AI-Powered Support Platforms

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.

Contact centers may not be the most exciting application for AI, but as our team has been digging into the category, I’ve been impressed by how far things have come — even since we last looked at it a few months ago. One area in particular is AI agent assistants. These copilot solutions are advancing rapidly, with capabilities such as: • Call summarization, classification, and structured data collection (i.e. filling out CRM fields) • Agent response and next-best-action support (for both chat and phone conversations) • Real-time caller sentiment analysis • Real-time QA and agent feedback • Automatic surfacing of relevant information (e.g. SOPs, help content, and customer info) Unlike many of the other areas we cover, the AI agent assistant category is primarily composed of vendors who are not specific to the healthcare industry. These products frequently show up as part of more comprehensive omnichannel Contact Center as a Service (CCaaS) platforms, such as: • Bright Pattern • Dialpad • Five9 • Genesys • NICE • Talkdesk • ujet.cx Additionally, there are a handful of industry-agnostic vendors who offer agent assistants as a standalone product or paired with broader intelligence features, like QA insights and performance analytics. These include: • Abstrakt • Balto • Convin • JustCall • Level AI Where the vendors above offer solutions that will work across all contact center use cases, there are situations where solutions for specific healthcare workflows — such as instances where clinical care and digital communication overlap — are needed. While these solutions may not work for your entire contact center, they can drive meaningful value for specific aspects of your operation. Examples include: • Birch.ai - healthcare-specific AI-powered agent assistants and call center intelligence • Laguna Health - AI-enabled conversational AI care management platform • Rotera Alyks - digital assistant for revenue cycle call center operations • Verbal - AI-enabled assistance and QA platform for virtual care clinicians We're interested to see whether organizations will be willing to implement multiple specialized solutions or will sacrifice specificity for efficiency with one-size-fits-all options. Like everything else in AI these days, this space is evolving rapidly.

We’ve just published major new industry research on applied agentic AI. Most organizations don’t realize how big a role agentic AI is already starting to play in customer support. What began as simple chatbots is now evolving into truly intelligent, contextual systems that anticipate needs, reason through complex requests, and act autonomously. It’s now reshaping the frontline of the customer support experience. At The Futurum Group, Keith Kirkpatrick, Nick Patience, and I set out to rigorously benchmark the leading vendors shaping this major market segment, representing many millions of support transactions per year. We didn’t take shortcuts. Our team poured over earnings calls, public filings, traffic data, vendor briefings, and our own intelligence platform to build a highly accurate model to create the most balanced, objective market view on agentic customer support available today. The results? Salesforce’s Agentforce on Help ranked #1 in full-stack, enterprise-grade agentic support, delivering the largest volume of governed, reasoning-enabled transactions of any vendor we studied. Microsoft, Amazon Web Services (AWS), SAP, Adobe, Oracle , IBM, Google Cloud, ServiceNow, Atlassian, and Intercom rounded out the field. This research makes clear: • Governance and reasoning separate hype from enterprise-ready systems. • Vendors are racing to prove agentic value with “customer-zero” strategies. • True self-optimizing agents remain early stage, but momentum is accelerating fast. Colorful insight: The real story isn’t that enterprises are experimenting with AI in support, it’s that agentic systems are quietly handling millions of real customer interactions every month, with governance and reasoning baked in. The future isn’t coming. It’s already in production today. You can download a copy of the research here, courtesy of Salesforce: https://buff.ly/c3GbGHq cc Daniel Newman Dan O'Brien Tiffani Bova Alex Smith Kathleen Barron Mitch Ashley Dave Eitler Fernando Montenegro

🧠 AI-First Use Cases for Customer Success, Account Management & Support It's not just sales that can benefit from AI-powered automation. We're also thinking on the customer experience and how we can better serve our customers leveraging AI in our workflows at Vanta: 🆕 Onboarding & Activation - Agentic AI-led Customer Onboarding – An autonomous AI agent walks customers through onboarding, dynamically adjusting based on user behavior, role, and progress. - Automated Customer Onboarding – AI sends tailored welcome messages, interactive walkthroughs, training content, and milestone reminders, with personalized progress tracking. - Onboarding Risk Prediction – AI flags customers likely to stall during onboarding based on usage signals, role, and industry, prompting human intervention at the right moment. 📊 Customer Health, Retention & Expansion - AI-generated Customer Health Scores – AI continuously monitors product usage, NPS scores, ticket volume, and sentiment to produce a dynamic, predictive health score. - AI-powered Renewal & Expansion Insights – Predictive models surface customers likely to churn or ready to expand based on product adoption, engagement signals, and historical behavior. - Automated QBR Generation – AI creates tailored quarterly business review decks using real-time usage data, benchmarks, and suggested action items for growth or risk mitigation. 🗣️ Feedback & Voice of the Customer - AI-powered Customer Feedback Collection & Tracking – AI gathers structured feedback from NPS, CSAT, support tickets, onboarding surveys, and calls, and categorizes it into themes for PM and GTM teams. - Product Feedback Loop Automation – When a customer submits a product request, AI logs and categorizes it, tracks request status, and automatically follows up when the request is fulfilled or addressed. 💬 Support & Issue Resolution - AI-driven Support Ticket Triage – AI prioritizes and routes incoming tickets by urgency, topic, and customer tier, suggesting answers or tagging the appropriate team. - Self-service AI Knowledge Assistant – A conversational AI assistant that provides customers with instant, contextual answers based on docs, past tickets, and product updates. - Auto-Response Suggestions – AI drafts first-response templates to support tickets, tailored to ticket context and customer profile, saving agents significant time. 🎯 Proactive Engagement - AI-Powered Play Recommendations – AI suggests proactive outreach plays for CSMs and AMs based on customer lifecycle stage, feature usage, or risk indicators. - Milestone Celebration Automation – Automatically send personalized emails or in-app messages when customers hit key milestones (e.g., passed audit, integrated first vendor), boosting engagement. - Usage Pattern Anomaly Detection – AI spots abnormal drops or spikes in usage and alerts the account team to investigate. Interested in solving these problems with us? Check out our Founder in Residence role opening! 🚀

The Future of Support: Assign Agents using AI-based predictive demand and skills availability: 🎢 Traditional support systems can't keep up with the complexities of global teams working in multiple shifts. When support managers need to balance SLAs, workloads, and ensure the best fit between agents and tasks, conventional rule-based algorithms fall short. What happens? 👉🏻Overload smarter engineers 👉🏻Not able to reduce the night shift loads (work-life balance) 👉🏻Not able to manage SLA proactively Customers are using Ascendo AI's Cognitive Routing models with existing CRMs. Imagine an advanced algorithm that: 👍 Forecasts demand by issue type, product, priority, customer entitlement, and region. 👍Assesses agent availability based on skill sets, scheduled shifts, and other factors. 👍Predicts resolution times to ensure timely responses and satisfied customers, and many more attributes. With cognitive routing, you can optimize your team's efficiency and performance by matching the right agent to the right task at the right time. Say goodbye to the inefficiencies of traditional systems and hello to smarter support management. 🌐 Ready to elevate your support operations? Let us engage in a conversation. Let's make meaningful outcomes with AI. #SupportManagement #CognitiveRouting #CustomerService #Innovation #ArtificialIntelligence

AI isn’t the future of customer support—it’s the present. The landscape is evolving rapidly, and companies that fail to adapt risk falling behind. Today’s best AI Agents already handle a significant portion of informational queries and personalized queries, with advancements in actions and troubleshooting accelerating quickly. The question is no longer if AI will transform support, but how fast and how effectively businesses can implement it. 💡 So how do we take action? The best approach is a clear roadmap that moves customer support from reactive to proactive AI systems: ✅ Phase 1 (Now): AI for triage, classification, and knowledge synthesis ✅ Phase 2 (Soon): AI orchestration & automation, reducing human effort on repetitive tasks ✅ Phase 3 (Future): Fully autonomous AI support, where AI anticipates and resolves issues before they escalate The organizations leading this shift are those treating AI not as an isolated tool, but as an intelligent, interconnected system—one that learns, anticipates, and evolves. The future of customer support isn’t just AI-assisted—it’s AI-powered. If you’re not already building for this future, the time to start is now. #AI #CustomerSupport #Automation #FutureOfWork #AIinBusiness

650. That’s the staggering number of companies offering conversational AI solutions for sales and service. The flood isn’t slowing: each week brings new entrants or announcements. A year ago, the market was already crowded; today, the latest wave of AI technologies has further lowered barriers to entry, fueling an unsustainable proliferation. Beyond the three hyperscalers, only a handful of providers have surpassed $100M in ARR. I spent the summer making sense of the mayhem. The result: nine categories mapped to the core jobs-to-be-done. Customer service and support solutions fall into four categories: • Virtual Agents. IVAs and their AI evolution operate across digital channels, handling transactional interactions and escalating to humans when necessary. • AI Answer Engines. These retrieve and format answers from knowledge bases. Generative AI has dramatically improved precision for informational inquiries. • Conversational IVR and Voice Agents. Voice remains complex; these agents primarily handle transactional interactions. • Conversational Engagement and Outreach Agents. These manage outbound communications across voice, SMS, and messaging channels, complying with regulations. Historically transactional, they increasingly enable dynamic engagement. Sales solutions are grouped into three categories: • Conversational Commerce & Concierge Agents. Mature agents replacing traditional chat with conversational experiences across pre- and post-sales. "Concierge" reflects their versatility in guiding customers seamlessly. • Autonomous SDRs (Sales Development Reps). Focused on complex B2B scenarios, they enrich and qualify leads, route them to sellers, and schedule appointments. Among the most mature AI applications for B2B sales. • Autonomous BDRs (Business Development Reps). These drive outbound sales motions where relevance is critical. Complex to implement and scale, they work best in highly targeted scenarios where personalization is flawless. Some providers span the full spectrum of service use cases and Conversational Commerce & Concierge Agents. Rather than duplicating them across categories, I group them under Conversational AI Platforms, relying on robust capabilities to design, deploy, and continuously improve applications and agents. Customer Support Automation is an emerging platform category, tailored for handling support requests and a natural fit for GenAI. These platforms deliver full resolutions when possible, automate workflows, and assist agents with context and guidance. It’s a mature use case for Agentic AI, with many providers publicly demonstrating transformative results. The visual landscape below captures this segmentation. A few vendors will emerge as true platforms, while others will focus on niches or become embedded in broader applications. The market remains in motion, and I welcome perspectives on what I may have overlooked. #conversationalai #agenticai #cx #salestech

AI in Customer Support: Beyond Cost Savings! Just listened to an eye-opening podcast with Alex Khoroshchak, CEO of CoSupport.ai, and it completely changed my perspective on AI in customer support! AI isn’t just about cutting costs—it’s about driving sales, extracting insights, and transforming customer interactions. 🔹 The AI Adoption Challenge Many businesses hesitate to adopt AI, fearing it’s a black box or prone to hallucinations. But shifting from human agents to AI-powered systems is a major transformation, not just a quick fix. 🔹 Agentic AI: The Future of Customer Support Unlike traditional chatbots, agentic AI doesn’t just respond—it acts. These systems: ✔️ Analyse data in real time ✔️ Follow SOPs to resolve complex requests ✔️ Process refunds, troubleshoot issues, and personalize support ✔️ Provide 24/7 multilingual assistance across platforms For e-commerce & tech, this is a game-changer—faster, better, and more scalable support with smaller human teams. 🔹 Build vs. Buy? The Hard Truth Building AI in-house sounds tempting, but it requires: ✅ Machine learning expertise ✅ Massive training data ✅ Deep system integrations For many, partnering with AI experts makes more sense than reinventing the wheel. 🔹 The Future: AI as a Business Intelligence Engine AI will not only solve queries but also: 📊 Extract insights for better product decisions 🎯 Optimize sales & marketing 💡 Proactively engage customers This isn’t just about automation—it’s business transformation. You can listen to the full episode here: https://lnkd.in/d9fCMau7 What do you think? • Will agentic AI replace human support or complement it? • Should companies build or buy AI solutions? • What’s the biggest challenge in AI adoption for customer support? Let’s discuss! 🔥👇 #AI #CustomerSupport #AgenticAI #BusinessIntelligence #Automation #FutureOfWork #CustomerExperience #AIinBusiness #DigitalTransformation