je.st
news
How Energy CIOs Can Innovate Without Risking Stability
2026-01-15 20:01:05| The Webmail Blog
How Energy CIOs Can Innovate Without Risking Stability jord4473 Thu, 01/15/2026 - 13:01 Cloud Insights How Energy CIOs Can Innovate Without Risking Stability January 20, 2026 by Matt Monteleone, Director, Solution Architecture, Rackspace Technology, Simon Bennett, CTO, Rackspace Technology Link Copied! Recent Posts How Energy CIOs Can Innovate Without Risking Stability January 20th, 2026 Seven Trends Shaping Private Cloud AI in 2026 January 15th, 2026 From Technical Debt to Digital Agility January 14th, 2026 The Cloud Evolution Why Modernization, Not Migration, is Defining the Next Decade January 12th, 2026 Continuing Success with VMware: How Rackspace Supports Customers and Partners January 12th, 2026 Related Posts Cloud Insights How Energy CIOs Can Innovate Without Risking Stability January 20th, 2026 AI Insights Seven Trends Shaping Private Cloud AI in 2026 January 15th, 2026 AI Insights From Technical Debt to Digital Agility January 14th, 2026 Cloud Insights The Cloud Evolution Why Modernization, Not Migration, is Defining the Next Decade January 12th, 2026 Cloud Insights, Products Continuing Success with VMware: How Rackspace Supports Customers and Partners January 12th, 2026 OT/IT convergence increases pressure on energy systems. Learn how leaders protect stability, close skill gaps and modernize critical operations with confidence. OT/IT convergence is accelerating, creating new risks when modernization moves faster than the safeguards that protect critical operations. In the energy sector, digital transformation presents a real paradox. The technologies that boost efficiency, safety and profitability can also create new operational risks when deployed without precision. Across the industry, operators report that a growing share of industrial cyber incidents now originate in IT-connected systems rather than traditional OT networks. Well-intended connectivity projects are creating new pathways into SCADA and control environments that were originally designed to operate in isolation. This shift reflects the pressure facing CIOs across oil and gas and utilities as OT and IT environments converge faster than many organizations can redesign their architectures and operating models. Why OT/IT convergence creates architectural pressure For decades, OT and IT operated in separate worlds. SCADA, ICS and DCS systems were engineered for stability and deterministic control. They werent designed for cloud connectivity, open data exchange or the data-intensive workloads required for modern analytics and AI. IT environments, meanwhile, evolved around agility, frequent updates and hybrid multicloud architectures. Those principles conflict with the uptime demands that define production operations. Today, these environments are intersecting faster than many organizations can redesign their architectures. Upstream: Rigs and offshore platforms generate massive volumes of telemetry, but operationalizing that data requires edge compute and real-time analytics at the wellsite. Extending connectivity to enterprise networks increases latency risks and broadens the attack surface. Midstream: Pipeline integrity monitoring depends on continuous visibility across distributed assets. Yet data is often isolated in operational networks that were never built to integrate with enterprise analytics, slowing incident response and complicating compliance. Downstream: APC systems rely on consistent performance data from legacy historians and ERP platforms. Modernizing these systems without interrupting production is like swapping components on an aircraft in flight. It requires careful sequencing, parallel operations and architectures that let new systems run safely alongside legacy environments until cutover. Utilities: The shift toward renewables, distributed energy resources and smart grid operations adds new layers of complexity. Generation, transmission and distribution teams must manage bidirectional flows, real-time demand response and customer-facing applications while maintaining NERC-CIP compliance and grid reliability. Why the skills gap heightens modernization risk Every technical challenge in energy modernization is amplified by a talent gap. Skilled professionals who understand both industrial systems and modern cloud architectures are in short supply. Retirements widen the gap further as experienced engineers leave without full knowledge transfer. Without the right expertise, organizations face added pressure: modernization timelines slow, operational safeguards weaken and the risk of misconfigurations increases across critical environments. How leading energy teams modernize safely Across the industry, the organizations that strive to reduce complexity and accelerate momentum share a common approach to modernizing critical systems without destabilizing operations. They modernize deliberately, applying practices that reinforce stability and create space for innovation to scale safely. 1. They treat OT/IT integration as a security architecture priority.They apply strict segmentation, zero trust principles and continuous monitoring, recognizing that even small misconfigurations can cascade into downtime or safety incidents. 2. They deploy edge intelligence with clear operational intent.Latency-sensitive processing stays close to the source, with operational data analyzed at the rig, compressor station, substation or refinery. This reduces latency, helps to maintain local control during connectivity issues and supports data governance requirements. 3. They engage partners who provide sustained, specialized expertise.OT security, hybrid cloud operations, edge computing and industrial AI require deep, ongoing capability. Energy operators work with partners who bring proven domain experience and 24x7x365 operational depth to close skill gaps and strengthen reliability. Real-world outcomes A leader in thermal monitoring for utilities and mining worked with Rackspace Technology to deploy predictive maintenance capabilities in four weeks. Within days, customers identified critical anomalies and avoided costly failures. A company specializing in drilling and forecasting analytics scaled its containerized application platform on AWS with support from Rackspace. The engagement strengthened the connection between field operations and enterprise systems and delivered results that would typically require a much larger internal team. These outcomes arent exceptions. They demonstrate whats possible when infrastructure modernization supports operational continuity instead of jeopardizing it. Innovation relies on a stable operational foundation To stay competitive, meet decarbonization targets and sharpen operational efficiency, your organization needs to modernize in ways that strengthennot strainthe systems that keep energy operations running. Success starts with infrastructure that delivers: Secure edge compute for real-time processing at remote sites OT/IT segmentation that reduces the risk of lateral movement and protects control environments Centralized data governance paired with local operational control Predictive analytics for maintenance, optimization and emissions visibility 24x7x365 managed operations to close skills gaps and maintain reliability For upstream teams, this supports drilling optimization and remote operations without exposing critical systems. For midstream operators, it enables continuous leak detection, automated reporting and secure visibility across assets. For downstream refineries, it strengthens APC stability, predictive maintenance and ESG reporting. And for utilities, it accelerates grid modernization and DER integration while maintaining protections that keep the grid stable. Why the balance between innovation and stability will define the next decade As an energy leader, you face pressure from every direction. Boards expect digital innovation. Operations teams demand reliability. Regulators increase requirements. CFOs expect measurable value. But modernization doesnt require choosing between innovation and stability. With the right architecture and the right operational partners, energy companies can modernize with confidence and strengthen resilience at the same time. The complexity doesnt disappear, but it becomes manageable. And once complexity is managed, momentum accelerates. Learn how modern infrastructure and managed operations support safer, faster innovation in the oil and gas industry. Tags: Private Cloud Cloud Insights Oil & Gas
Category:Telecommunications
LATEST NEWS
Seven Trends Shaping Private Cloud AI in 2026
2026-01-14 19:55:34| The Webmail Blog
Seven Trends Shaping Private Cloud AI in 2026 jord4473 Wed, 01/14/2026 - 12:55 AI Insights Seven Trends Shaping Private Cloud AI in 2026 January 15, 2026 By Amine Badaoui, Senior Manager AI/HPC Product Engineering, Rackspace Technology Link Copied! Recent Posts Seven Trends Shaping Private Cloud AI in 2026 January 15th, 2026 From Technical Debt to Digital Agility January 14th, 2026 The Cloud Evolution Why Modernization, Not Migration, is Defining the Next Decade January 12th, 2026 Continuing Success with VMware: How Rackspace Supports Customers and Partners January 12th, 2026 Continuing Success with VMware: How Rackspace Supports Customers and Partners January 12th, 2026 Related Posts AI Insights Seven Trends Shaping Private Cloud AI in 2026 January 15th, 2026 AI Insights From Technical Debt to Digital Agility January 14th, 2026 Cloud Insights The Cloud Evolution Why Modernization, Not Migration, is Defining the Next Decade January 12th, 2026 Cloud Insights, Products Continuing Success with VMware: How Rackspace Supports Customers and Partners January 12th, 2026 Cloud Insights, Products Continuing Success with VMware: How Rackspace Supports Customers and Partners January 12th, 2026 AI is moving from experimentation into sustained use. This article explores the key trends shaping private cloud AI in 2026 and what they mean for enterprise architecture, cost and overnance. As AI moves beyond early experimentation, production environments begin to expose new operational demands. What started as proof-of-concept work with large language models, copilots and isolated workloads is now moving into day-to-day use across core business functions. Over the course of 2026, many organizations will move past asking whether AI can deliver value and focus instead on how to operate it reliably, securely and cost-effectively over time. Once teams begin planning for sustained use, their priorities around AI architecture tend to change. Cost behavior, data protection and performance predictability start to matter as much as model capability. Public cloud remains essential for experimentation and elastic scaling, but it is no longer the default execution environment for every AI workload. Private cloud increasingly becomes part of the execution layer, particularly for workloads that benefit from tighter control, closer data proximity and more predictable operating characteristics. In 2026, architecture decisions reflect a more deliberate balance between experimentation and long-term operation. The trends below highlight the architectural pressures and tradeoffs that surface as AI systems mature and take on a sustained role in enterprise operations. Over the course of the year, these architectural decisions will increasingly influence cost predictability, governance posture, system performance and long-term operational reliability. Trend 1: Hybrid AI architectures become the norm In 2026, AI architecture will be shaped less by platform loyalty and more by how individual workloads actually behave. Many organizations are moving away from treating AI as a single deployment decision and toward managing it as a portfolio of workloads with different execution needs. AI workload placement now spans public cloud, private or sovereign environments, specialized GPU platforms and, in some cases, edge systems. Teams make these placement decisions based on cost predictability, latency tolerance, data residency constraints and governance expectations, not adherence to a single cloud strategy. Private cloud is often a strong fit for workloads that require consistency and control. These include steady-state inference pipelines with predictable demand, RAG systems colocated with regulated or proprietary data, and latency-sensitive agentic loops that depend on proximity to internal systems. Data-sensitive training or fine-tuning workloads also tend to align well with controlled environments. As teams balance experimentation with production workloads, hybrid routing patterns begin to take shape. Training and experimentation may continue to burst into public or specialized GPU clouds, while inference shifts toward private cloud to support more stable economics. Sensitive retrieval and embedding pipelines often remain local, while non-sensitive augmentation selectively calls external models. In this model, GPU strategy evolves toward cross-environment pool management, with capacity placed where it best supports utilization efficiency, workload criticality and data classification requirements. Hybrid AI increasingly functions as an operating model rather than an exception. Trend 2: Agentic AI moves into controlled private environments Agentic AI systems are moving beyond early prototypes and into active enterprise evaluation. These systems rely on multi-step reasoning, autonomous decision-making and interaction with internal tools and data sources. As teams begin planning for production use, certain requirements become more visible. Agentic workflows benefit from deterministic performance to maintain consistent behavior across chained actions. They also require deeper observability to understand how decisions are made and where failures occur, along with stronger isolation around sensitive actions and more predictable resource allocation. Private cloud environments align well with these needs. They provide safer integration points with ERP, CRM and operational systems, closer proximity to proprietary data and clearer boundaries around what agents can access or execute. I think these characteristics will become increasingly important as organizations explore agent-driven automation beyond isolated use cases. Over the course of 2026, agentic AI is likely to become a stronger private cloud use case, particularly where automation intersects with governed data and internal systems. Trend 3: Inference economics drive platform decisions As AI systems begin running continuously rather than occasionally, inference economics become harder to ignore. As inference supports more users, workflows and operational dependencies, cost behavior becomes more visible and more difficult to manage. Public cloud offers flexibility and speed, but for long-lived or high-throughput inference workloads, cost predictability can become a challenge. Variable concurrency, premium GPU pricing and sustained demand introduce uncertainty that is manageable during pilots, but harder to absorb as inference moves into steady, production use. What I see is teams underestimating how quickly inference costs grow once models move beyond experimentation. This typically surfaces as organizations connect AI to real operational workflows with defined latency, availability and reliability expectations. Private cloud supports more stable cost models through reserved or fractional GPU allocation, hardware-aware optimization and more controlled scaling paths. Local inference pipelines can also reduce overhead associated with repeated external calls and data movement. As a result, organizations increasingly separate experimentation from execution. Public cloud remains valuable for exploration and burst activity, while private cloud becomes a foundation for more cost-stable inference as AI systems mature over the course of 2026. Trend 4: Data sovereignty and regulation drive architectural choices Data sovereignty and regulatory requirements will continue to shape how AI systems are deployed. As AI touches more sensitive and regulated information, compliance considerations extend beyond where data is stored to include how it is processed, retrieved and generated. When AI workloads involve regulated, proprietary or region-bound data, architectural choices often become compliance decisions. This is especially relevant in financial services, healthcare, energy and public sector environments, where auditability and data lineage are essential. Private cloud environments make it easier to define and enforce these boundaries. They support full data custody, clearer residency controls and stronger oversight of training inputs, embeddings and retrieval pipelines. As governance expectations mature, architectural control can simplify compliance rather than introduce additional friction. Over time, the compliance perimeter for AI is moving closer to private cloud as systems begin to influence more regulated and operationally sensitive decisions. Trend 5: Zero-trust security extends into AI pipelines Zero-trust security principles are increasingly applied beyond networks and identities and into AI pipelines themselves. AI workloads introduce new execution paths through embeddings, vector databases, agent orchestrators and internal tools, each of which becomes a potential control point. As these pipelines mature, organizations tend to require more explicit identity and policy enforcement around model-serving endpoints, retrieval stages, fine-tuning datasets and agentic actions. Trust is established at each stage rather than assumed across the system. This is why I think well see zero-trust move from a conceptual model into a concrete architectural requirement. Private cloud environments support deeper enforcement through microsegmentation, isolated data stores and polic-driven access layers. This makes it easier to define and maintain clear trust boundaries between ingestion, retrieval, inference and action execution. Over the course of 2026, AI security increasingly becomes data-path centric, with zero-trust applied end to end. Private cloud plays an important role in making this level of enforcement more practical and consistent. Trend 6: RAG pipelines and sensitive workloads shift on-premises Retrieval-augmented generation (RAG) continues to move toward production use across enterprise workflows. As RAG systems support operations, compliance and internal knowledge access, they increasingly interact with highly sensitive information. As RAG systems mature, teams often discover that they surface far more sensitive material than initially expected. This often changes how teams think about placement and control. Hosting RAG pipelines in private cloud supports lower latency, more consistent inference performance and greater control over proprietary documents. Cost stability also becomes more relevant as retrieval frequency increases and knowledge bases grow. As RAG becomes central to enterprise AI during 2026, private cloud is well positioned to serve as its operational foundation. Trend 7: GPU strategy evolves toward utilization efficiency Early AI deployments often focus on GPU availability. As deployments mature, attention shifts toward how efficiently those resources are used. When teams begin running multiple AI pipelines in parallel, GPUs can quickly become underutilized without careful scheduling and right-sizing. At that point, architecture matters as much as raw capacity. Private cloud architectures support multi-tenant GPU pools, fractional allocation and workload-aware scheduling, helping organizations improve utilization without overspending. They also enable optimization techniques such as quantization, distillation and batching, which can reduce compute pressure while maintaining functional performance. Rather than serving solely as a compute layer, private cloud increasingly acts as an efficiency layer, aligning GPU resources more closely with actual workload behavior. What these trends signal for enterprise AI strategy These trends point to a clear shift in how AI is operated as it moves from experimentation into day-to-day use. Public and private cloud continue to play important roles, but their responsibilities are becoming more clearly defined as systems mature. Private cloud increasingly supports AI workloads that benefit from greater control, closer data proximity and more predictable operating characteristics. Public cloud remains essential for experimentation, burst capacity and rapid innovation. The most effective strategies combine both, placing workloads intentionally based on behavior, sensitivity and risk. As organizations plan and adapt throughout 2026, architectural choices play a larger role in how reliably and responsibly AI systems operate. For many teams, private cloud becomes an important execution layer as AI moves into sustained, enterprise-scale use. Learn more about how private cloud supports AI workloads that require control, predictability and scale. Tags: AI Private Cloud AI Insights
Category: Telecommunications
Using Agentic AI to Modernize VMware Environments on AWS
2026-01-12 18:51:35| The Webmail Blog
Using Agentic AI to Modernize VMware Environments on AWS jord4473 Mon, 01/12/2026 - 11:51 AI Insights Using Agentic AI to Modernize VMware Environments on AWS January 22, 2026 by Dean Bantleman, EMEA Lead Architect AWS, Rackspace Technology Link Copied! Recent Posts Using Agentic AI to Modernize VMware Environments on AWS January 22nd, 2026 How Energy CIOs Can Innovate Without Risking Stability January 20th, 2026 Seven Trends Shaping Private Cloud AI in 2026 January 15th, 2026 From Technical Debt to Digital Agility January 14th, 2026 The Cloud Evolution Why Modernization, Not Migration, is Defining the Next Decade January 12th, 2026 Related Posts AI Insights Using Agentic AI to Modernize VMware Environments on AWS January 22nd, 2026 Cloud Insights How Energy CIOs Can Innovate Without Risking Stability January 20th, 2026 AI Insights Seven Trends Shaping Private Cloud AI in 2026 January 15th, 2026 AI Insights From Technical Debt to Digital Agility January 14th, 2026 Cloud Insights The Cloud Evolution Why Modernization, Not Migration, is Defining the Next Decade January 12th, 2026 Explore how agentic AI supports VMware modernization on AWS by accelerating analysis, planning and execution while keeping architectural decisions and governance in human hands. Cloud migration is a strategic lever for modernization, agility and scle, but it can demand more time and attention from your teams than it should. Manual analysis, repetitive configuration tasks and complex coordination across teams often stretch timelines and divert skilled engineers away from higher-value work. That friction isnt inevitable, and migration shouldnt consume the capacity you need to keep your business moving forward. By combining agentic AI with experienced cloud migration consultants, solutions architects and DevOps engineers, were structuring our migration processes so that AI agents can take on data-intensive analysis and repeatable tasks, while human experts provide architectural judgment, business context and oversight. The challenge with traditional cloud migration Traditional cloud migrations tend to follow a predictable pattern. Readiness assessments, application portfolio analysis and business case development require teams to process large volumes of data gathered from workshops, discovery tools and technical documentation. Before execution begins, significant effort is spent organizing inputs, validating assumptions and aligning stakeholders on next steps. As migration planning moves closer to execution, teams shift their focus to highly repeatable but time-intensive activities creating runbooks, analysing firewall rules, validating configurations and documenting application states. These tasks are essential to a successful migration, but they demand sustained attention and careful coordination, particularly when timelines are compressed. Over time, this work can absorb the capacity of architects and engineers who are best positioned to guide architectural decisions, modernization priorities and risk management. The challenge isnt a lack of expertise its that too much of that expertise is applied to migration mechanics rather than higher-value planning and execution. Finding that balance depends on where AI is applied, how decisions are governed and how modernization progresses without disrupting proven operating models. This is where agentic AI changes the equation. How agentic AI transforms cloud migration and modernization Rackspace approaches cloud migration not as a one-time event, but as a modernization journey. By structuring migration processes around agentic AI capabilities, were able to apply automation where it delivers the most impact while keeping architectural judgment, governance and business decisions firmly in human hands. For organizations running VMware environments, this model supports modernization on AWS without forcing immediate architectural change. VMware services on AWS provide continuity and operational familiarity, while agentic AI accelerates the analysis, planning and execution required to move forward with confidence. Rather than replacing expertise, agentic AI is designed to amplify it. AI agents can support modernization across the migration lifecycle by taking on data-intensive analysis and repeatable tasks, while we remain accountable for strategy, sequencing and risk management across four phases: Assess Mobilize Migrate and modernize Day two operations Assessment often accounts for a meaningful portion of overall migration timelines, particularly in complex or VMware-based environments. As you move into this phase, discovery workshops and portfolio analysis generate large volumes of technical and operational data that must be reviewed and interpreted before planning can move forward. In this phase, Rackspace migration consultants lead structured discovery workshops, including the Migration Readiness Assessment (MRA) and Migration Portfolio Assessment (MPA), to gather input across applications, infrastructure and business priorities. Once discovery is complete, AI agents analyze workshop outputs and supporting documentation to surface patterns, risks and readiness gaps that would take human teams significantly longer to identify. These findings span both business and technical considerations, including organizational readiness issues and end-of-support operating systems that may influence migration and modernization decisions. With portfolio data analyzed, AI agents can: Categorize applications and servers by complexity Propose migration approaches and wave sequencing Highlight candidates for modernization versus rehosting Migration consultants review and refine these recommendations, applying business context and customer-specific requirements before plans are finalized. AI agents can also use Model Context Protocol (MCP) servers to generate estimated cloud costs and total cost of ownership. Solutions architects validate assumptions, confirm estimates and support business case development and funding approvals. Throughout the assessment phase, Rackspace experts remain directly engaged in workshops and planning discussions, helping your team understand the options available and the decisions that shape the path forward. Mobilizing for migration at scale Mobilization translates planning into executable work. This phase depends on a deliberate division of responsibility between human expertise and agentic AI so that your preparation moves quickly without sacrificing control or clarity. Solutions architects lead activities that require architectural judgment and business insight, including documenting current and target states for each application and defining sequencing, standards and governance requirements. AI agents then support mobilization by: Analyzing release plans and cross-checking dependencies Validating technical feasibility and sequencing Generating runbooks and deployment artefacts Deploying discovery agents and validating replication workflows Creating pre- and post-migration checks All agent outputs are reviewed and validated by migration consultants before execution begins, maintaining quality control as preparation scales. AI agents can also validate existing or greenfield landing zones against AWS security best practices and Well-Architected Framework principles. Migration governance, organizational alignment and stakeholder engagement remain human-led throughout. Migrate and modernize at scale Execution is where agentic AI helps scale migration and modernization activities while maintaining continuity and control, particularly for VMware environments. For VMware-based workloads, Rackspace uses agent-driven automation alongside VMware HCX to support lift-and-shift migrations with minimal manual effort. By referencing previously captured application and infrastructure data, workloads can be moved individually or in batches using live vMotion while maintaining uptime. For workloads modernizing toward native Amazon EC2, AWS Transform supports agent-assisted conversions. AI agents validate preparation steps, analyze collected data and document outcomes, while human oversight ensures business-critical workloads are sequenced appropriately and receive focused attention. Modernization extends beyond infrastructure. For platform modernization scenarios, consultants lead the work while AI agents validate firewall rules, configurations and application-specific infrastructure builds. When deeper transformation is required, AI coding assistants such as Kiro and Amazon Q Developer support refactoring for cloud-native architectures, with human architects guiding design decisions and quality standards. CloudOps for day two operations and beyond Migration is not the end of the journey. Once workloads are running on AWS, Rackspace supports ongoing operations through AIOps designed to improve stability, responsiveness and operational efficiency. AIOps agents can help reduce mean time to recovery by detecting failures, supporting incident response and initiating predefined recovery actions with limited manual intervention. These agents use rerieval-augmented generation (RAG) with runbooks stored in knowledge bases, providing the contextual understanding needed to respond in line with established operational practices. This model supports a more proactive approach to operations, maintaining reliability and consistency across environments, while allowing your teams to focus on optimization, modernization and ongoing improvement rather than routine firefighting. A technology foundation built on AWS best practices To support this model in practice, we rely on a set of AWS services that are designed for secure, governed and scalable agent deployment. These services give you the ability to apply agentic AI across migration, modernization and operations while maintaining the security controls, observability and integration points your environment requires. At a foundational level, this approach is built on: Amazon Bedrock AgentCore, which provides a secure execution environment for AI agents with IAM- and OAuth-based authentication, along with access to Model Context Protocol (MCP) servers through the AgentCore Gateway Amazon Strands Agents, which supply prebuilt tools for common migration and operations tasks, supporting consistent behavior and reducing the overhead required to design and manage agent workflows Amazon Bedrock Knowledge Bases, which store runbooks, best practices and operational knowledge in vector databases, enabling agents to use retrieval-augmented generation (RAG) for context-aware analysis and response Together, these services allow agentic AI to operate within defined security boundaries while remaining flexible enough to support migration, modernization and day two operations at scale. Expertise and automation working together Across migration and modernization, agentic AI is applied alongside experienced delivery teams to support complex enterprise environments, including VMware estates running on AWS. Automation contributes consistency and scale, while architectural decisions, governance and execution remain guided by experienced practitioners throughout the journey. As an AWS Premier Tier Services Partner, we ready to help eligible customers navigate AWS migration funding programs that can offset a portion of modernization costs. This support allows technical execution and business planning to move forward in parallel, without slowing momentum or sacrificing oversight. If youre planning a migration, theres a more efficient path forward. Rackspace can help you move faster while maintaining the quality, control and oversight your organization requires. Learn more today. Tags: AI Public Cloud AI Insights Amazon Web Services (AWS)
Category: Telecommunications