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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 top insurer aligned data and AI to cut waste, speed up delivery, and turn preauthorization into a fast, accurate, real-time process.
Energisa modernized 70 systems by migrating to AWS with e-Core’s support, gaining scalability, availability, and performance in the energy sector.
Introduction In today’s digital economy, IT Service Management (ITSM) platforms are more than just support tools, they are strategic enablers that impact operational efficiency, user satisfaction, and business agility. With an increasing focus on automation, AI, and user-centric service delivery, selecting the right service desk solution has become a critical decision for CIOs, IT leaders, and digital transformation teams. Why choosing the right service desk tool matters The service desk is the heart of IT operations. Beyond resolving tickets, it supports governance, ensures compliance, and drives consistent service experiences. A well-chosen ITSM platform enables faster resolution, proactive problem management, better user engagement, and alignment with digital transformation goals. On the other hand, a misaligned tool can lead to inefficiencies, user dissatisfaction, and mounting operational costs. When to use each tool Choosing the ideal tool depends on your organization’s size, IT maturity, integration needs, and strategic objectives: Jira Service Management (JSM): Best suited for organizations already using the Atlassian ecosystem or looking for agility and fast ROI. Strong in DevOps integration and automation via Rovo. ServiceNow : Preferred for large enterprises needing robust enterprise-grade features, deep AI capabilities, and high levels of customization. Ideal for those with higher budgets and complex service architectures. Freshservice: A favorite among mid-sized companies that need a full-featured ITSM without the complexity. Known for ease of use and rapid implementation. Zendesk: Strong contender for businesses that need multichannel support, especially customer-facing teams. Good choice for aligning IT support with customer experience. ManageEngine ServiceDesk Plus: Ideal for organizations needing strong ITIL compliance, asset management, and on-premise or hybrid deployment options.
Throughout my career as a Project Manager, I’ve seen many companies, even those whose core business is technology, struggle to adopt Agile project management models like Scrum and Kanban. While many claim to use these methodologies, most actually operate under a hybrid approach, applying selected Agile practices while maintaining a traditional mindset. Even more than 20 years after the Agile Manifesto, many organizations still have difficulty executing core Agile practices. Issues like inconsistent ceremonies , poor task prioritization , and lack of delivery planning often delay product launches and lead to team dissatisfaction. Recently, I served as a Scrum Master for a client project where, over six months, we improved the team’s Agile processes and project management maturity. Working in an extended team model with the client’s development team and Product Owner, I helped optimize ceremonies and workflows while the client team remained focused on strategic business efforts. By the end of this period, the squad was operating more efficiently, with better backlog management, faster task execution, and shorter release times. This article shares the Agile improvements we implemented and the benefits they brought to the te am. Optimizing the daily me eting Agile frameworks recommend that the Daily Meeting (or stand-up) lasts no more than 15 minutes. When I joined the project, these meetings often exceeded 30 minutes because they had become status report sessions, reviewing each task and its delivery timeline. To fix this, we refocused the Daily on its core purpose: having each developer share (a) what they worked on, (b) what they planned to work on next, and (c) whether they had any blockers. To support the change, we created a shared page where team members could write their answers before the meeting. This helped them prepare concise updates. Eventually, this support wasn’t needed anymore, the team internalized the practice. We also noticed that completed tasks weren’t advancing through the Kanban board because developers weren’t updating the workflow status. This created bottlenecks despite tasks being technically done. To address this, we encouraged the team to update the task status daily, document the adopted solution, identify the next step, and assign the task to the next person in the execution flow . Eventually, the team became autonomous in keeping the board up to date. Another challenge was that unrelated discussions often took place during the Daily, such as deep dives into technical solutions. To resolve this, we created a separate 15-minute daily meeting for technical discussions and a weekly refinement session for reviewing complex tasks. This kept the Daily focused and efficient. By the end of the project, the Daily Meeting had gone from 40 to 15 minutes thanks to these adjustments . Product backlog prioritization One of the most common challenges in Agile teams is backlog prioritization. When everything is a priority, nothing truly is. Trying to deliver too many features at once reduces focus and often results in rework. In our project, the Product Managers had a long list of features they wanted released as soon as possible. We worked with the PO to rank tasks based on business value, impact, and urgency , using prioritization techniques such as customer value, cost of delay, and effort-to-impact ratios. We ensured this prioritization was clearly reflected in the Kanban board so developers would know what to tackle next without needing to ask. This increased autonomy and avoided idle time between tasks. The prioritized backlog also became a prerequisite for Sprint Planning. Developers would estimate each task, and based on team velocity , we could forecast what was achievable in the next Sprint. By avoiding work on lower-priority items before higher-priority ones were completed, we reduced WIP (Work in Progress) and increased delivery focus. Over six months, we reduced WIP by 20% and doubled the number of completed tasks per Sprint. Improving task detail Developers need well-defined tasks to execute efficiently. Yet in this project, many tasks only had a title or vague descriptions, requiring developers to chase down details, which delayed development.
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 top insurer aligned data and AI to cut waste, speed up delivery, and turn preauthorization into a fast, accurate, real-time process.
Energisa modernized 70 systems by migrating to AWS with e-Core’s support, gaining scalability, availability, and performance in the energy sector.
Introduction In today’s digital economy, IT Service Management (ITSM) platforms are more than just support tools, they are strategic enablers that impact operational efficiency, user satisfaction, and business agility. With an increasing focus on automation, AI, and user-centric service delivery, selecting the right service desk solution has become a critical decision for CIOs, IT leaders, and digital transformation teams. Why choosing the right service desk tool matters The service desk is the heart of IT operations. Beyond resolving tickets, it supports governance, ensures compliance, and drives consistent service experiences. A well-chosen ITSM platform enables faster resolution, proactive problem management, better user engagement, and alignment with digital transformation goals. On the other hand, a misaligned tool can lead to inefficiencies, user dissatisfaction, and mounting operational costs. When to use each tool Choosing the ideal tool depends on your organization’s size, IT maturity, integration needs, and strategic objectives: Jira Service Management (JSM): Best suited for organizations already using the Atlassian ecosystem or looking for agility and fast ROI. Strong in DevOps integration and automation via Rovo. ServiceNow : Preferred for large enterprises needing robust enterprise-grade features, deep AI capabilities, and high levels of customization. Ideal for those with higher budgets and complex service architectures. Freshservice: A favorite among mid-sized companies that need a full-featured ITSM without the complexity. Known for ease of use and rapid implementation. Zendesk: Strong contender for businesses that need multichannel support, especially customer-facing teams. Good choice for aligning IT support with customer experience. ManageEngine ServiceDesk Plus: Ideal for organizations needing strong ITIL compliance, asset management, and on-premise or hybrid deployment options.
Throughout my career as a Project Manager, I’ve seen many companies, even those whose core business is technology, struggle to adopt Agile project management models like Scrum and Kanban. While many claim to use these methodologies, most actually operate under a hybrid approach, applying selected Agile practices while maintaining a traditional mindset. Even more than 20 years after the Agile Manifesto, many organizations still have difficulty executing core Agile practices. Issues like inconsistent ceremonies , poor task prioritization , and lack of delivery planning often delay product launches and lead to team dissatisfaction. Recently, I served as a Scrum Master for a client project where, over six months, we improved the team’s Agile processes and project management maturity. Working in an extended team model with the client’s development team and Product Owner, I helped optimize ceremonies and workflows while the client team remained focused on strategic business efforts. By the end of this period, the squad was operating more efficiently, with better backlog management, faster task execution, and shorter release times. This article shares the Agile improvements we implemented and the benefits they brought to the te am. Optimizing the daily me eting Agile frameworks recommend that the Daily Meeting (or stand-up) lasts no more than 15 minutes. When I joined the project, these meetings often exceeded 30 minutes because they had become status report sessions, reviewing each task and its delivery timeline. To fix this, we refocused the Daily on its core purpose: having each developer share (a) what they worked on, (b) what they planned to work on next, and (c) whether they had any blockers. To support the change, we created a shared page where team members could write their answers before the meeting. This helped them prepare concise updates. Eventually, this support wasn’t needed anymore, the team internalized the practice. We also noticed that completed tasks weren’t advancing through the Kanban board because developers weren’t updating the workflow status. This created bottlenecks despite tasks being technically done. To address this, we encouraged the team to update the task status daily, document the adopted solution, identify the next step, and assign the task to the next person in the execution flow . Eventually, the team became autonomous in keeping the board up to date. Another challenge was that unrelated discussions often took place during the Daily, such as deep dives into technical solutions. To resolve this, we created a separate 15-minute daily meeting for technical discussions and a weekly refinement session for reviewing complex tasks. This kept the Daily focused and efficient. By the end of the project, the Daily Meeting had gone from 40 to 15 minutes thanks to these adjustments . Product backlog prioritization One of the most common challenges in Agile teams is backlog prioritization. When everything is a priority, nothing truly is. Trying to deliver too many features at once reduces focus and often results in rework. In our project, the Product Managers had a long list of features they wanted released as soon as possible. We worked with the PO to rank tasks based on business value, impact, and urgency , using prioritization techniques such as customer value, cost of delay, and effort-to-impact ratios. We ensured this prioritization was clearly reflected in the Kanban board so developers would know what to tackle next without needing to ask. This increased autonomy and avoided idle time between tasks. The prioritized backlog also became a prerequisite for Sprint Planning. Developers would estimate each task, and based on team velocity , we could forecast what was achievable in the next Sprint. By avoiding work on lower-priority items before higher-priority ones were completed, we reduced WIP (Work in Progress) and increased delivery focus. Over six months, we reduced WIP by 20% and doubled the number of completed tasks per Sprint. Improving task detail Developers need well-defined tasks to execute efficiently. Yet in this project, many tasks only had a title or vague descriptions, requiring developers to chase down details, which delayed development.
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
Background Ricksoft is known for its popular work and project management apps on the Atlassian Marketplace, including WBS Gantt-Chart for Jira and Excel-like Bulk Issue Editor for Jira. Originating in Japan, the company has grown to serve over 8,000 customers worldwide through nine apps. Their tools are trusted by some of the world’s most innovative companies to boost productivity and manage complex work. By 2019, Ricksoft was seeing growing demand from customers in the Americas and Europe, and needed to scale its support operation beyond Japan. To serve new markets effectively, they had to provide fast, high-quality service in English and across time zones, while maintaining the responsiveness and care that defined their brand. That’s when they turned to e-Core. Our approach We designed a support solution based on three key principles: timezone alignment, consistent high-quality service, and cultural alignment . To get things right from the start, our analysts flew to Japan for in-depth training. They didn’t just learn the products; they learned how Ricksoft works , communicates, and supports customers. Once training wrapped, we built the support operation around Ricksoft’s priorities: timely responses, consistency, and a thoughtful, human tone. We created detailed documentation and a clear process for hiring and onboarding, ensuring every new analyst could step into the role with confidence and alignment. We also introduced monthly updates to the knowledge base and took responsibility for keeping existing articles relevant. That work paid off: most tickets are now resolved in just two to three exchanges , and self-service performance has improved across the board. Today, Ricksoft holds a satisfaction rate of 89 percent and a CSAT of 4.63 out of 5 , well above industry averages. As new analysts joined over the years and support expanded to more products, the service quality remained consistent. “We place great confidence in e-Core’s hiring decisions,” says Alessandro Cristiano, Support Manager at Ricksoft. “They understand how we work, and their mission, values, and culture attract good talent. We had five different generations of agents in seven years, and the work was consistent all the time.” Building long-term value through support. To make support even more impactful, we set up a feedback loop between support and product. Recurring issues and feature requests are flagged and shared with product managers, turning support into a valuable channel to inform product strategy. Tone and empathy matter, too. Ricksoft’s brand is rooted in respect, clarity, and thoughtful communication. We greet returning customers by name, tailor our closings, and have consistently met their SLA targets—with 99% first-response compliance within 12 hours or less. “What is special about e-Core is that they listen first,” Cristiano says. “They don’t try to mold things just for efficiency if it doesn’t work for you. First, they absorb your culture, and then transform it.” To strengthen Ricksoft’s presence in the Atlassian Marketplace , we introduced a post-ticket review request process. That simple step led to more customer reviews, better app ratings, and increased visibility. “We’re now at the top search results, which helps increase our app installs, and ultimately our revenue,” says Cristiano. We also monitor Ricksoft-related activity in the Atlassian Community. When their apps are mentioned, our team responds quickly and accurately, helping Ricksoft remain visible, helpful, and credible in the ecosystem .
A successful cloud migration doesn’t start with tools, it starts with the right plan . Download this guide to discover a structured, strategic approach to migrating and modernizing workloads on AWS . Whether you’re just starting your cloud journey or looking to optimize an existing environment, this whitepaper gives you the clarity and framework to do it right. What you’ll learn: A 3-phase cloud assessment and planning framework AWS tools that support discovery, migration, and modernization How to reduce cloud spend, improve security, and scale with confidence Real-world planning strategies, including containerization, serverless, and data modernization
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.