Technology Radar 2025
An LTIMindtree Crystal Report
Technology Radar 2025: An Interactive View
Click on any technology element to discover:
- Adoption phase
- Market potential
- Horizon classification
- Relationships with other technology elements
For example, selecting Generative AI reveals it belongs to Horizon 1, is in the Improving adoption phase and has High market potential. It also connects with 7 other technologies, aiding its adoption.
Business Operations
Business Operations aim to boost efficiency and productivity by focusing on digitalization and automation while also meeting specific operational goals. Emerging technologies empower organizations to manage their resources and workloads with agility while positioning them for scalability. The primary goal is to harness the full potential of these Technologies through effective adoption and use in operational practices.
Hyperautomation
Hyperautomation involves using advanced technologies like AI, ML, and RPA to automate numerous business and IT processes. The aim is to improve efficiency, reduce costs, and streamline operations by minimizing human involvement in repetitive tasks. Hyperautomation integrates multiple automation tools to manage complex workflows and decision-making.
Featured Story
A French utility company aimed for operational excellence, innovation, business adoption, system reliability, cost reductions, performance visibility, and scalable solutions through cloud migration. They adopted LTIMindtree’s next-gen application managed services with hyperautomation and cognitive tools, resulting in a 60% reduction in MTTR and 58% incident prevention through a converged operations command center.
Connected enterprise
A connected enterprise integrates functions, processes, and technologies for seamless, efficient operation. Digital transformation enhances connectivity across all levels, from plant operations to enterprise networks. Technologies like 5G, edge computing, AI, and cloud create new value chains and digital touchpoints.
Featured Story
A top-heavy vehicle manufacturer partnered with LTIMindtree to make telemetry data actionable. Using a Microsoft Azure-based platform, they developed aftermarket services like range estimation and driving behavior analysis. This improved data quality, prevented vehicle failures and reduced maintenance costs for fleet owners.
Application programming interfaces (API) economy
The API economy refers to the way in which APIs are transforming businesses and industries by enabling new business models, improving efficiency, and fostering innovation. APIs allow different software systems to communicate with each other, making it easier to integrate and leverage various services and data.
Featured Story
A global consumer conglomerate revamped its brand website, focusing on data security after previous issues exposed payment info. They partnered with LTIMindtree for Hacking-as-a-Service and Managed Security Services. The misconfigured APIs were fixed to avoid siphoning of data from the website. LTIMindtree set up a CI/CD pipeline with secure coding rules to automate security tests and provided guidelines for ongoing threat mitigation.
Financial Ops ( FinOps)
FinOps focuses on managing an organization’s financial resources and processes for optimal health and efficiency, aiding in effective planning and resource allocation. It enables informed decisions for long-term profitability. In addition, it is responsible for developing financial statements and budgets, monitoring accounts payable and receivable, preparing financial reports for management review, reconciling bank statements, and performing audits of financial transactions.
Featured Story
A leading vertical transportation systems manufacturer faced high costs from underutilized Azure subscriptions. After implementing LTIMindtree’s FinOps solution, they achieved a 65% improvement in cloud utilization, a 30% reduction in overall spending, and $6.2 million in annual savings across Azure and 100% increase in cost saving by implementing Azure Arc
Industry cloud platforms (ICPs)
ICPs are specialized cloud services designed to meet the unique needs of specific industries. Unlike generic cloud solutions, these platforms combine software, platform, and infrastructure as a service (SaaS, PaaS, and IaaS) to provide tailored solutions for different verticals.
Featured Story
A major Qatari conglomerate aimed to transform digitally with SAP S/4HANA, moving from 15+ customized ERPs. They faced challenges with unreliable insights and siloed operations. LTIMindtree implemented over 10 industry-specific solutions via the SAP business technology platform, resulting in a 60% reduction in payroll cycle times, cost savings in strategic e-sourcing, and significant improvements in customer invoice and sub-contractor payment processing.
Application programming interfaces (API) economy
The API economy refers to the way in which APIs are transforming businesses and industries by enabling new business models, improving efficiency, and fostering innovation. APIs allow different software systems to communicate with each other, making it easier to integrate and leverage various services and data.
Featured Story
A global consumer conglomerate revamped its brand website, focusing on data security after previous issues exposed payment info. They partnered with LTIMindtree for Hacking-as-a-Service and Managed Security Services. The misconfigured APIs were fixed to avoid siphoning of data from the website. LTIMindtree set up a CI/CD pipeline with secure coding rules to automate security tests and provided guidelines for ongoing threat mitigation.
Connected enterprise
A connected enterprise integrates functions, processes, and technologies for seamless, efficient operation. Digital transformation enhances connectivity across all levels, from plant operations to enterprise networks. Technologies like 5G, edge computing, AI, and cloud create new value chains and digital touchpoints.
Featured Story
A top-heavy vehicle manufacturer partnered with LTIMindtree to make telemetry data actionable. Using a Microsoft Azure-based platform, they developed aftermarket services like range estimation and driving behavior analysis. This improved data quality, prevented vehicle failures and reduced maintenance costs for fleet owners.
Financial Ops (FinOps)
FinOps focuses on managing an organization’s financial resources and processes for optimal health and efficiency, aiding in effective planning and resource allocation. It enables informed decisions for long-term profitability. In addition, it is responsible for developing financial statements and budgets, monitoring accounts payable and receivable, preparing financial reports for management review, reconciling bank statements, and performing audits of financial transactions.
Featured Story
A leading vertical transportation systems manufacturer faced high costs from underutilized Azure subscriptions. After implementing LTIMindtree’s FinOps solution, they achieved a 65% improvement in cloud utilization, a 30% reduction in overall spending, and $6.2 million in annual savings across Azure and 100% increase in cost saving by implementing Azure Arc
Hyperautomation
Hyperautomation involves using advanced technologies like AI, ML, and RPA to automate numerous business and IT processes. The aim is to improve efficiency, reduce costs, and streamline operations by minimizing human involvement in repetitive tasks. Hyperautomation integrates multiple automation tools to manage complex workflows and decision-making.
Featured Story
A French utility company aimed for operational excellence, innovation, business adoption, system reliability, cost reductions, performance visibility, and scalable solutions through cloud migration. They adopted LTIMindtree’s next-gen application managed services with hyperautomation and cognitive tools, resulting in a 60% reduction in MTTR and 58% incident prevention through a converged operations command center.
Industry cloud platforms (ICPs)
ICPs are specialized cloud services designed to meet the unique needs of specific industries. Unlike generic cloud solutions, these platforms combine software, platform, and infrastructure as a service (SaaS, PaaS, and IaaS) to provide tailored solutions for different verticals.
Featured Story
A major Qatari conglomerate aimed to transform digitally with SAP S/4HANA, moving from 15+ customized ERPs. They faced challenges with unreliable insights and siloed operations. LTIMindtree implemented over 10 industry-specific solutions via the SAP business technology platform, resulting in a 60% reduction in payroll cycle times, cost savings in strategic e-sourcing, and significant improvements in customer invoice and sub-contractor payment processing.
Voice of change
“The Technology Radar 2025 casts a spotlight on advancements in business operations. From hyperautomation for streamlining complex workflow tasks, to connected enterprises that enable seamless communication and collaboration, and lowcode/nocode as a way to transform and modernize your landscape, technological disruptions around Agentic AI, IDP2.0 and NoCode are reshaping the way businesses function.
GenAI in the from of Conversational AI, Voice AI, Brain AI & Action AI is further changing operations by offering better customer engagement and experience. Through strategic adoption of these technologies, businesses can refine their operations, uncover new growth opportunities, and maintain a decisive edge in an increasingly digital world.
”
____
Ramesh RevuruGlobal Head of Engineering, BlueVerse
LTIMindtree
Digital Foundation
Cloud, infrastructure, and security are critical pillars of a resilient digital foundation. The swift acceleration of digital transformation has heightened enterprises' demands for service-oriented, secure, and agile solutions. Emerging technologies will significantly bolster the delivery of dependable, secure, and efficient digital infrastructure, culminating in robust and effective digital systems.
Green computing
With increasing emphasis on sustainability, green computing is gaining traction. This involves designing, manufacturing, using, and disposing of computers and related systems in an environmentally friendly way. Green computing is not just about reducing the environmental impact of technology; it's about creating a sustainable future where technology and nature coexist harmoniously
Featured Story
A US-based electric service organization required agile capacity management, technical debt reduction, financial savings, and IT operations modernization. They used LTIMindtree’s Green Ops solution to identify underutilized resources and applied AI-enabled recommendations to optimize their environment. This reduced their carbon footprint and saved 20% on infrastructure costs in less than a year.
Distributed infrastructure
Distributed infrastructure is gaining attention and is expected to become mainstream soon. It enables organizations to scale efficiently, quickly adapt to changing demands, and optimize resource usage. By spreading resources across multiple locations, it enhances system resilience, ensuring continuous operation even if one node fails. This approach also reduces the need for large, centralized data centers, leading to significant savings in capital and operational expenses.
Featured Story
A European utility company faced issues with system integration, platform complexity, and low performance. By implementing LTIMindtree’s hybrid infrastructure solution, enabling 24x5 application management and 24x7 infrastructure support by leveraging cloud brokerage platform. They achieved a future-ready platform capable of supporting business growth, improving user experience, maintaining high performance, and controlling operational costs
Heterogeneous computing
Heterogeneous computing refers to the use of different types of processors and computing resources within a single system to optimize performance and efficiency. This approach leverages the strengths of various processing units, such as CPUs, GPUs, FPGAs, and other specialized processors, to handle specific tasks more effectively.
Featured Story
An Australian local government struggled with data extraction from legacy systems, governance issues, high integration costs, and performance problems. By adopting LTIMindtree’s heterogeneous cloud capabilities to integrate on-premises and cloud applications, they achieved 20% cost reduction and fully compliant data security.
Cybersecurity mesh
Cybersecurity mesh is a modern security approach designed to provide flexible, scalable, and reliable protection across distributed environments. It decentralizes security perimeters and integrates security controls to ensure comprehensive protection, regardless of where assets or users are located.
Featured Story
A US-based retail energy services organization faced high costs due to outdated security operations, poor tool integration, and fragmented configurations. LTIMindtree implemented a cybersecurity mesh solution providing 24/7 managed security, improved threat level analysis, and configured over 30 new email firewall rules. They achieved a drop in false positives from 95% to 57% and scanned over 1 million vulnerabilities with 100% critical vulnerability remediation.
Zero trust
Zero trust is a security model that operates on the principle of "never trust, always verify." Unlike traditional security models that assume everything inside an organization's network is trustworthy, zero trust requires continuous verification of every user and device attempting to access resources, regardless of their location.
Featured Story
One of the largest insurance brokerage service providers was experiencing challenges, including an absence of a centrally managed Azure security program, insufficient security controls, and a lack of an automated threat mitigation system. They adopted LTIMindtree's Azure cloud solution and utilized a zero-trust approach to secure their Azure cloud environment. This implementation led to a 75% improvement in threat detection efficacy, a reduction in surface attacks, and a decreased mean time to detect, contain, and respond.
Cybersecurity mesh
Cybersecurity mesh is a modern security approach designed to provide flexible, scalable, and reliable protection across distributed environments. It decentralizes security perimeters and integrates security controls to ensure comprehensive protection, regardless of where assets or users are located.
Featured Story
A US-based retail energy services organization faced high costs due to outdated security operations, poor tool integration, and fragmented configurations. LTIMindtree implemented a cybersecurity mesh solution providing 24/7 managed security, improved threat level analysis, and configured over 30 new email firewall rules. They achieved a drop in false positives from 95% to 57% and scanned over 1 million vulnerabilities with 100% critical vulnerability remediation.
Distributed infrastructure
Distributed infrastructure is gaining attention and is expected to become mainstream soon. It enables organizations to scale efficiently, quickly adapt to changing demands, and optimize resource usage. By spreading resources across multiple locations, it enhances system resilience, ensuring continuous operation even if one node fails. This approach also reduces the need for large, centralized data centers, leading to significant savings in capital and operational expenses.
Featured Story
A European utility company faced issues with system integration, platform complexity, and low performance. By implementing LTIMindtree’s hybrid infrastructure solution, enabling 24x5 application management and 24x7 infrastructure support by leveraging cloud brokerage platform. They achieved a future-ready platform capable of supporting business growth, improving user experience, maintaining high performance, and controlling operational costs
Green computing
With increasing emphasis on sustainability, green computing is gaining traction. This involves designing, manufacturing, using, and disposing of computers and related systems in an environmentally friendly way. Green computing is not just about reducing the environmental impact of technology; it's about creating a sustainable future where technology and nature coexist harmoniously
Featured Story
A US-based electric service organization required agile capacity management, technical debt reduction, financial savings, and IT operations modernization. They used LTIMindtree’s Green Ops solution to identify underutilized resources and applied AI-enabled recommendations to optimize their environment. This reduced their carbon footprint and saved 20% on infrastructure costs in less than a year.
Heterogeneous computing
Heterogeneous computing refers to the use of different types of processors and computing resources within a single system to optimize performance and efficiency. This approach leverages the strengths of various processing units, such as CPUs, GPUs, FPGAs, and other specialized processors, to handle specific tasks more effectively.
Featured Story
An Australian local government struggled with data extraction from legacy systems, governance issues, high integration costs, and performance problems. By adopting LTIMindtree’s heterogeneous cloud capabilities to integrate on-premises and cloud applications, they achieved 20% cost reduction and fully compliant data security.
Zero trust
Zero trust is a security model that operates on the principle of "never trust, always verify." Unlike traditional security models that assume everything inside an organization's network is trustworthy, zero trust requires continuous verification of every user and device attempting to access resources, regardless of their location.
Featured Story
One of the largest insurance brokerage service providers was experiencing challenges, including an absence of a centrally managed Azure security program, insufficient security controls, and a lack of an automated threat mitigation system. They adopted LTIMindtree's Azure cloud solution and utilized a zero-trust approach to secure their Azure cloud environment. This implementation led to a 75% improvement in threat detection efficacy, a reduction in surface attacks, and a decreased mean time to detect, contain, and respond.
Voice of change
“The Technology Radar 2025 serves as a technology guide for enterprises to sharpen their strategies, identify opportunities, and craft innovative solutions for a future-ready secure enterprise. Trends like heterogeneous computing, zero trust, and green computing are crucial for building resilient, safe and adaptive infrastructure that can seamlessly integrate with emerging technologies., These advancements are essential building blocks for a future where technology seamlessly integrates with emerging innovations.
”
____
Chandan PaniChief Information Security Officer
LTIMindtree
Digital Innovation
This segment explores how data analytics and digital engineering merges to design and enhance digital and enhance digital products and services. It shows how emerging technologies are revolutionizing traditional engineering through rapid prototyping, smart automation, and improved user experiences.
Custom language models (CLMs)
CLMs are modified versions of Large Language Models (LLMs) that have been adjusted to meet specific requirements or tasks. These models are based on pre-trained LLMs and undergo additional training on targeted datasets or modifications to improve their performance for tasks. Advancements in training techniques and multitask model architecture are primary factors in enhancing the capabilities of compact language models.
Featured Story
An American multinational company specializing in air-conditioning and refrigeration needed to automate and optimize its marketing content creation across various platforms like emails, LinkedIn etc. LTIMindtree applied retrieval-augmented generation (RAG) to extract information from user-uploaded documents and leveraged prompt engineering techniques to customized large language models for optimal performances across mediums. This implementation achieved 40% reduction in writing costs compared to traditional methods and increased platform traffic by 10% with higher quality content.
Edge computing
Edge computing is a distributed framework for processing data at the source, such as IoT devices or local servers. This reduces latency and bandwidth issues, enables real-time analysis, improves response times, and leverages in-device computing for predictive analytics. It drives innovation, enhances decision-making, and improves customer experiences across various industries, including connected vehicles and smart factories
Featured Story
One of the largest construction company in the Asia was facing major challenges in non-compliance instances on their fabrication site and detection process was also not robust. LTIMindtree deployed an AI solution that captured real-time data from different components of machine and fed into AI engine performing condition monitoring predictive maintenance based on real-time data received. The solution helped expand visibility by supporting simultaneous multi-zone analysis of incoming video feeds, making the non-compliance process seamless as the concerned authority was automatically notified via email within minutes of any non-compliance occurrence.
Machine learning (ML)
ML, an essential branch of artificial intelligence (AI), pertains to the development of algorithms and statistical models that allow computers to execute tasks without explicit programming. Currently, machine learning is experiencing several significant advancements that are enhancing AI capabilities. Moreover, multimodal learning has emerged as a key focus, enabling models to process and integrate diverse data types such as text, images, and audio
Featured Story
A global bank required data integrity, accuracy and regulatory reporting across 100+ countries. LTIMindtree developed a scalable platform with an inbuilt engine that supported machine learning and pattern detection for data monetization. The solution included the creation of a cognitive data lake encompassing global finance and regulatory data into Hadoop-based big data infrastructure. This implementation helped the bank achieve a 30% reduction in manual efforts for data adjustments and established a single source of truth for data.
Synthetic data
Synthetic data is created artificially and used as a substitute for test data sets of production or operational data, to validate mathematical models and to train Machine Learning (ML) models. The primary application of synthetic data is in the training of neural networks and ML models, where developers require labeled data sets that can range from a few thousand to tens of millions of items.
Featured Story
An American multinational digital communications technology conglomerate faced difficulties due to the presence of multiple disparate ERP and eCommerce systems, which led to compatibility issues. LTIMindtree utilized UiPath to create a wrapper around the various testing solutions and integrated Gen AI to generate synthetic data for testing. This implementation accelerated the testing cycle by 30% and enabled seamless test data generation with AI, reducing manual effort and increasing testing efficiency.
Data fabric
Data fabric is an architecture that integrates and manages data across on-premises, cloud, and hybrid systems. Organizations often face data silos, where information is isolated in different departments or platforms. Data fabric unifies these disparate sources, enabling better decision-making.
Featured Story
An engineering, construction, and consulting solutions firm was facing the challenges in data accumulation as the data from from each construction project housed in multiple locations, leading to delays in getting timely feedback to governance teams, leadership, and project teams. LTIMindtree implemented a data fabric architecture using Microsoft Azure services, creating a central repository for standardized data which is accessible across organization. This implementation resulted in real-time data access, improved decision making and enhanced efficiency in project execution and governance.
Custom language models (CLMs)
CLMs are modified versions of Large Language Models (LLMs) that have been adjusted to meet specific requirements or tasks. These models are based on pre-trained LLMs and undergo additional training on targeted datasets or modifications to improve their performance for tasks. Advancements in training techniques and multitask model architecture are primary factors in enhancing the capabilities of compact language models.
Featured Story
An American multinational company specializing in air-conditioning and refrigeration needed to automate and optimize its marketing content creation across various platforms like emails, LinkedIn etc. LTIMindtree applied retrieval-augmented generation (RAG) to extract information from user-uploaded documents and leveraged prompt engineering techniques to customized large language models for optimal performances across mediums. This implementation achieved 40% reduction in writing costs compared to traditional methods and increased platform traffic by 10% with higher quality content.
Data fabric
Data fabric is an architecture that integrates and manages data across on-premises, cloud, and hybrid systems. Organizations often face data silos, where information is isolated in different departments or platforms. Data fabric unifies these disparate sources, enabling better decision-making.
Featured Story
An engineering, construction, and consulting solutions firm was facing the challenges in data accumulation as the data from from each construction project housed in multiple locations, leading to delays in getting timely feedback to governance teams, leadership, and project teams. LTIMindtree implemented a data fabric architecture using Microsoft Azure services, creating a central repository for standardized data which is accessible across organization. This implementation resulted in real-time data access, improved decision making and enhanced efficiency in project execution and governance.
Edge computing
Edge computing is a distributed framework for processing data at the source, such as IoT devices or local servers. This reduces latency and bandwidth issues, enables real-time analysis, improves response times, and leverages in-device computing for predictive analytics. It drives innovation, enhances decision-making, and improves customer experiences across various industries, including connected vehicles and smart factories
Featured Story
One of the largest construction company in the Asia was facing major challenges in non-compliance instances on their fabrication site and detection process was also not robust. LTIMindtree deployed an AI solution that captured real-time data from different components of machine and fed into AI engine performing condition monitoring predictive maintenance based on real-time data received. The solution helped expand visibility by supporting simultaneous multi-zone analysis of incoming video feeds, making the non-compliance process seamless as the concerned authority was automatically notified via email within minutes of any non-compliance occurrence.
Machine learning (ML)
Machine learning (ML), an essential branch of artificial intelligence (AI), pertains to the development of algorithms and statistical models that allow computers to execute tasks without explicit programming. Currently, machine learning is experiencing several significant advancements that are enhancing AI capabilities. Moreover, multimodal learning has emerged as a key focus, enabling models to process and integrate diverse data types such as text, images, and audio
Featured Story
A global bank required data integrity, accuracy and regulatory reporting across 100+ countries. LTIMindtree developed a scalable platform with an inbuilt engine that supported machine learning and pattern detection for data monetization. The solution included the creation of a cognitive data lake encompassing global finance and regulatory data into Hadoop-based big data infrastructure. This implementation helped the bank achieve a 30% reduction in manual efforts for data adjustments and established a single source of truth for data.
Synthetic data
Synthetic data is created artificially and used as a substitute for test data sets of production or operational data, to validate mathematical models and to train Machine Learning (ML) models. The primary application of synthetic data is in the training of neural networks and ML models, where developers require labeled data sets that can range from a few thousand to tens of millions of items.
Featured Story
An American multinational digital communications technology conglomerate faced difficulties due to the presence of multiple disparate ERP and eCommerce systems, which led to compatibility issues. LTIMindtree utilized UiPath to create a wrapper around the various testing solutions and integrated Gen AI to generate synthetic data for testing. This implementation accelerated the testing cycle by 30% and enabled seamless test data generation with AI, reducing manual effort and increasing testing efficiency.
Experience
Customer experience is at the core of any relationship with end users, clients, partners, employees, or prospects. In this segment, we explore how leveraging new-age AI technologies such as computer vision and conversational and generative AI will allow us to transform and adapt to the ever-changing needs of the primary stakeholders.
Computer vision
Computer vision, a branch of AI, that enables computers to obtain relevant information from videos, digital images, and other visual inputs to interpret information, much like humans. Like how AI empowers computers to think, computer vision empowers them to observe and comprehend. Computer vision encompasses identifying objects, processing, tracking, and understanding images.
Featured Story
A fortune 100 automobile manufacturer, sought to reduce time and improve efficiency in quality inspection processes that eliminates manual errors during quality checks. LTIMindtree developed a computer vision-based solution at the edge using AWS services for porosity detection on case aluminum cylinder heads. The solution also involved processing decisions on a cloud-based machine learning model analyzing images and videos. This implementation led to 100% defect detection and quality conformity and wastage control, enabling the recovery of finished products.
Digital twins
A digital twin is a virtual replica of an asset enabling simulations to predict real-life performance. These are based on real-world data and provide a dynamic approach to monitoring and optimization. Recent advancements integrate digital twins with IoT and 5G technologies, facilitating improved decision-making and reduced latency.
Featured Story
A leading metal and mining company facing challenges in capturing condition of the existing, non-availability of structural drawings etc. Also the structure encountered various problems like corrosion, cracks, unwarranted modifications, seepage, and improper welds. LTIMindtree built LiDAR Laser Scanning, As-Built Drawings, BOM Extraction and conducted structural auditing with the help of digital twin creation. This implementation resulted in 90% reduction in-person site visit, 60% reduction in timeline with quicker capture of as-built conditions.
Mixed reality (MR)
MR is a cutting-edge technology that combines virtual reality (VR) and augmented reality (AR) to create immersive experiences. It enables natural and intuitive 3D interactions between humans, computers, and the environment. This new reality leverages advancements in computer vision, graphics processing, display technologies, input systems, and cloud computing
Featured Story
A global paint manufacturer aimed to enhance customer engagement by enabling users to visualize paint colors on their walls before making a purchase. LTIMindtree developed an augmented reality (AR) visualizer application that allows users to virtually paint their walls using mobile devices, providing real-time preview of different color options. This application was rolled out in more than 60 countries, achieving 2.8 millions downloads. This application created an immersive customer experience that drove engagements and supported business objectives of the company.
Conversational systems
Conversational systems are smart technologies that can communicate with humans through text and speech. They automate human interactions in multiple languages through text and voice queries. Companies use conversational systems to enhance consumer interactions and create personalized products using consumer segmentation data.
Featured Story
US-based Fortune 500 life & annuity insurer, sought to enhance customer engagement as it was facing operational challenges majorly due to long waiting times in both call centers and live chat and availability of additional staff during peak times for query resolution. LTIMindtree team implemented a conversational solution leveraging NLP and also trained the NLP engine with life & annuity intents and utterances. The team also built chatbot solutions to address generic queries like FAQs and policy-related enquiries. This implementation resulted in 30% less volumes in call centers, more than 35% of automation of all voice calls through bots.
Sensor tech
Sensor technology encompasses devices that detect environmental changes and convert them into measurable signals for analysis and action. The integration of artificial intelligence (AI) with sensor technology will facilitate local data processing and interpretation, significantly reducing latency and enabling more accurate and timely real-time decision-making.
Featured Story
A leading utility company aimed to ensure the reliable functioning of critical transformers by implementing an online monitoring & diagnostic solution to prevent failures and minimize maintenance. LTIMindtree developed XmerIOT, an IoT based solution that utilizes sensors to continuously monitor transformer parameters, providing real-time data. This implementation led to enhanced reliability of transformers, reduced unplanned downtimes and maintenance, thereby improving overall operational efficiency.
Computer vision
Computer vision, a branch of AI, that enables computers to obtain relevant information from videos, digital images, and other visual inputs to interpret information, much like humans. Like how AI empowers computers to think, computer vision empowers them to observe and comprehend. Computer vision encompasses identifying objects, processing, tracking, and understanding images.
Featured Story
A fortune 100 automobile manufacturer, sought to reduce time and improve efficiency in quality inspection processes that eliminates manual errors during quality checks. LTIMindtree developed a computer vision-based solution at the edge using AWS services for porosity detection on case aluminum cylinder heads. The solution also involved processing decisions on a cloud-based machine learning model analyzing images and videos. This implementation led to 100% defect detection and quality conformity and wastage control, enabling the recovery of finished products.
Conversational systems
Conversational systems are smart technologies that can communicate with humans through text and speech. They automate human interactions in multiple languages through text and voice queries. Companies use conversational systems to enhance consumer interactions and create personalized products using consumer segmentation data.
Featured Story
US-based Fortune 500 life & annuity insurer, sought to enhance customer engagement as it was facing operational challenges majorly due to long waiting times in both call centers and live chat and availability of additional staff during peak times for query resolution. LTIMindtree team implemented a conversational solution leveraging NLP and also trained the NLP engine with life & annuity intents and utterances. The team also built chatbot solutions to address generic queries like FAQs and policy-related enquiries. This implementation resulted in 30% less volumes in call centers, more than 35% of automation of all voice calls through bots.
Digital twins
A digital twin is a virtual replica of an asset enabling simulations to predict real-life performance. These are based on real-world data and provide a dynamic approach to monitoring and optimization. Recent advancements integrate digital twins with IoT and 5G technologies, facilitating improved decision-making and reduced latency.
Featured Story
A leading metal and mining company facing challenges in capturing condition of the existing, non-availability of structural drawings etc. Also the structure encountered various problems like corrosion, cracks, unwarranted modifications, seepage, and improper welds. LTIMindtree built LiDAR Laser Scanning, As-Built Drawings, BOM Extraction and conducted structural auditing with the help of digital twin creation. This implementation resulted in 90% reduction in-person site visit, 60% reduction in timeline with quicker capture of as-built conditions.
Mixed reality (MR)
MR is a cutting-edge technology that combines virtual reality (VR) and augmented reality (AR) to create immersive experiences. It enables natural and intuitive 3D interactions between humans, computers, and the environment. This new reality leverages advancements in computer vision, graphics processing, display technologies, input systems, and cloud computing
Featured Story
A global paint manufacturer aimed to enhance customer engagement by enabling users to visualize paint colors on their walls before making a purchase. LTIMindtree developed an augmented reality (AR) visualizer application that allows users to virtually paint their walls using mobile devices, providing real-time preview of different color options. This application was rolled out in more than 60 countries, achieving 2.8 millions downloads. This application created an immersive customer experience that drove engagements and supported business objectives of the company.
Sensor tech
Sensor technology encompasses devices that detect environmental changes and convert them into measurable signals for analysis and action. The integration of artificial intelligence (AI) with sensor technology will facilitate local data processing and interpretation, significantly reducing latency and enabling more accurate and timely real-time decision-making.
Featured Story
A leading utility company aimed to ensure the reliable functioning of critical transformers by implementing an online monitoring & diagnostic solution to prevent failures and minimize maintenance. LTIMindtree developed XmerIOT, an IoT based solution that utilizes sensors to continuously monitor transformer parameters, providing real-time data. This implementation led to enhanced reliability of transformers, reduced unplanned downtimes and maintenance, thereby improving overall operational efficiency.
Voice of change
“The 2025 Technology Radar highlights the profound transformative potential of interactive technologies, including augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and computer vision, in redefining user experiences. These innovations are driving a fundamental shift in how businesses engage with customers, facilitating the creation of personalized and intuitive interactions.
Emerging disruptive technologies, such as mixed reality, digital twins, and conversational AI systems, stand poised to reshape the future of customer engagement. By leveraging these advancements, organizations can develop next-generation solutions that seamlessly integrate the physical and digital realms, enabling businesses to deliver immersive, intelligent, and highly tailored experiences that enhance both customer satisfaction and operational efficiency.
”
____
Dr. Sujay SenGlobal Head - Interactive & Salesforce
LTIMindtree
Top 10 Technologies
Custom language models (CLMs)
CLMs are modified versions of large language models (LLMs) that have been adjusted to meet specific requirements or tasks. These models are based on pre-trained and undergo additional training on targeted datasets or modifications to improve their performance for tasks. Advancements in training techniques and multitask model architecture are primary factors in enhancing the capabilities of compact language models.
Featured Story
An American multinational company specializing in air-conditioning and refrigeration needed to automate and optimize its marketing content creation across various platforms like emails, LinkedIn, etc. LTIMindtree applied retrieval-augmented generation (RAG) to extract information from user-uploaded documents and leveraged prompt engineering techniques to build customized large language models for optimal performances across media. This achieved a 40% reduction in writing costs compared to traditional methods and increased platform traffic by 10% with higher quality content.
Zero trust
Zero trust is a security model that operates on the principle of "never trust, always verify." Unlike traditional security models that assume everything inside an organization's network is trustworthy, zero trust requires continuous verification of every user and device attempting to access resources, regardless of their location.
Featured Story
One of the largest insurance brokerage service providers was experiencing challenges, including an absence of a centrally managed Azure security program, insufficient security controls, and a lack of an automated threat mitigation system. They adopted LTIMindtree's Azure cloud solution and utilized a zero-trust approach to secure their Azure cloud environment. This implementation led to a 75% improvement in threat detection efficacy, a reduction in surface attacks, and a decreased mean time to detect, contain, and respond.
Edge computing
Edge computing is a distributed system processing data near its source, such as IoT devices or local servers. This reduces latency and bandwidth issues, enables real-time analysis, improves response times, and leverages in-device computing for predictive analytics. It drives innovation, enhances decision-making, and improves customer experiences across various industries, including connected vehicles and smart factories.
Featured Story
One of the largest construction companies in Asia was facing major challenges with non-compliance instances on their fabrication site and their detection process was also not robust. LTIMindtree deployed an AI solution that captured real-time data from different machine components and fed it into an AI engine for condition monitoring and predictive maintenance based on such real-time data. The solution helped expand visibility by supporting simultaneous multi-zone analysis of incoming video feeds, making the non-compliance detection process seamless as the concerned authority was automatically notified via email within minutes of any non-compliance occurrence.
Synthetic data
Synthetic data is created artificially and used as a substitute for test data sets of production or operational data, to validate mathematical models and to train machine learning (ML) models. The primary application of synthetic data is in the training of neural networks and ML models, where developers require labeled data sets that can range from a few thousand to tens of millions of items.
Featured Story
An American multinational digital communications technology conglomerate faced difficulties due to the presence of multiple disparate ERP and e-commerce systems, which led to compatibility issues. LTIMindtree utilized UiPath to create a wrapper around the various testing solutions and integrated Gen AI to generate synthetic data for testing. This implementation accelerated the testing cycle by 30% and enabled seamless test data generation with AI, reducing manual effort and increasing testing efficiency.
Artificial intelligence (AI)
AI is a transformative technology reshaping industries by enabling machines to simulate human intelligence, automate complex processes and drive data-driven decision-making. AI enhances operational efficiencies, accelerates innovation and unlocks new business values. The AI market is projected to grow at a 37% CAGR, reaching USD 1.8 trillion by 2030.
Featured Story
An American multinational assurance company faced inefficiencies in their underwriting process due to manual document review. LTIMindtree developed an Outlook plug-in to automate document retrieval and insight generation, cutting manual tasks. Additionally, a chatbot was introduced to swiftly answer underwriters' questions. These innovations saved 30-35% of underwriters' time and reduced error rates.
Conversational systems
Conversational systems are smart technologies that can communicate with humans through text and speech. They automate human interactions in multiple languages through text and voice queries. Companies use conversational systems to enhance consumer interactions and create personalized products using consumer segmentation data.
Featured Story
A US-based Fortune 500 life and annuity insurer sought to enhance customer engagement as it faced operational challenges primarily due to long wait times in both call centers and live chat. Another issue was the availability of additional staff during peak times for query resolution. The LTIMindtree team implemented a conversational solution leveraging NLP and trained the NLP engine with life and annuity intents and utterances. The team also built chatbot solutions to address generic queries such as FAQs and policy-related inquiries. This implementation resulted in a 30% lower volume in call centers and >35% automation of all voice calls through bots.
Cybersecurity mesh
Cybersecurity mesh is a modern security approach designed to provide flexible, scalable, and reliable protection across distributed environments. It decentralizes security perimeters and integrates security controls to ensure comprehensive protection, regardless of where assets or users are located.
Featured Story
A US-based retail energy services organization faced high costs due to outdated security operations, poor tool integration, and fragmented configurations. LTIMindtree implemented a cybersecurity mesh solution providing 24/7 managed security, improved threat level analysis, and configured over 30 new email firewall rules. They achieved a drop in false positives from 95% to 57% and scanned over 1 million vulnerabilities with 100% critical vulnerability remediation.
Data fabric
Data fabric is an architecture that integrates and manages data across on-premises, cloud, and hybrid systems. Organizations often face data silos, where information is isolated in different departments or platforms. Data fabric unifies these disparate sources, enabling better decision-making.
Featured Story
An engineering, construction, and consulting solutions firm faced challenges in data accumulation as the data from each construction project was siloed. It was housed in multiple locations, leading to delays in timely feedback for governance teams, leadership, and project teams. LTIMindtree implemented a data fabric architecture using Microsoft Azure services, creating a central repository for standardized data that was accessible across the organization. This implementation resulted in real-time data access, improved decision-making and greater efficiency in project execution and governance.
Industry cloud platforms (ICPs)
ICPs are specialized cloud services designed to meet the unique needs of specific industries. Unlike generic cloud solutions, these platforms combine software, platform, and infrastructure as a service (SaaS, PaaS, and IaaS) to provide tailored solutions for different verticals.
Featured Story
A major Qatari conglomerate aimed to digitally transform with SAP S/4HANA, evolving from 15+ customized ERPs. They faced challenges with unreliable insights and siloed operations. LTIMindtree implemented over 10 industry-specific solutions via the SAP business technology platform, resulting in a 60% reduction in payroll cycle times, cost savings in strategic e-sourcing, and significant improvements in customer invoice and sub-contractor payment processing.
Post-quantum cryptography (PQC)
PQC is a specialized field of cryptography dedicated to developing encryption algorithms that are resilient against the prospective threats presented by quantum computing.Q-day is a term referring to stage when quantum computers become powerful enough to break widely-used existing cryptographic systems, procted by techniques like RSA and ECC.
Featured Story
LTIMindtree successfully launched and tested a quantum-safe VPN link in London, in collaboration with Quantum Xchange and Fortinet. This initiative showcased LTIMindtree's quantum-safe VPN's practical application of PQC within a live network. It utilizes quantum-based key generation and out-of-band key delivery, secured by PQC algorithms that are progressing towards standardization by NIST. This development significantly enhanced the security and integrity of encrypted data.
Voice of change
“As we step into 2025, it is evident that we are on the cusp of extraordinary technological advancements. The insights from Technology Radar 2025 unveil a future where artificial intelligence, cybersecurity, and productivity tools are intricately integrated into our everyday lives. These top 10 technologies are set to dramatically enhance productivity and transform customer interactions. The innovations we spotlight today will unquestionably shape the world of tomorrow, enabling both businesses and individuals to flourish in an evolving landscape.
”
____
Indranil MitraVice President, LTIMindtree Research
New and Notable
Every year, the technology landscape evolves with new and disruptive innovations that redefine industry benchmarks. In 2025, several new technologies have surfaced, promising to drive efficiency, agility, and intelligence across enterprises. This section of the Technology Radar 2025 delves into these emerging advancements, highlighting their business relevance, real-world applications, and strategic considerations for enterprises looking to stay ahead in the digital era.
Cyber-Physical Systems (CPS) security
Cyber-physical systems security focuses on protecting the integration of physical processes with computational and networked systems. It addresses the vulnerabilities of the systems that are interconnected like autonomous vehicles, smart grid etc. Currently, organizations are increasingly adopting AI-driven anomaly detection and edge computing for real-time threat mitigation. With the increased reliance of interconnected systems, ensuring the security of CPS is critical to prevent operational disruptions, safeguard sensitive data and maintain trust in automated systems.
Featured Story
A Fortune 50 Oil and Gas Major faced challenges with multiple plant locations and independent infrastructures. LTIMindtree conducted a cyber physical system security assessment, identifying gaps and formulating a roadmap to enhance security. By implementing industry best practices, they provided an integrated view of the security status, enabling effective central response to cyber incidents
Optical computing
Optical computing or photonic computing, uses light for calculations, reasoning and AI tasks. It employs photons from lasers or diodes to represent data and perform computations through wave propagation and interference patterns, enabling instantaneous latency-free processing. Its potential for significant energy efficiency gains makes it ideal for the emerging era of AI and machine learning.
Featured Story
An all-optical, cross-domain XPU computing architecture was introduced for high-performance tasks, featuring the first fully optical general-purpose CPU. This architecture eliminates data conversion to the electronic domain. The study addressed misconceptions about optical computing, highlighting its performance and efficiency. The CPU can handle trillions of operations per second and integrates digital, analog, and quantum elements. Additionally, a roadmap was provided for expanding this CPU into a full XPU, focusing on energy-efficient designs using 2D materials for ultra-high bandwidth and low latency computing.
Polyfunctional bots
Polyfunctional robots are advanced machines designed to perform multiple tasks, often beyond their original programming. They adapt to new tasks through learning and human instruction, eliminating the need for reprogramming. These robots can switch between tasks, making them highly adaptable and collaborative with humans. A leading research firm predicts that by 2030, 80% of humans will interact daily with smart robots, up from less than 10% today.
Featured Story
A versatile polyfunctional robot is being designed to support Germany's healthcare sector by performing tasks such as transporting patients and documenting care activities. Enhanced to handle non-rigid objects and recognize speech, field tests show potential to alleviate nursing workload. However, challenges in ease of use, safety, and empathy remain, requiring further development.
Fractional GPU
Fractional Graphics Processing Unit (FGPU) is a software-driven mechanism for partitioning a GPU's computing and memory resources. It allows efficient utilization of high-powered GPUs by dividing them into smaller and scalable units. Demand for cost-effective GPU resources and cloud-based AI workloads are influencing the rising adoption of FGPUs. It plays vital role in running multiple AI tasks concurrently without interference. FGPUs enables better utilization of GPU resources, reducing costs and increasing GPU utilization.
Featured Story
An autonomous vehicle company, faced GPU scheduling issues, leading to underutilization. By implementing fractional GPUs for advanced scheduling on Kubernetes, they transitioned from static GPU allocation to dynamic pooling. This change increased GPU utilization from 45% to over 80%, allowing more efficient training and testing of their AI models. Additionally, the system supports multi-node training, enhancing scalability and performance.
Custom Language Models (CLMs)
CLMs are modified versions of Large Language Models (LLMs) that have been adjusted to meet specific requirements or tasks. These models are based on pre-trained algorithms and undergo additional training on targeted datasets or modifications to improve their performance for tasks. Advancements in training techniques and multitask model architecture are primary factors in enhancing the capabilities of compact language models.
Featured Story
An American multinational company specializing in air-conditioning and refrigeration needed to automate and optimize its marketing content creation across various platforms like emails, LinkedIn, etc. LTIMindtree applied retrieval-augmented generation (RAG) to extract information from user-uploaded documents and leveraged prompt engineering techniques to build customized large language models for optimal performances across media. This achieved a 40% reduction in writing costs compared to traditional methods and increased platform traffic by 10% with higher quality content.
Agentic AI
Agentic AI is designed to operate alongside humans in shared environments, improving productivity, safety, and efficiency. It facilitates safe interactions, easy programming, and adaptability to various tasks across different industries by utilizing advanced sensors, IoT devices, machine learning modules, and intuitive interfaces. With the advancement of Agentic AI, bots are expanding their capabilities from responding queries to executing complex tasks. This requires robust algorithm design to detect events in real-time and execute tasks at optimal speeds.
Featured Story
Claim adjudicators spent excessive time reviewing multiple sections of claims to gather key information, leading to a tedious and error-prone process, especially with complex claims. LTIMindtree has built Claim Summarizer Tool that automates the consolidation of claim information, providing adjudicators with a quick, single-click view of claim status and critical details, eliminating manual cross-referencing. This increases efficiency and reduces claim analysis time, improves decision-making accuracy by minimizing human error and enhances productivity, allowing focus on higher-value tasks.
Financial Ops (FinOps)
FinOps focuses on managing an organization’s financial resources and processes for optimal health and efficiency, aiding in effective planning and resource allocation. It enables informed decisions for long-term profitability. In addition, it is responsible for developing financial statements and budgets, monitoring accounts payable and receivable, preparing financial reports for management review, reconciling bank statements, and performing audits of financial transactions.
Featured Story
A leading vertical transportation systems manufacturer faced high costs from underutilized Azure subscriptions. After implementing LTIMindtree’s FinOps solution, they achieved a 65% improvement in cloud utilization, a 30% reduction in overall spending, and $6.2 million in annual savings across Azure and 100% increase in cost saving by implementing Azure Arc.
Green computing
With increasing emphasis on sustainability, green computing is gaining traction. This involves designing, manufacturing, using, and disposing of computers and related systems in an environmentally friendly way. Green computing is not just about reducing the environmental impact of technology; it's about creating a sustainable future where technology and nature coexist harmoniously
Featured Story
A US-based electric service organization required agile capacity management, technical debt reduction, financial savings, and IT operations modernization. They used LTIMindtree’s Green Ops solution to identify underutilized resources and applied AI-enabled recommendations to optimize their environment. This reduced their carbon footprint and saved 20% on infrastructure costs in less than a year.
Synthetic data
Synthetic data is created artificially and used as a substitute for test data sets of production or operational data, to validate mathematical models and to train Machine Learning (ML) models. The primary application of synthetic data is in the training of neural networks and ML models, where developers require labeled data sets that can range from a few thousand to tens of millions of items.
Featured Story
An American multinational digital communications technology conglomerate faced difficulties due to the presence of multiple disparate ERP and e-commerce systems, which led to compatibility issues. LTIMindtree utilized UiPath to create a wrapper around the various testing solutions and integrated Gen AI to generate synthetic data for testing. This implementation accelerated the testing cycle by 30% and enabled seamless test data generation with AI, reducing manual effort and increasing testing efficiency.
Cyber-Physical System (CPS) security
Cyber-physical systems security focuses on protecting the integration of physical processes with computational and networked systems. It addresses the vulnerabilities of the systems that are interconnected like autonomous vehicles, smart grid etc. Currently, organizations are increasingly adopting AI-driven anomaly detection and edge computing for real-time threat mitigation. With the increased reliance of interconnected systems, ensuring the security of CPS is critical to prevent operational disruptions, safeguard sensitive data and maintain trust in automated systems.
Featured Story
A Fortune 50 Oil and Gas Major faced challenges with multiple plant locations and independent infrastructures. LTIMindtree conducted a cyber physical system security assessment, identifying gaps and formulating a roadmap to enhance security. By implementing industry best practices, they provided an integrated view of the security status, enabling effective central response to cyber incidents
Optical computing
Optical computing or photonic computing, uses light for calculations, reasoning and AI tasks. It employs photons from lasers or diodes to represent data and perform computations through wave propagation and interference patterns, enabling instantaneous latency-free processing. Its potential for significant energy efficiency gains makes it ideal for the emerging era of AI and machine learning.
Featured Story
An all-optical, cross-domain XPU computing architecture was introduced for high-performance tasks, featuring the first fully optical general-purpose CPU. This architecture eliminates data conversion to the electronic domain. The study addressed misconceptions about optical computing, highlighting its performance and efficiency. The CPU can handle trillions of operations per second and integrates digital, analog, and quantum elements. Additionally, a roadmap was provided for expanding this CPU into a full XPU, focusing on energy-efficient designs using 2D materials for ultra-high bandwidth and low latency computing.
Polyfunctional bots
Polyfunctional robots are advanced machines designed to perform multiple tasks, often beyond their original programming. They adapt to new tasks through learning and human instruction, eliminating the need for reprogramming. These robots can switch between tasks, making them highly adaptable and collaborative with humans. A leading research firm predicts that by 2030, 80% of humans will interact daily with smart robots, up from less than 10% today.
Featured Story
A versatile polyfunctional robot is being designed to support Germany's healthcare sector by performing tasks such as transporting patients and documenting care activities. Enhanced to handle non-rigid objects and recognize speech, field tests show potential to alleviate nursing workload. However, challenges in ease of use, safety, and empathy remain, requiring further development.
Fractional GPU
Fractional Graphics Processing Unit (FGPU) is a software-driven mechanism for partitioning a GPU's computing and memory resources. It allows efficient utilization of high-powered GPUs by dividing them into smaller and scalable units. Demand for cost-effective GPU resources and cloud-based AI workloads are influencing the rising adoption of FGPUs. It plays vital role in running multiple AI tasks concurrently without interference. FGPUs enables better utilization of GPU resources, reducing costs and increasing GPU utilization.
Featured Story
An autonomous vehicle company, faced GPU scheduling issues, leading to underutilization. By implementing fractional GPUs for advanced scheduling on Kubernetes, they transitioned from static GPU allocation to dynamic pooling. This change increased GPU utilization from 45% to over 80%, allowing more efficient training and testing of their AI models. Additionally, the system supports multi-node training, enhancing scalability and performance.
Custom language models (CLMs)
CLMs are modified versions of Large Language Models (LLMs) that have been adjusted to meet specific requirements or tasks. These models are based on pre-trained algorithms and undergo additional training on targeted datasets or modifications to improve their performance for tasks. Advancements in training techniques and multitask model architecture are primary factors in enhancing the capabilities of compact language models.
Featured Story
An American multinational company specializing in air-conditioning and refrigeration needed to automate and optimize its marketing content creation across various platforms like emails, LinkedIn, etc. LTIMindtree applied retrieval-augmented generation (RAG) to extract information from user-uploaded documents and leveraged prompt engineering techniques to build customized large language models for optimal performances across media. This achieved a 40% reduction in writing costs compared to traditional methods and increased platform traffic by 10% with higher quality content.
Agentic AI
Agentic AI is designed to operate alongside humans in shared environments, improving productivity, safety, and efficiency. It facilitates safe interactions, easy programming, and adaptability to various tasks across different industries by utilizing advanced sensors, IoT devices, machine learning modules, and intuitive interfaces. With the advancement of Agentic AI, bots are expanding their capabilities from responding queries to executing complex tasks. This requires robust algorithm design to detect events in real-time and execute tasks at optimal speeds.
Featured Story
Claim adjudicators spent excessive time reviewing multiple sections of claims to gather key information, leading to a tedious and error-prone process, especially with complex claims. LTIMindtree has built Claim Summarizer Tool that automates the consolidation of claim information, providing adjudicators with a quick, single-click view of claim status and critical details, eliminating manual cross-referencing. This increases efficiency and reduces claim analysis time, improves decision-making accuracy by minimizing human error and enhances productivity, allowing focus on higher-value tasks.
Financial ops (FinOps)
FinOps focuses on managing an organization’s financial resources and processes for optimal health and efficiency, aiding in effective planning and resource allocation. It enables informed decisions for long-term profitability. In addition, it is responsible for developing financial statements and budgets, monitoring accounts payable and receivable, preparing financial reports for management review, reconciling bank statements, and performing audits of financial transactions.
Featured Story
A leading vertical transportation systems manufacturer faced high costs from underutilized Azure subscriptions. After implementing LTIMindtree’s FinOps solution, they achieved a 65% improvement in cloud utilization, a 30% reduction in overall spending, and $6.2 million in annual savings across Azure and 100% increase in cost saving by implementing Azure Arc.
Green computing
With increasing emphasis on sustainability, green computing is gaining traction. This involves designing, manufacturing, using, and disposing of computers and related systems in an environmentally friendly way. Green computing is not just about reducing the environmental impact of technology; it's about creating a sustainable future where technology and nature coexist harmoniously
Featured Story
A US-based electric service organization required agile capacity management, technical debt reduction, financial savings, and IT operations modernization. They used LTIMindtree’s Green Ops solution to identify underutilized resources and applied AI-enabled recommendations to optimize their environment. This reduced their carbon footprint and saved 20% on infrastructure costs in less than a year.
Synthetic data
Synthetic data is created artificially and used as a substitute for test data sets of production or operational data, to validate mathematical models and to train Machine Learning (ML) models. The primary application of synthetic data is in the training of neural networks and ML models, where developers require labeled data sets that can range from a few thousand to tens of millions of items.
Featured Story
An American multinational digital communications technology conglomerate faced difficulties due to the presence of multiple disparate ERP and e-commerce systems, which led to compatibility issues. LTIMindtree utilized UiPath to create a wrapper around the various testing solutions and integrated Gen AI to generate synthetic data for testing. This implementation accelerated the testing cycle by 30% and enabled seamless test data generation with AI, reducing manual effort and increasing testing efficiency.
Voice of change
“Technologies are not merely enablers of innovation; they form the bedrock of a smarter, more secure, and efficient future. The Technology Radar 2025 offers a visionary glimpse into the future, spotlighting emerging and pivotal technologies. This year's edition introduces eight groundbreaking technologies poised to revolutionize our current work paradigms. With AI's omnipresence, these advancements will accelerate the realization of AI's benefits, transforming industries at an unprecedented pace.
”
____
Sachin JainPrincipal Director, LTIMindtree Research
LTIMindtree
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