 🚀 CollabLLM generalizes well across different domains. Tested on the Abg-CoQA conversational QA benchmark, CollabLLM proactively asked clarifying questions 52.8% of the time, compared to just 15.4% for GPT-4o. This demonstrates its ability to handle ambiguous queries effectively, making it more adaptable to real-world scenarios. 🔬 Real-world studies confirm efficiency and engagement gains. A 201-person user study showed that CollabLLM-generated documents received higher quality ratings (8.50/10) and sustained higher engagement over multiple turns, unlike baseline models, which saw declining satisfaction in longer conversations. It is time to move beyond the single-step LLM responses that we have been used to, to interactions that lead to where we want to go. This is a useful advance to better human-AI collaboration. It's a critical topic, I'll be sharing a lot more on how we can get there.

Boring content doesn’t build brands. So why are we still making it? Things are changing at a speed we never thought possible, and it's getting scary, especially if you're into marketing. A few years ago, simply providing valuable information was enough. There was less competition, audiences had more patience to consume detailed content, and social media wasn’t as saturated with endless options fighting for attention. But today? The way audiences consume content has evolved. They still seek information, but they also expect it to be engaging, easy to digest, and even fun. 🔴 The challenge? We’re competing not just with other brands but with everything else fighting for attention—trending reels, viral tweets, and endless scrolls of content. So what happens if we don’t adapt? 😕 Engagement drops → Purely educational content without an engaging hook gets overlooked. 😖 Brand recall weakens → People remember stories, humor, and emotions—not just facts. 😑 Reach shrinks → Algorithms prioritize engaging content, and if yours doesn’t hook people, it won’t be seen. The solution? Edutainment. Instead of choosing between educational and entertaining content, combine the two. Here’s how: ✅ Traditional educational content: Blog posts, case studies, reports, how-to guides. ✅ Entertainment-based content: Interactive quizzes, polls, short-form videos, memes, storytelling posts. ✅ Hybrid (Edutainment) content: Infotainment-style videos, gamified learning, storytelling-based lessons, social media threads with humor. Why does this work? Because people crave dopamine, not just data. → A well-told story makes an audience listen. → A fun quiz makes them engage. → A short, entertaining video makes them stay. If you make your audience enjoy learning, they’ll keep coming back. Remember, your audience doesn’t owe you their attention. You need to earn it. Thoughts?

One thing that LLM agents can't do well: Any old-school chatbot can stay on script, while LLM agents tend to go rogue and lead customers into weird conversations. But of course, old chatbots feel robotic, and customers don't want to talk to them. They are reliable, but people don't like them, LLM agents are the opposite. They're fluid and adaptive, but they can say anything. You're literally one hallucination away from a disaster. The guys behind Parlant are doing something really smart with their new version: You can build an agent with the best of both worlds. The agent can dynamically switch between an LLM agent and strict mode based on what's happening in the conversation. Risk isn't uniform across a conversation: 1. When a customer asks a casual product question, Parlant engages the LLM to generate a fluid and helpful answer. 2. When a customer asks for a refund, Parlant engages strict mode to only return approved, contextually-driven response templates. You control the agent's "composition mode" based on natural-language observations about the current state of the conversation. This is a really cool idea. It should significantly improve the current state of the art in chatbots. You can check it out here: parlant.io The attached diagram shows how the dynamic composition mode works.

There’s a big difference between 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 and 𝘴𝘵𝘰𝘳𝘺𝘵𝘦𝘭𝘭𝘪𝘯𝘨. A 10-second social reel might get a few easy likes (vanity metrics, mostly). But a well-produced, story-driven video has the power to 𝘴𝘵𝘪𝘤𝘬… To shape how people see your business, your culture, and your impact. Take Bronte Webb from Martinus Rail. Her journey as an apprentice isn’t just another corporate highlight reel, it’s a story that puts a face to opportunity, growth, and the future of the industry. A quick social reel wouldn’t do it justice. A longer-form piece allows the story to breathe, giving the audience time to connect, engage, and truly feel something. A well-crafted video can: - Build credibility and trust in your business - Strengthen brand perception (quality content = a quality company) - Showcase real people and real impact - not just quick sugar hits - Shift how your audience feels about your company, not just what they know Marketing and b2b content creation isn’t just about pushing content out there, it’s about taking the time to tell the business stories that matter most to your audience. That’s what makes a B2b brand more memorable, and leads to better business outcomes. Social reels certainly have their place. But the best content isn’t the quickest to produce. It’s the content that leaves a lasting impression. More companies in the industry need to get this balance right. And the marketing and comms managers that understand this will generate better business outcomes longterm from the marketing content they create. If your company had the chance to tell a truly powerful story, what would it be?

Want to create story-driven marketing campaigns? Try the Rick Rubin Method 👇 Rick Rubin, the producer behind albums from Beastie Boys to Jay-Z to Johnny Cash, has a creative process that's surprisingly applicable to B2B marketing and sales storytelling. Recently, I used this method to enhance a client's campaign with stories. Here's how: 𝗚𝗮𝘁𝗵𝗲𝗿 𝘀𝗲𝗲𝗱𝘀 🌱 We collected customer stories, not just as testimonials, but as raw material for our creative process. This gave us authentic voices to work with. 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁 🔬 Using empathy maps, we delved deep into each customer's perspective. This wasn't just about understanding their needs, but truly walking in their shoes. 𝗖𝗿𝗮𝗳𝘁 🧶 Armed with these insights, we created lead generators that addressed real pain points. Each piece of content was a mini-story, showing how our solution could transform their business. 𝗖𝗼𝗺𝗽𝗹𝗲𝘁e 🔁 We set up feedback loops, constantly refining our tools until they were pitch-perfect. The beauty of this method? It's both systematic and wildly creative. It forces you to start with real human experiences, then sculpt them into something that speaks directly to your target audience. How could you apply the Rick Rubin Method in your work? Share your ideas in the comments, or if you'd like to dive deeper into storytelling strategies for B2B, let's connect! __ ♻️ Share this with your network to help more people revolutionise their B2B storytelling with a systematic yet creative method. 🚀 Follow Rob D. Willis for more daily tips to unlock your team's potential with clear storytelling techniques.