In 2025, the enterprise telecom segment moved beyond digital upgrades towards a phase of transformation. Enterprises across industries embedded advanced technologies, such as artificial intelligence (AI) and machine learning (ML), generative AI (GenAI) and agentic AI systems, cloud-native platforms, and internet of things (IoT)-driven automation, into their core operations. However, alongside the rapid adoption of these technologies, cybersecurity emerged as a critical concern. Representatives from leading enterprises share their insights on new and emerging technologies, private 5G adoption, cybersecurity challenges and the way forward…

What were the key technologies that reshaped the enterprise segment during 2025?

Sudhir Chiplunkar

In 2025, enterprise technology matured from digital enablement to operational indispensability. At Hyderabad Metro, this shift was visible in how technology directly influenced day-to-day service reliability across a 69.2 km fully elevated network with 57 stations.

Key technologies included integrated command and control systems, IoT-based asset monitoring and cloud-led enterprise platforms. These allowed us to break down silos between rolling stock, traction power, signalling, stations and operation control centre (OCC) operations. Earlier, information moved sequentially; today, it moves in real time.

For example, alerts from station equipment, power substations, or trains now flow directly into central systems, enabling faster diagnosis and coordinated response. This integration has significantly reduced mean time to resolution during service-impacting incidents, particularly during peak-hour operations. The most important change in 2025 was not the adoption of new tools, but the ability to see the system as one operational whole.

Lalit Desiraju

What stood out in 2025 was how decisively enterprises narrowed their technology focus. Instead of chasing multiple innovations, organisations concentrated on strengthening service delivery in a more complex and distributed operating environment.

The key technologies that reshaped the enterprise space during 2025 were:

  • GenAI embedded into service operations: GenAI found its way into contact centres, service desks and internal platforms, mainly to support agents, improve knowledge access and bring more consistency into customer interactions.
  • Data and platform modernisation: There was a strong push to clean up and modernise data architectures, helping enterprises support real-time operations, reporting and AI-enabled services more reliably.
  • Hybrid and multi-cloud operating models: Rather than adding more cloud services, organisations focused on making existing environments work better across geographies, regulations and cost structures.
  • Security modernisation: As digital interactions rise, enterprises are strengthening identity controls, automation and monitoring to protect service continuity and customer data.

Together, these technologies reshaped how enterprises delivered services at scale in 2025.

Rajneesh Garg

The year 2025 marked a decisive shift in how enterprises approached digital transformation, moving from experimentation to scaled adoption of outcome-driven technologies. Enterprises increasingly focused on resilience, intelligence and real-time decision-making, driven by volatile supply chains and rising customer expectations. The need for a scalable and infrastructure-ready approach also became a significant priority.

Key technologies that reshaped the enterprise landscape included the following:

  • AI and advanced analytics: AI moved beyond pilot projects to enterprise-wide deployments. Predictive analytics, intelligent automation and AI-driven decision support systems became central to improving operational efficiency, demand forecasting and risk mitigation. For large enterprises, AI-enabled insights significantly reduced turnaround times and improved resource utilisation.
  • Cloud-native and hybrid architectures: Enterprises accelerated migration towards hybrid and multi-cloud environments to balance scalability, security and regulatory requirements. Cloud-native platforms enabled faster innovation cycles, seamless collaboration and improved business continuity across distributed operations.
  • IoT and real-time visibility platforms: The proliferation of IoT devices transformed asset tracking and operational monitoring. Real-time data from connected devices allowed enterprises to enhance visibility, reduce downtime and improve service reliability especially in asset-heavy sectors like logistics and manufacturing.
  • Cybersecurity and zero-trust frameworks: With expanding digital footprints, cybersecurity became a board-level priority. Enterprises adopted zero-trust architectures and AI-driven threat detection to safeguard critical systems, data and digital supply chains.
  • Convergence of  IT and operational technology (OT): For decades, IT focused on data while OT focused on physical systems. Now they are colliding. In IT, downtime means financial loss. In OT, it can mean hazards to human safety and the environment.

Dr Sushil K. Meher

After the Covid pandemic, technology has witnessed a deep penetration in the healthcare sector and found acceptance among doctors and patients. The year 2025 marked a significant turning point for the penetration of latest technology in healthcare. AI moved from experimentation to real-world use, virtual care using AI became a necessity, connected devices reshaped remote monitoring, and AI-based automation in healthcare eased the workload of doctors.

As we move into 2026, this momentum will continue, with digital health shifting from innovation to infrastructure and agentic AI. The focus is now on improving operational efficiency and accuracy, enabling personalised care, and delivering quality healthcare. Here are some of the key tech trends that will shape the future of healthcare in 2026:

  • Immersive technologies to transform training and treatment,
  • Internet of medical things (IoMT), bio-patches, and connected health ecosystems to expand rapidly,
  • Robotics to redefine surgical and operational efficiency and accuracy of surgery,
  • Home care, geriatric care and remote patient monitoring (RPM) to become central to chronic care,
  • Personalised treatment with AI and low-code platforms,
  • Enhanced diagnostic accuracy through AI and custom healthcare apps,
  • 5G-enabled telemedicine will emerge as a game-changer for virtual care,
  • Use of AI agents for enhanced care delivery and workflow automation,
  • Multimodal AI for predictive diagnostics,
  • AI for medical diagnosis, improving efficiency, accuracy and speed.

Dr Abhinanda Sarkar

All information technology eventually rests on hardware. Physical computers and peripherals generate the computational speed and host the data storage needed for AI and other advancements. These computers run on microprocessors, which use specialised materials such as the “rare earth” elements. So, any disruption in their supply chain has a disproportionate impact on technology, from cars to computers. The world discovered this the hard way in 2025. Some countries leveraged this disruption to their benefit, with strategic shifts to position themselves favourably. India reoriented its manufacturing and trade policies in line with the country’s priorities.

How has the year been in terms of AI and agentic AI adoption? What were the key developments?

Sudhir Chiplunkar

At Hyderabad Metro, AI adoption in 2025 moved decisively into operational decision support. AI-driven analytics are now used across rolling stock maintenance, traction power consumption and passenger demand analysis.

A practical example is condition-based maintenance. By analysing historical fault data and live sensor inputs, AI models help maintenance teams identify early signs of component degradation. This has directly supported over 90 per cent peak-hour train availability, even during high-demand periods and special events.

Agentic AI, in our context, is not about autonomous decision-making. In a safety-critical environment, its role is to assist experienced engineers by analysing multiple scenarios, flagging anomalies and recommending responses. Human oversight remains non-negotiable. The real value in 2025 came from AI augmenting operational judgement not replacing it. From an enterprise perspective, the emphasis has been on responsible AI adoption, ensuring transparency, cybersecurity and governance, particularly in safety-critical systems as in railways.

Lalit Desiraju

AI adoption in 2025 was steady and deliberate. Enterprises became more selective about where AI was deployed, especially in CX and IT environments where reliability and service quality are critical.

GenAI was commonly used in agent-facing scenarios. It supported assisted resolution, content creation and faster access to information, with deployments closely tied to performance and quality metrics rather than broad experimentation.

Agentic AI, in contrast, remained limited and carefully controlled. Enterprises tested it in specific areas such as workflow coordination, ticket handling and IT service processes, always with defined boundaries and human oversight. Fully autonomous use cases were still rare.

One clear development through the year was the increased focus on operational controls. Organisations invested more effort in governance, monitoring and escalation mechanisms, recognising that the value of AI depends as much on integration and oversight as on the technology itself.

Rajneesh Garg

The year 2025 was a pivotal year for AI adoption, marked by a transition from rule-based automation to more autonomous, context-aware systems. Enterprises increasingly embraced AI not just as a productivity tool, but as a strategic enabler of intelligent operations.

Key developments included the following:

  • Enterprise AI at scale: Organisations expanded AI deployments across functions such as operations, finance, customer service and supply chain planning. AI-driven demand sensing, anomaly detection and predictive maintenance delivered tangible improvements in efficiency and cost optimisation. AI-driven SOCs can filter noise, detect anomalies and even automate responses.
  • Emergence of agentic AI: Agentic AI systems capable of executing tasks autonomously with minimal human intervention gained traction. These AI agents began handling repetitive operational workflows, exception management and decision support, enabling faster responses and reducing manual effort.
  • Responsible and governed AI: As AI adoption expanded, enterprises placed a strong emphasis on AI governance. Enterprises focused on explainability, data integrity and ethical AI frameworks to ensure compliance, trust and long-term sustainability of AI initiatives.
  • Integration with core enterprise systems: AI capabilities were increasingly embedded within enterprise resource planning (ERP), transportation management system (TMS) and analytics platforms, enabling seamless integration into day-to-day business processes rather than operating as stand-alone solutions.

Dr Sushil K. Meher

AI-powered medical scribe tools help doctors document patient visits more efficiently. These tools listen to doctor–patient conversations and automatically create structured clinical notes in electronic health records. By cutting down paperwork, they help ease clinician burnout, allow providers to focus more on patient care, and speed up the process of arriving at final diagnoses.

AI-driven diagnostic models analyse medical images, pathology slides, ECGs and bio-markers to support faster and more accurate diagnoses. By identifying patterns that may be hard to spot manually, these tools help clinicians detect diseases early on and make more informed decisions. AI agents automate routine administrative and clinical tasks, such as appointment scheduling, patient follow-ups, insurance processing and initial triage. This reduces manual effort and improves efficiency across healthcare operations.

Beyond administration, AI agents support the end-to-end patient journey, from symptom assessment to follow-up and ongoing care. They are also used in laboratories to assist with research and enable patients to monitor and manage their health more effectively.

AI optimises treatment planning and drug management to enhance patient care efficiency and safety. It analyses patient data, previous diagnosis, medical history, laboratory results, age, weight and negative drug interactions. Continuous data analysis helps in managing chronic diseases with proactive healthcare interventions.

Dr Abhinanda Sarkar

In this century, AI has evolved from symbolic AI (“good old-fashioned AI”) to predictive AI based on deep learning to GenAI built around large language models. In 2025, the shift was from GenAI to agentic AI. In many ways, AI agents are an answer to the value question: How do we derive value from GenAI?

Every value proposition involves two considerations of capital and labour. From a capital perspective, enterprises are discovering that AI can get expensive. This may be due to cloud and service subscriptions (for smaller companies) or the cost of storing and processing data (for larger companies). In the context of labour, the steady advancement of AI is driving efficiencies, typically reducing headcount, while creating demand for new skills. However, out of this churn, new applications continue to emerge. In India and other countries, AI combined with geographic information systems, for instance, is advancing crop planning, soil health analysis and pest control. A host of agritech start-ups have started working with government agencies to set up foundational enablers such as data exchanges and last-mile service delivery. If this works, small and medium-scale farms will come closer to becoming technology-enabled enterprises.

What are your thoughts on private 5G adoption for enterprises? What are the key use cases for your sector?

Sudhir Chiplunkar

For a metro system, communication reliability is as critical as physical infrastructure. Private 5G has the potential to become a key enabler of operational resilience in environments like ours.

At Hyderabad Metro, operational communication spans trains in motion, stations, depots, OCCs and emergency response teams. Supporting over 0.5 million passenger journeys daily, even a few seconds of communication latency can impact response effectiveness.

Private 5G offers the possibility of secure, low-latency connectivity for use cases such as:

  • High-definition CCTV analytics for crowd management
  • Real-time monitoring of assets across stations and depots
  • Reliable communication for maintenance and emergency teams
  • Support for future automation and digital twin applications

While public networks are suitable for passenger services, metro operations demand deterministic performance, making private 5G a compelling option as part of a layered communication architecture.

Rajneesh Garg

From an enterprise perspective, private 5G networks enable real-time communication between systems, devices and applications, which is essential for mission-critical operations. Moreover, they provide enterprises greater control over data traffic, network performance and security, an important consideration for large-scale, distributed operations.

Key use cases in the logistics sector include:

  • Real-time tracking and monitoring of cargo, vehicles and yard operations (with data residing in private networks)
  • Smart warehouses enabled by connected sensors, robotics and automation
  • Seamless integration of IoT devices for condition monitoring and predictive maintenance
  • Improved safety and surveillance across large logistics parks and terminals/CFT (freight stations)
  • Powering smart factories, connected vehicles and autonomous operations.

Dr Sushil K. Meher

5G technology will transform telemedicine by overcoming previous limitations, such as network instability and slow data speeds. Here is how 5G enhances telehealth services:

  • Enhanced video quality: 5G will support high-quality video consultations, making it easier for doctors to conduct detailed virtual exams. Clear, uninterrupted video calls will ensure more accurate diagnoses, especially in fields where visual assessments are essential such as dermatology.
  • Real-time data sharing: 5G will enable the immediate transmission of large medical files, such as MRIs and CT scans. Healthcare providers can quickly review diagnostic images and collaborate with specialists in real time, which is particularly important for emergency care and situations requiring fast decision-making.
  • Remote surgeries and procedures: With 5G, surgeons can perform complex remote procedures using robotic systems. The ultra-low latency of 5G will allow precise, real-time control during surgeries, enabling healthcare providers to offer surgical care from miles away.
  • Wearable health devices and continuous monitoring: 5G technology will make wearable health devices more effective, allowing continuous monitoring of vital signs such as heart rate and blood pressure. This data can be shared with healthcare providers instantly, enabling them to adjust treatments and detect potential issues before they become serious.
  • Expanding telemedicine to rural areas: 5G has improved telemedicine accessibility in rural or underserved areas where reliable internet connections are often lacking. It will bridge the gap in healthcare services and enable patients in remote regions to consult with specialists and receive timely care.

Dr Abhinanda Sarkar

As is well known, 5G offered many advantages over 4G. Some of these rely on low latency, driven by a dense cellular architecture. In simpler terms, 5G is suitable when faster communication is needed over shorter distances. Public networks, however, are optimised for longer distances and can be a little more tolerant of delay. Enterprises have recognised this distinction and leveraged it to create private 5G networks. In 2025, more companies adopted this approach. The numbers are still low, but climbing rapidly.

Outside of manufacturing and device-to-device communication, private networks may need specialised use cases. In many situations, such as hospitals and schools, the privacy element that private networks provide may be valuable. That said, Wi-Fi, coupled with good cybersecurity, may be able to do an adequate job as well, particularly if cell towers can be conveniently located. However, it is possible to envision “smart campuses” with private 5G networks where devices are connected student-to-student or teacher-to-student. Experiments in teaching using augmented reality/virtual reality are underway, and such innovations could benefit significantly from 5G private networks.

What will be the emerging trends for the enterprise segment in 2026?

Sudhir Chiplunkar

For infrastructure-led enterprises, 2026 will be about operational convergence and resilience.

At Hyderabad Metro, we see three clear trends. First, IT-OT convergence will deepen, with operational data flowing seamlessly between maintenance, safety, energy management and customer systems. Second, AI-driven planning will move from analytics to execution, supporting maintenance scheduling, energy optimisation and service planning. Third, cybersecurity will become a core operational function, not just an IT responsibility. As systems become more connected, protecting critical assets and passenger data becomes as important as physical safety.

These trends are particularly relevant for metros, where thousands of assets operate continuously in public spaces. Technology in 2026 will be judged by its ability to keep systems running safely, predictably and efficiently, not by novelty.

Fourth, edge computing and real-time analytics will gain prominence, enabling faster decision-making closer to the point of action. This will be particularly relevant in environments where milliseconds matter.

Finally, enterprises will focus on technology that delivers measurable outcomes, reduced downtime, improved safety, energy efficiency and enhanced customer experience, rather than adopting technology for its own sake.

As we move into 2026, enterprises that align digital innovation with operational discipline will be the ones that deliver resilient, future-ready infrastructure.

Lalit Desiraju

Looking ahead towards 2026, enterprises are expected to place greater emphasis on simplification and commercial discipline. Many organisations will focus on reducing complexity by consolidating platforms and utilising existing resources more effectively.

Technology decisions are likely to be judged more closely on service outcomes, cost efficiency and resilience. AI will continue to be used within defined processes, supported by clearer accountability and performance tracking rather than widespread expansion.

Hybrid and multicloud environments will continue to evolve, with a greater emphasis on predictability and governance. Cybersecurity will remain closely linked to this shift, particularly around identity and continuous monitoring.

Overall, 2026 is shaping up to be a year where enterprises focus less on adding new capabilities and more on making their technology estates easier to run and easier to scale.

Rajneesh Garg

In 2026, the enterprise segment will clearly move from being digitally enabled to becoming truly autonomous, with intelligence built into the core of operations rather than layered on top. AI will no longer be viewed as a stand-alone capability but as an embedded decision engine across supply chains, networks and enterprise systems, enabling real-time, self-correcting operations. Enterprises will increasingly combine private 5G, edge computing and IoT to create highly responsive, low-latency environments that support mission-critical use cases at scale, particularly in asset-intensive sectors. At the same time, there will be a decisive shift toward platform-led, data-first architectures that eliminates silos and allow enterprises to act on insights instantly rather than retrospectively.

Sustainability will also progress from intent to execution, with technology playing a central role in optimising resources, reducing emissions and improving accountability. Overall, 2026 will mark the year when enterprises stop experimenting with emerging technologies and start measuring them purely on business outcomes, resilience and long-term value creation. All initiatives outlined above will also be executed in accordance with environmental sustainability principles.

Dr Sushil K. Meher

By 2026, advanced technologies, such as AI, internet of medical things (IoMT), 3D printing, immersive technologies, blockchain, quantum computing and robotics will transform healthcare to a large extent. These innovations will enhance patient care, accuracy and improve operational efficiency. AI will drive faster diagnostics, IoMT will enable remote monitoring and 3D printing will assist in personalised treatments, contributing to more precise and connected healthcare. Further, AI can contribute to significant cost reductions, deliver meaningful social impact and improve productivity in healthcare. It can diagnose quickly and accurately, prepare the discharge summary in two minutes, personalise treatments, optimise resource allocation and improve diagnostic accuracy. This will ultimately lead to more efficient and effective healthcare delivery and minimise medical errors. It will automate repetitive and time-consuming business processes such as appointment scheduling, data entry, inventory management, insurance claims processing and staff scheduling.

To stay competitive, healthcare organisations are embracing low-code, no-code platforms and citizen development healthcare programmes. These tools can empower non-IT professionals to build custom applications, addressing IT backlogs and allowing faster responses to evolving patient needs. By investing in these platforms, providers can accelerate digital transformation, deliver superior care and improve operational efficiency.

Dr Abhinanda Sarkar

Given the pace of change and the disruptive nature of current innovation, it will not be surprising if 2026 throws up entirely unexpected breakthroughs. Some expectations are as follows:

  • New regulations and standards for AI will be legislated and ratified in many countries worldwide. Most of these are already underway, like the EU AI Act and the DPDP Act in India, but progress has been slower than the advancement of the technology.
  • Cryptocurrencies and stablecoins will continue their resurgence, driven by supportive policies. This will power research and development in cryptography, which, in turn, can revive interest in blockchain, NFTs and digital assets.
  • Warfare will be even more automated, with drones and other robotic devices replacing soldiers and manned planes and ships. Ongoing conflicts will continue to provide fertile training grounds for such expendable autonomous devices.
  • Semiconductor manufacturing capability will continue to determine the technology, transportation and defence leadership of countries. Chip fabrication facilities will determine AI and data storage localisation to an increasing degree.
  • A “NewSpace” economy will emerge, driven by private enterprises and not just governments. Logistics and communications industries will benefit from satellite deployments. Space junk will steadily increase.

However, as technologies evolve and emerge, dangers will continue to lurk. Cybersecurity will continue to struggle to keep up with hackers, fraudulent telecallers will keep innovating to exploit the unsuspecting, deepfakes will continue to threaten privacy and human dignity, and agencies will start using facial and other invasive recognition techniques. Enterprises, governments and individuals must all play a role in making and keeping technology safe and useful to all.