Evaluating Customer Experience Initiatives

In an AI-first customer journey, shoppers start in ChatGPT—and, with the new Instant Checkout and the Agentic Commerce Protocol (ACP), they don’t have to leave. This is Agentic Customer Experience (ACX): chat → consider → buy. What moves first: • Near term: “considered” specialty purchases shift to AI chat commerce—think bikes, strollers, mixers—where an agent can shortlist, explain trade-offs, and convert. • Longer term: commodity buys flip from search-led (Google/Amazon) to agent-led replenishment. Your assistant will keep you stocked and price-optimized. What doesn’t change (for specialty purchases): • Social discovery still sparks demand. • Brand still matters and earns trust and premium. What does change: • The front door to commerce becomes chat. • Storefronts become APIs. • The funnel compresses: intent, evaluation, and checkout collapse into one conversation. Implications for incumbents: Platforms around commerce, content management, personalization, analytics, and ads (Adobe, Salesforce, and more) will be re-architected for ACX. The surface area for differentiation shifts from page design to data, agents, and outcomes. Opportunities for founders: • Agent-native merchandising & attribution. • ACP connectors/adapters for major platforms; merchant tooling for offers, returns, identity, and fraud. • Product knowledge graphs & RAG for complex catalogs. • Compliance, safety, and brand controls for autonomous purchases. • Journey analytics for agentic flows (from prompt to purchase). ACX won’t kill commerce—but it will reroute the path to purchase for many brands.

Has an AI agent bought you anything nice today? AI agents are evolving from simple chatbots into sophisticated systems that are reshaping the entire shopping experience. Leading Retail AI agent companies are building the foundation for fully autonomous commerce. Enterprise platforms are racing to integrate these capabilities, while nimble startups are creating purpose-built solutions for retail. Today's Retail AI agents are embedding deep into merchant infrastructure, creating the foundation for what comes next. Agents aren't just handling customer support anymore. They're managing product recommendations, order tracking, customer preferences, and building the data foundation for tomorrow's autonomous shopping experiences. Companies deploying these agents today are: → Building consumer trust before the autonomous shift → Gathering critical behavioral data → Preparing their infrastructure for agent-driven transactions → Driving measurable ROI across every aspect of the customer journey What is behind the momentum for leading Retail AI agent companies? 1) Data & Technical Maturity: AI infrastructure finally supports real-time, context-aware interactions at scale and retailers finally have the unified customer data infrastructure to power intelligent agents 2) Consumer Readiness: Post-pandemic shoppers demand instant, personalized service across all touchpoints 3) Economic Pressure: With retail margins under pressure, automation and AI-driven efficiency becomes essential As we see more deployment of agents across the commerce stack, leading implementations reveal clear areas where AI and agents are already creating value for retailers: → Discovery & Personalization: Big Sur AI's adaptive quizzes and AI data scientists optimize product discovery, merchandising, and inventory placement in real-time → Transaction Support: Agents manage cart recovery, payment resolution, and shipping preferences – reducing friction at critical conversion points → Post-Purchase Excellence: From order modifications to loyalty programs, agents handle complex, multi-step processes that previously required human intervention → Channel Integration: Agents are enabling unified experiences across web, mobile, and in-store interactions – ex: DRUID's enterprise deployments show how agents connect CRM, inventory, and fulfillment systems to create seamless experiences AI agents will transform retail. Retailers investing in agent infrastructure now are building competitive moats through better data, refined AI models, and customer trust – advantages that compound over time. The companies building the agentic foundations today will power the shopping experiences of tomorrow. For more data and insights on the companies building the future of commerce, comment “retail agent” for *free* access to CB Insights' data and insights on the Retail AI agents markets.

What CTOs in Banking Should Do with AI for Customer Experience A few months ago, I sat with the CTO of a major bank who shared a familiar frustration: “We’ve invested millions in AI, but our customer experience hasn’t improved the way we expected.” I asked a simple question: “Are you using AI to solve real customer pain points, or are you using it because it’s expected?” That conversation led us down a path that many banking leaders are navigating today—leveraging AI not just for efficiency, but to truly enhance customer relationships. AI and the Future of Banking Customer Experience The global AI in banking market is expected to reach $130 billion by 2030, growing at a CAGR of 32% (Allied Market Research). This isn’t just about chatbots or fraud detection anymore; AI is redefining how banks engage with customers at every touchpoint. McKinsey reports that banks effectively using AI can increase customer satisfaction by 35% while reducing operational costs by up to 25%. The challenge, however, is execution—CTOs must ensure AI is seamlessly integrated into both digital and human interactions. How Leading CTOs Use AI for Customer Experience 1- Hyper-Personalization Example: JPMorgan Chase uses AI to analyze customer behavior and provide real-time loan and investment suggestions, increasing engagement by 40%. 2- AI-Powered Virtual Assistants Example: Bank of America’s Erica, an AI-powered assistant, has handled over 1.5 billion interactions, offering personalized financial insights. 3- Predictive Analytics for Proactive Engagement Example: A European bank using AI-driven insights reduced customer churn by 22% by proactively addressing financial concerns. 4- AI-Enhanced Fraud Detection Example: Mastercard’s AI-based fraud prevention has reduced false declines by 50%, improving trust and security. A Real-World Impact: AI in Action One of our banking clients struggled with high customer complaints about slow loan approvals. By integrating AI-driven document verification and risk assessment, approval times dropped from 5 days to 5 minutes. The result? A 30% increase in loan applications and a significant boost in customer satisfaction. The Human-AI Balance in Banking Despite AI’s capabilities, customers still value human interaction. 88% of banking customers want a mix of AI-powered convenience and human support when dealing with financial decisions (PwC). The key for CTOs is to balance automation with empathy—ensuring AI enhances, rather than replaces, the personal touch. The Road Ahead AI is no longer a futuristic concept in banking—it’s a strategic necessity. CTOs who embrace AI for customer experience, not just efficiency, will lead the industry forward. At Devsinc, we believe the future of banking isn’t just digital—it’s intelligent, personalized, and deeply customer-centric. The question is, are we using AI to replace transactions, or to build trust? Because in banking, trust isn’t just a feature—it’s the foundation.

Every delightful customer interaction begins with the marketer, and it can only be as powerful as the #CRM and #metadata underpinning it. With agents supporting them at every step of the customer journey creation process, marketers and #customerengagement teams can now create superior experiences shaped by intelligent and emotionally resonant conversations. At a cognitive level, the human brain no longer perceives AI as a “chatbot.” It perceives a relationship. This emotional shift fundamentally changes how consumers relate to brands, fostering deeper loyalty and trust. When customers interact with agents in a way that feels natural, their engagement deepens. The implications go far beyond engagement. Every AI-driven interaction generates a wealth of contextual data, far richer than what brands could ever collect from a single web form or survey. In one conversation, an agent can gather insights about a customer’s preferences, behaviors, and intent, building a more complete, dynamic customer profile. This continuous intelligence loop allows brands to maximize the value of every interaction. Let’s bring this to life with an example... Imagine Melanie, one of your many potential customers. She’s been thinking about joining Posh Fitness, a popular gym chain in her city. Instead of filling out a form, she decides to engage with the agent on their website. As they chat, it quickly feels more like a friendly exchange than a transaction. Melanie shares her fitness goals, whether she wants to lose weight, gain muscle, or improve flexibility, and the agent listens closely, asking the right questions to understand her needs and intent. The agent gathers valuable insights through this conversation that a simple web form could never capture. Melanie mentions her dietary restrictions, her preference for a supportive personal trainer style, and that she loves outdoor workouts but needs a flexible schedule due to her busy life. In just a few minutes, the agent collects a wealth of data about Melanie: her goals, preferences, and availability—all essential to crafting a personalized experience. And because the conversation feels human-like and emotionally resonant, it creates an immediate connection to Posh Fitness. By collecting this richer data early in the relationship, Posh Fitness can offer tailored recommendations and build Melanie’s loyalty well before she signs up. This isn’t just about closing a sale. It’s about building trust and delivering personalized experiences that evoke emotions and feel deeply human. Brands that will thrive in the era of #Agentic #AI are those that recognize the shift from transactional interactions to relationship-driven engagement. This isn’t just about personalization; it’s about creating experiences and dialogues that feel alive—where AI and marketers co-create journeys that adapt in real time, amplifying the impact of every customer moment.

AI can significantly enhance efficiency, personalization, & responsiveness, ultimately leading to a better customer experience :- Chatbots & Virtual Assistants: AI-powered chatbots & virtual assistants can handle routine customer inquiries 24/7. They can provide instant responses, answer frequently asked questions, and assist with basic problem-solving, freeing up human agents for more complex issues. Natural Language Processing (NLP): NLP allows AI systems to understand & process human language. AI can analyze customer inquiries to identify sentiment, extract important information, & provide context-aware responses. Personalization: AI can analyze customer data to create personalized experiences. This includes recommending products or services based on past behavior, addressing customers by name, and tailoring content to individual preferences. Predictive Analytics: AI can predict customer behavior & needs by analyzing historical data. This helps companies address issues before a customer even asks for it. Automated Email & Text Responses: AI can automatically categorize and respond to emails & text messages based on predefined criteria. Voice Recognition: AI-driven voice recognition systems can be used in call centers to transcribe and analyze customer-agent conversations. This helps improve agent performance. Sentiment Analysis: AI can analyze customer feedback, reviews, and social media comments to gauge customer sentiment. This information can be used to make improvements in products and services and address negative feedback promptly. Self-Service Portals: AI can power self-service portals that allow customers to find information, track orders, and troubleshoot issues on their own. This reduces the volume of inquiries to human agents. Automated Ticketing and Routing: AI can automatically categorize & prioritize support tickets, ensuring that urgent issues are addressed promptly and assigned to the right team or agent. Knowledge Management: AI can help companies build & maintain extensive knowledge bases by automatically categorizing and tagging content, making it easier for both agents & customers to find information. Emotion Recognition: AI can be used to detect customer emotions during interactions, allowing agents to adjust their approach accordingly & provide empathetic responses. Cross-Channel Consistency: AI can ensure that customer interactions are consistent across various channels (web, mobile, chat, email, phone), providing a seamless experience. Training and Onboarding: AI-driven tools can assist in training and onboarding customer service agents, helping them acquire product knowledge and best practices more efficiently. Quality Assurance: AI can monitor and analyze agent-customer interactions to assess performance, provide feedback, and maintain service quality. Multilingual Support: AI-powered translation and language processing can help companies provide support to customers in multiple languages.

AI + HI = Improved CX In today’s digital world, businesses strive to deliver exceptional customer experiences (CX) to stand out. While artificial intelligence (AI) has revolutionized CX by enabling automation, personalization, and efficiency, it cannot fully replace the human touch. AI enhances CX by processing vast amounts of data in real time, predicting customer preferences, and providing instant responses through chatbots, recommendation engines, and self-service options. It reduces wait times, offers 24/7 support, and ensures consistency across interactions. However, AI alone has limitations—it lacks emotional intelligence, creativity, and the ability to handle complex, nuanced customer concerns. Human agents bring empathy, critical thinking, and problem-solving skills that AI cannot replicate. When combined with AI, human agents become more efficient, as AI handles routine tasks, provides insights, and allows them to focus on high-value interactions. Impact on BPO KPIs 1. First Call Resolution (FCR) Improvement: • AI-driven knowledge bases and predictive analytics equip human agents with real-time solutions, reducing repeat calls. • Virtual assistants handle routine inquiries, allowing human agents to focus on complex issues. 2. Reduction in Average Handling Time (AHT): • AI-powered tools like speech analytics and automated summaries minimize the time agents spend on after-call work (ACW). • Virtual assistants can gather customer information before handing over to a live agent, speeding up resolutions. 3. Increased Customer Satisfaction (CSAT): • AI ensures faster response times and personalized interactions based on past behavior. • Human agents, equipped with AI-driven insights, can provide more empathetic and accurate solutions, improving overall satisfaction. 4. Enhanced Agent Productivity and Utilization: • AI automates repetitive tasks such as data entry, ticket classification, and FAQs, freeing up agents for complex interactions. • Sentiment analysis tools help agents adjust their approach in real time for better engagement. 5. Lower Cost Per Contact: • AI-driven self-service options reduce the volume of inbound calls and chats, lowering operational costs. • Intelligent routing ensures the right agent handles the right query, optimizing workforce efficiency. 6. Improved Net Promoter Score (NPS): • Personalized AI-driven recommendations and proactive outreach enhance customer engagement. • The combination of AI efficiency and human empathy fosters long-term customer loyalty. The synergy of AI and HI leads to an improved CX by ensuring speed, accuracy, and emotional connection. AI-driven insights empower human agents to offer proactive solutions, while human empathy ensures customers feel valued. AI and HI are not competitors but collaborators. Businesses that successfully integrate both will deliver superior CX, optimize BPO performance, and achieve sustainable growth in an increasingly digital world.

💡 The Future of Customer Experience Is Already Here — And These 5 Startups Prove It Most CX programs talk about “delight” and “innovation.” These up-and-coming startups are actually doing it. Here are 5 that caught my eye — each showing a fresh, scalable way to rethink how businesses serve customers: 🌟 1. Qvasa – Real-time Customer Trend Radar Qvasa integrates directly with Zendesk to surface customer pain points and trends in real time. With AI-driven tagging + instant alerts via Slack or email, support teams don’t just respond—they anticipate. This turns CX from reactive firefighting into proactive strategy. 🌟 2. Monterey AI – Feedback That Actually Drives Roadmaps Customer feedback often dies in static docs. Monterey AI changes that with smart tagging, AI-based workflow automation, and natural language Q&A. It helps product and CX teams connect insights directly to decisions—shortening the distance between what customers ask for and what companies build. 🌟 3. Aisera – Generative AI for Enterprise Support Already trusted by Zoom and Snowflake, Aisera uses generative AI to handle repetitive support tasks instantly. Think password resets, FAQs, or IT tickets—all automated. The result: higher CSAT scores, lower costs, and human agents freed up for complex, empathy-driven work. 🌟 4. Lucidya – CX Intelligence for the Arabic World Serving the GCC region, Lucidya built one of the most advanced Arabic sentiment analysis engines—covering 15 dialects with 92% accuracy. This means brands can finally understand customers in their authentic voice. It’s a perfect example of CX tuned to cultural and linguistic nuance. 🌟 5. Rwazi – On-the-Ground Consumer Insights in Africa Rwazi collects real-time, consent-based consumer signals across African markets. Their AI engine, Sena, transforms raw local data into actionable insights for global brands. This is customer understanding at scale—helping businesses meet fast-changing demand in one of the world’s most dynamic regions. ✨ Why This Matters ▪️ They’re using AI as an enabler, not a gimmick. ▪️ They focus on bridging the gap between feedback and action. ▪️ They show that CX innovation is global, not just Silicon Valley. If you’re in CX or CS, these startups are worth watching closely. They’re building the playbook we’ll all be using tomorrow. 👉 Which of these resonates most with you? #CustomerExperience #CXInnovation #Startups #AI #CustomerSuccess

🎯 Enhancing Customer Service with AI in Microsoft Dynamics 365 CRM 🚀 Customer expectations are evolving, and businesses must adapt to provide faster, more personalized, and efficient support. Microsoft Dynamics 365 Customer Service is leading this shift with AI-driven innovations and automation-first experiences. Here’s what’s new in 2025: 🛠️ Key Enhancements in Dynamics 365 Customer Service 🔹 AI-Powered Case Summarization • Copilot now generates automatic case summaries from past interactions, enabling agents to get up to speed quickly. • Reduces manual effort and improves first-contact resolution rates. 🔹 Intelligent Routing with AI • Uses machine learning and natural language processing (NLP) to classify and route cases to the right agent or bot. • Ensures faster resolution times and improved customer satisfaction. 🔹 Proactive Issue Resolution with IoT & AI • Integrated IoT monitoring detects potential issues before customers even notice them. • Triggers automated case creation and assigns tasks to service teams before a failure occurs. 🔹 Virtual Agent Improvements • The built-in Copilot chatbot now offers more natural conversations and automated workflows for common issues. • Seamlessly integrates with Omnichannel for Customer Service, reducing agent workload. 🔹 Omnichannel Customer Engagement • Deeper integration with WhatsApp, SMS, and social messaging apps to enable real-time, AI-assisted interactions. • Supports live sentiment analysis to help agents tailor responses effectively. 🔹 Enhanced Knowledge Management • AI-driven knowledge article recommendations ensure agents always have the most relevant information. • Automated content updates keep documentation accurate without manual intervention. 💡 Business Impact ✔️ Faster response times and reduced case resolution effort. ✔️ More personalized customer experiences with AI-driven insights. ✔️ Improved agent productivity through automation and knowledge suggestions. ✔️ Cost savings by deflecting simple queries to AI-powered chatbots. 📌 Want to learn more? Check out Microsoft’s official release notes on Customer Service: https://lnkd.in/gvCtGZFd What’s your experience with AI in customer service? Have you implemented these features yet? Let’s discuss in the comments! 👇 #MicrosoftDynamics365 #CustomerService #AI #Automation #DigitalTransformation #CRM

Struggling to fine-tune your Amazon Connect bots? Self-service logs and Contact Lens dashboards give you the clarity you need. Let’s see why they’re the secret to smarter, more efficient bot management! These tools don’t just track activity… They show what really works in customer interactions and where bots can improve. Here’s why self-service logs and Contact Lens dashboards optimize bot management👇🏻 ✅ Complete Interaction Visibility Capture every user input, bot response, and conversational path to identify successes and failures. ✅ Intent Accuracy Insights Understand which intents are recognized correctly and which need refinement. ✅ Customer Behavior Patterns Spot where users struggle, abandon interactions, or escalate to human agents. ✅ AI-Powered Analysis Leverage sentiment trends and analytics to pinpoint optimization opportunities. ✅ Data-Driven Decisions Enable proactive bot improvements, increasing efficiency and customer satisfaction. With these insights, bot management becomes precise, proactive, and data-backed. PS: Are your bots really performing at their best? Share your experience or challenges with bot management in the comments 👇 #AmazonConnect #CustomerExperience #AWS