Modernizing IT confidently means assessing data access, workload needs, security, operations, and device lifecycles before choosing where to invest. Cloud, AI, and security readiness are connected decisions—not a hardware purchase or a single migration project.
How do I modernize IT without disrupting the business?
Start with the business services and workloads the organization needs to protect, improve, or scale. Then identify what prevents each one from meeting its requirements: limited compute, slow storage or data access, unclear data ownership, security controls, aging endpoints, or gaps in operational skills. This helps separate urgent constraints from attractive but unnecessary upgrades.
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A useful plan moves through four decisions: establish a baseline, choose a suitable location for each workload, design security and governance into that choice, and schedule infrastructure and endpoint changes around business continuity. Record owners, dependencies, success measures, and rollback plans for consequential changes.
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- Workloads: Record business purpose, users, dependencies, utilization patterns, performance needs, recovery objectives, and planned growth.
- Data: Identify where data lives, who can access it, how quickly workloads need it, how it is classified, and whether it can be moved or used by AI services.
- Infrastructure: Check compute, storage performance, network capacity, backup and recovery, monitoring, and the age and support status of equipment.
- Risk and operations: Map applicable security and compliance obligations, current controls, staff capabilities, service ownership, and the ongoing effort needed to operate each option.
- Endpoints: Assess business PCs for performance, manageability, support status, security needs, and the safe handling of devices being retired.
Use readiness evidence as a prompt, not a verdict
Dell’s September 1, 2026 summary of the June 2026 Modern Enterprise Readiness Study, attributed to Dell Technologies and Vanson Bourne, reports that 66% of 2,950 business and IT decision-makers surveyed across 35 countries said their data center and infrastructure were not fully ready for their most demanding workloads at production scale, including AI and analytics. In the same survey, 80% said storage performance and data access were bigger bottlenecks than raw compute for many critical workloads, and 50% said they lacked a clear, actionable roadmap across AI, data, and security.
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Those are reported survey responses, not a census of organizations or proof that any one investment will solve a readiness problem. Dell’s summary gives the sample size, countries, and fieldwork month and year, but not the full questionnaire or methodology. Use the results to ask whether your own organization has measured data access and agreed on a roadmap; do not assume the percentages describe your environment.
Is our infrastructure ready for AI?
AI readiness depends on more than accelerator availability. An AI workload needs suitable data, predictable access to it, enough compute and storage capacity, an appropriate deployment location, security controls, and a team able to operate and monitor the system. A pilot that works with a small dataset may not perform or remain economical at production scale.
Before moving from pilot to production, answer these questions:
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- Data: Which sources may be used, what rights and restrictions apply, how current must the data be, and how will it be prepared and governed?
- Capacity: What are the expected concurrency, latency, storage, and data-movement needs at realistic usage levels?
- Exposure: Could prompts, inputs, or outputs reveal confidential information, regulated data, or intellectual property to a third party?
- Operations: Who owns access controls, model and application updates, monitoring, incident response, and service recovery?
- Economics: What will the full operating model cost, including compute, storage, networking, cloud usage, software, support, and staff time?
Dell’s summary of the 2026 study says 60% of respondents identified security and compliance concerns as the primary reason their organization had slowed or paused AI adoption; 68% expressed concern about regulatory compliance when using their data for AI; and 68% said their data and intellectual property were too valuable to place in a generative AI tool where a third party may have access. These findings describe the surveyed decision-makers’ responses, not the prevalence of a specific security incident or a legal conclusion. They point to practical questions about data handling, access, and approved AI services that each organization must answer for itself.
Should this workload run in the cloud or on premises?
Choose a location workload by workload. Compare variability, data location and access, security and compliance obligations, infrastructure readiness, operational capability, and cost predictability. A workload’s current location is not automatically its best long-term location, and cloud is not automatically cheaper or more secure.
| Option | Questions to assess | Potential fit to investigate |
|---|---|---|
| Public cloud | How variable is demand? What are the data-transfer and service costs? Can the required controls, residency, performance, and recovery objectives be met? Does the team have cloud operations expertise? | Workloads that benefit from flexible capacity or managed services, if data, governance, operational, and cost requirements can be met. |
| Private cloud | Is dedicated or controlled infrastructure important? Can the organization operate or support the platform? What are the upfront and recurring costs, capacity limits, and lifecycle responsibilities? | Workloads that need a cloud-like operating model on infrastructure under the organization’s control, subject to capability and cost validation. |
| On premises | Can existing equipment meet performance, resilience, and support needs? What investment is needed to refresh it? How will the organization handle capacity peaks and maintenance? | Steady workloads or workloads with specific local data-access, latency, or control requirements, where the infrastructure and team are capable. |
Dell’s May 19, 2026 AI infrastructure article recommends a hybrid pattern in which consistent workloads use right-sized on-premises capacity and variable peaks use cloud. Treat this as Dell’s vendor guidance, not a universal cost rule: actual results depend on workload behavior, sizing, data movement, service pricing, and operating capability. Model those factors with your own usage and contract assumptions before deciding.
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What should we assess before moving sensitive data or AI workloads?
Set security and governance requirements before selecting the platform or service. The relevant controls need to span cloud, on-premises systems, and edge devices; otherwise a workload can inherit gaps at the point where data enters, moves, is processed, or is accessed.
Make data handling explicit
- Classify data and document permitted uses, retention, residency, and deletion requirements.
- Determine whether a provider or service can access prompts, source data, outputs, logs, or backups, and under what terms.
- Limit access by role and task; review identities, credentials, service accounts, and administrative access.
- Protect data in transit and at rest, and establish how keys, backups, and recovery are managed.
- Define how AI outputs will be reviewed, logged, and used in decisions with business or regulatory consequences.
Include infrastructure and endpoint protections
Security design should account for operating systems, firmware, hardware, networks, identity, and recovery—not just the application layer. In a December 3, 2025 Dell article, Intel and Dell representative George Murphy discussed threats below the operating system and hardware-level protections in the context of running AI on sensitive data. That is a vendor-published security perspective, not an independent assessment of attack prevalence or proof that a particular configuration is secure. Dell’s August 2026 customer article also presents examples involving Dell Pro PCs with Intel Core Ultra processors, Intel vPro, and CrowdStrike Falcon; its customer testimonials are not independent product testing, and feature availability can vary by configuration.
Ask vendors and internal security teams for configuration-specific evidence: what protections are enabled, how they are managed and updated, what events are visible to administrators, and how the organization can recover if a device or workload is compromised. Validate the answers against your threat model and compliance obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do we modernize while keeping older infrastructure useful?
Modernization does not require replacing every system at once. Classify existing infrastructure by business criticality, support status, capacity, security exposure, and dependency on other systems. Keep equipment that still meets requirements and can be maintained safely; isolate, upgrade, migrate, or retire assets that cannot. Set a review date so a temporary exception does not become an unexamined permanent dependency.
For each workload, compare the cost and risk of extending its current environment with the cost, disruption, and operational demands of moving it. Older systems may remain useful for stable workloads, but only if security patches, backup and recovery, compatible interfaces, and support arrangements remain adequate. A staged migration can reduce disruption when dependencies are mapped, changes are tested, and rollback is defined.
Plan endpoint refresh and retirement together
Business PCs are part of the same lifecycle decision. Refresh devices when their performance, manageability, support status, or security capabilities no longer meet business needs; choose specifications from actual application and fleet requirements rather than an unsupported model recommendation. At the same time, decide how retiring devices will be inventoried, sanitized, reused, recycled, or destroyed, and retain records appropriate to the organization’s policies.
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Dell’s February 11, 2025 refresh article describes its Asset Recovery Services, including data sanitization, possible onsite shredding, reuse or recycling, and reporting through TechDirect. These are descriptions of Dell services; confirm current service scope, availability, and terms directly before relying on them. The article also mentioned a 0% financing offer for credit-qualified US and Canadian business customers that expired August 1, 2025; it is not a current offer.
What does a private-cloud option involve?
Private cloud is one architecture choice, not a synonym for every on-premises deployment. Dell’s Private Cloud product page describes a software subscription that can run on new or existing Dell PowerEdge compute, Dell storage, and customer-selected networking. Dell also describes automation and lifecycle management, validated blueprints, and support for named software ecosystems including VMware by Broadcom, Red Hat OpenShift, Nutanix, and Microsoft Azure Local.
Dell positions the platform for infrastructure modernization, multicloud strategy, and scalability. Those are vendor product claims, not independent proof that the offer will suit every environment. An evaluation should establish whether the organization’s chosen hardware and software are supported, what the subscription includes, who operates each layer, how capacity is expanded, what migration work is needed, and how costs compare over the planned lifecycle. Confirm current technical and commercial details with Dell rather than assuming a product-page description applies to a specific configuration.
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Quick Recap
How can we turn the assessment into a low-risk plan?
- Inventory services and dependencies. Identify the applications, datasets, infrastructure, endpoints, teams, and providers behind critical business services.
- Set requirements. Define performance, availability, recovery, data handling, security, compliance, and support needs for each workload.
- Choose candidate locations. Compare public cloud, private cloud, and on-premises options against workload variability, data access, operational capability, and modeled cost.
- Test a representative case. Use realistic data volume and usage patterns; verify performance, security controls, recovery, monitoring, and operating effort before scaling.
- Sequence changes. Prioritize by business value and risk, map dependencies, schedule around critical periods, and document rollback and communications.
- Measure after deployment. Track the agreed outcomes—such as service performance, recovery readiness, access-control compliance, utilization, and operating cost—and revisit placement when workload or business needs change.
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