Tech Mahindra & Dell Technologies: Redefining Virtualization with Innovative Solutions

Today, businesses are transitioning from traditional to modern hypervisor models due to constrained scalability, escalating costs, and the limitations imposed by vendor lock-ins. Modern workloads require greater operational flexibility, resource efficiency, and cloud-readiness. As a result, organizations are adopting open, disaggregated infrastructure solutions to improve agility, optimize costs, and maintain long-term flexibility. In light of these shifting business needs, Tech Mahindra and Dell Technologies have formed a strategic partnership to deliver innovative hypervisor solutions that enhance the resilience, adaptability, and future readiness of modern IT environments.

Joint Solution Overview: Driving Innovation with Tech Mahindra and Dell

The collaboration between Tech Mahindra and Dell aims to simplify access to IT infrastructure by delivering an open, adaptable ecosystem capable of supporting multiple hypervisor environments. This is particularly significant as it addresses the growing dissatisfaction with traditional hypervisors by offering a comprehensive suite of end-to-end solutions, including strategic consulting and the development of customized hypervisor roadmaps. These offerings enable enterprises to accelerate IT modernization efforts and enhance infrastructure agility more effectively.

Disaggregated Infrastructure: The Key Differentiator

One of the defining strengths of Dell’s software-defined infrastructure solution, along with its robust managed services, is its adoption of disaggregated infrastructure. This delivers a level of flexibility and scalability that traditional hyper-converged infrastructure (HCI) often lacks.

Unlike HCI, which integrates computing, networking, and storage into a single unified system and can lead to vendor lock-in, disaggregated infrastructure offers several distinct benefits:

Flexibility Beyond Vendor Lock-in: Open, adaptable infrastructure supporting multiple hypervisors and seamlessly running diverse workloads without being constrained by proprietary platforms, which avoids vendor lock-in.
Independent Scaling: Facilitates the ability to independently scale, compute, and store resources, ensuring optimal resource utilization and a reduced hardware footprint. The technology is especially valuable for demanding workloads such as AI, edge computing, and containerized applications.
Cost Efficiency: Enhances server utilization, minimizing the need for additional hardware. The resulting efficiency lowers software licensing costs and reduces physical infrastructure requirements, such as the number of network ports.
Operational Efficiency: Simplifies lifecycle management through faster patching, streamlined updates, and minimal downtime. Storage remains available without requiring node-by-node offline periods.
Financial Flexibility: Enables organizations to maximize the value of their existing compute resources through a consumption-based commercial model while ensuring investment protection and greater cost predictability.

Dell’s Disaggregated Infrastructure Solutions

The following solutions address specific infrastructure demands and workload requirements:

PowerFlex: A robust, software-defined infrastructure solution that supports multiple operating systems through a unified storage architecture. The solution is ideally suited for workload consolidation and high-performance use cases, including AI, analytics, and business-critical applications such as SAP.
ACP Azure with SDS PowerFlex: This integration with Dell APEX Cloud Platform for Microsoft Azure delivers enterprise-grade storage, optimized workload management, and scalable infrastructure, ensuring a consistent and reliable Azure experience across multi-cloud environments
ACP Red Hat OpenShift: A turnkey platform engineered to streamline and enhance on-premises OpenShift deployments with deep integration and optimized operations for managing containerized applications with greater efficiency.
NativeEdge: A simplified edge infrastructure management solution that automates deployments and minimizes IT overhead, enabling streamlined operations at distributed sites.

Dell X TechM: Disaggregated Infrastructure Solutions and Services for Diverse Workloads

Dell Technologies and Tech Mahindra offer a comprehensive portfolio of hypervisor alternatives and infrastructure solutions to meet the diverse needs of modern enterprises. The collaboration combines Dell’s industry-leading technologies and Tech Mahindra’s domain expertise to support workloads across various operating environments.

Consulting and Infrastructure Services:

Tech Mahindra takes a consulting approach to help customers identify the correct alternative hypervisor and infrastructure solution through various phases, including assessment, design, deployment, and operation. These phases are interconnected to deliver comprehensive IT solutions.

Assessment Phase: Begins with evaluating the customer’s current capabilities and future needs. This involves gathering data, conducting interviews, and presenting findings that include detailed recommendations.
Design Phase (Day 0): In this phase, bespoke services are designed, including technology onboarding, architecture design, and planning a robust implementation strategy.
Deployment Phase (Day 1): Focuses on the practical implementation of the designed solutions. Activities include hardware installation, software configuration, end-to-end testing, project documentation, and knowledge transfer.
Operation Phase: Involves ongoing support and management to ensure the solutions continue to meet performance standards. This includes lifecycle management, monitoring, and addressing any operational issues that arise post-deployment.

A Future-Ready IT Strategy

The hypervisor solutions developed collaboratively by Dell Technologies and Tech Mahindra represent a game-changing advancement for businesses aiming to modernize and streamline their IT environments. Disaggregated infrastructure provides organizations with increased flexibility, improved cost management, and enhanced operational efficiency. This approach also enables them to overcome the limitations associated with traditional hypervisor models.

The collaboration exemplifies both companies' commitment to innovation and customer-centricity, equipping businesses with the tools they need to embrace future opportunities.

TAGS: Cloud and Infrastructure Services

About the Author

Ravindra Shinde

AVP-Hybrid Cloud, Tech Mahindra

Ravindra Shinde is Tech Mahindra's AVP, Hybrid Cloud Solutions; he leads infrastructure transformation by embracing new and innovative technologies. He focuses on developing infrastructure solutions and pursuing strategies for existing and new accounts to expand services. His strategic approach facilitates business transformation by refining infrastructure solution strategies.

Puneet Garg

Global Solutions Architect, Dell Technologies

Puneet Garg is a Global Solutions Architect within the Dell Technologies Global Alliances team, working with Partners and Customers to advise them on cutting-edge technology solutions. Puneet helps partners enhance their technological capabilities, improve operational efficiency, and maximize organizational performance. Puneet Garg has expertise in various technologies, including generative AI, cybersecurity, and multi-cloud.

Mohammad Ghouse (MG) Mohideen

Global Architect, Dell Technologies

MG Mohideen leads the Global Alliances for GSIs in BFS, Retail, TTH, manufacturing, Telecom, Automotive, Smart Cities, Metro, Cleantech, and Smart Grid through industry-recognized Cyber Resilience, Modern Data Center, and Multi-Cloud Solutions, with coverage across US, EMEA, and APJC theaters. An experienced SME and a technology strategist with broad and deep expertise in Energy Efficient Digital Utilities (Electricity /Water /Gas) and Smarter Cities Landscape (Smart, Safe, Resilient and Sustainable Cities). Consulting and Advisory partner in formulating industry PoV for ultra mega and marquee projects for the public sector, large corporates, and enterprises

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Ravindra Shinde

AVP-Hybrid Cloud, Tech Mahindra

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Puneet Garg

Global Solutions Architect, Dell Technologies

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The Reality of Enterprise AIArtificial intelligence is no longer confined to pilots or innovation labs. It is now embedded as the operational spine of banks, insurers, hospitals, and public institutions. From influencing treatment pathways, pricing decisions, and claims routing to transaction monitoring, eligibility assessments, and more, AI now plays a critical role in core operations.On executive dashboards, the AI impact looks positive. Accuracy is higher, unit costs are lower, and turnaround times appear to have dropped. From a distance, progress seems to be measurable and real. But leadership teams are noticing an alarming pattern: unease among customers and citizens, reluctance from professionals expected to rely on these models, and increasing scrutiny from regulators. As AI moves into critical decision flows, performance metrics alone cannot define outcomes or control. This is where responsible AI shifts from a slogan to a boardroom imperative.As AI usage expands, responsible AI shifts from a mere slogan to a boardroom imperative.Challenge: Progress on Paper, Fragility in PracticeThe central problem is simple to state and hard to ignore—technical capability is accelerating, and decision control is not. Enterprises are building increasingly powerful AI models without aligning on what those models are authorized to decide.In regulated industries in particular, this shows up in recognizable ways:A bank demonstrates strong model performance, yet struggles to explain why similar customers receive different credit outcomes.A hospital deploys a triage model to improve throughput, but clinicians override it, as they are unsure when to trust it and when to ignore it.A public agency uses risk scores to prioritize cases, only to face public criticism when outcome patterns by geography or demographics cannot be justified.In each scenario, the models are operating according to their design goals. But the strain emerges when institutions have to justify those decisions against their own policies, regulatory obligations, and social responsibilities. This is the illusion of progress: a false sense of confidence built on internal metrics, while the gap between what the model is optimized for and what the institution is accountable for continues to widen.The Structural Drivers Behind the BreakdownAs mentioned above, this disconnect is not due to bad intent or weak technology. It emerges from three structural issues in how enterprises approach AI.Decisions Without Clear OwnershipAI is still framed as a technology investment rather than a change in decision capability. The typical process is as follows: business teams define the use case and expected benefits, data science teams build and fine-tune the models, and risk and compliance review documentation and controls. Boards, meanwhile, see aggregated outcomes.What is missing here is a clear accountability of the decision logic the AI model embodies, such as the thresholds, priorities, and trade-offs that shape how it behaves in edge cases. Therefore, when a regulator or internal committee seeks clarification on who authorized the way the model treats marginal applicants or high-risk cases, the answer is rarely clear. This ambiguity is a risk in itself.Trade-offs Limited to the DataEvery meaningful AI model encodes value trade-offs, such as fairness versus efficiency, privacy versus personalization, sensitivity versus specificity, and automation versus human discretion. If these trade-offs are not discussed and decided upfront, they end up being determined implicitly by historical data and optimization objectives. For instance:A claims model might minimize average handling time at the cost of systematically under-serving certain complex segments.A fraud system might aggressively block suspicious transactions, increasing safety metrics while creating silent attrition among legitimate customers.A clinical model trained on incomplete labels may over- or under-prioritize certain conditions, and this distortion often surfaces only when outcome patterns come under scrutiny.From a model perspective, this performance is sound. From an institutional standpoint, decisions may no longer align with stated policies or social commitments. Such misalignments invite scrutiny and erode trust.Governance Confined to DeploymentMany organizations have robust governance on paper: model approval processes, documentation standards, policy statements, and risk registers. However, the real stress test begins after deployment. Model drift, population changes, new regulatory expectations, and evolving public norms all shift the context in which AI operates. If monitoring focuses only on technical metrics, early warning signs go unnoticed, such as:Growing volumes of human overrides in certain scenariosRepeated complaints about certain decision patternsClinicians or case workers informally revert to manual processesRegulators raise the same accountability questionsIf these signals are not collected and treated as first-class data, governance remains static while the operating environment continues to evolve.The real stress test begins after deployment, yet most governance frameworks remain static while operating conditions evolve.How Can Enterprises Respond: Turning Capability into ControlCombating the control conundrum does not require slowing AI adoption. It requires redefining how AI success is measured. Enterprises moving past the illusion of progress make four deliberate shifts.Redefine Success as Decision Quality, Not Just as Model PerformanceStakeholders need to expand their evaluation methodology while assessing AI programs. Alongside accuracy, cost, and cycle-time improvements, leading enterprises are examining:Which classes of decision does the model have authority over, and where does it only take an advisory position?What is the institution’s position on acceptable error types for this use case?How are fairness, explainability, and recourse being measured over time, not just at launch?This reframing anchors AI in the broader question of decision quality and institutional risk, rather than treating it as a point solution.Establish Explicit Decision OwnershipEvery high-impact AI model should have formalized ownership that is accountable not only for the benefits but also for the rules of engagement, such asWhat can the model decide autonomously?Where is human approval mandatory?Which trade-offs have been consciously accepted, and which are off-limits?When must the logic be revisited? (e.g., material regulatory changes, significant shifts in data)Defining clear responsibilities is not about adding procedural approvals. It ensures that automated decisions remain clearly accountable to the institution.Design Constraints and Autonomy Limits in the ModelOperationalizing control demands the establishment of guardrails beyond policies. When constraints are designed into the core functions of the model, governance is streamlined. For traditional AI, this calls for hard limits on decision ranges in sensitive segments, guardrails around how certain demographic attributes can be used, and explainability thresholds below which automation is not allowed.On the other hand, for the emerging autonomous agentic systems, it translates to defining which actions an agent executes end-to-end and where it needs intervention. Also, it establishes boundaries on how far it can adjust terms, priorities, or treatment strategies without escalation while ensuring logs, justifications, and intermediate steps are captured for behavior analysis and auditing.Embed Decision Observability and Feedback LoopsFinally, enterprises need greater visibility into how AI-enabled decisions play out over time. In practice, this requires complementing traditional model monitoring with operational signals such as override and escalation patterns, complaint and appeal themes, behavioral data from professionals and case workers (adoption, workarounds), and external signals from regulators, professional bodies, or the media.Rather than treating these signals as noise, enterprises should recognize them as early indicators of misalignment between system logic and lived reality. Treating these signals as structured feedback allows organizations to adjust course before risk escalates.The Future Direction: Readiness for Agentic AIAs agentic AI gains enterprise prominence, the urgency to strengthen decision governance intensifies. These models do not merely recommend; they coordinate, decide, and act across workflows. For instance, in BFSI, an agentic system might manage collections strategies across thousands of accounts, adjusting outreach and managing terms as circumstances change. In healthcare, it might orchestrate diagnostics, appointments, and referrals across an entire care pathway. In the public sector, it might manage complex, multi-agency cases. In such scenarios, the question shifts. It is no longer sufficient to ask whether the model is fair and accurate. It should also focus on:How does the model behave across time and population?What happens when its objectives interact with other automated systems?How quickly and cleanly can intervention occur if outcomes begin to diverge from intent?Enterprises that act now will build the foundation to scale agentic AI with safety and control. Conversely, organizations that stall restrict their operational agility and growth.From Illusion to DisciplineAI now sits at the center of enterprise strategy and cannot be solely evaluated through the lens of technical achievement. In regulated industries, especially, the real differentiator will be the ability to scale AI while ensuring decision-making is aligned with intent, regulatory expectations, and public trust. Making this strategic leap requires enterprises to institutionalize responsible AI as a sustained governance discipline, embedding accountability directly into how models are designed and deployed. With this approach, enterprises capture AI’s productivity and performance gains while maintaining clear authority over automated decisions. This is the missing link to scale AI responsibly.

From Assisted Intelligence to Autonomous ExecutionA supply chain exception arises at 2 a.m. A pricing anomaly appears in a key market. A customer churn risk is detected mid-journey.Traditionally, each of these would trigger dashboards, alerts, and human escalation. Today, agentic AI systems are beginning to resolve them autonomously—analyzing context, selecting actions, and executing decisions in real time. According to Gartner, agentic AI is a leading strategic technology trend for 2025,1 and by 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of daily tasks to be completed autonomously.2The shift in AI applications from assistance to autonomy introduces new and complex security challenges. When introducing these new systems, the organizations need to align agentic AI with their values. Organizations also need to adapt and address security challenges related to operational integrity, mitigation, and the prevention of harmful autonomous actions.Agentic AI will only scale when autonomy is paired with governance, traceability, and security by design.Key Areas of Difference between AI Security and Agentic AI SecurityAutonomy: Agentic AI systems operate independently and can execute complex tasks without human input to achieve their goals. Whereas generative AI focuses on a single model to create content (such as text or images) in response to a specific prompt and does not initiate further actions independently.Decision Complexity: Agentic AI is capable of adaptive and context-driven decisions. These actions are taken in real-time or in response to novel scenarios with minimal human oversight. Typical AI systems execute simple, task-oriented automations within a closed scope or defined boundaries.Proactive Risk Exposure: The attack surface of agentic AI systems expands due to their adaptive, interconnected behavior. Compared to conventional AI, they can create unpredictable threat vectors.Identity and Access Challenges: Traditional AI operates with transient authentication within clearly defined service boundaries. On the other hand, agents may maintain state across interactions. They can impersonate users or other digital entities with stored credentials. Agents can make autonomous access decisions based on goal-directed reasoning and potentially escalating privileges. Such capabilities require robust ephemeral, delegated, and cross-domain identity controls.Continuous Governance and Auditability: Agentic systems require real-time and dynamic policy enforcement. They also require traceability and continuous audit for self-directed workflows. In contrast, regular AI usually relies on more static security oversight and accountability frameworks.Agentic AI Security Risks and ChallengesAgentic AI systems introduce various security threats impacting the application layer, APIs, and ML/LLMs. OWASP also highlighted the security threats and mitigation measures for agentic AI. Below are key security threats to consider when building an agentic AI system.Goal Integrity and Alignment AttacksGoal integrity and alignment attacks target the reasoning processes that determine agent objectives. Attackers use techniques such as prompt injections and reward manipulation. They also poison the training data or exploit system gaps. Such attacks can cause agents to pursue unintended or harmful outcomes.Agent Hallucinations and Factual ErrorsAgent hallucinations occur when AI agents generate false information or act on incorrect or fabricated data. This can negatively affect business operations and cause minor issues or major system failures. These attacks can be mitigated by using human oversight, verification checkpoints, and confidence thresholds.Memory and Data PoisoningAttackers attempt to compromise AI decision-making by injecting malicious content into an agent's contextual memory. Attackers also insert harmful content in an attempt to manipulate training data and poison Retrieval-Augmented Generation (RAG). To mitigate these attacks, AI, data, and MLOps teams must validate all data ingested into agent memory and RAG pipelines through automated and human-in-the-loop controls.Tools and API MisuseAgents often rely on external tools and APIs to perform tasks. Without proper safeguards, these AI agents can misuse these tools. Agents can exceed their authorization boundaries or manipulate external systems. They can overutilize resources by making too many requests, potentially triggering a denial-of-service attack.Non-Human Identities (NHIs) often lack proper session oversight. This makes them vulnerable to token abuse or credential leakage. Implementing robust key management and limiting API usage helps in mitigating these risks. Applying least privilege principles and regularly monitoring for anomalous activity can prevent abuse or data leaks.Identity Spoofing and Privilege EscalationAttackers can manipulate agents to impersonate users or services and perform unauthorized actions. This risk increases as agents often access multiple systems with different permissions. Attackers exploit the agent’s trust or escalate privileges using malicious prompts. Clear boundaries, strict privilege isolation, and monitoring of agent activity can help reduce these risks.Agent-to-Agent Security and Supply Chain AttackAgent-to-agent interactions involve multiple autonomous AI systems that communicate, exchange information, and make collaborative decisions. In a multi-agent environment, agents might share unintended data, manipulate results to benefit themselves, or even deceive one another. When agents do not coordinate, their actions can clash and waste resources. Agents also depend on external models, libraries, or plugins. If not managed properly, they can also introduce vulnerabilities. It is important to secure supply chain interactions and control data flow between agents. If one agent is compromised, it can trigger cascading effects across multiple systems, resulting in widespread impact beyond the initial point of compromise. To mitigate these risks, it is important to verify agent identities, use secure messaging, and monitor for anomalous behavior.In interconnected agent ecosystems, a single breach can propagate across workflows, amplifying impact far beyond the initial point of compromise.Agent Clone and Rogue AgentCloning is the unauthorized duplication of an agent, which can lead to serious security breaches and trust violations. Attackers create ‘evil twins’ of trusted AI agents. They use reverse engineering or replicate training datasets to create clones. They also intercept and mimic API responses to conduct sophisticated social engineering attacks. Clones can harvest sensitive information from users who believe they are interacting with the legitimate system. They can also damage brand reputation through harmful outputs associated with a trusted AI identity.Similarly, a rogue AI agent can deviate from its intended purpose. Due to goal misalignment, malicious compromise, or software defects, an agent can operate outside authorized parameters. This can cause widespread damage, as agents may execute unauthorized transactions and leak sensitive information. They can also manipulate the connected systems or consume excessive resources.AI Agent Lifecycle SecuritySecuring agentic AI is an ongoing process that spans the agent’s entire lifecycle. Below is the entire lifecycle:Design and Development PhaseOrganizations should establish clear security requirements and guardrails defining agents' operational boundaries and permissible actions. Employing secure coding practices and regularly reviewing codes helps identify vulnerabilities. Access controls should be designed using the principle of least privilege. To safeguard sensitive information and data, implement encryption, data minimization, and privacy controls. Additionally, developers should implement input/output validation and secure configuration management. It is also important to maintain detailed documentation of all security decisions.Deployment PhaseThe deployment phase of agentic AI systems includes establishing a secure CI/CD pipeline with integrity verification, code signing, and vulnerability scanning. It also involves hardening the runtime environment through proper network segmentation, container security, and endpoint protection. Secure all APIs with proper encryption, rate limiting, and access controls. Conduct final pre-deployment security validation through penetration testing and compliance checks.Operation PhaseDuring the operation phase, continuously monitor agents to detect anomalous patterns and deviations. Implement input/output data validations to prevent injection attacks and prompt manipulation. Also, ensure responses are secure and comply with ethical guidelines. Organizations should also keep detailed logs of agent actions for security checks and compliance. Regularly monitor system performance to detect attacks or slowdowns caused by resource overuse. Set up automated actions to address security issues quickly.Maintenance PhaseThe maintenance phase of agentic AI systems focuses on keeping the agent secure while the agent is active. Regularly update all parts of the system with security patches and test the changes. Organizations must conduct security checks, such as penetration testing and vulnerability scanning, to identify new risks. Analyze security incidents and continually improve defenses based on new threat intelligence. Regularly review access privileges and configuration changes. Also, keep documentation up to date to track all security controls.Decommissioning PhaseAI platform, security, and IT operations teams should jointly plan the agent decommissioning stage. This includes formally approving shutdowns, terminating all agent processes, preventing unauthorized restarts, and securely wiping or destroying sensitive data in accordance with governance and compliance requirements. This includes training data, operational logs, authentication credentials, and cached content across all storage locations. Systematically remove all agent credentials, API keys, certificates, and permissions from every system it previously interacted with. The final steps include comprehensive documentation of the decommissioning process for audit purposes. Conduct a post-decommissioning security assessment to confirm no residual access or data remains.Securing agentic AI is not a one time control but a continuous discipline, spanning design, deployment, operation, maintenance, and decommissioning.AWS Services for Agentic AI Risk MitigationAWS provided the Agentic AI Security Scoping Matrix,3 a framework for securing autonomous AI systems. Based on the scope of application, it helps identify the appropriate security controls.Amazon Bedrock and Amazon Bedrock AgentCoreAmazon Bedrock Guardrails help in content filtering by defining custom boundaries for AI agent behavior and responses.Amazon Bedrock AgentCore Identity secures AI agents by managing their access to AWS resources and third-party services. It uses authentication protocols such as OAuth 2.0 and provides secure credential storage for OAuth tokens and API keys, ensuring that only authorized agents can access specific resources.Amazon Bedrock AgentCore tracks agent operations in real time through its identity and observability capabilities, enabling rapid issue troubleshooting and performance optimization to ensure reliable execution.Amazon Bedrock helps track the entire AI decision-making chain for audit purposes.Systematically test models for hallucinations and security vulnerabilities.Amazon SageMakerAmazon SageMaker Model Monitoring helps detect drift in model performance that may indicate compromiseAmazon SageMaker Clarify helps detect bias and explain predictions in machine learning modelsAmazon SageMaker Model Cards help document model characteristics and limitations to improve risk assessmentAWS IAM (Identity and Access Management)AWS IAM ensures fine-grained permission policies for AI agent actions.AWS IAM enables service control policies to limit agent capabilities.AWS IAM allows permission boundaries to prevent privilege escalation.AWS IAM offers role-based access control for different AI agent functions.AWS KMSEncrypt sensitive data used or generated by AI agents.Amazon MacieAutomatically discover and classify sensitive data, such as PII, financial data, and intellectual property. Amazon GuardDutyIt is machine-learning-powered threat detection that identifies abnormal agent behavior.It continuously monitors the system to identify potential security issues.It provides AWS Lambda integration for automated remediation.AWS CloudTrailProvide comprehensive API logging for all agent interactions.Immutable audit trails for compliance and investigation.Insights to detect unusual patterns in agent behavior.Amazon CloudWatchReal-time monitoring of agent activities and resource usage.Anomaly detection to identify potential security incidents.Custom dashboards for security operations visibility.AWS ConfigTrack configuration changes in AI systems.Enforce compliance rules for agent deployments.Assess overall security posture continuously.Amazon InspectorThe tool assists in assessing vulnerabilities for AI infrastructure.It facilitates security assessment of environments where agents operate.AWS Security HubIt provides a centralized view of security alerts across AI systems.The tool helps with compliance checks against security standards.It provides integration with other security services for comprehensive protection.ConclusionAs agentic AI applications and use catch up and grow, it is critical to adapt and address emerging challenges. Organizations will need to develop systems and cultivate a decision-making culture that prioritizes proactive security rather than being reactionary, with a focus on defending against known attacks. AWS provides a comprehensive suite of services that help build secure foundations for AI initiatives. Organizations can harness the power of autonomous AI systems while minimizing associated risks by understanding the unique security challenges they pose and implementing appropriate safeguards.

Today’s competitive environment of enterprise cloud migrations, traditional proposal development methods no longer meet the demanding timelines and accuracy requirements of modern businesses. As solution architects at Tech Mahindra, we worked across multiple RFX proposals using AWS Transform to simplify and speed up our migration proposal process. This blog explores how we adapted our approach when a customer needed a quick, directional business case for their cloud migration journey. The challenge was to deliver fast, actionable insights that would help the customers understand the value proposition of cloud migration within a compressed timeline. This solution helped set new standards for efficiency and precision in cloud migration planning while meeting the urgent needs of our customers’ decision-making process.Understanding the Cloud Migration JourneyCloud migration proposals traditionally require analysis of existing infrastructure, technical assessments, and direction for business case development. Our team faced the challenge of responding to an RFP to migrate more than 2,000 servers across three data centers to AWS. The client required directional cost projections, instance-sizing recommendations, licensing strategies, TCO analysis, and migration timelines, all elements that traditionally needed weeks of analysis and multiple team members’ involvement.Challenges in the Traditional ApproachThe conventional proposal development process was often inefficient and risky. Teams spent two to three weeks conducting manual analyses, wrestling with complex spreadsheet calculations, and making numerous assumptions. This approach not only consumed valuable resources but also introduced the risk of human error in calculations. Generic TCO calculators based on industry averages often failed to capture client-specific nuances, making it difficult to build compelling business cases that would resonate with stakeholders.While tool-based approaches offer significant advantages in streamlining these processes, their successful implementation hinges on effective coordination across the organization's technology landscape. This requires close coordination across multiple IT teams, including application owners, database administrators, security teams, and network specialists, to manage different aspects of the infrastructure. Each group typically maintains its own tools, methodologies, and approval processes, creating a complex web of dependencies that can impact data collection timelines. Additionally, varying security protocols and access restrictions across departments often introduce additional layers of complexity to the assessment phase, making it crucial to establish clear communication channels and data collection protocols from the project's outset.Implementing AWS TransformTo overcome the challenges of the traditional approach, AWS Transform provides a framework that combines assessment methodologies, proven migration strategies, and business case development capabilities to accelerate the proposal process. It delivers data-driven strategic plans that reflect a thorough understanding of the client's environment and outline practical pathways to effective cloud adoption.AWS Transform helps in:AI-Powered Analysis for InfrastructureI used AWS Transform to generate migration assessments from data collected from on-premises environments. It also helped in estimating the cost of running on-premises servers on AWS.To automate assessment, we create an AWS Transform job and upload the on-premises server data using an AWS MPA .xlsx or RVTools export file (max 10MB, up to 30,000 servers per assessment). We then specify either a region or a multi-region deployment for the assessment.Data Analysis and RefinementIt is easy to interact with AWS Transform Assessment using natural language to provide detailed workload characteristics. The process takes a detailed look at multi-availability zone strategies, multi-region architecture options, disaster recovery needs, and compliance requirements, resulting in migration proposals that address resilience needs with technically sound and cost-effective solutions. In our case, AWS Transform Assessment supported the proposal through rightsizing, cost modeling, and migration strategies for each workload. Recommendations provided by AWS Transform include:EC2 Instance Mapping AWS Transform Assessment uses AI algorithms to analyze current server configurations and usage patterns, automatically identifying optimal EC2 instance types based on workload characteristics. It maps current on-premises servers to right-sized EC2 instances using performance metrics rather than just specifications, and provides recommendations for specialized instance types such as compute-optimized, memory-optimized, etc.Storage Optimization The solution analyzes current storage utilization, access patterns, and performance requirements and recommends appropriate storage services – such as EBS, S3, EFS, and FSx – based on workload requirements. It also identifies opportunities for storage tiering to optimize costs and calculates potential savings from migrating to AWS storage solutions with specific configurations.License Mobility Assessment The solution identifies existing software licenses that can be transferred to AWS and analyzes BYOL (Bring Your Own License) opportunities versus AWS license-included options. This provides a cost comparison between license mobility scenarios and new licensing models, along with recommendations for optimizing licenses during migration.Reserved Instance Opportunities AWS Transform uses AI-driven forecasting to identify stable workload components suitable for Reserved Instances (RI) and analyzes potential savings across different RI term lengths and payment options. It also identifies workload patterns that could benefit from savings plans and provides optimization recommendations for commitment-based discount opportunities.Developing a Data-Driven Business CaseAWS Transform helped us create the business case within days by automating 80% of the calculations. The business case is based on the current on-premises environment understanding and provides accurate TCO projections. It also identified license mobility opportunities, RI benefits, and storage optimization options. The assessment revealed immediate cost-saving opportunities that meet performance requirements.The assessment further strengthens the business case through customized RI strategies that analyze workload stability patterns and usage consistency to recommend optimal commitment terms, payment options, and coverage levels that balance upfront investments against long-term savings. All these elements helped in creating a compelling financial narrative that demonstrates both immediate cost reductions and long-term optimization opportunities. The AWS Migration business case created by AWS Transform presents decision-makers with a clear, data-validated economic rationale for cloud migration that addresses both capital expense reduction and operational cost optimization. Automated generation of business cases with both hard and soft benefits helps us in making the migration proposal more persuasive.Table 1 presents a three-year TCO comparison for different AWS pricing and tenancy options, showing total costs, potential savings versus on-demand pricing, and annual costs. This analysis helps stakeholders understand the financial impact of various licensing and instance strategies, providing comparisons for cost-optimization opportunities.3-Year TCO ComparisonPricing Option3-Year Total CostSavings vs. On -DemandAnnual CostOn-Demand (Shared Tenancy)$730,513.02-$243,504.341-Year NURI (Shared Tenancy)$567,361.5622.33%$189,120.521-Year NURI (Mixed Tenancy, Windows Server pre-2022 BYOL)$1,080,290.91-47.88%$360,096.973-Year NURI (Shared Tenancy)$478,262.0434.53%$159,420.68Key Benefits in the RFP Process Observed by TechMUsing AWS Transform reduces proposal development time by approximately 60%.Reduced analysis time from 2-3 weeks to 5 days with 80% automated technical calculations.Eliminated manual calculation errors and generated consistent recommendations across workloads.Provided data-driven recommendations for rightsizing, BYOL, and optimal RI mix.Provided more consistent quality across proposals.Freed up team members for proposal narrative development and support more clients simultaneously.ConclusionAWS Transform accelerated TechM responses to client migration proposals, enabling teams to deliver higher-quality recommendations in less time. The AWS Transform Assessment delivers comprehensive business case justification through detailed cost breakdowns that illuminate financial implications across multiple dimensions. AWS Transform’s tools help avoid over-provisioning by recommending appropriate instance types based on actual workload requirements. By leveraging AWS Transform, TechM solutions architects now help clients realize the full benefits of the cloud, including increased scalability, cost savings, and enhanced security.

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Tech Mahindra & Dell Technologies: Redefining Virtualization with Innovative Solutions

Joint Solution Overview: Driving Innovation with Tech Mahindra and Dell

Disaggregated Infrastructure: The Key Differentiator

Dell’s Disaggregated Infrastructure Solutions

Dell X TechM: Disaggregated Infrastructure Solutions and Services for Diverse Workloads

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