Integrating AI with Customer Experience Software Solutions

7 ways to seamlessly integrate AI into your users journey 1. The core purpose of AI directly shapes the user’s journey. 
 Conduct user research to identify key pain points or tasks users want AI to solve. ↳ if the startup’s AI helps automate content creation, what’s the user’s biggest friction in the current workflow? 2. Where will the AI interact with users within the product flow? Map out where AI should intervene in the user journey. For instance, ↳ does it act as an assistant (suggesting actions)
 ↳ a decision-maker (making recommendations)
 ↳ a tool (executing commands) 3. Simplify feedback loops help build trust and comprehension
 Focus on how users will receive AI feedback. ↳ What kind of feedback does the user need to understand why the AI made a recommendation? 4. Build a modular, responsive interface that scales with AI’s complexity. Visual elements should adapt easily to different screen sizes, user behaviors, and data volume. ↳ if the AI recommends personalized content, how will it handle hundreds or thousands of users while maintaining accuracy?
 
 5. Use layers of transparency At first glance, provide a simple explanation, and offer deeper insights for users who want more detailed information. Visual cues like "Why?" buttons can help. For more on how layered feedback can improve UX, check out my post here 
https://lnkd.in/eABK5XiT 6. Leverage Emotion Detection patterns that shift the tone of feedback or assistance. ↳ when the system detects confusion, the interface could shift to a more supportive tone, offering simpler explanations or encouraging the user to ask for help. For tips on emotion detection, check this https://lnkd.in/ekVC6-HN 7. Prototype different AI patterns ⤷ such as proactive learning prompts ⤷ goal-based suggestions ⤷ confidence estimation based on the business goals and user needs Run usability tests focusing on how users interact with AI features. ↳ Track metrics like user engagement, completion rates, and satisfaction with AI recommendations. Check out the visual breakdown below 👇 How are you integrating AI into your product flows? #aiux #scalability #designsystems #uxdesign #startups

I’ve been asked a lot in the last few weeks about how we started to use AI for support…. We aren’t perfect and we definitely haven’t arrived, but for all the support leaders out there, here are a few things to consider as you transition to using more AI to support your customers. 1️⃣ Assessment of Support Processes: Start by assessing your current support workflows. Identify pain points, bottlenecks, and opportunities for improvement. Highlight the top 2-3 areas where AI could speed up the resolution for your customers. 2️⃣ Invest in AI Technologies: Embrace AI tools tailored to your support needs. From natural language processing to chatbots 🤖, explore solutions that align with your support goals and customer expectations. No two businesses are exactly the same, so do your homework. 3️⃣ Assess the numbers: Should you build it or buy it (more posts to come on this topic). Regardless of if you choose to build it or buy it, outlining a clear business case for the investment to share with other stakeholders is an important part of the adoption of any AI tools. 4️⃣ Build a Knowledge Hub: Lay the foundation for AI success by developing a comprehensive knowledge base. This foundation of information serves as the backbone for AI-driven responses, ensuring accuracy and efficiency. LLM’s thrive when the knowledge they are fed is extensive, accurate and detailed. 5️⃣ Prioritize Continuous Improvement: Monitor key performance metrics and gather feedback from both customers and agents. Use insights to refine AI algorithms, optimize processes, and deliver exceptional support experiences. You wouldn’t cut a brand new agent loose without QA and the same can be said for your “AI agent” Transitioning to AI does not have to lead to a degradation of service or even be scary for your team. Coupled with the right strategy it can enhance the experience for your customers and your agents and allow your team the time to focus on other areas of customer support. #CustomerSupport #AIInnovation #SupportLeadership #ContinuousImprovement #FutureReadySupport

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #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/gFjXBrPe

Had a really interesting demo recently with Zingly.ai. They position themselves as an AI-native platform built for modern customer interactions: persistent, intelligent, and blended across channels and timelines. After my Customer Contact Week barrage of solutions talking about this, Zingly wanted to show me their take on what it can actually look like. Three things stood out: 1. Persistent digital experiences that carry context Zingly’s Rooms are always-on engagement spaces. Customers don’t lose their place when they leave a session. They return to an environment where context, documents, history, and human support are already in play. This structure fits the reality of journeys that unfold over days or weeks, like rolling over a retirement account or resolving a service issue. 2. AI that does real work Their agentic AI is built to take action. Qualifying leads, updating CRMs, triggering workflows. These outcomes are powered by backend connectivity, not just language models. The architecture is modular, so it can adapt to different enterprise tech stacks. This has the potential to reduce friction while expanding capability. 3. Intelligent orchestration between automation and people Zingly’s “Relationship AI” determines when to escalate a conversation, not just based on customer requests but by analyzing behavior and intent. It can push to sales when someone is ready to convert or route to service when frustration shows up. It’s proactive, not reactive. Zingly's focused on building an engagement layer that connects what’s already there and fills the gaps those systems weren’t designed to solve. I appreciate that they're not trying to replace the core platforms that are already in place. When I look at the CX landscape and see most businesses running a patchwork of CRM, CCaaS, and support tools, solutions like this could be the missing connective tissue. Too often, transformation efforts stall because each new tool adds complexity. What CX leaders need now are solutions that create harmony across what already exists. I'm closely watching the companies that are mindful of how they can most effectively integrate, align, and amplify instead of disrupt, duplicate, or distract. #CustomerExperience #AIDrivenCX #DigitalEngagement Metric Sherpa

Today, we're publishing the AI Agent Engine! 🙌 It's a distillation of our learnings from many successful deployments of AI agents at enterprises. Ultimately, this is what's required for a successful implementation of AI agents. Of course, this is specific to our space (customer service & experience), but the themes will carry over to any vertical. 1. First, you have the "AI agent", defined as a software system that can autonomously do the work of a human agent, such as looking up data, taking actions, making complex decisions, and writing personalized responses. This is the holy grail that everyone wants to get to. 2. Around it, the rest of the engine is designed to reinforce the AI agent and allow it to continuously improve. This starts with a mechanism (i.e. "Routing") that determines when the conversation should be escalated to a human in the loop. This is key because it allows you to roll out your AI agent incrementally. 3. Next, you have the AI tooling for your human agent to use that automates away mundane tasks, like drafting an answer, finding relevant information, polishing tone, etc. We call this "Agent Assist", and it's akin to a copilot. 4. Then, the conversations all feed into a central data platform, our "Admin Dashboard", that allows the leaders of the team to use LLMs to analyze the conversations. This will surface themes, trends, and anomalies in the data easily. It'll also identify gaps in your knowledge, for example, and proactively tell you how to fix them. 5. Finally, you need a way for human staff to "QA" the conversations to constantly give feedback. We've built this directly into the product. These components form the AI Agent Engine, a helpful framework for thinking about AI implementations. The full post written by Bihan Jiang, Kaylee George, and Cynthia Chen is below! 👇