Tech Mahindra and Microsoft announced on June 30, 2026, that they are collaborating on an AI-driven 5G Network Digital Twin for telecom operators, combining Microsoft Azure, Microsoft Fabric, Azure Digital Twins, Azure AI Foundry-era tooling, and Tech Mahindra’s telecom integration expertise. The pitch is not simply another dashboard for network engineers. It is a bet that the next phase of 5G will be won by operators that can model, predict, and automate their networks fast enough to sell enterprise-grade services with credible guarantees. For WindowsForum readers, the story matters because Microsoft is again trying to turn Azure from a cloud platform that hosts telecom workloads into the control plane through which telecom networks are observed, reasoned over, and eventually acted upon.
The collaboration lands in a telecom market that has spent years selling 5G as a platform for transformation while struggling to turn that promise into enough new revenue. Faster mobile broadband was never the whole business case. The bigger prize has always been private networks, edge computing, industrial automation, network slicing, and service-level agreements that let carriers sell differentiated connectivity to enterprises rather than commodity bandwidth to consumers.
That is where the “network digital twin” label starts to matter. A digital twin is a software representation of a real-world system, kept current with live or near-real-time data. In Microsoft’s Azure Digital Twins architecture, the model is not merely a diagram; it is a graph of entities, relationships, properties, and events that can be queried and integrated with analytics and AI services.
Telecom networks are especially tempting candidates for this treatment because they are messy, expensive, and constantly changing. A large operator may be managing radio access network gear from multiple vendors, transport links, core network functions, edge sites, cloud-native workloads, security policies, customer-specific SLAs, and regulatory obligations. No single pane of glass has ever solved that complexity because the problem is not glass. The problem is whether the operator can understand cause and effect quickly enough to make better operational decisions.
Tech Mahindra and Microsoft are presenting their joint solution as a move from passive monitoring to active decisioning. That phrase is doing a lot of work. It implies a shift from “show me the alarm” to “simulate the likely failure domain, recommend a remediation, and eventually orchestrate the fix.” The difference is the difference between a network operations center staring at dashboards and a network operations model that can test interventions before the customer notices degradation.
That is why the announcement emphasizes SLA-driven offerings such as network slicing and edge orchestration. Network slicing is often described as carving a virtual network out of shared infrastructure for a particular application or customer. In practice, selling slices is less about marketing language and more about proving that the slice can be provisioned, monitored, protected, and repaired according to a business contract.
The operational challenge is brutal. If a slice spans RAN, transport, core, and edge compute, then a degradation event may come from radio interference, backhaul congestion, cloud resource contention, misconfiguration, software defects, or a policy conflict. An operator cannot monetize such services at scale if every incident requires heroic manual correlation across separate tools.
A digital twin promises to make those dependencies explicit. The operator can map network assets, services, customers, and performance conditions into a semantic model. Once telemetry is unified through a data estate such as Microsoft Fabric, AI systems have a better shot at identifying patterns that are invisible when data is trapped in vendor-specific systems.
The phrase AI-ready data estate sounds like the sort of thing that emerges from a press release committee, but the underlying point is real. AI in network operations is only as useful as the freshness, cleanliness, and context of the data it receives. A model that can see alarms but not topology, customer impact, maintenance history, or policy constraints will confidently recommend nonsense.
That matters because telecom modernization is not a greenfield cloud migration. Operators do not rip out decades of infrastructure because a hyperscaler has discovered an attractive diagram. They integrate. They wrap legacy systems. They translate between old operational support systems, newer cloud-native network functions, vendor EMS and NMS platforms, field inventory systems, ticketing systems, and bespoke scripts that nobody wants to admit are business critical.
Tech Mahindra has previously worked with Microsoft around Azure Operator Nexus, Microsoft Fabric, and telecom transformation. This latest announcement looks less like a one-off product launch and more like another tile in a mosaic: Azure as the infrastructure and data layer, Tech Mahindra as the industry solution builder, and AI as the accelerant that makes the combined proposition more urgent.
There is a strategic convenience here for both companies. Microsoft gets another proof point for Azure in telecom, a sector where hyperscalers have been trying to convince carriers that cloud platforms can support mission-critical network functions and operations. Tech Mahindra gets to sell higher-value AI-led modernization work rather than competing only on integration labor.
For operators, the pitch is appealing but not automatically persuasive. Telecom buyers are rightly suspicious of platforms that promise autonomy before they prove observability. The history of network management is full of expensive systems that ingested data, generated dashboards, and failed to produce operational trust.
In a telecom setting, a closed loop might detect congestion, simulate alternative routing or resource allocation, select a policy-compliant response, execute it through automation, and verify that the service returned to target performance. At modest levels, that could mean generating recommended actions for human approval. At higher levels, it could mean autonomous remediation under tightly bounded conditions.
This is where the distinction between useful automation and reckless automation becomes central. Operators have historically been cautious for good reason. A bad action in an enterprise IT system may disrupt an application. A bad action in a telecom network may degrade emergency communications, affect regulated services, or cascade across thousands of customers.
The best version of a network digital twin is therefore not an AI system that blindly “fixes” the network. It is a system that lets operators ask safer what-if questions. What happens if this cell is taken down for maintenance? Which enterprise slices are exposed if this edge site loses capacity? How would rerouting traffic affect latency for a customer with a strict SLA? Which remediation improves the most customer-impacting condition with the least operational risk?
That is the practical value of combining simulation with live telemetry. Operators need a way to test actions against a model of the network before those actions hit production. In mature environments, the twin becomes an operational rehearsal space: a place to predict consequences, refine policies, and reduce the blast radius of automation.
Fabric is Microsoft’s attempt to bring data engineering, real-time analytics, data science, and business intelligence into a more integrated platform. In the telecom context, that means the twin can be fed not only by live operational streams but also by historical patterns that reveal seasonality, chronic trouble spots, capacity trends, and customer-impact correlations.
This is also where Microsoft’s broader AI story enters. Azure AI Foundry and related agent frameworks are meant to help organizations build and manage AI applications that can reason over enterprise data and tools. In this announcement, the agentic AI language suggests a future where software agents do more than summarize incidents. They may compare scenarios, trigger workflows, call orchestration APIs, and learn from outcomes.
The caveat is that telco data governance is unforgiving. Operators will need strict controls over what data enters models, which systems agents can touch, how recommendations are audited, and how customer-specific or regulated data is protected. A network digital twin that becomes a sprawling data lake with a chatbot bolted on is not modernization. It is risk with a nicer interface.
That is why Fabric IQ and semantic intelligence are more than buzzwords in this context. If Microsoft and Tech Mahindra can help operators create a consistent business and network vocabulary across data sources, AI tools become easier to constrain and evaluate. If they cannot, the agentic layer will inherit every ambiguity and inconsistency that already frustrates network operations teams.
Microsoft’s telecom strategy is not about making Windows Server the center of the carrier network. It is about making Azure the substrate for workloads, data, automation, and AI systems that carriers depend on. The more network operations move into Azure-connected platforms, the more telecom operations begin to resemble cloud operations: identity-first, policy-driven, API-mediated, and deeply dependent on observability pipelines.
That has practical consequences. Telecom operators adopting this kind of architecture will need people who understand Kubernetes, Azure governance, data engineering, AI operations, and security posture management alongside traditional telecom domains. The boundary between network engineer and cloud engineer continues to blur.
For enterprise IT teams, the downstream effect could be more interesting than the telecom plumbing itself. If operators can actually deliver more reliable network slices, edge services, and SLA-backed connectivity, enterprises may start treating carrier networks as programmable extensions of their own infrastructure. That could affect how organizations design branch connectivity, industrial networks, remote operations, and latency-sensitive applications.
The catch is that those benefits only arrive if carriers can turn the platform into dependable service offerings. Enterprises have heard a lot of 5G promises. They will care less about whether an operator uses Azure Digital Twins and more about whether the service is measurable, enforceable, secure, and priced sensibly.
There is a difference between showcasing a solution and proving it at national carrier scale. The announcement describes a solution intended for medium and large telecom operators, but it does not name live customers, deployment counts, production performance metrics, or measured cost reductions from this specific collaboration. That does not make the technology unserious. It means readers should separate product capability from market validation.
The strongest version of this story is not that Microsoft and Tech Mahindra have solved autonomous 5G operations. The stronger and more credible story is that they are assembling the ingredients operators need if they want to move in that direction: cloud-scale data ingestion, digital twin modeling, semantic context, predictive analytics, AI-assisted reasoning, and orchestration hooks.
That assembly is still hard. Network data is noisy. Topology records are often inaccurate. Multi-vendor environments resist neat abstraction. Operational teams may distrust recommendations they cannot explain. Regulators may require auditable human oversight. Security teams will ask whether AI agents have too much operational reach.
The industry should welcome ambition while demanding evidence. A network twin that reduces mean time to repair, improves capacity planning, predicts SLA violations, or prevents outages is valuable. A network twin that mainly produces impressive demos is another layer of management software.
That does not mean operators should avoid the architecture. In fact, a well-governed twin could improve security by making network state more visible, policy drift easier to detect, and incident impact easier to assess. The same contextual model that helps troubleshoot performance can help identify suspicious changes or abnormal traffic patterns.
But the access model must be ruthless. AI agents that can reason across telemetry and trigger actions need clear permissions, logging, approval workflows, and rollback mechanisms. The operational convenience of an agent that “just fixes it” must not override the discipline of least privilege.
There is also the question of model poisoning and bad data. If the twin’s view of the network is wrong, the recommendations built on top of it may be wrong. If telemetry pipelines are manipulated, the AI layer may be nudged toward harmful actions. Traditional network monitoring already faces data quality problems; adding automation raises the stakes.
For Microsoft, this is both an opportunity and a liability. Azure’s security, identity, compliance, and governance tooling can be a selling point, especially for operators that already use Microsoft technologies. But the more Microsoft participates in telecom operational control planes, the more scrutiny it will face when reliability or security failures occur.
Operators invested heavily in spectrum and infrastructure. Many expected 5G to open new enterprise revenue streams. Instead, a large portion of the market has remained stuck between consumer data plans and bespoke enterprise projects that are difficult to scale. A platform that makes service assurance more predictable could help convert custom engineering into repeatable products.
SLA-backed network slicing is the obvious example. Edge orchestration is another. If an operator can model capacity, predict risk, and automate remediation across access, transport, core, and cloud edge layers, it can sell a stronger product to enterprises. If it cannot, it is still selling aspiration.
The digital twin becomes a kind of commercial confidence engine. It lets the operator understand what it can safely promise. It helps product teams design offers around measurable capabilities rather than theoretical 5G features. It gives account teams a way to discuss risk, assurance, and performance in terms that enterprise customers recognize.
That may be where Tech Mahindra’s role becomes especially important. Telecom modernization projects often fail not because the cloud service is inadequate, but because the operator cannot translate technology into operating model change. The twin has to connect engineering, operations, finance, product, and customer assurance. Otherwise, it remains a sophisticated tool used by a narrow technical audience.
The concrete takeaways are narrower, but more useful.
Microsoft Wants Azure to Become the Telecom Network’s Memory
The collaboration lands in a telecom market that has spent years selling 5G as a platform for transformation while struggling to turn that promise into enough new revenue. Faster mobile broadband was never the whole business case. The bigger prize has always been private networks, edge computing, industrial automation, network slicing, and service-level agreements that let carriers sell differentiated connectivity to enterprises rather than commodity bandwidth to consumers.That is where the “network digital twin” label starts to matter. A digital twin is a software representation of a real-world system, kept current with live or near-real-time data. In Microsoft’s Azure Digital Twins architecture, the model is not merely a diagram; it is a graph of entities, relationships, properties, and events that can be queried and integrated with analytics and AI services.
Telecom networks are especially tempting candidates for this treatment because they are messy, expensive, and constantly changing. A large operator may be managing radio access network gear from multiple vendors, transport links, core network functions, edge sites, cloud-native workloads, security policies, customer-specific SLAs, and regulatory obligations. No single pane of glass has ever solved that complexity because the problem is not glass. The problem is whether the operator can understand cause and effect quickly enough to make better operational decisions.
Tech Mahindra and Microsoft are presenting their joint solution as a move from passive monitoring to active decisioning. That phrase is doing a lot of work. It implies a shift from “show me the alarm” to “simulate the likely failure domain, recommend a remediation, and eventually orchestrate the fix.” The difference is the difference between a network operations center staring at dashboards and a network operations model that can test interventions before the customer notices degradation.
The 5G Monetization Problem Was Always an Operations Problem
The industry’s recurring 5G headache is that many of the most lucrative use cases require more operational precision than traditional telecom systems were built to provide. A consumer streaming video over a best-effort mobile connection can tolerate variable performance. A factory floor using private 5G for robotics, a port using connected logistics, or a hospital relying on edge-connected devices cannot be sold vague promises about coverage and speed.That is why the announcement emphasizes SLA-driven offerings such as network slicing and edge orchestration. Network slicing is often described as carving a virtual network out of shared infrastructure for a particular application or customer. In practice, selling slices is less about marketing language and more about proving that the slice can be provisioned, monitored, protected, and repaired according to a business contract.
The operational challenge is brutal. If a slice spans RAN, transport, core, and edge compute, then a degradation event may come from radio interference, backhaul congestion, cloud resource contention, misconfiguration, software defects, or a policy conflict. An operator cannot monetize such services at scale if every incident requires heroic manual correlation across separate tools.
A digital twin promises to make those dependencies explicit. The operator can map network assets, services, customers, and performance conditions into a semantic model. Once telemetry is unified through a data estate such as Microsoft Fabric, AI systems have a better shot at identifying patterns that are invisible when data is trapped in vendor-specific systems.
The phrase AI-ready data estate sounds like the sort of thing that emerges from a press release committee, but the underlying point is real. AI in network operations is only as useful as the freshness, cleanliness, and context of the data it receives. A model that can see alarms but not topology, customer impact, maintenance history, or policy constraints will confidently recommend nonsense.
Tech Mahindra Supplies the Telecom Scar Tissue Microsoft Lacks
Microsoft brings the cloud platform, data services, digital twin architecture, and AI branding. Tech Mahindra brings something Microsoft cannot manufacture in Redmond: years of telecom delivery work inside the sort of heterogeneous environments where elegant reference architectures go to die.That matters because telecom modernization is not a greenfield cloud migration. Operators do not rip out decades of infrastructure because a hyperscaler has discovered an attractive diagram. They integrate. They wrap legacy systems. They translate between old operational support systems, newer cloud-native network functions, vendor EMS and NMS platforms, field inventory systems, ticketing systems, and bespoke scripts that nobody wants to admit are business critical.
Tech Mahindra has previously worked with Microsoft around Azure Operator Nexus, Microsoft Fabric, and telecom transformation. This latest announcement looks less like a one-off product launch and more like another tile in a mosaic: Azure as the infrastructure and data layer, Tech Mahindra as the industry solution builder, and AI as the accelerant that makes the combined proposition more urgent.
There is a strategic convenience here for both companies. Microsoft gets another proof point for Azure in telecom, a sector where hyperscalers have been trying to convince carriers that cloud platforms can support mission-critical network functions and operations. Tech Mahindra gets to sell higher-value AI-led modernization work rather than competing only on integration labor.
For operators, the pitch is appealing but not automatically persuasive. Telecom buyers are rightly suspicious of platforms that promise autonomy before they prove observability. The history of network management is full of expensive systems that ingested data, generated dashboards, and failed to produce operational trust.
The Real Product Is Not the Twin, It Is the Feedback Loop
The most important phrase in the announcement is not “digital twin.” It is closed-loop orchestration. A digital twin by itself is a model. A dashboard is a view. Predictive analytics is a forecast. Closed-loop orchestration is the dangerous and valuable step where the system begins to act.In a telecom setting, a closed loop might detect congestion, simulate alternative routing or resource allocation, select a policy-compliant response, execute it through automation, and verify that the service returned to target performance. At modest levels, that could mean generating recommended actions for human approval. At higher levels, it could mean autonomous remediation under tightly bounded conditions.
This is where the distinction between useful automation and reckless automation becomes central. Operators have historically been cautious for good reason. A bad action in an enterprise IT system may disrupt an application. A bad action in a telecom network may degrade emergency communications, affect regulated services, or cascade across thousands of customers.
The best version of a network digital twin is therefore not an AI system that blindly “fixes” the network. It is a system that lets operators ask safer what-if questions. What happens if this cell is taken down for maintenance? Which enterprise slices are exposed if this edge site loses capacity? How would rerouting traffic affect latency for a customer with a strict SLA? Which remediation improves the most customer-impacting condition with the least operational risk?
That is the practical value of combining simulation with live telemetry. Operators need a way to test actions against a model of the network before those actions hit production. In mature environments, the twin becomes an operational rehearsal space: a place to predict consequences, refine policies, and reduce the blast radius of automation.
Fabric Gives the Pitch Its Data Gravity
Microsoft Fabric is an important part of the collaboration because digital twins do not thrive on topology alone. Telecom networks generate enormous volumes of telemetry, including performance counters, logs, events, configuration changes, inventory data, trouble tickets, geospatial data, customer data, and service metrics. Without a unified data layer, the twin risks becoming an isolated model that knows the shape of the network but not its history.Fabric is Microsoft’s attempt to bring data engineering, real-time analytics, data science, and business intelligence into a more integrated platform. In the telecom context, that means the twin can be fed not only by live operational streams but also by historical patterns that reveal seasonality, chronic trouble spots, capacity trends, and customer-impact correlations.
This is also where Microsoft’s broader AI story enters. Azure AI Foundry and related agent frameworks are meant to help organizations build and manage AI applications that can reason over enterprise data and tools. In this announcement, the agentic AI language suggests a future where software agents do more than summarize incidents. They may compare scenarios, trigger workflows, call orchestration APIs, and learn from outcomes.
The caveat is that telco data governance is unforgiving. Operators will need strict controls over what data enters models, which systems agents can touch, how recommendations are audited, and how customer-specific or regulated data is protected. A network digital twin that becomes a sprawling data lake with a chatbot bolted on is not modernization. It is risk with a nicer interface.
That is why Fabric IQ and semantic intelligence are more than buzzwords in this context. If Microsoft and Tech Mahindra can help operators create a consistent business and network vocabulary across data sources, AI tools become easier to constrain and evaluate. If they cannot, the agentic layer will inherit every ambiguity and inconsistency that already frustrates network operations teams.
Windows and Azure Admins Should Watch the Telecom Cloud Creep
At first glance, this announcement may seem far removed from the concerns of Windows administrators. It is a telco story, not a desktop story. But the direction of travel should feel familiar to anyone who has watched Microsoft move from operating systems into identity, device management, security, productivity telemetry, and cloud governance.Microsoft’s telecom strategy is not about making Windows Server the center of the carrier network. It is about making Azure the substrate for workloads, data, automation, and AI systems that carriers depend on. The more network operations move into Azure-connected platforms, the more telecom operations begin to resemble cloud operations: identity-first, policy-driven, API-mediated, and deeply dependent on observability pipelines.
That has practical consequences. Telecom operators adopting this kind of architecture will need people who understand Kubernetes, Azure governance, data engineering, AI operations, and security posture management alongside traditional telecom domains. The boundary between network engineer and cloud engineer continues to blur.
For enterprise IT teams, the downstream effect could be more interesting than the telecom plumbing itself. If operators can actually deliver more reliable network slices, edge services, and SLA-backed connectivity, enterprises may start treating carrier networks as programmable extensions of their own infrastructure. That could affect how organizations design branch connectivity, industrial networks, remote operations, and latency-sensitive applications.
The catch is that those benefits only arrive if carriers can turn the platform into dependable service offerings. Enterprises have heard a lot of 5G promises. They will care less about whether an operator uses Azure Digital Twins and more about whether the service is measurable, enforceable, secure, and priced sensibly.
The Vendor Language Is Ahead of the Deployed Reality
The announcement uses phrases such as autonomous decision-making, agentic AI, intelligent reasoning, and real-time decisioning. Those phrases should be read as directional rather than proof of widespread production autonomy. Telecom operators are cautious institutions, and “autonomous network operations” is a journey measured in trust boundaries, not press release verbs.There is a difference between showcasing a solution and proving it at national carrier scale. The announcement describes a solution intended for medium and large telecom operators, but it does not name live customers, deployment counts, production performance metrics, or measured cost reductions from this specific collaboration. That does not make the technology unserious. It means readers should separate product capability from market validation.
The strongest version of this story is not that Microsoft and Tech Mahindra have solved autonomous 5G operations. The stronger and more credible story is that they are assembling the ingredients operators need if they want to move in that direction: cloud-scale data ingestion, digital twin modeling, semantic context, predictive analytics, AI-assisted reasoning, and orchestration hooks.
That assembly is still hard. Network data is noisy. Topology records are often inaccurate. Multi-vendor environments resist neat abstraction. Operational teams may distrust recommendations they cannot explain. Regulators may require auditable human oversight. Security teams will ask whether AI agents have too much operational reach.
The industry should welcome ambition while demanding evidence. A network twin that reduces mean time to repair, improves capacity planning, predicts SLA violations, or prevents outages is valuable. A network twin that mainly produces impressive demos is another layer of management software.
The Security Argument Cuts Both Ways
Any system that unifies network telemetry, models operational dependencies, and connects to orchestration workflows becomes a high-value target. If compromised, it could reveal sensitive topology, customer relationships, operational weaknesses, and automation pathways. In security terms, a network digital twin is not just an observability asset. It may become a map of the kingdom.That does not mean operators should avoid the architecture. In fact, a well-governed twin could improve security by making network state more visible, policy drift easier to detect, and incident impact easier to assess. The same contextual model that helps troubleshoot performance can help identify suspicious changes or abnormal traffic patterns.
But the access model must be ruthless. AI agents that can reason across telemetry and trigger actions need clear permissions, logging, approval workflows, and rollback mechanisms. The operational convenience of an agent that “just fixes it” must not override the discipline of least privilege.
There is also the question of model poisoning and bad data. If the twin’s view of the network is wrong, the recommendations built on top of it may be wrong. If telemetry pipelines are manipulated, the AI layer may be nudged toward harmful actions. Traditional network monitoring already faces data quality problems; adding automation raises the stakes.
For Microsoft, this is both an opportunity and a liability. Azure’s security, identity, compliance, and governance tooling can be a selling point, especially for operators that already use Microsoft technologies. But the more Microsoft participates in telecom operational control planes, the more scrutiny it will face when reliability or security failures occur.
The Telecom Twin Is Really a Business Model Test
The most compelling part of the collaboration is not the technology stack. It is the business claim that better network intelligence can unlock new 5G revenue. That claim has haunted the industry for years.Operators invested heavily in spectrum and infrastructure. Many expected 5G to open new enterprise revenue streams. Instead, a large portion of the market has remained stuck between consumer data plans and bespoke enterprise projects that are difficult to scale. A platform that makes service assurance more predictable could help convert custom engineering into repeatable products.
SLA-backed network slicing is the obvious example. Edge orchestration is another. If an operator can model capacity, predict risk, and automate remediation across access, transport, core, and cloud edge layers, it can sell a stronger product to enterprises. If it cannot, it is still selling aspiration.
The digital twin becomes a kind of commercial confidence engine. It lets the operator understand what it can safely promise. It helps product teams design offers around measurable capabilities rather than theoretical 5G features. It gives account teams a way to discuss risk, assurance, and performance in terms that enterprise customers recognize.
That may be where Tech Mahindra’s role becomes especially important. Telecom modernization projects often fail not because the cloud service is inadequate, but because the operator cannot translate technology into operating model change. The twin has to connect engineering, operations, finance, product, and customer assurance. Otherwise, it remains a sophisticated tool used by a narrow technical audience.
The Small Print Behind the Big Promise
The announcement is best read as a signpost rather than a finished map. It shows where Microsoft and Tech Mahindra think telecom operations are heading: toward cloud-scale models, AI-assisted decisions, and tighter integration between network state and business outcomes. It also shows how much work remains before “autonomous networks” become routine.The concrete takeaways are narrower, but more useful.
- Tech Mahindra and Microsoft are positioning the Network Digital Twin as a cloud-scale operational platform for medium and large telecom operators, not as a consumer-facing 5G feature.
- The solution combines Azure, Microsoft Fabric, Azure Digital Twins, AI tooling, and Tech Mahindra telecom integration expertise to unify telemetry and model complex multi-vendor networks.
- The business target is enterprise 5G monetization, especially services that need stronger assurance such as network slicing, edge orchestration, and SLA-backed connectivity.
- The most consequential technical claim is closed-loop orchestration, because that is where the system moves from observing the network to recommending or triggering operational actions.
- The announcement does not yet provide named production customers or hard performance metrics, so claims about autonomy and measurable outcomes should be treated as goals to verify rather than results already proven at scale.
- Security, governance, data quality, and operator trust will determine whether the twin becomes an operational control plane or just another expensive visualization layer.
References
- Primary source: The Fast Mode
Published: Wed, 01 Jul 2026 01:28:50 GMT
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/PRNewswire/ -- Tech Mahindra (NSE: TECHM), a leading global provider of technology consulting and digital solutions to enterprises across industries,...
www.prnewswire.com
- Official source: marketplace.microsoft.com
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- Related coverage: digitaltwinconsortium.org
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