Implementing AI Solutions For Omnichannel Support

Over 50% of our support chats were resolved by our AI assistant last week. No human intervention! This didn’t happen by accident. For small business owners looking to automate support, the real work happens before you flip the AI switch. It starts with building a strong foundation, and getting your team onboard. Here’s how we did it: The Process 1. Audit your support history We analyzed thousands of past tickets and chats to identify the most common and repetitive questions. Yes, we did this with AI. 2. Build (or expand) your knowledge base We created over 1,000 new help articles in a single quarter—filling gaps, refining answers, and making sure every article was easy to follow. Yes, we also created new articles with AI. 3. Train the AI assistant We integrated our knowledge base with our AI assistant and ran extensive testing to improve responses and coverage. 4. Educate and align the team We openly communicated how AI would help, not replace our support team. We showed how it would reduce mundane work and free them up to focus on more strategic, meaningful customer conversations. 5. Monitor, learn, and iterate We continuously tracked resolution rates, flagged weak responses, and kept refining the system. The Results • Faster, more consistent support for customers • 50% drop in manual support chats • A more energized support team, now focused on deeper issues, proactive outreach, and customer success initiatives The Takeaway AI isn’t just a tool. It’s a mindset shift. If your team sees it as a threat, you’ll hit resistance. But if you bring them along—show them how it removes the boring parts of the job so they can focus on the impactful ones, you unlock a whole new level of engagement. The real power of AI isn’t about replacement. It’s about elevation. Elevate your team. Serve your customers better. And don’t skip the groundwork. #AI #CustomerSupport #Automation #SmallBusiness #SaaS #Leadership #CustomerSuccess #ecommerce

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.

Wizr Cx platform provides advanced and enterprise-grade AI agents for customer support. A few key observations deploying these AI agents live with some of our customers.  - Use Case Selection is Important With one of our enterprise clients in the automotive space, we started by AI agents for automating dealer support. The focus was on streamlining inquiries regarding parts availability and service scheduling. By choosing these high-volume tasks, we saw a 29% reduction in response times within 2 months. Selecting well-defined, impactful areas for automation drives early momentum, tangible ROI and better acceptance.  - AI-Augmented, Not AI-Only For a SaaS company handling L1 and L2 support, we found that the most effective model was one where AI agents handled the initial triage and common troubleshooting. AI agents deflected routine inquiries while human agents took over more complex, technical issues at L2. This hybrid approach resulted in a 41% improvement in case resolution times without sacrificing the personalized touch customers value.  - Continuous Tuning for Business Changes is Critical During a major software release for a SaaS client, AI agents struggled with new feature-related queries that weren’t yet part of the existing knowledge base. After the client used our AIOps services to update their knowledge base with release-specific documentation and retrained the AI agents on new workflows, accuracy in handling release-related questions improved by 72%, restoring high effectiveness.  - Agent Training is Just as Important as AI Training In the software industry, rolling out AI wasn't friction-free. Some support agents were initially hesitant to trust AI assistants. By co-training agents and demonstrating how AI-generated solutions could enhance their work, adoption rates soared to 92%, creating a smoother collaboration between AI assistants and human agents, and resulting in significantly higher solve rates. Would love your thoughts on what you are seeing in similar real-life implementations. #AIAgents #CustomerSupport #Enterprise Sirish Kosaraju Srinivas K

After 35+ years running contact centers, I've watched every technology promise come and go. Most fail because they're designed by people who've never managed 4,000 agents during Black Friday or handled executive escalations at 2 AM. AI is different. But only when implemented by operators who understand the business.   Here's what 1 Billion interactions taught us about AI in contact centers:   What doesn't work: Replacing human judgment with algorithms What does work: Enhancing human performance with intelligence What doesn't work: AI as a cost reduction strategy What does work: AI as a performance multiplier What doesn't work: Technology-first implementations What does work: Operations-first integration   Real numbers from our Fortune 500 deployments: 23% improvement in first-call resolution through predictive routing 34% increase in customer satisfaction with real-time sentiment analysis 28% reduction in average handle time without sacrificing quality   The difference between success and failure? Successful implementations treat AI as operational enhancement, not technology replacement. Companies winning with contact center AI focus on integration complexity, change management, and new performance measurements rather than feature lists and vendor demos.   𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐜𝐞𝐧𝐭𝐞𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬: 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐛𝐮𝐲𝐢𝐧𝐠 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 𝐚 𝐥𝐚𝐛, 𝐨𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧?   After managing through every major technology transition in our industry, I can tell you the difference matters more than budget or timeline. Thoughts? What's your experience with AI implementations in contact center environments? . . . . #ContactCenter #AI #CustomerExperience #BPO #Leadership