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Defining value from AI projects early on ensures measurable business outcomes. Learn frameworks, costs to consider, and a real-world case study.
Learn essential AI failures lessons that help organizations build safer, smarter, and more responsible artificial intelligence systems.
Discover how ITSM for mid-sized companies improves efficiency, reduces costs, and boosts ROI through smart automation and self-help practices.
Discover how agile frameworks help scale AI projects from pilots to real outcomes.
Uncover the six pillars of support maturity. See how e-Core’s framework helps you strengthen your support organization and prepare for the future.
Enterprise clients bring high expectations and visibility. Learn how to strengthen your support structure with escalation managers, SLA adjustments, and proactive workflows.
Discover how AI streamlines AWS migration with automation, visibility, and post-migration optimization for scalable, cost-efficient operations.
Streamline your cost-performance strategy with Lambda Power Tuning for smarter application management.
Elevate your operations with SLA Automation: set new standards for reliability and exceptional customer service.
Discover AI delivery best practices to prevent technical debt, improve scalability, and ensure reliable, long-term business impact.
A strategic approach to modernizing code At e-Core, we often partner with organizations whose software technically “works” in production, but just barely. Over time, shifting teams, competing priorities, and tight deadlines lead to codebases that are fragile, expensive to maintain, and risky to enhance. In this context, software refactoring becomes more than a technical task. It’s a business strategy that preserves continuity while unlocking future growth. Defining refactoring: Evolution without disruption Software refactoring is the disciplined restructuring of existing code, improving readability, modularity, and performance, without altering its external behavior. Unlike a full rewrite, refactoring delivers incremental value by: Accelerating feature delivery through cleaner, more modular code. Reducing operational risk by eliminating brittleness and regressions. Lowering total cost of ownership by making enhancements less labor-intensive. Why refactoring matters for business  Healthy codebases have a direct business impact: Faster Time-to-Market: Modular components support parallel development and faster releases. Greater resilience: Decoupled services isolate failures and simplify maintenance. Easier onboarding: Clear architecture and design patterns help new developers ramp up quickly. Sustainable cost savings: Less firefighting, fewer regressions, and lower maintenance overhead. What may look like a simple “cleanup” is often the first step toward digital reinvention. Refactoring techniques In enterprise environments built on Java, C#, or Python, we apply proven object-oriented refactoring techniques to increase cohesion and extensibility: Class decoupling: Split monolithic classes into focused, single-responsibility components. SOLID principles: Apply interface segregation and dependency inversion to stabilize architecture. Composition over inheritance: Build flexible, interchangeable structures. Testability enhancements : Isolate side effects and inject dependencies for better test coverage. These techniques form the foundation of our incremental software refactoring roadmaps. Beyond OO: Choosing the right tools for each paradigm Modern languages blend programming paradigms. We help clients apply the right tools to the right problems: Functional patterns in Python to create concise, side-effect-free data pipelines. Kotlin/Scala hybrids that combine OO structure with expressive functional constructs. C# LINQ and Async for declarative data queries and scalable concurrency. This multi-paradigm approach ensures that systems remain robust, flexible, and expressive. A refactoring story: From debt to differentiation Case Study: Regional logistics provider
AI customer support has moved beyond simple automation; it’s now a critical driver of efficiency, cost reduction, and customer satisfaction. However, many organizations still struggle with implementation, especially when it comes to balancing AI-driven automation with human expertise. This article brings together key insights and market observations to answer some of the most pressing questions about AI customer support, highlighting practical ways teams are using AI to enhance operations. How can AI improve efficiency in customer support? AI is transforming customer support far beyond the realm of simple chatbots. By automating repetitive tasks, optimizing workflows, and delivering actionable insights, AI enables support teams to resolve tickets faster, reduce costs, and enhance customer satisfaction. AI-driven models have already improved ticket severity assessment, escalation management, and automated first responses, allowing teams to operate more efficiently and focus on high-value interactions. What are the most impactful AI-based use cases in support? Many companies are moving beyond basic automation to adopt high-impact solutions in AI customer support. Examples include: Chatbot Agents : When integrated with knowledge bases, modern AI chatbots now reach over 85% accuracy in self-service scenarios ( Gartner, 2024 ), easing the load on support teams. Predictive Analytics : By analyzing historical interactions, predictive models help anticipate and prevent issues before they escalate, cutting resolution times by up to 30%. Sentiment Analysis : AI detects emotional tone in customer messages, enabling timely interventions and improving loyalty. Smart Ticket Distribution : Assigning tickets based on agent expertise and customer sentiment ensures faster resolutions and fewer escalations. Organizations testing AI-driven models have seen manual effort on routine tickets reduced by as much as 70–80%, demonstrating AI’s value in scaling support efficiently. How do you ensure high-quality chatbots in AI customer support? The evolution of AI chatbots has made them far more capable than their rule-based predecessors. Success with AI customer support chatbots hinges on: Up-to-date knowledge bases Seamless handoffs to humans Sentiment-aware, context-driven responses Combining chatbots with analytics and automated ticket handling can help turn them into proactive enablers of support, rather than just reactive tools. What’s the ROI of GenAI in AI customer support? Many organizations implementing AI customer support solutions are already seeing measurable ROI: reduced resolution times, lower operational costs, and increased agent productivity. GenAI-powered tools contribute to more accurate ticket handling, proactive insights, and the ability to scale operations, turning support into a strategic function that goes beyond issue resolution. Is AI replacing human agents in support? Not at all. The real story of AI customer support is empowerment, not replacement. 75% of leaders using AI in support report that their teams have taken on more strategic responsibilities ( Gartner, 2024 ). By offloading repetitive tasks, AI allows agents to focus on empathy, complex resolutions, and innovation. Why is knowledge management essential for AI customer support? A strong knowledge base is the backbone of successful AI customer support. Structured, searchable, and regularly updated repositories ensure that AI solutions, from chatbots to sentiment analysis tools, can deliver accurate and reliable assistance. Consolidating documentation, feedback, and ticket insights in a single platform enables both human agents and AI to perform at their best.