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How to Build AI-Enabled Operations and Achieve Measurable Outcomes
2025-12-11 00:15:38| The Webmail Blog
How to Build AI-Enabled Operations and Achieve Measurable Outcomes jord4473 Wed, 12/10/2025 - 17:15 AI Insights How to Build AI-Enabled Operations and Achieve Measurable Outcomes December 10, 2025 by Rackspace Technology Link Copied! Recent Posts How to Build AI-Enabled Operations and Achieve Measurable Outcomes December 10th, 2025 Prioritize Strategy to Strengthen Your Cloud Transformation December 8th, 2025 Modern IT Service Management is Transforming Managed Services - Part 1 December 4th, 2025 AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation - Part 2 December 4th, 2025 How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1st, 2025 Related Posts AI Insights How to Build AI-Enabled Operations and Achieve Measurable Outcomes December 10th, 2025 Cloud Insights Prioritize Strategy to Strengthen Your Cloud Transformation December 8th, 2025 Cloud Insights Modern IT Service Management is Transforming Managed Services - Part 1 December 4th, 2025 AI Insights AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation - Part 2 December 4th, 2025 AI Insights How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1st, 2025 Explore the strategy, data foundations, governance and expertise required for AI-enabled operations, and how Rackspace AI Launchpad accelerates readiness. Organizations across every industry want to harness and accelerate the power of AI to innovate, optimize operations and compete more effectively. Yet, despite the excitement surrounding AI, many companies are struggling to turn their AI aspirations into measurable outcomes. One of the leading reasons is that AI initiatives fail when organizations focus solely on technology and overlook the strategy, governance and operating models required to sustain it. Key considerations for the rollout of scalable, production-ready AI To employ AI in ways that genuinely advance your organizations goals, you need more than promising use cases. You need clear objectives, a reliable data foundation, fit-for-purpose infrastructure, efficient model execution and a workforce thats prepared to use and govern AI responsibly. These elements give you the conditions required to move from experimentation to consistent, measurable impact. Define clear business objectives AI adoption should start with a well-defined problem to solve or outcome to improve. You need to identify where AI can deliver measurable value improving customer experience, accelerating product development or automating repetitive processes. Anchoring each initiative to a concrete business objective helps set you up for long-term ROI. Build a data foundation AI is only as good as the data from which it learns and this is where many pilot projects fall short. Before you scale AI, you need to know what data is required, where it will come from and how youll access it. That data must be accurate, accessible and compliant. Start by modernizing your data platforms, removing silos and establishing governance frameworks that safeguard sensitive information. Choose the right infrastructure AI workloads demand flexible, scalable compute and storage resources, and those needs vary based on whether you're training, fine-tuning or inferencing. Evaluate whether public cloud, private cloud or a hybrid model best supports your performance, cost and data sovereignty requirements. Choosing the right environment up front helps you avoid complexity as your AI footprint grows. Use lightweight technology where appropriate Many AI tasks are lightweight and benefit from fast, local inference using CPUs or NPUs already deployed at the edge in CDN nodes, telco POPs or even mobile devices. Examples include: Autocomplete in email Ranking search results Recalling recent chat messages Translating short text Tagging images with metadata Recognizing these patterns helps you deploy AI more efficiently and reduce unnecessary infrastructure overhead. Empower your workforce Successful AI programs depend as much on people as on technology. Equip your teams with the skills to work alongside AI systems, including data literacy, model interpretation and responsible AI practices. As employees understand how AI works and where it applies, adoption strengthens and outcomes improve. Enterprise barriers to AI adoption, and how to address them As organizations scale AI beyond early pilots, there are four common challenges that we see them face: complex and inconsistent data, unclear strategy, limited internal expertise and increasing security and compliance pressures. Lets examine each of these challenges and outline some actions you can take to reduce risk, improve alignment and keep your AI initiatives on track. Challenge 1: Data complexity and quality problems Fragmented systems, inconsistent formats and incomplete records make it difficult to train reliable models and generate accurate outputs. Solution: Invest in integration and data management capabilities that unify sources, standardize formats and automate quality checks. This gives your AI systems a consistent and trustworthy foundation to learn from. Challenge 2: Unclear strategy Without a clear roadmap, AI efforts often become a set of disconnected pilots that never progress into production or deliver meaningful impact. Solution: Define a phased adoption framework that moves from a proof of concept (PoC) to a pilot and then into production. Each phase should validate value, refine requirements and prepare your teams for ongoing operational responsibilities. Challenge 3: Skills gap Most organizations lack deep expertise in data engineering, model development and machine learning operations (MLOps). This slows progress and increases dependence on a small number of specialists. Solution: Combine targeted upskilling with support from partners who specialize in AI operations. Automation platforms can also reduce manual work and allow your teams to focus on higher-value tasks instead of managing pipelines and infrastructure. Challenge 4: Security and compliance AI models can introduce new exposure points, including unintended data access paths and governance gaps if security controls are not embedded from the start. Solution: Adopt an AI governance framework that enforces secure data access, validates model behavior and aligns your deployments with regulations such as GDPR and HIPAA. Treat governance as a continuous practice rather than a one-time requirement. How Rackspace AI Launchpad accelerates AI adoption Rackspace AI Launchpad gives you a clear, proven pathway for evaluating and deploying AI workloads without the complexity and delay of building custom environments from scratch. Its designed for organizations that already have a defined use case in mind, as well as those working with Foundry for AI by Rackspace (FAIR) to shape their AI strategy. Rackspace AI Launchpad helps you move from concept to production through a structured three-phase framework: Proof of concept: Identify a high-impact AI use case and validate it quickly in a secure, managed environment. Pilot: Refine data pipelines, model performance and workflows while integrating with your existing systems. Production deployment: Scale AI applications across the enterprise with Rackspace-managed infrastructure, operations and continuous support. Rackspace AI Launchpad is built on Rackspace Private Cloud AI. It delivers a curated AI architecture aligned to your requirements for training, fine-tuning and inferencing, whether in your data center or at the edge. This gives you the performance and reliability needed to advance AI initiatives without taking on the operational burden of designing and maintaining the environment yourself. With global expertise, 24x7x365 support and deep hybrid cloud experience behind the platform, AI Launchpad helps you adopt AI with confidence and turn promising use cases into production-ready outcomes faster. Case study: Compass accelerates patient record review Compass is a U.S.-based healthcare provider that manages large volumes of service notes requiring extensive manual review. The organization needed a secure way to modernize and automate its electronic health record (EHR) note review process. Rackspace Technology developed a private-cloud-hosted AI workflow for EHR note review that brings together natural language querying, automated documentation analysis and real-time reporting. With this workflow in place, Compass reduced manual review time by 80% while also improving ocumentation accuracy and giving clinicians faster access to actionable insights. Build AI-enabled operations with Rackspace AI Launchpad AI enablement becomes achievable when you pair new technology with new ways of working across the organization. When you focus on clear outcomes, build solid data foundations and equip your teams to use AI responsibly, you can move beyond experimentation and start generating meaningful, measurable impact. Rackspace AI Launchpad gives you a trusted guide as you navigate that shift. It brings together the infrastructure, expertise and operational support you need to make AI deployment faster, more secure and easier to scale across your environment. Learn how Rackspace AI Launchpad can help you move beyond pilots and build AI-enabled operations across your organization. Tags: AI Private Cloud AI Insights
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AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation (Part 2)
2025-12-04 19:20:37| The Webmail Blog
AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation (Part 2) jord4473 Thu, 12/04/2025 - 12:20 AI Insights AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation - Part 2 December 4, 2025 by Jason Rinehart, Sr. Product Architect, Rackspace Technology Link Copied! Recent Posts Prioritize Strategy to Strengthen Your Cloud Transformation December 8th, 2025 Modern IT Service Management is Transforming Managed Services - Part 1 December 4th, 2025 AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation - Part 2 December 4th, 2025 How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1st, 2025 Is Your AI Operation Achieving Long-Term, Sustainable Growth? November 25th, 2025 Related Posts Cloud Insights Prioritize Strategy to Strengthen Your Cloud Transformation December 8th, 2025 Cloud Insights Modern IT Service Management is Transforming Managed Services - Part 1 December 4th, 2025 AI Insights AI Revolution in Service Management Features Intelligent Operations and Continuous Innovation - Part 2 December 4th, 2025 AI Insights How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1st, 2025 AI Insights Is Your AI Operation Achieving Long-Term, Sustainable Growth? November 25th, 2025 In part two of his AI in service management series, Jason Rinehart examines how AI and intelligent operations transform service delivery, support and continuous improvement. Heres the reality: AI is no longer just a buzzword. Its transforming the way IT services are delivered and supported. In my previous article, I outlined how modern service management establishes the structure and discipline needed for resilient operations. In this article, we look at how AI and intelligent operations accelerate that evolution and make service management smarter, faster and more proactive. Ill share what you need to know to navigate this shift with confidence. How AI and intelligent operations are changing service management Smarter service delivery Imagine predicting what your customers need before they even ask. AI-powered demand forecasting is making this possible. By analyzing historical data and real-time signals, machine learning models help optimize capacity and cut costs. No more relying on gut feelings, outdated spreadsheets or antiquated alerting. AI also helps you understand your customers on a deeper level. By analyzing feedback and use patterns, you can personalize services and anticipate needs. Suddenly, youre not just reacting; youre leading the conversation. Thought starter: Try using AI analytics to spot trends in your service usage. Ask yourself: What patterns do I see? How could I adjust our offerings to deliver even more value? Operations that run themselves Incident management doesnt have to be a bottleneck. AI-driven bots and automation scripts resolve common incidents instantly. This can help free your team to tackle your complex challenges. Downtime typically drops, and your users get back to work faster. AI is also changing problem management. By crunching telemetry and historical data, it can spot patterns, predict failures and recommend fixes sometimes before anyone even notices a problem. Thought starter: Consider deploying AI-powered monitoring tools. Theyll not only alert you to issues but can also suggest or execute fixes automatically. Management and support that never sleep AI is always on. It watches for anomalies, correlates events across your environment and triggers automated remediation. This means fewer false alarms, faster recovery and no more operational ticket fatigue. Your service catalog can be smarter, too. By analyzing user behavior and your business goals, AI can recommend services that keep the catalog dynamic and relevant. And when it comes to security and compliance, AI acts as a tireless watchdog, monitoring threats, automating checks and enforcing policies in real time. Thought starter: Integrate AI-driven security tools that adapt to new threats and automate compliance reporting. How much manual effort could you save? Humans and AI: better together AI isnt here to replace your team; its here to empower them. Cross-functional teams can focus on strategy and innovation while AI handles routine tasks. Knowledge management gets a boost, too, with AI curating and delivering context-aware information right when you need it. Thought starter: Encourage your team to tap into AI-powered knowledge bases and collaboration tools. How much faster can they solve problems and spark new ideas? The future: intelligent operations and continuous innovation Whats next? The future of service management is intelligent, predictive and proactive. Heres what you can expect as AI and intelligent operations become the norm. Proactive prevention: Predictive planning and incident management will help you anticipate needs and issues before they happen. Outages will be prevented, not just resolved. Intelligent evolution: Continuous analysis of performance and feedback will drive real-time enhancements. Your services will evolve on the fly. User experience elevation: Hyper-personalization will deliver tailored experiences to every user and team, powered by AI insights. Stronger teamwork: Seamless collaboration will break down silos, letting IT, business and support teams work together effortlessly. Innovation expansion: With AI handling the routine, your team members can focus on strategic projects and creative solutions. Thought leadership challenge: Are you ready to let AI handle the heavy lifting so your teams can focus on what really matters? What would you do with the time and resources freed up by intelligent automation? Wrapping up: your next steps AI and intelligent operations arent just the future; theyre already here. By embracing these technologies, you can deliver services that are faster, smarter and more customer centric. Rackspace is here to help! Ideas to explore Pilot: Launch an AI-powered incident management tool and track how resolution time improves. Analyze: Use machine learning to analyze service use and predict future needs. Secure: Automate compliance and security monitoring to stay ahead of threats. Inform: Foster a culture of continuous learning with AI-driven knowledge management. Final thought Organizations that thrive in our AI-driven world will be those that combine human ingenuity with the power of intelligent automation. The future of service management is proactive, predictive and continuously improving and its yours to shape into your vision of success. Ready to take action? Lets talk about how you can start piloting AI-driven approaches to service management. Tags: AI Insights
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How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis
2025-12-01 17:17:07| The Webmail Blog
How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis jord4473 Mon, 12/01/2025 - 10:17 AI Insights How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1, 2025 by Brian Lichtle, CTO, Rackspace Technology Link Copied! Recent Posts How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1st, 2025 Is Your AI Operation Achieving Long-Term, Sustainable Growth? November 25th, 2025 Strengthening Healthcare Operations Through Cyber Resilience November 24th, 2025 The Hidden Complexity of Microsoft 365 Copilot and How to Get Ready for It November 19th, 2025 The Hidden Complexity of Microsoft 365 Copilot and How to Get Ready for It November 19th, 2025 Related Posts AI Insights How Kiro AI Agents Accelerate Development from Modernization to Cloud Migration Analysis December 1st, 2025 AI Insights Is Your AI Operation Achieving Long-Term, Sustainable Growth? November 25th, 2025 Cloud Insights Strengthening Healthcare Operations Through Cyber Resilience November 24th, 2025 Cloud Insights, Products The Hidden Complexity of Microsoft 365 Copilot and How to Get Ready for It November 19th, 2025 Cloud Insights, Products The Hidden Complexity of Microsoft 365 Copilot and How to Get Ready for It November 19th, 2025 Among the many new agentic AI development environments is the one that Rackspace Technology has started working with to help modernize enterprise development Kiro. Our CTO explains why. Shipping engineering work on time and within budget has become more challenging for developers. Kiro simplifies it with AI agents that assist throughout the entire process. From our perspective, Kiro is transforming how enterprises modernize legacy systems, accelerate innovation and deliver value to customers. By fundamentally transforming the software development lifecycle, Kiro is helping empower teams to transition from legacy systems to modern solutions in minutes instead of months. Changes like this are affecting not just how we develop, but also how we test, architect, and approach software development practices. Launching an AI-assisted transformation catalyst Kiro acts as a transformation catalyst, reimagining the entire AI-assisted software development lifecycle (SDLC) through four key capabilities: New architecture patterns: Transforms application structure by identifying reusable components and implementing modern architectural approaches that scale. Application modernization: Evolves legacy systems to contemporary platforms rapidly with minimal risk. Automated testing frameworks: Accelerates quality assurance and defect resolution while maintaining reliability. Rapid defect resolution: Streamlines debugging and issue remediation that keeps projects moving forward. Advancing from legacy to modern in minutes Traditional modernization projects are complex, costly and high risk. For example, migrating a legacy Python application typically takes months and substantial resources. Kiro flips this paradigm. For example, we modernized a full-stack Python 3.7 application in under ten minutes updating dependencies, patching security vulnerabilities and optimizing performance with minimal manual effort. The value can be immediate and measurable, including: 87.5% time savings on report analysis and migration tasks* Thousands of hours freed across hundreds of reports and projects Cost savings from less hiring for modernization work Its not just about speed. It's about gaining the ability to tackle technical debt that organizations have put off for years because the traditional approach was too slow, expensive and high risk. Accelerating cloud migration and assessment Kiro's impact extends far beyond code modernization. Teams can leverage its functionality to build sophisticated tools for database modernization, cloud assessment and migration analysis. For example, a Kiro-powered PowerShell data collection tool has solved pre-cloud connectivity challenges, enabling the rapid assessment of hundreds of servers in minutes versus weeks. Automated recommendation engines integrate real-time pricing data and generate comprehensive reports that empower fast and accurate migration strategies. When evaluating time saved by project category enhancements, traditionally one of the most time-intensive development activities, at Rackspace, we have experienced timeline compression from weeks to days or even hours. This acceleration transforms the speed with which organizations can respond to market demands and customer needs. Building collective excellence through community Success with AI-powered development isn't just about the technology; it's about creating an environment where teams learn, share and compound their capabilities over time. A comprehensive center of excellence approach includes: Top-down leadership buy-in: When leadership sees documented, proven benefits, investment in AI enablement becomes strategic rather than experimental. Open collaboration channels: Accessible forums for questions, concerns and real-time problem-solving break down silos and accelerate learning. Shared knowledge libraries: Curated collections of best practices, prompt libraries, use cases and onboarding materials help eliminate redundant learning curves. Continuous learning culture: High-cadence collaboration helps ensure blocks are resolved quickly, wins are celebrated and replicated, and lessons are learned to inform future work. This ecosystem approach means that when someone begins a new modernization project, they can immediately access relevant examples, reuse proven libraries and build on past experiences. The 80% time savings that AI enables expands to 85%, then 90%, as organizational knowledge compounds, based on internal work at Rackspace. Individual productivity gains are expected to become collective transformation. Accelerating workflow enterprise-wide Kiro integrates into daily development workflows across planning, coding, testing, deployment, and maintenance. Agentic capabilities enable parallel workflows, persistent data sharing across teams, and agile, data-driven development practices. Teams operate at a pace and scale that fundamentally changes whats possible not just for individual projects, but for entire work portfolios. With 31-plus active projects deployed globally within Rackspace, the pattern is clear: organizations that embed AI deeply into their development practices don't just work faster; they work smarter. They build institutional knowledge that makes every subsequent project more efficient than the last. Delivering compounding strategic advantages The real differentiator isnt the AI itself. Its what becomes possible when AI-driven automation combines with deep technical expertise and battle-tested processes. Faster migrations translate to accelerated time-to-market. More accurate assessments mean better architectural decisions and optimized cloud spending. Higher quality outcomes reduce post-migration issues and technical debt. Modernization transforms from a risky and expensive endeavor that organizations avoid into a strategic capability they can leverage for competitive advantage. As one Rackspace cloud specialist observed, It doesn't just enhance my work. It has transformed our entire team of developers and the functions they perform. Individual growth is important, but collective excellence is unstoppable. Enterprise adoption is scaling rapidly through controlled expansion and rigorous outcome tracking. Each project informs business case development, validates best practices and identifies new opportunities for AI integration. As new agents are developed and embedded more deeply into development workflows, the compounding effect accelerates. Advancing the AI agent transformation path The AI agent journey centers on three imperatives: Driving measurable real-world impact Capturing and sharing organizational knowledge Continuously evolving both technology and strategy for an AI-powered future Kiro represents more than an AI tool. It's a blueprint for SDLC transformation that any enterprise can adopt. Organizations can shift from viewing AI as a productivity enhancement to recognizing it as a transformation platform enabling rapid modernization, changing how software gets built and fostering continuous learning through community-driven excellence. We're not just developing better software faster. We're rethinking whats possible when human expertise and AI capabilities collaborate seamlessly, creating value that compounds exponentially over time rather than diminishing as the novelty fades. The question isn't whether AI will change software development for everyone; it's whether your organizaion will lead that change or follow it. Learn more in our Kiro demo video. Tags: AI AI Insights Amazon Web Services (AWS)
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