FOREWORD
An exponentially increasing number of products that use artificial intelligence are being introduced into the market in various industries The public discourse on artificial intelligence can be characterized as a dialogue between AI enthusiasts, who emphasize myriad use cases of automated decision-making, and AI skeptics, who are concerned about the ethical issues surrounding the automation of decision-making. This edition of AGram takes the former stand emphasizing the role of artificial intelligence in building resilience. Resilience is a virtue in human societies and civilizations. People have built these societies and civilizations by showing resilience in the face of adversity. As societies evolved, they developed technologies that further enhanced their resilience, thus resilience and technology mutually reinforcing each other In this vein, artificial intelligence may be seen as the next generation of technologies enabling human resilience
While the age-old threats to human societies, such as natural disasters, continue to exist, new threats have manifested as the by-products of human progress. For example, social media, the technology that allows citizens to organize social movements and fight for their rights threatens the right to privacy of the same citizens In general, even as digitization allows individuals and organizations to become more efficient, it also exposes them to various forms of cybersecurity risks Individuals, organizations, and societies have to build resilience into their systems to face the inevitable threats of disasters, both natural and man-made
This edition of A-Gram features a collection of articles that discuss the role of artificial intelligence in building resilience in social and technical systems. Collectively, the articles provide a comprehensive perspective on some use cases that are often overlooked because of the focus on the application of artificial intelligence that solves problems at hand rather than impending problems. Thanking the authors for their valuable contributions, I invite the readers to engage actively with the content of this magazine.
PERSPECTIVES ON THE ROLE OF ARTIFICIAL INTELLIGENCE IN CRISIS MANAGEMENT
This article discusses some emerging perspectives on how artificial intelligence impacts our collective ability to manage crises It problematizes crisis management as a complex task and explores a couple of ways in which artificial intelligence helps mitigate the complexity and increase our effectiveness in managing crises
ARTIFICIALINTELLIGENCE:SCOPE
Most definitions of artificial intelligence allude to the ability of machines to emulate human-like reasoning and decision-making For the sake of this article, I will assume a wider definition of artificial intelligence Specifically, one school of thought within the Information Systems discipline considers AI as simply the next and the most advanced stage in the development of information technology. One may trace the evolution of the systems that use this technology to the decision support systems (DSS) that were built around half a century ago. These systems evolved from being good summarizers of data fed to them to systems that can collect, store, and process big data, thus enabling users to augment their decision-making capabilities to unprecedented levels In short, any technology that allows users to find patterns in past data to predict the future and thereby enhance the users’ decision-making capability fits under the definition of artificial intelligence in this article.
Dr Sunil ReddyKunduru
PROFESSOR IndianInstituteof ManagementAmritsar
FACULTY CORNER
CRISISANDMANAGEMENT
Human society has evolved from strength to strength because of its ability to handle crises. The most common crises humans continue to face since prehistoric times are the natural disasters. Extreme weather conditions, unpredictable behavior of other species on earth, epidemics and pandemics, etc have always threatened human societies. The ability of humans to collectively handle such crises was critical in establishing civilizations and, subsequently, the modern world. Though natural disasters continue to create crises for human societies, other crises also threaten the way of life of humans
At a macro level, organizational continuity is a matter of concern to leaders across various domains of life. Business continuity deals with the external factors that threaten the ability of an organization to continue pursuing its goals. For example, the COVID-19 pandemic and the intermittent lockdowns that followed threatened many businesses with the question of their survival. Crises may be more localized as well. For example, there could be domain-specific crises such as a cyberattack, raising questions about IT continuity management. A crisis may also be localized in time such as making public a piece of information affecting a firm’s brand reputation, the share prices of large corporations, political stability in a troubled nation, etc.
The very survival of human societies depends on how the society is prepared to handle various crises. Experts in various domains have created structured approaches to crisis management Disaster management is usually considered to have the following steps: prevention, mitigation, response, and recovery. That is, disaster management involves efforts to reduce the risk of a disaster; in case the disaster occurs minimizing its impact on people; saving the lives and property of people who are impacted; and rebuilding the communities affected by the disaster In general, any crisis management plan involves the following components:
1. Risk analysis
2. Organization of response
3. Response protocol
4. Communication strategy
5. Post-crisis assessment
CRISISMANAGEMENTANDARTIFICIAL INTELLIGENCE
The five components of crisis management listed above are by no means easy to achieve in any crisis Crises are by definition unpredictable and raise a wide range of uncertainties in their wake. Add the available time to respond to the uncertainty in the crises, and one may begin to fathom the complexity of the task. Lauras and Comes (2015) eloquently summarize the working conditions of decision-makers managing crises as “ill-defined goals and ill-structured tasks, uncertainty, shifting and competing goals, dynamic and continually changing conditions, actionfeedback loops (real-time reactions), time stress, high stakes, multiple players, organizational goals and norms”. Crisis managers can use some help.
Enter AI
FACULTY CORNER
The use of predictive models to do risk analysis is common knowledge today. From our daily newsfeeds to predicting tomorrow’s weather, machines are finding patterns in past data to provide the probabilities of likely future events. While these models do present a very obvious use case for artificial intelligence in crisis management, this article focuses on less obvious ways in which artificial intelligence can improve crisis management Specifically, it is important to understand the role artificial intelligence can play in collaborative efforts to organize, initiate, and execute responses to a crisis.
COLLABORATIONANDARTIFICIAL INTELLIGENCE
In a recent article, Benaben et al. (2020) proposed a framework for collaborative situational modeling. Essentially, the model organizes information across four dimensions in the form of a graph. The nodes of the graph are concepts from these dimensions and the edges in the graph are the relationships between these concepts. The four dimensions of the crisis are Context, Objectives, Partners, and Behavior. Within each dimension, concepts are defined
For example, the Context has concepts such as environment, environment components, characteristics, opportunities, and threats. Relationships can be defined for these concepts such as a threat may be linked to a characteristic of the environment or its components Similarly, the environment contains environmental components. Thus, using these concepts and relationships between concepts within and between dimensions, one can build a complete model for the collaborative efforts driving crisis management.
Once the collaborative situation metamodel, described above, is developed, it paves the way for effective collaboration when crisis strikes. It can be used to implement the protocols, recruit and mobilize crisis workers, procure and mobilize materials, monitor the context, and evaluate the progress in crisis management. The base model may be augmented with additional artificial intelligence tools such as computer vision and natural language processing to improve monitoring of progress and communication between stakeholders
FACULTY CORNER
ARTIFICIALINTELLIGENCEANDRISK ANALYSIS
Although it has become common knowledge today, this article would be remiss not to mention the more obvious uses of artificial intelligence in crisis management. Risk analysis involves a continuous assessment of the likelihood of a crisis.
Such assessment is done using some kind of predictive modelling. Artificial intelligence technologies, especially the models using neural networks and deep learning, have improved the performance of the prediction and classification models significantly. As a result crisis managers have a better ability to prevent crises
In summary, artificial intelligence augments the ability of human society to handle crises. Using this technology can not only improve our ability to prevent crises, but it can also enable us to collaborate more effectively to respond to those crises that cannot be averted. While the possibility of using artificial intelligence in different aspects of crisis management should be continuously explored, it is important to remember that it is just another technology that can merely help us to better what we have been doing all along albeit less efficiently and less effectively. The use of predictive models to do risk analysis is common knowledge today. From our daily newsfeeds to predicting tomorrow’s weather, machines are finding patterns in past data to provide the probabilities of likely future events While these models do present a very obvious use case for artificial intelligence in crisis management, this article focuses on less obvious ways in which artificial intelligence can improve crisis management. Specifically, it is important to understand the role artificial intelligence can play in collaborative efforts to organize, initiate, and execute responses to a crisis
AI’S ROLE IN CRISIS MANAGEMENT
Effective crisis response has emerged as a key component of global resilience in an increasingly uncertain world. The application of artificial intelligence (AI) is transforming crisis management, whether it be for handling complicated geopolitical events, public health emergencies, or natural disasters AI is influencing a future where resources are optimized, recuperation is accelerated, and lives are saved by utilizing the power of real-time decisionmaking, predictive technology, and advanced analytics This document provides a data-enriched perspective on AI applications in crisis management, supported by relevant statistics, real-life examples, and sources.
PREDICTIVEANALYTICSAND MODELLING:THEPOWEROF PREDICTION
By anticipating possible calamities, such as weather-related incidents or public health outbreaks, artificial intelligence's predictive powers facilitate proactive crisis management The United Nations Office for Disaster Risk Reduction (UNDRR) estimates that AI-powered early warning systems might avert up to $1 billion in annual economic losses and save 23,000 lives Early warning systems use real-time data analysis to predict disasters and give vital advance notice for resource allocation and evacuation.
Dhriti Khurana
MDI Gurgaon, PGDM-HRM (2024-26)
Infographic illustrating reductions in hurricane forecasting errors (2017-2022) and economic savings through early warning systems. (Source: NOAA, UNDRR)
STUDENT CORNER
Real-Life Example: AI-driven models during Hurricane Florence (2018) provided precise flood predictions, enabling timely evacuations and saving thousands of lives in North Carolina.
AI is excellent at analyzing enormous amounts of data in real-time and producing insights that can be put to use Making decisions quickly might make the difference between life and death in emergency situations. Through social media trend analysis and sensor network monitoring, artificial intelligence (AI) solutions offer a thorough awareness of changing circumstances, assisting decision-makers and first responders in efficiently allocating resources.
Real-Life Example: In the 2015 Nepal earthquake, AI systems analyzedover 20 million tweets, efficiently directing resources to urgent needs.
Sophisticated algorithms minimize waste and inefficiency by prioritizing resource distribution based on real-time assessments For instance, AIpowered systems can evaluate satellite photos to determine damage and allow for the precise distribution of supplies.
Real-Life Example: After Hurricane Harvey, the American Red Cross utilized AI to optimize relief distribution, ensuring critical supplies reached affected areas promptly.
Managing a crisis frequently entails balancing scarce resources while under tremendous pressure. By allocating resources optimally, AI makes sure that assistance reaches the most vulnerable.
Platforms with AI capabilities improve the accuracy and speed of information sharing. Whether via automated alert systems or natural language processing technologies, artificial intelligence (AI) makes sure that important information reaches stakeholders promptly and accurately. Furthermore, in times of crisis, large language models (LLMs) like LLAMA2 are essential for assessing public opinion and combating false information
Real-Life Example: During the COVID-19 pandemic, WHO utilized AI chatbots to disseminate accurate health information to over 10 million people worldwide
STUDENT CORNER
Bar graph showing evacuation compliance rates before and after automated alerts. (Source: Government of Australia)
ACCELERATINGRECOVERYEFFORTS: POST-CRISISRECOVERY
Planning for a sustained recovery requires the use of AI. AI assists businesses in identifying areas that need immediate attention and developing longterm resilience strategies by evaluating post-crisis data According to research, areas that use AI tools to rebuild after a disaster recover 20% more quickly (OECD, 2022) Making well-informed decisions throughout recovery is facilitated by the capacity to process contextual and historical information.
Real-Life Example: After the 2011 Fukushima disaster, AI tools helped Japan efficiently rebuild infrastructure and allocate resources to affected areas.
ADDRESSINGCHALLENGESANDETHICAL CONSIDERATIONS
Although AI has the potential to revolutionize crisis management, there are several obstacles to overcome, such as algorithmic biases, ethical dilemmas, and data privacy difficulties Building stakeholder trust in AI deployment requires ensuring accountability and transparency Furthermore, to ensure that AI tools function dependably in real-world scenarios, thorough testing and validation are required.
Real-Life Example: During Hurricane Katrina, disparities in data collection highlighted the importance of addressing algorithmic bias in resource allocation.
COLLABORATIONFORSUCCESS
Governments, businesses, and humanitarian organizations must work together to successfully apply AI in crisis management. Collaborations guarantee that a range of stakeholders contribute to the development of AI technologies, promoting creativity and inclusivity. These partnerships increase the efficacy of AI-driven solutions by pooling resources and expertise.
STUDENT CORNER
AI's contribution to crisis management will only increase in importance as it develops further To fully realize AI's promise, strategic investments in infrastructure and research are necessary The world community can use AI to make the planet safer and more robust by tackling ethical and technological issues. With new technology like drones reducing reaction times by 40%, it is predicted that investment for AI in crisis management will reach $1.3 billion by 2025.
Real-Life Example: AI-powered dronesin Turkey’s 2023 earthquake responsereduced search- andrescue times significantly, locating survivors in record time.
CONCLUSION
The use of AI in crisis management is not only necessary, but also an improvement Stakeholders can guarantee that AI maximizes its capacity to save lives in emergency situations by tackling current issues and utilizing data-driven innovations. A future where technology and people come together to create resilience against global crises is promised by the field's ongoing expansion.
STUDENT CORNER
AI FOR RESILIENCE PREPARING FOR UNCERTAIN FUTURE
In the interwoven and ever-changing framework of global trade, supply chains are the blood vessels of every business activity However, the COVID-19 pandemic and other geopolitical conflicts have made the companies as well as the governments realize how fragile their supply chains really are. Events such as the chip shortage, port traffic jams and the blockage of the Suez Canal have all displayed a bitter reality; the supply chains that were designed for ‘just-in-time’ delivery systems fail when confronted with ‘just-in-case’ requirements.
In this period of uncertainty, the adage ‘Failing to prepare is the same as preparing to fail’ appears most appropriate. Leading firms are meeting this challenge, changing their supply chains from weak links to strong networks. These companies are embedding resilience into their operations- by integrating AI, embracing the circular economy principles, and adopting various sourcing strategies- that allows them to emerge stronger than before
At present supply chains are working in a much riskier environment where uncertainty is brought about by economy volatility, politics and byincreased competition in the market.Some of the recent developments include: Semiconductor Shortage (2021) - Global demand as well as supply chain disruptions resulted in losses in the automotive market of around 210 billion US dollars during the year 2021.
IIMLucknow
Suez Canal Blockage (2021) – This has been the case just with one ship being stuck for six days, thus showing how the upset of one single element can reverberate throughout the whole supply chain.
Evita Bansal
STUDENT CORNER
Port Congestion (2023) - Cities like Shanghai and Los Angeles have experienced major disruptions because of the burgeoning e-commerce industry and the shrinking workforce. This is leading to increased costs and longer delivery times. These disruptions have served to be resilient scenario as companies now have shifted their operational model from being reactive to proactive measures.
Redundancy: A strategy of combining a number of suppliers together with localized production clusters has the potential to ensure consistent output even where major suppliers cannot deliver. Beyond buildingresilience, companies must also rethinktheir operating models to embrace sustainability and circularity.
The notion of circular economy incorporates notions that are not otherwise linked purely with resource recovery, namely those that help alleviate issues embedded in the supply chain. It moves away from a typical linear model and is enhanced with sustainability, resource longevity and restoration
KeyPractices:
Remanufacturing and Recycling: Enabling the extension of the life cycle of the current product for the purpose of reducing the new raw material input requirement.
Regionalization: The local construction of material recuperation and retrieval systems and loops to avoid wastage and improve efficiency
Resilience is no longer just a catchphrase; it has become a way of doing business The ability to build an efficient supply chain is built on three key elements:
Visibility: IoT sensors and control towers provide real-time insights into inventory levels and supplier activities
Flexibility: Flexible manufacturing and agile logistics networks allow the swift pivots in response to demand shifts or disruptions.
Design for Sustainability: Designing productsthat increase recyclingand repair convenience so that the products are made to be more eco-friendly.
STUDENT CORNER
PROACTIVERISKMANAGEMENT:A PARADIGMSHIFT
Organizations are shifting from reactive to proactive risk management, where threats are predicted before they escalate. Such organizations move from active defense to reactive risk management, as threats are always predicted before they blow out of proportion.
CoreStrategies:
Scenario Planning: Digital twins and Monte Carlo simulations devise the possible threats, so measures can be applied before damage is caused.
Supplier Diversification: Multi-sourcing strategies create minimizes the vulnerability of different geographical areas
Dynamic Inventory Models: Safety stocks developed with the aid of AI will ensurea right mix between availability and operational cost management
Example: By applying multi-sourcing strategies, General Motors met the demand for semiconductors so that production had minimal adverse effects
AGILELOGISTICSNETWORKS:SPEED MEETSSCALABILITY
Agility is the linchpin of resilient logistics. It allows businesses to adapt swiftly, thereby maintaining service levels even under duress.
InnovationsDrivingAgility: Omnichannel Fulfilment: Looking at customer demands by offering both online and offline services seamlessly.
AI-Driven Routing: It enhances efficiency and minimizes delays through optimization.
Micro-Fulfilment Centres: Warehouses that are positioned strategically shorten lead times and enhance last-mile delivery.
Example: Amazon increased the number of microfulfillment centers in 2022 and the delivery time decreased by 20% This shows the importance of proximity in logistics-delivery systems.
GAME-CHANGINGTECHNOLOGIES:
Supply chain technologies are developing with smarter chains being more visible, resilient, and able to make precise decisions effortlessly
IoT Sensors: Extremely useful in monitoring moving equipment and hence minimizing chances of damage, loss, and spoilage.
Blockchain: A system that helps to provide clarity about the supply chains which addresses the needs of the critical sectors like Pharma that demand traceability and provenance.
AI Forecasting: A discipline whose primary objective is proactive planning and forecasting demand and embargoing potential supply chain bottlenecks displaying great precision.
In 2023, Unilever showed that a forecasting system based on AI could reduce excess inventory by 15% while maintaining service quality.
STUDENT CORNER
THEWAYFORWARD:BUILDINGSUPPLY CHAINSTHATWITHSTANDTHETESTOF TIME
Resilience has gone from corporate goals to outstanding competitive advantage. One of the best investments is in predictive analytics that incorporates AI-IoT to make actionable insights, enable fast decision-making, and give the company data-driven strategies.
Operational flexibility is achievable by using circular economy principles, which at the same time minimizes environmental impact The businesses that put resilience at the core of their activities will not only weather the storms but also harness opportunities offered by the prevailing global uncertainties ‘Be prepared, and fortune will come’ is how the adage goes. Embedding resilience and agility into the operations would not only enable businesses to withstand future disruptions but also transform challenges into opportunities for growth and sustainability.
AI-POWERED ORGANIZATIONAL AGILITY THRIVING
IN A DYNAMIC WORLD
"Organizational agility is no longer just a competitive advantage—it's a survival strategy "
THENEEDFORAGILITYINACOMPLEX WORLD
Organizational agility has been more than a competitive advantage; it has become a survival strategy in today's fast-changing business landscape. The COVID-19 pandemic, geopolitical tensions, and rapidly changing market dynamics have proven that businesses must pivot quickly and intelligently to survive.
Through this tool, the organizations start putting strategic adaptive decisions into action They are then proactive and decisive in handling hindrances. Due to this backdrop of turbulence among markets at an international level, it offers them real-time exposure, predict analysis, and dynamic adaptability of the strategy in these ventures.
STUDENT - MBA(BA)
IIM AMRITSAR
STUDENT CORNER
TRANSFORMATIVEROLEOFAIIN ORGANIZATIONALAGILITY
Organisational agility is essentially the strength of AI whereby it can process an immense volume of complex data in real time, yielding actionable intelligence to assist dynamic decisions
For instance, through the use of advanced machine learning algorithms and predictive analytics, firms can apply early warning systems that identify disruptions in advance, specify the risks involved, and advise on how best to intervene at strategic levels well before problems intensify. That is what keeps companies resilient in their operations and with competitive advantage, even in turbulent economic times.
AIINWORKFLOWREIMAGINATIONAND OPERATIONALOPTIMIZATION
AI extends beyond analytics and redesigns the workflow, resource allocation, and response to new challenges in an organization Using intelligent automation and cognitive computing, companies can redefine real-time operational procedures, deploy workers optimally,and design fluid organizational structures that quickly respond to the evolving needs of the market.
This high-tech allows the organization to get out of its hierarchical constraints and develop more responsive and innovative cultures.
"AI shifts organizational agility from being a survival mechanism to a platform for proactive innovation "
STRATEGICPLANNINGWITHAI-DRIVEN INSIGHTS
The use of AI-driven insights fuels holistic views that fuel strategic planning, incorporating the rich mix of market trends, consumer behavior, technological disruptions, and geopolitical dynamics.
Comprehensive, multi-dimensional perspectives that build on context and foresight enable leadership teams to learn how to build strategy that goes beyond just the anticipation and mitigation of risk, thereby stating fresh opportunity.
STUDENT CORNER
BUILDINGANAI-ENABLEDCULTURE
The fast-evolving global economy is not a choice; rather, it has been an enabler of resilience and longterm success through AI.
The AI will be there to enable the reimagined workflow, enhance strategic planning, and encourage innovation in the culture of businesses
Businesses can, with such help, cut through uncertainty and uncertainty, to be better positioned businesses for the future-those that survive but most importantly, thrive-unlocking possibilities that redefine what is possible in an increasingly interconnected world
To fully exploit the potential of AI, organizations must undergo a transformation that is holistic in nature. This transformation is not just technological but also cultural, with data-driven decisions, continuous learning, and adaptability.
The main elements of this shift are:
Investment in AI infrastructure
Upskilling the workforce.
Building ecosystems that unite human creativity with AI
This will make sure that businesses are ready for an increasingly complex and uncertain global landscape
AFUTURE-READYBUSINESS ECOSYSTEM
AI technologies redefine the limits of strategic flexibility by providing smart insights to facilitate fast adaptation and fostering a culture of continuous innovation
Challenges become opportunities with AI-driven strategy implementation Organizations can convert uncertainty into sources of competitive advantage in a robust adaptive ecosystem ready for the next wave of disruption, thanks to AI.
EXPERT CORNER
RESILIENT AI
PREPARING FOR UNCERTAIN FUTURES
"Resilient AI refers to robust, adaptable systems designed to withstand and respond to unforeseen challenges"
Artificial Intelligence (AI) has emerged as a transformational force that is changing industries, economies, and societies at a rapid pace. The more uncertain the global landscape, the more resilience in AI has become important.
Do you know about The Great Depression from 1929to1939?
This was one of the worst economic collapses, bringing mass unemployment, poverty, and social unrest. Could AI have at least mitigated this scenario, or could it have at least minimized the damage? We'll look at that below, but first, let's look at how this resilience can build human readiness for uncertain futures i e by application in crisis management, predictive analytics, scalability, and supply chain innovation
DIRECTOR OF PRODUCT MANAGEMENT
It will save many lives and resources if a timely and effective response is made in cases of crisis, like natural disasters, pandemics, or geopolitical conflicts.
AI has shown that it can actually help improve strategies related to crisis management. For example, during the COVID-19 pandemic, AIpowered tools tracked the spread of the virus, identified hotspots, and optimized resource allocation.
EXPERT CORNER
AI also enables real-time decision-making during disasters. Machine learning algorithms can analyze satellite images to give a damage assessment, areas of safety, and lead rescue operations. As crises get more complex, the resilient AI must remain adaptable, reliable, and transparent for trust and effectiveness to be achieved.
HARNESSINGPREDICTIVEANALYTICS FORASECUREFUTURE
Predictive analytics allows an organization to look ahead of the curve and take proactive decisions regarding possible disruptions. AI systems, analyzing massive datasets, find patterns and trends that might otherwise go unnoticed.
For example, predictive models allow governments to forecast the impacts of climate change and take preventive measures and allocate resources accordingly
In healthcare, predictive analytics can detect disease outbreaks before they worsen, allowing for timely interventions. This ability gives decisionmakers the confidence to make decisions in uncertain environments.
SCALABILITYANDFLEXIBILITY:THE BACKBONEOFRESILIENTAI
Resilient AI systems should be scalable and flexible in order to be able to adjust to fluctuating demands. Scalability ensures that solutions provided by AI may manage increasing workloads without a performance impact.
For instance, AI-based cloud computing services enable an organization to surge the power of its computing during emergency periods, such as natural disasters or economic downturns Flexibility helps systems of AI adapt to novel situations or datasets without needing deep reconfiguration
EXPERT CORNER
SUPPLYCHAININNOVATION:AIIN ACTION
Global supply chains become more susceptible to disruption by trade, war and natural disasters. Artificial intelligence-driven innovation is part of the recipe in building supply chain resilience. Machine learning algorithms optimize logistics, predict demand fluctuations, and even spot vulnerabilities real-time data related to shipping routes, warehouses, and retailers provides the ability to manage inventory efficiently.
For instance, in the COVID-19 pandemic, AIpowered platforms assisted companies in real-time to reroute supplies, find alternative suppliers, and delay shipments during this period. Robust AI systems can also do "what if" scenarios as preparation for eventual disruption so businesses can create their contingency plans.
LOOKINGBACK:COULDAIHAVEHELPED DURINGTHEGREATDEPRESSION?
Now that we have gone through the topics, let us go back to our question left at the beginning Could AI have prevented or limited the damage broughtaboutbytheGreatDepression?
I am quite sure you would know the answer by now If AI did exist back then, some of the following could have helped:
1. Predictive analytics would have probably simulated how various economic situations could play out and alerted for a potential fall.
Machine learning models could analyze financial data to detect unsustainable trends in the markets and warn the investors and the regulators
2. The central banks would have been using AI to check the economic indicators for stabilization and thereby adjusted the interest rate or money supply to keep things in order. 3
AI tools like Natural Language Processing (NLP) could have analyzed the public sentiment to give insight into social unrest or loss of consumer confidence, and the governments could have acted proactively to restore the trust.
CONCLUSION:BUILDINGARESILIENT TOMORROW
In conclusion, the future is not so sure anymore, but investment in resilient AI is no longer optional-it's a necessity Applying AI's strength in crisis management, predictive analytics, scalability, and supply chain resilience enables building t th t d ti d i h
EXPERT CORNER
RESILIENT AI PREPARING FOR UNCERTAIN FUTURES
"Resilience is no longer a luxury—it's a necessity"
THENEEDFORRESILINTAIINSUPPLY CHAINS
In today's volatile business landscape, the question is how enterprises can maintain operational continuity when facing disruptions ranging from geopolitical crises, local events, competition, and channel disruptions to the black swan events like natural disasters
The answer would lie in the transformative power of Machine Learning and AI, particularly within supply chain management.
Rahul RanjanSrivastava
FOUNDER INTELLIMARK AI
EXPLAINABLEAI:BUILDINGTRUSTIN AUTOMATION
Explainable AI (XAI) brings another layer of sophistication. The predictive and prescriptive capabilities of AI are truly impressive, but decision-makers often do not find recommendations satisfying if they don't understand the transparency behind them.
EXPERT CORNER
Explainable AI bridges that gap by showing logic and data behind the output For example, if an AI system is suggesting a reallocation of inventory, XAI can show how regional demand fluctuations or supplier delays influenced that recommendation. This type of transparency can bring trust and ensure more informed decisions.
AIINACTION:REAL-WORLDSUCCESS STORIES
A multinational CPG firm used AI to optimize its supply chain operations. When there was an unforeseen surge in demand due to a severe heat wave in one of the European countries, its AI systems recognized that there was a need for more bottlenecks in real-time, provided alternative shipping routes, and focused on high-demand SKUs for production. This reduced the lead time by 25%, and all the shelves stayed well-stocked despite the all-encompassing disturbances. This illustrates how AI solutions not only alleviate shortterm pains but also bolster long-term robustness.
DEMANDSENSINGANDFORECASTING: PIVOTINGWITHPRECISION
In addition to long-term forecast, AI promotes resilient supply chains by analyzing real-time data streams ranging from point-of-sale transactions and weather updates, to social media activity. Through demand sensing, businesses can detect sudden changes in demand and make a turn accordingly; they can allocate resources in realtime on the fly, without carrying much inventory. Hence, companies can dynamically optimize their supply chains toward minimal out-of-stock situations and yet carry minimal stock. Responsive yet efficient positioning is a definition of AI in supply chains.
One of the severe cyclones struck the Tamil Nadu coast in 2024. Several manufacturing companies operating their plants in Chennai and Pondicherry saw the sudden shutdown of manufacturing facilities and loss of access to critical facilities and capacity.
EXPERT CORNER
Businesses from the automotive and consumer goods sector had to redesign their supply chains on the fly to stay in business. Companies identified alternative supply routes, critical shipments, and made changes in production schedules using AIpowered solutions These systems enabled them to respond within hours, thus averting service level drops and disruption This is a very practical example of how AI can convert the response to crisis from reactive scramble to a proactive approach.
The predictive power of AI amplifies its usage in supply chain management. Advanced analytics allow businesses to predict potential risks and prepare ahead and enhance their pricing, media, manufacturing, demand, supply, and logistics planning.
Companies using such predictive models have seen logistics costs reduced by as much as 30% and response times to disruptions improve by 40%. AI provides demand forecasting for even months ahead based on historical trends, seasonality, and other macroeconomics, ensuring that production and other supply chains are in step with the projected needs to eliminate waste and make best use of resources.
Generative AI enabled real-time insights is a major unlock for business users, because they are not dependent on the analytics teams anymore for advanced analyses Imagine: a supply chain manager asks: "What's the current level of inventory by Supplier A?" AI systems fetch and analyze these data in near real time; hence, appropriate adjustments can quickly be made given the shifting nature of demand and supply. Thus, supply chain management becomes no longer a reactively driven activity but a proactively driven discipline
"AI transforms crisis response from a reactive scramble to a proactive strategy "
The path to AI-driven resilience is not without its challenges Quality data remains the foundation of a successful AI system, but most organizations are dealing with fragmented or inconsistent datasets. In addition, implementing AI solutions requires investment in both technology and talent. Teams need to be trained not just to use AI tools but to interpret and act on their insights effectively.
Still, it has high returns. Implementations of AI with a supply chain will have numerous cases where investment recouping has accelerated its strategy becomes successful over a toughly competitive environment.
EXPERT CORNER
"The
message is
clear:
Looking ahead, the quantum computing and edge AI promise to take the supply chain to a higher level of resilience Quantum computing promises to enhance exponentially the speed and complexity of data processing in AI, at the source of data collection.
AI is no longer optional It is a critical enabler of resilient, future-ready supply chains As the business world becomes more unpredictable, those who leverage AI will not just survive but thrive The question is not whether to adopt AI, but how quickly you can integrate it into your operations "
CLUB ACTIVITIES
W-INNINGS
The intra-college fantasy cricket competition offers a thrilling opportunity for students to engage with the sport in a unique and interactive way Participants can create and manage their own virtual cricket teams by selecting real players from various leagues and tournaments. This competition takes place on a dedicated website that not only allows students to showcase their cricketing knowledge and strategic thinking but also provides a platform for friendly rivalry among peers. As they track player performances and make critical decisions on team composition, participants experience the excitement of cricket management, all while fostering a sense of community and camaraderie within the college.
The intra-college competition tests participants' Excel proficiency, focusing on speed and accuracy. Competitors will complete complex tasks involving data manipulation, formula creation, report generation, and advanced features like pivot tables and data visualization. The event aims to showcase individual skills while promoting collaboration and learning among students through real-world scenarios requiring quick thinking and attention to detail.
CLUB ACTIVITIES
NEW EVENT
PROMPTOPIA
Promptopia is a dynamic competition designed to test participants’ creativity, critical thinking, and AI expertise Through engaging challenges, students explore the power of AI to craft innovative ideas, reimagine possibilities, and develop forwardthinking solutions
This event encourages participants to push boundaries, blending technology and imagination to tackle real-world scenarios with a fresh perspective
CALL FOR ARTICLES
The Call for Articles competition challenges students to showcase their creativity and writing prowess through impactful submissions. Open to both inter-college and intra-college participants, it encourages exploration of diverse ideas and themes. The event provides a platform for students to refine their writing, share unique perspectives, and engage in meaningful intellectual expression.
VYAKRITI THE IT & ANALYTICS CONCLAVE CONCLAVE
IIM Amritsar’s 4th edition of annual IT & Analytics conclave, ‘Vyakriti’ focused on the theme "Digitization and Trust: A Double-Edged Sword Shaping Our Future" which explores the dual nature of digital transformation. The event explored how businesses can leverage innovations while managing the crucial balance between growth and trust.
OUR TEAM
Sumedha Shruti Ansh
Dinesh j Radhika
Siddhant
Ishan
GAMES
PUZZLE
ACROSS
3 An unexpected event that disrupts normal operations.
5 The process of rebuilding after a crisis 6 AI-powered models used to predict future outcomes
7. AI technique that nds patterns in past data for decision-making
9. The concept of increasing system adaptability and redundancy
10. Using data to foresee potential disruptions
DOWN
1 The ability of a system to withstand and recover from challenges.
2 A strategy focusing on sustainable recovery in supply chains.
4 A machine-learning tool that processes human language.
8 Technology enabling machines to mimic human intelligence
QUIZ
GAMES
1.
WhatdoesAIforresiliencefocusonachievinginuncertainfutures?
Reducing AI development costs a
Enhancing systems to adapt and recover from challenges b.
Eliminating human oversight in critical systems c.
Improving gaming performance d.
2.
WhichofthefollowingisapotentialuseofAItoincreaseresilience?
Predicting natural disasters and planning response strategies a.
Automating routine office tasks b.
Increasing sales in e-commerce platforms c.
Replacing all human decision-makers d.
3.
HowcanAIsupportorganizationsduringunexpectedcrises?
By automating physical repairs to infrastructure a
By analyzing data to provide actionable insights quickly b.
By reducing the complexity of governance structures c.
By halting operations temporarily d
4.
Why is resilience important when implementing AI systems in real-world scenarios?
To handle sudden changes and unpredictable events effectively a.
To reduce the computational cost of AI models b
To prevent the need for data cleaning c.
To avoid integrating AI with existing systems d.
5
Whatroledoesmachinelearningplayinenhancingresilience?
It allows AI systems to continuously improve and adapt based on new data. a.
It eliminates the need for training datasets. b.
It restricts the AI system to predefined scenarios. c.
It focuses solely on visual recognition tasks. d.
"Resilience in AI isn't about avoiding challenges, but preparing to meet them with agility, precision,andstrength."
~AndrewNg
THEIMITATIONGAME THE POWER OF DATA AND HUMAN INGENUITY
"SOMETIMES IT IS THE PEOPLE NO ONE IMAGINES ANYTHING OF WHO DO THE THINGS NOONECANIMAGINE."
The Imitation Game does more than tell the story about Alan Turing and his mission to crack the Enigma code during World War II; it teaches insights about data analytics, pattern recognition, and what lies behind technological breakthroughs. Out of a cipher having millions of combinations, Turing easily understood that a brute-force method was not going to cut it. Instead, he was using climatic reports among other data to identify patterns, pioneering techniques that have become foundational in modern data science and analytics. What makes the movie so powerful is that it achieves a balance of showing Turing's intellectual brilliance as well as the human struggle he faced. Despite its revolutionary spirit, the movie more so leads the audience to understand the resistance facing Turing from his colleagues and superiors They could not swallow this unorthodox approach. This is a common problem with data analysts today: convincing people that the results of datadriven solutions are better than intuition or tradition.
The second major theme is teamwork. Turing did not work alone; he collaborated with engineers, linguists, and other experts to develop the Bombe machine. This illustrates that the most complex problems are best solved through collaboration, where diverse skills come together to create effective solutions. Success in today's data-driven business world often depends on the synergy between data scientists, business analysts, and decision-makers
Ultimately, The Imitation Game is not the technology but the people and the way around it One learns that having faith in numbers, questioning a long-standing assumption, and then working together all contribute to successful innovation And with his work as the foundation for the data-centric world we now live in, Turing's life reminds us all that it is the human who makes the machine, from war to business, and vice versa.
