Software precision — through forecasting, simulation, and optimisation — becomes essential to maintaining energy security and reliability. As India accelerates towards a renewable-first energy system, AI-driven forecasting, digital twins, and cybersecurity are becoming essential for grid reliability and operational efficiency. The article explores how software-defined energy infrastructure is reshaping power system management in the clean energy era.
India’s energy transition is no longer just about adding renewable capacity — it is about reimagining how power systems are planned, operated, and secured in a software-defined world.
Read the full article published in RenewEdge website, where I have discussed India’s aspiration to build the software backbone of a renewable-first grid by embedding intelligence, resilience, and efficiency into its energy systems.
In the last two years, during conversations with Energy & Utilities (E&U) clients, one question consistently comes up: “How can Digital Twin technology help our organization?”Despite its growing popularity, many still perceive a Digital Twin as nothing more than a digital replica or 3D visualization of a physical asset. While visualization is part of the story, it is far from the whole.
The truth is, Digital Twin is not just a model; it is an evolving intelligence layer that spans the entire lifecycle of an asset: from design and engineering, to real-time monitoring, simulation-based optimization, predictive maintenance, and ultimately, decommissioning and recycling.
Read the full article in my LinkedIn post which explores the role of exponential technology in energy and utilities sector.
Generative AI (GenAI) technology is emerging as a powerful catalyst, capable of reshaping how utilities create knowledge, engage customers, and manage complexity. But the story doesn’t end there. The evolution toward Agentic AI (the AI systems that not only generate insights but also reason, plan, and act autonomously): signals a fundamental shift in what is possible. Together, GenAI and Agentic AI chart a pathway from assisted intelligence to autonomous intelligence, unlocking possibilities that mirror Telecom’s leap from chatbots and predictive analytics to self-healing, self-optimizing networks and others.
Read the full article in my LinkedIn post, where I have explained why GenAI is more than just “chatbots plus text generation”. It’s real power lies in combining large language models (LLMs), domain adaptation, multimodal capabilities, and decision support to transform how information is created, consumed, and operationalized.
The Energy and Utilities (E&U) sector stands at the crossroads of an unprecedented digital evolution. While telecom companies swiftly adopted digital innovations over the past two decades- achieving operational efficiency gains of up to 30% and embedding analytics in more than 70% of their processes (Deloitte, 2023; IDC, 2024); the E&U sector has traditionally lagged, hindered by legacy systems and stringent regulations. However, the tide is rapidly turning. Recent reports by Gartner (2024) forecast a significant ramp-up in digital investment across the utilities sector, predicting an accelerated adoption. Now, as utilities embrace modular platforms, AI-driven analytics, and digital O&M solutions, the opportunity is immense- potentially reducing operational costs by up to 25% (McKinsey, 2024).
Read the full article in my LinkedIn post, which explores how the E&U industry can leverage strategic lessons from telecom to accelerate digital transformation, unlock customer value, and seize this growing market opportunity.
According to the latest available data, there are approximately 17 billion connected Internet of Things (IoT) devices and this figure is expected to almost double to 29 billion by 2030. As per verified market research, IoT Devices Market size was valued at USD 125 B$ in 2023 and is projected to reach 620 B$ by 2031 (1).
The growing adoption of smart devices has driven demand for IoT devices, making sustainability more critical than ever as IoT’s role in business continues to expand. Most of the IoT devices have dangerous elements inside, ranging from heavy metals such as lead, mercury, cadmium and beryllium to hazardous chemicals like brominated flame retardants.
They are smaller in size!
The good news is; compared to other technology products, IoT devices has lesser environmental impact and they can deliver greener results. IoT devices are small physical devices, so their production requires fewer raw materials and lesser plastics and packaging materials, thus producing less e-waste.
The unique design of IoT devices has inherent restrictions in memory, so they use embedded systems programming which can run in such restricted conditions. IoT industry dictates the development of more complex systems that can be run on limited resources. IoT devices are compact and smaller than regular computers, designed for seamless integration into various environments. Despite their size, they can perform critical tasks, including real-time monitoring and data analysis. Their efficiency and versatility make them essential in smart systems and automation. Manufacturing of IoT devices requires less material as well as less energy than a large computers.
IoT devices are used primarily for data collection. They continuously collect information and send it to data centers for analysis. The whole process takes a lot of energy and is perhaps not always worth it. It would be a good practice to evaluate the real need for IoT use, in terms of what we gain and how much we spend in the race for popular digital instruments.
Where is the challenge?
The challenge is in it’s waste disposal, and associated environmental impact. Some of the critical concerns are:
Increased Volume of E-Waste: The proliferation of IoT devices leads to a significant increase in electronic waste, contributing to the growing global e-waste problem. 17B IoT devices as of today and it is growing exponentially.
Resource Consumption and Toxicity: IoT devices often contain rare earth metals and hazardous materials that are challenging to recycle and can be toxic to the environment if not disposed of properly.
Short Lifespan: Many IoT devices have short lifespans and are not designed for easy disassembly or recycling, leading to more frequent disposal and accumulation of waste in landfills.
As we discussed at the beginning, IoT devices often contain heavy metals such as lead, mercury, and cadmium. These metals can leach into soil and water, causing environmental contamination and health issues like neurological damage, kidney disease, and cancer. Lead exposure can cause high blood pressure and brain, kidney and reproductive health issues in adults. Exposure to mercury may cause irritation to the eyes, skin, and stomach, cough, chest pain, or difficulty breathing, insomnia, irritability, among others. IoT devices often contain lithium-ion batteries in them, which can pose fire hazards if damaged. Improper disposal can lead to toxic leaks, causing soil and water contamination.
What are sustainable approaches?
To reduce the environmental impact of IoT devices in terms of e-waste, several approaches can be considered:
Design for Longevity and Repairability: Develop IoT devices with longer lifespans, modular components, and easy repairability to reduce the need for frequent replacements.
Use of Sustainable Materials: Utilise recyclable and non-toxic materials in the manufacturing of IoT devices to minimise environmental harm.
Improved Recycling Programs: Establish and promote efficient e-waste recycling programs to ensure proper disposal and recovery of valuable materials from discarded IoT devices.
Standardisation and Interoperability: Encourage standardisation of components and interoperability between devices to reduce the need for multiple, redundant gadgets.
Manufacturer Take-Back Schemes: Implement take-back programs where manufacturers are responsible for collecting and recycling their products at the end of their lifecycle.
Consumer Awareness and Education: Educate consumers about the environmental impact of e-waste and encourage responsible purchasing, usage, and disposal of IoT devices.
Legislation and Regulation: Advocate for stricter regulations and policies that mandate environmentally friendly practices in the production, usage, and disposal of IoT devices.
Can GenAI come to the rescue?
Identifying alternative materials used in IoT devices is crucial as we use more and more connected devices and in-order-to address environmental concerns. The search for eco-friendly materials can reduce the environmental impact and promote the development of greener technologies. Generative AI (GenAI) can play a pivotal role in this process by analysing vast amounts of data to predict and discover new materials with desirable properties. GenAI can simulate the performance of these materials in various conditions, accelerating the research and development process. Additionally, GenAI can optimise manufacturing processes to incorporate these new materials efficiently, ensuring that IoT devices are not only sustainable but also cost-effective and high-performing.
Conclusion
The market demand for IoT devices is rapidly increasing as industries and consumers want to harness the power of connected technologies for enhanced efficiency, convenience, and innovation. However, this surge in demand brings about significant environmental challenges due to the materials and manufacturing processes currently used. Adopting sustainable approaches in the production and deployment of IoT devices is critical to mitigating these environmental impacts. Sustainable practices, such as using eco-friendly materials, improving energy efficiency, and implementing robust recycling programs, ensure that the growth of IoT does not come at the expense of our planet. By prioritising sustainability, manufacturers not only will meet regulatory requirements but also will contribute to a greener future while maintaining the technological advancements that IoT devices offer.
Generative AI is dominating conversations due to its transformative impact across industries. From generating creative content to aiding in problem-solving, these systems are revolutionizing how we approach tasks. With their ability to innovate, automate, and personalize, Generative AI technologies are at the forefront of shaping the future of technology and human interaction. Ensuring technology advancements benefit all, not just the privileged few, is paramount. While major tech players and governments strive for inclusive and equitable technology use, individuals must also acquire basic skills to prevent widening skill gaps. Accessible technology empowers everyone to participate fully in the digital age, fostering a more equitable and prosperous society.
Crafting well-designed prompts empowers users to steer the AI towards desired outcomes, maximizing creativity and relevance
Introduction
Rule based systems vs Trained systems
Trained systems learn from the data, they adapt and improve over time. Whereas, programmed systems follow predefined instructions without any learning capabilities. Today’s intelligent systems (using AI technology) are trained, not programmed. Instead of programming or writing specific rules to solve a problem, AI systems are fed with examples of ‘what it will encounter’ in the real world. AI systems then detect the patterns and produce their own ‘rules’ based on the examples. While trained systems offer flexibility and adaptation, programmed ones excel in precise execution.
So, Artificial Intelligence (AI) is the science and engineering of making intelligent machines, that can perform tasks which typically requires human intelligence (John McCarthy, 2007). At its simplest form, Artificial Intelligence is a field which combines Computer Science with robust data set.
AI systems typically perform functions like ‘classifying data’ (e.g. assigning labels to images), ‘grouping data’ (e.g. identifying customer segments with similar purchasing behavior), or ‘choosing actions’ (e.g. steering an autonomous vehicle).
What is Generative AI?
Generative AI (GenAI) is the latest development in AI field, where the primary function of the AI system is to generate content, content that is similar to/ indistinguishable from human-created content.
Generative AI is crucial because it empowers us to create new content, ranging from art to music to text, with minimal human intervention. Mastering generative AI allows for innovative problem-solving, creative expression, and automation across various fields, making it a valuable skill in today’s rapidly evolving technological landscape.
Who are the consumers?
Consumers of generative AI systems can be diverse, ranging from individual creators and artists to businesses and organizations across industries such as entertainment, marketing, design, education, healthcare, and more. These systems cater to anyone seeking automated content generation, personalized recommendations, enhanced creativity, or improved efficiency in their work processes. Additionally, consumers could include developers and researchers interested in advancing the capabilities of generative AI itself.
Why to embrace generative AI?
Understanding generative AI skills can be incredibly valuable for several reasons:
Creative Expression: Generative AI empowers individuals to express their creativity in new and exciting ways, whether it’s through generating art, music, literature, or even entirely new concepts. This skill allows for innovative exploration and pushes the boundaries of traditional creative processes.
Problem Solving: Generative AI techniques can be applied to various problem-solving scenarios, such as generating synthetic data for training machine learning models, designing optimized structures in engineering, or simulating complex systems for research purposes. By mastering generative AI, individuals gain a powerful toolset for addressing real-world challenges.
Career Opportunities: With the increasing integration of AI technologies across industries, proficiency in generative AI can open up numerous career opportunities. From roles in software development and data science to creative fields like design and entertainment, individuals with generative AI skills are in high demand and can command competitive salaries in the job market.
Can Gen AI systems make mistakes?
A big yes. While it strives for accuracy and coherence in it’s response; there might be occasions where AI systems can misinterpret the context and provide information that’s outdated or inaccurate.
Large Language Models (LLM)
Large language models are advanced AI systems trained on vast amounts of text data, capable of understanding and generating human-like language. They exhibit impressive language comprehension, enabling them to perform tasks such as text generation, summarization, translation, and question answering.
Generative AI systems are based on large language models which forces users to understand how they are trained. Training a LLM involves feeding it vast amounts of text data to learn patterns and language nuances. The process begins with pre-training on a diverse dataset to impart general knowledge and language understanding to the model. Fine-tuning is the next step which involves training the LLM on a more specific dataset for a particular task or domain. During training, the model adjusts its internal parameters through iterations to minimize prediction errors and improve performance. Finally, validation datasets are used to monitor the model’s performance and prevent overfitting during training.
Despite their capabilities, challenges such as ethical concerns, biases, and energy consumption highlight the need for responsible development and deployment of these models.
Key advantages of Gen AI systems
Creative Output: Gen AI can generate novel and creative content, including images, music, and text, which can be valuable for artistic endeavors, content creation, and design projects.
Automation of Tasks: Gen AI can automate repetitive tasks, such as data entry, image generation, and text summarization, freeing up human resources for more complex and creative endeavors.
Personalization: Gen AI can analyze large datasets and user preferences to generate personalized recommendations and experiences, enhancing customer engagement and satisfaction in various industries such as e-commerce, entertainment, and marketing.
Problem-solving: Gen AI can assist in problem-solving by generating solutions, optimizing processes, and identifying patterns in data, enabling organizations to make data-driven decisions and innovate more effectively.
Efficiency and Scalability: Gen AI can improve efficiency and scalability by automating workflows, optimizing resource allocation, and accelerating innovation cycles, leading to cost savings and competitive advantages for businesses and organizations.
Known dis-advantages of Gen AI systems.
While Generative AI (Gen AI) offers numerous benefits, it also presents some disadvantages:
Ethical Concerns: Gen AI raises ethical concerns regarding the authenticity and ownership of generated content. There are debates surrounding the potential misuse of AI-generated content for spreading misinformation, creating fake news, and manipulating public opinion.
Bias and Discrimination: Gen AI models trained on biased datasets may inadvertently perpetuate biases and discrimination in generated content. This can lead to unfair outcomes, reinforce stereotypes, and exacerbate societal inequalities.
Security Risks: Gen AI poses security risks, including the potential for malicious actors to exploit vulnerabilities in AI systems to generate fake identities, bypass authentication mechanisms, or launch cyberattacks such as phishing scams and social engineering attacks.
Loss of Jobs: The automation capabilities of Gen AI may lead to job displacement and unemployment in certain industries, as AI systems can perform tasks more efficiently and cost-effectively than humans. This can have socio-economic implications and require workforce retraining and reskilling initiatives.
Hallucination/ Dependence on Data: Gen AI models require large volumes of high-quality data for training, which can be expensive, time-consuming, and resource-intensive. Additionally, reliance on data may raise privacy concerns and require compliance with data protection regulations. Moreover, if the data is biased or incomplete, it can lead to inaccurate or unreliable AI-generated outputs.
As per Mckinsey report, economic potential of Generative AI is $4.1 Trillion each year, that is 4.4% of the total economic output. Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI.
How to unlock the potential of Gen AI?
To unlock the potential of Generative AI as an individual, one should learn and experiment by diving into learning Generative AI techniques/ tools/ frameworks. Experiment with small projects to understand its capabilities and limitations.
Continuous Learning is needed to keep up with the latest research, advancements, and best practices in Generative AI. Individual may want to share findings, insights, and projects output with the community in order to foster collaboration and exchange ideas, expertise for growth and learning.
The key is to also stay informed about ethical considerations surrounding generative AI, such as bias mitigation and privacy concerns; and ensure responsible usage and deployment of AI-generated content.
Be a prompt engineer
Prompt engineering is a strategic and creative endeavor that shapes the interactions between humans and artificial intelligence systems. This is the process of crafting effective and specific prompts to guide artificial intelligence systems. It involves designing questions or statements that elicit desired responses from AI models. It is crucial in influencing the output and behavior of AI, ensuring accuracy and relevance in the generated content. By carefully constructing prompts, engineers can control the direction and focus of AI models, leading to more tailored and useful responses.
The effectiveness of prompt engineering can greatly impact the overall user experience and usefulness of AI-powered tools and services.
Conclusion
In summary, prompt engineering serves as a strategic tool for harnessing the power of Generative AI, by guiding the model’s output towards desired outcomes. Crafting well-designed prompts can steer the generative AI model to produce outputs that align with specific goals or criteria, ensuring precision and relevance in the generated content. Prompt engineering can help mitigate biases in generative AI outputs by providing prompts that encourage fair and unbiased content generation, promoting diversity and inclusivity. And finally, tailoring prompts to specific domains or tasks enables generative AI models to produce content that is relevant and useful within those contexts, enhancing their practical applicability.
Railway systems/networks are divided into sections (also known as blocks) to avoid collision between trains; because more than one train is NOT permitted to run on the same section of the track at the same time. The recent train accident in India along with the casualty numbers has warranted to revisit the train signalling system and how technology can come for rescue. The probe report flags multiple protocol breaches including signalling system as reason for the accident (1).
Combination of Blockchain, IoT and AI technology will play a big role in addressing this real-life challenge.
Signalling system plays a vital role in a Railways network to monitors the trains movement and operates the trains in a safe manner. Finding issues with signal failure before time will not save lives and national infrastructure. Key objectives of a Rail signalling system are:
Maintain a safe distance between trains
Control the trains movement at junctions
Prioritise track allotments based on business rules (Fast train vs slower trains vs goods trains)
Internet of Things (IoT) based event logger are being used in many countries to monitor and log the data related to train movements, but many a times this logger is not integrated or overlooked by personnels during the decision making which are primarily manual in nature. Also the usage of IoT systems are limited to predict failure of equipments to alert signal engineer for maintenance rather than using in real-time decision makings.
Problem statements:
In the above diagram, a passenger train has got priority over a goods train. When both the trains approaches a station which is equipped with a “passing loop” track, Peer to Peer (P2P) negotiation takes place. In this case, passenger trains negotiate with Station master to get access to the main line, and good train negotiate with station master to divert into passing loop track. Just imagine when trains are also passing in opposite track at the same time, the station master will get engaged in similar P2P negotiations. Such a P2P coordination can become with more number of trains participating in the network and also if train lines are crossing each other (diamond crossing). The real treat to safety is, the outcome of such a bilateral coordination (P2P) is provisional until all the coordinations are completed (between station master and participating trains).
Concurrent one-to-one coordinations are potentially complex and inefficient or even dangerous if done manually or by unsophisticated approach. All the coordinations between peers must be able to execute the same safeguarding logic that take into consideration of all relevant actors within the given railway network.
Secondly, in some cases it is seen that, once a section of track is allowed to one train post P2P negation, the track fails to switch as intended and hence creating accident situation.
Solution Components:
Blockchain: Blockchain technology provides both centralized and decentralized aspects of a solution.
It will hold the network-wide data at central level which is unique truth and can be shared across all participating nodes (information like train routes, timetables, passenger reservations, infrastructure reservations etc.). It will hold network-wide rules to safeguard trains in terms of smart contracts (stored on the ledger). These are software defined rules and will get executed when certain conditions are met.
The governance will be decentralized, business network administration is based on consensus and on transparency. Transactions are verified (endorsed) and ordered (serialized) by peers/orderers distributed over the participating network. When participating trains in a section or within a defined proximity met certain criteria consensus being taken in defined channel and corresponding smart contracts are verified and ordered for execution. This way, any bottleneck from central broker that is responsible for information distribution is avoided.
Internet of Thing: IoT devices are used to provide real-time situational data from the ground.
Once consensus is arrived in Signalling system for a train to get access to a particular section, the Switch operators makes sure the required switches happens before a green signal provided to the train driver.
So the IoT system will be a participant of the Blockchain system to provide a finality to the transactions before it get executed. IoT devices can be used to monitor the track changes and pass/fail flag will come back to Blockchain system as input before it commits the transaction. Computer vision/ camera solutions can be also used alternatively.
The status of the signal (green or red) on a particular track can be easily broadcasted by a wireless transmitter to any near-by approaching train. This come handy especially early morning hrs with less visibility where train driver need not have to look for the signal outside, but will get the status inside the driver cabin itself.
IoT devices using RFID and other technology can be used to build Anti Collision Systems for railway which can sense static or slow moving objects on the track. Indian railway has been using Train Collision Avoidance System in certain sections of the network successfully (2).
Conclusion
The Blockchain-based signalling System will be a software defined safety mechanisms that can be considered to automate train traffic management. Exponential technologies such as Edge Computing, 5G, IoT and AI can be used to bring right capability for implementation of such a system.
Blockchain and IoT solutions together will provide the real time data to all the stakeholders including Station Masters, Section Controllers, Signal Engineers and others. By sharing real time data on train movement with data integrity and transparency in place, this solution will enhance decision making capability. All the participating trains can communicate with each other, and decisions will be executed through smart contract that can avoid train delays which typically happens due to information gap and lack of trust with the existing systems.
Data will be stored in a distributed ledger using cryptography which will ensure data integrity and tamper proof. Applications and user interfaces can be build on Blockchain system to authenticate participants (driver, dispatcher, station master) based on face recognition or similar technology to avoid security risks.
Digital Twin technology is much more than representation of 3 dimensional view. This can produce value when augmented with real time data collected from IoT devices and analysed with Machine Learning/ AI techniques. As the data is expanding exponentially with billion devices getting connected where sensors are providing reliable data on real time basis; Digital Twin technology are coming handy in analysing and assessing products, services and processes for efficiency, better maintenance, and bringing many other benefits. Since the data continues to flow, simulations made by the digital twins can learn, improve, get continuously updated on near real time basis and models can be deployed in the physical world very quickly.
This is a promising technology with a time to acceptance of 5 to 10 years as featured in Gartner’s hype cycle for emerging technology trends for 2017 (Gartner, 2017). Market forecast is to have one-half of companies using Digital Twins to improve their products, services, and to solve real life challenges like traffic management, climate changes among others in next five years or so.
What is a Digital Twin?
Digital twin is a digital representation of a physical entity or system. The object can be an actual physical assets like building or can be a product like vehicle or a city by itself. These virtual replica gives control over the product or process from the design phase to the deployment phase, hence we will see a growing demand of digital twin technology globally. Digital Twin is getting widely adopted due to multiple benefits it offers, such as real-time monitoring, reduce product defects, shorten time to the market, extend the life of assets and reduction in production cost.
Why Digital Twin technology is important?
Digital twins are powerful masterminds to drive innovation and performance. Digital twin technology helps companies improve the customer experience by better understanding customer needs, develop enhancements to existing products, operations, and services, and can even help drive the innovation of new business.
Digital twins are virtual replicas of physical devices that data scientists and IT pros can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as IoT, AI and analytics are optimized. Digital twins can marry human and AI to produce something far greater and simulate complex systemswhich wasn’t possible otherwise. Also combined with AR, VR and related technologies provide a framework to overlay intelligent decision making into day-to-day operations. Digital twins offer a real-time look at what’s happening with physical assets, which can radically alleviate maintenance burdens.
Where Digital Twin technology can be used?
Electronics & Manufacturing Industry are expected to b ethe largest users of this technology. But increasingly Digital Twin technology is used in government, Automotive, Energy and Utilities, Retail, Healthcare and many other industries. The applications range from Product Design & Development, Inventory Management, Manufacturing Process & Planning, city Planning, Traffic managements and others.
Retail Industry: During the pandemic retail industry was one of the most affected business next to travel and hospitality. Consumer experience is critical in the retail industry and Digital twin implementation played a crucial role in augmenting customer experience by creating virtual twins for customers and modelling fashions for them on it. Other benefits of Digital Twins are in the area of floor space management, security features implementation and energy management among others.
Smart Cities: Creating a replica of the physical world Digital Twin is rapidly becoming indispensable technology to visualize the city in real-time; layered with buildings data, urban infrastructure, utilities network, traffic data and others helped city authority to take real time decisions while planning new and making adjustments to existing conditions.
Tourism: Many nations have started using Digital Twin as a tool to promote their cities to attract tourists by creating high-quality 3D models supplemented with Augmented Reality and Virtual Reality applications. This virtual tourism also got a boost during COVID-19 pandemic with limited travel options for tourists.
Industry 4.0: Digital twin technology applied to in-use products provide comprehensive insights into usage patterns, workload capacity; a holistic view of the health and performance of equipment enable companies to carry out Maintenance and replenishment of spare parts to minimize time-to-service and avoid costly asset failures.
Manufacturing: Usage pattern and use feedback are incorporated into product designing where Digital twin has a significant influence simulating the next generation of products and aligning them to manufacturing pipeline.
Healthcare: Digital twin technology can be used for pre-operative planning by simulating the medical conditions, taking all medical risks into account and plan for better outcome. In addition to simulating process flows to identify inefficiencies and bottlenecks.
Current challenges for adoption
Three Dimensional drawings are the foundation of Digital twin. Most of the industries are still working on two Dimensional drawings currently and adoption of Digitization is the biggest bottleneck. As number of digital Twins increases; version control and storing the up-to date replica of physical assets in digital format can be being considered as a challenge to address. Finally, as massive amounts of data being collected and utilized in Digital Twin initiatives, it has potential risk for Data security and privacy which needs to be addressed carefully and responsibly.
Covid-19 pandemic has not only exposed how unprepared the entire world is, but also surfaced social and economic inequalities in the community. The administration and vision of all world leaders are being tested during this time. Role of independent international bodies like WHO has been doubted and there could be drastic changes going forward. Carrying out business remotely with minimum mobility and decreased levels of industrial activities have brought down pollution levels in urbanised areas. This has resulted into daily global CO2 emissions to decrease by –17%; by far the ONLY positive externality of the Covid-19 pandemic (Nature.com, 2020). New economic policies need to be established quickly to cushion the impact of declining world’s GDP as production units are severely affected by shutdown caused due to pandemic. Immediate priority for each nation is to contain the outbreak and at the same time to maintain a sustainable level of economic activities. Each nation is also constrained with technical capabilities and available capacity in terms of healthcare professionals, medical infrastructure; this is where private public partnership can come handy to address the crisis jointly.
As world is desperately looking for a comeback on it’s economic activities, we all need to play our part by following safe distance measures and quarantine rules imposed by the respective governments. IBM has developed offerings for Worker Insight Solutions which receives inputs from optical and thermal imaging cameras, Bluetooth beacons and mobile phones to provide analytical insights to workers and supervisors for a wide range of critical use cases targeting for COVID-19 situation.
