How AI can Improve Seller Support

I have been working with AI in customer support for a while now. And lately, one thing is becoming clear. This space is getting crowded. Every vendor claims their AI is the magic wand. Just plug it in, and your support problems disappear. But the reality is different. AI isn’t magic. It’s a strategy. It has to be planned, adapted, and rolled out based on: 🔹 Your goals 🔹 Your current challenges 🔹 And your team’s capacity Most support leaders we speak with aren’t confused about the tech. They are confused about where to use it. That’s the real challenge. So we created a simple matrix to help teams make better AI decisions. It’s built on just two questions: 1. What’s the risk if AI gets this wrong 2. How complex is the task When you map support work using this lens, things get clearer: - Use AI fully for low risk, repetitive tasks like tagging, triaging, or summarising. - Use AI as a helper for pattern based tasks like routing, recommending actions, or drafting replies. - Keep humans in control for high risk, complex issues like escalations, complaints, or anything tied to revenue.   And here’s the other mindset shift: Don’t think of support AI as one giant bot. Think of it as a system of specialised agents: 🔹 Analyzers – Understand queries, profiles, logs 🔹 Orchestrators – Manage workflows, routing 🔹 Reasoners – Diagnose problems 🔹 Recommenders – Suggest next steps 🔹 Responders – Write or send replies Each agent plays a specific role, just like your support team does. Done right, AI doesn’t replace humans. It supports them, speeds them up, and helps them focus where it matters most. This approach is also being recognised by the front-runners in the space. At a recent ServiceNow event I attended, many speakers echoed the same thought: AI is not one size fits all. It must be tailored to each organisation’s structure, systems, and bandwidth.   Let’s stop using AI for the sake of it. Let’s start using it where it actually makes a difference.   If you are building or evaluating AI for support and want to walk through the matrix, Feel free to drop me a message.  Always happy to exchange notes.

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

𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝐡𝐨𝐰 𝐜𝐮𝐬𝐭𝐨𝐦 𝐆𝐞𝐧𝐀𝐈 𝐢𝐬 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐡𝐲𝐩𝐞𝐫 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐬𝐚𝐥𝐞𝐬 𝐩𝐢𝐭𝐜𝐡𝐞𝐬 𝐭𝐨 𝐢𝐧𝐬𝐭𝐚𝐧𝐭, 𝐞𝐦𝐩𝐚𝐭𝐡𝐞𝐭𝐢𝐜 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐝𝐞𝐞𝐩𝐞𝐫 𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐥𝐨𝐲𝐚𝐥𝐭𝐲. The future of customer experience is tailored, efficient, and scalable. Here’s how businesses are winning with custom GenAI: 𝐇𝐲𝐩𝐞𝐫-𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐚𝐥𝐞𝐬 𝐎𝐮𝐭𝐫𝐞𝐚𝐜𝐡 ↳ A SaaS company increased demos by 40% using AI-tailored email campaigns. ↳ Microsoft Azure AI analyzed buyer intent to craft resonant messages. ↳ Sales teams focused on closing deals instead of chasing leads. 𝐄𝐦𝐩𝐚𝐭𝐡𝐞𝐭𝐢𝐜 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 ↳ An e-commerce brand reduced complaints by 25% with AI-powered chatbots. ↳ Azure Cognitive Services provided real-time sentiment analysis for better responses. ↳ AI escalated complex issues directly to human agents, cutting wait times. 𝐅𝐚𝐬𝐭𝐞𝐫 𝐐𝐮𝐞𝐫𝐲 𝐑𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 ↳ A fintech startup cut support resolution time by 50%. ↳ Azure OpenAI Service referenced transaction history to resolve disputes instantly. ↳ Customers trusted the speed and accuracy of automated responses. 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 ↳ A subscription service boosted renewals by 30% with GenAI insights. ↳ Microsoft’s AI tools flagged inactive users for re-engagement. ↳ Personalized emails brought 70% of these customers back. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 ↳ A marketing agency generated 500 ad variations in 24 hours. ↳ AI, hosted on Azure, adapted copy for cultural nuances and languages ↳ Campaign ROI doubled without adding more creative resources. 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐔𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐃𝐚𝐭𝐚 ↳ A healthcare provider used AI to analyze patient feedback. ↳ Azure’s capabilities uncovered service gaps and addressed them within weeks. ↳ Improved patient satisfaction scores by 15%. 𝐒𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐌𝐮𝐥𝐭𝐢𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬 ↳ A retail brand unified its online and in-store journeys with GenAI. ↳ AI recommended products based on in-store visits and browsing history. ↳ Cross-channel conversions surged by 35%. Custom GenAI isn’t just a tool. It’s your secret weapon for sustainable growth and customer loyalty. ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #AIforCustomerExperience #CustomGenAIImpact #PersonalizedAI #FutureOfCX #GenAIRevolution

Lately I’ve been reading a lot about hyper-personalization and AI customer experience, something we’re evolving day in and out with Zendesk AI. A recent CMSWire article caught my attention, alluding to the retail industry leading the charge on personalized AI experiences and I couldn’t agree more. Many retailers have nailed tailoring interactions to individual customer needs, because in such a competitive marketplace they need their customers to feel seen, heard, and understood to retain them.  And retailers aren’t just focusing on personalization with AI, but efficiency and customer satisfaction as well. A great example of this is one of our global retail customers, Next, who has found Zendesk AI has allowed their customer representatives to focus less on simpler tickets and more on complex issues. This has led to a 15% decrease in average handling time and the ability to roll out AI tools at scale across the 127 different countries they operate in. As Head of Customer Contact Experience Technology Raz Razaq says, “The driver [for adoption] was to maintain our high-level service, especially as we’re growing organically.” For retailers operating at scale, AI can be a well-managed solution to fully transform the CX experience, from personalization to self-service to omni-channel support. I love great stories like the one from NEXT, the kind that really show the practical application and far-reaching potential of AI in the industry. Learn more: https://lnkd.in/gZxc6Aip #CX #CustomerStory

When you're deploying AI agents for a CX function, having a good Knowledge Base is a non-negotiable. Why? When optimized, it can empower your AI agents to deliver fast, accurate responses. When neglected, it can leave customers frustrated and agents underperforming. If you want to make sure your help center actually HELPS, here are 5 strategies you can deploy: 1. Structure your content in a Q&A format with clear headings and concise instructions to make it easy for both customers and AI to find relevant information. 2. Use precise keywords. If you have membership tiers, explicitly say which tier you're talking about. 3. Update content regularly with release dates for new features and remove outdated articles. 4. Use visuals (carefully). Reference images and annotations can improve usability—just make sure you have the bandwidth to keep them accurate. 5. Make agents accessible by providing a clear link to the AI agent channels for when customers need help beyond the answers available to them. A lot of companies view help centers as a nice-to-have but the truth is, the ROI is massive. And if you're thinking of using (or already use) AI agents for your customer support, you need to keep it well maintained so the agents can: → Identify knowledge gaps → Make suggestions to make your documentation easier to understand When your help center is optimized, AI agents can perform at their best, which translates to happier customers and less workload for your team. Read the full article for more strategies we recommend—link in the comments! 👇

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.