Integrating AI with Customer Experience Software Solutions

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.

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.

A lot of people think the toughest part about deploying AI agents in enterprise environments is to figure out the best model to use - OpenAI vs Claude vs DeepSeek. Completely wrong. We have worked with top enterprises and multiple public companies to deploy AI support agents, and here’s what we’ve learned: the real question isn’t whether AI can automate support, it’s how to make AI work effectively in the complex, human-centric world of enterprise operations. Yesterday, I was on a call with the Senior VP of Operations for a company handling 4 million annual support issues, and the top questions were: 1. How do we test and monitor the AI at scale? What will effective QA from humans look like? 2. What are the guardrails in the model? Will the AI self-QA before the humans have to QA? 3. What's the workflow to manage the knowledge - can the AI go and update our knowledgebase when it learns new topics? 4. How do we design a hybrid support model so that AI<>Humans can collaborate depending on who is best equipped to respond 6. Most importantly, how do you integrate AI agents into complex enterprise systems without disrupting workflows? - Zendesk + Confluence + Notion + Slack These aren’t just technical challenges, they’re operational and strategic challenges that require deep expertise in both AI and customer experience. The future of AI in customer support isn’t just about the models themselves. While foundational AI infrastructure will inevitably become commoditized (Welcome DeepSeek AI), the real value lies in application layer - the tools and systems that bring AI agents to life and deliver real value in the messy, hybrid environments of large enterprises, with minimal changes. At Fini, we’re building the future of AI-driven support by tackling these questions head-on and delivering real value for our enterprise customers. Out platform makes it dead easy for enterprises to self-deploy, and let their CX teams manage AI<>Human collaboration. The future of customer support is here, and it’s hybrid. Let’s build it together.

Building the AI-Powered Revenue Engine: Right Tool, Right Job There’s no shortage of AI hype and excitement, especially in GTM. But the reality is, companies that are implementing AI strategically are seeing double-digit improvements in revenue metrics. The key insight? Right tool, right job. Successful AI adoption isn’t about replacing humans; it’s about augmenting where people excel and automating where they don’t. Here’s a framework I am thinking through: Where humans should lead: Trust-building, complex negotiations, navigating ambiguity. One SaaS company reduced their SDR, redeploying these reps to focus exclusively on high-value conversations. AI handled initial outreach and qualification. Pipeline increased. Where AI should augment: AI-powered deal inspection improves qualification accuracy, predictive analytics uncovered expansion triggers that boosted upsell revenue, and intelligent prospecting tools identified “in-market” accounts, that sales teams might have missed. Where AI should automate: Routine support queries, onboarding workflows, renewal reminders. Deploying agentic AI solutions can significantly cut simple support tickets and drive increased CSAT. Done right, automation enhances the customer experience. ⚠️ The biggest risk, in my mind, is tool proliferation. I’ve seen teams with multiple point solutions, creating fragmented experiences for both customers and employees. The companies winning are the ones who are: ·      Mapping the entire customer journey, not just individual touchpoints ·      Identifying the highest-friction areas with the greatest impact potential ·      Applying AI with surgical precision at these critical junctures ·      Ensuring data flows seamlessly between systems for unified intelligence The outcome? A revenue engine that balances human expertise with AI efficiency, delivering growth and customer delight. 👉 So, where all of this is heading and what is the integrated, AI-first revenue engine of 2030 going to look like? #AIStrategy #RevOps #CustomerExperience #SalesEffectiveness

AI isn’t one-size-fits-all. Yesterday Adobe announced the general availability of our AI Agents to transform how businesses orchestrate and optimize customer experiences and marketing campaigns. I wanted to share some thoughts on what makes enterprise AI agents fundamentally different from consumer AI tools. 1. Architecture Requirements LLMs are powerful at interpreting prompts and mapping them to likely intents. But in the enterprise, “intent” means more than understanding language. It means turning a request into multi-step, context-aware, and compliant action. That’s why our Agent Orchestrator combines decision science with language models. Enterprises need agents that not only understand but can also plan, prioritize, and execute across systems. The key principle is contextual awareness: knowing who is asking, what data they’re authorized to access, and what organizational rules apply. That requires a very different architecture than general-purpose AI. 2. Domain Specialization vs. Generalization Enterprise agents need to be purpose-built for workflows. A Site Optimization Agent must understand web performance, content performance, and brand guidelines. A Journey Agent must orchestrate across channels while respecting compliance and brand voice. Specialization makes recommendations actionable. The harder part is collaboration across domains, since real business processes rarely live in one function. 3. Human-in-the-Loop Design Unlike consumer AI tools, enterprise contexts are too nuanced for full autonomy. Agents must propose actions, explain their reasoning, and adapt based on human feedback. This isn’t just about safety. It’s how agents learn organizational preferences and align with unique business requirements that no training set can capture. 4. Platform Integration Enterprise agents must be embedded where the work happens, not bolted on as extra tools. That’s why our agents surface inside Adobe’s Real-Time CDP, Experience Manager, Journey Optimizer, and Customer Journey Analytics. The goal is invisible integration, enhancing existing workflows instead of creating new ones for teams to manage. Building enterprise AI agents isn’t about adding AI on top of existing tools. It’s about integrating AI capabilities with organizational context and controls. This is the shift from AI as a helper to AI as a true organizational collaborator. Check out the full announcement here: https://lnkd.in/ghfdK8j8 Get a deeper dive of Adobe Experience Platform agents: https://lnkd.in/gCbV3DMy #AI #EnterpriseAI #CustomerExperience #FutureOfWork #FutureOfExperience Adobe for Business

💎 4 Core Design Principles for Integrating AI into Existing Products More and more companies are integrating AI into their products. When done correctly, AI can streamline interaction and improve UX. When done poorly, users won't have much motivation to use AI. In the worst-case scenario, AI takes the role of MS Clippy and causes annoyance. Here are 4 core principles that org should follow when introducing AI: 1️⃣ Help user express intent (input UX) Usability studies show that chat-based interfaces that many orgs use as a primary medium of interaction with AI engines don't help users express and articulate their intent. Generally, it takes around a minute for an average user to express what they want to achieve. And that's because users often have to start from scratch, typing a text prompt. Practical design recommendations ✅ Provide prompt "templates" or smart auto-completion (much easier if you design AI for a particular niche, such as AI customer support assistant) ✅ Use visual input (allow user to attach visuals that provide additional details) 2️⃣ Presenting results in a user-friendly manner (output UX) Plain text often doesn't help users derive insight fast because users have to read and comprehend the text (which can be slow process when AI provides long blocks of text as an output) Practical design recommendations ✅ Use widgets (widgets are pre-defined content containers for a certain type of content; for example, when a user asks about the weather, we can show a weather widget) ✅ Prioritize results (forced ranking) to avoid choice overload 3️⃣ Make it easy to tweak the output (refinement UX) There is a low chance that AI will solve a problem users have on the first attempt (I mean, the first prompt the user submits). As a result, users often need to polish, trim, and remix AI output. Doing so by writing more prompts is tedious. Practical design recommendations ✅ Expose inline controls (sliders, buttons) to adjust output  ✅ Let users highlight parts to refine or regenerate  ✅ Provide presets or "bookmarkable" states 4️⃣ Choose where AI should live in the workflow (Integration UX) When companies introduce AI, all too often, they add another navigation option called "Assistant" to the list of top-level navigation options. This feels more like an ad-hoc rather than a solid solution to this problem, and it typically leads to a situation where AI remains unused as users resist jumping to a separate tool or mode. Practical design recommendations ✅ Embed AI naturally in tools users already use (for example, Slack uses contextual intros for AI)  ✅ Let AI work "in situ" (AI jumps in in a particular situation and assist the user with a task at hand) 📕 How To Avoid Prompt Treadmill When Designing With AI https://lnkd.in/dBB2Vq2Y 🖼️ Microsoft Clippy, the most annoying Office Assistant ever #AI #design #productdesign

Most people think slapping a chatbot will make the product AI-first. The true value of AI is seen when you integrate AI in a foundational way, not as a surface design. Deep context → high-leverage outcomes. Here are four ways AI can be integrated meaningfully: 1. Map out the root cause of the problem and define the context. If AI is the hammer, you need to know exactly where to hit to make the most impact. 2. Design the AI system first to accomplish the best outcome. Design teams don’t need to be AI engineers, but they do need to learn how AI systems work and build the capability to prototype these systems. 3. Don’t just build AI experiences since it’s trendy and hope for a magic, Continue improving AI experiences by integrating evals. 4. Don’t forget UX heuristics, they’re still relevant. They’re grounded in human psychology. Especially designing for feedback is more important than ever. I could write more, but here are four for now. How do you think design teams can integrate AI meaningfully into products?

AI in CX is evolving fast. For years, AI was primarily used for deflection, automating responses to reduce customer interactions and cut costs. But as businesses scale, they’re realizing something critical: not every interaction is a cost - many are opportunities. We kicked off our AI + CX thought leadership series with Ken Easthouse, former CX and System Analytics Lead at Spoton Fence, to dive into this shift. 𝗔 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝗵𝘂𝗺𝗮𝗻 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗵𝗲𝗺. 🔹 AI-powered agent assistance – Suggesting responses, summarizing conversations, and freeing agents to focus on solving problems. 🔹 Smart knowledge management – AI that dynamically learns, improving accuracy over time. 🔹 AI-driven quality assurance – Automating insights into agent performance and customer satisfaction. 🔹 Personalized AI-driven CX – Identifying customer intent and driving revenue, not just reducing calls. Ken shared an eye-opening insight: 💡 𝗧𝗵𝗲𝗶𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲 𝘄𝗮𝘀 0.5%, 𝗯𝘂𝘁 𝘄𝗵𝗲𝗻 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝘀𝗽𝗼𝗸𝗲 𝘁𝗼 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁, 𝗶𝘁 𝗷𝘂𝗺𝗽𝗲𝗱 𝘁𝗼 20%. Deflection-first AI misses this. But when AI works with human agents, businesses don’t just save costs - they increase revenue and build stronger relationships. A big thank you to Phani for leading this conversation and setting the stage for deeper insights into AI and CX. This is just the beginning! We’ll be speaking with more CX leaders to uncover how AI is shaping the future. If you’re a CX leader with insights on AI’s role in customer experience, we’d love to hear from you! Let’s push the boundaries together 🚀 #customerexperience #cxleaders #ai #customersupport