Tag: Artificial Intelligence

Artificial Intelligence and the Future of Railway Transportation: Promise, Perils, and Pathways


“The railway industry, one of the oldest enablers of industrial transformation, now stands on the cusp of another revolution—this time powered by Artificial Intelligence.”

From the steam engines of the 19th century to today’s high-speed trains, railways have been symbols of innovation. Now, as we move deeper into the 21st century, Artificial Intelligence (AI) promises to redefine how rail networks are managed, how trains are operated, and how passengers experience travel.

But like every major transformation, the rise of AI in railway transportation is not without its challenges. The genesis of this article stems from the fact that I started my work life in the Indian Railways Service of Mechanical Engineers nearly half a century back. More recently when I was doing a Wharton Business School program on AI applications, the idea of this piece came to me.

  In this article, I have tried to explore the promise, perils, and pathways of integrating AI into one of the most vital sectors of modern infrastructure, particularly for a dense population country like India.

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🚄 The Promise: Efficiency, Safety, and Customer Experience

AI does hold considerable potential to make a high-density transportation mode like the Railways smarter, safer, and more responsive. Here are just a few areas where the promise can be seen:

  • Predictive Maintenance: Machine learning models can analyze vibration, temperature, and operational data to detect potential failures before they occur—reducing costly downtime and enhancing safety.

Predictive maintenance, powered by sensor analytics and machine learning, are reducing unplanned downtime by up to 30% in Germany (Deutsche Bahn). In India, AI-equipped SMART coaches can now monitor vibrations, structural wear, and staff behavior, leading to substantial maintenance savings and enhanced safety.

  • Optimized Scheduling and Routing: AI can dynamically adjust train schedules based on real-time data—weather, demand, or disruptions—minimizing delays and maximizing throughput.

In dense rail networks like India’s or Japan’s, such precision translates into better asset utilization, optimized route rationalization, and more efficient capacity deployment.

  • Safety and Reliability: AI enhances safety through real-time monitoring and automated diagnostics. Computer vision systems are today identifying track defects, unauthorized access, and obstacles with over 90% accuracy. AI-powered drones can now inspect tracks and overhead equipment faster than traditional crews, improving both safety and inspection efficiency.

Train operations benefit from AI-assisted driver alertness monitoring and automatic braking recommendations based on track conditions. These advancements reduce human error—still a leading cause of railway incidents.

  • Passenger Experience and Multimodal Connectivity: In many places, AI-driven chatbots and journey planners have started offering personalized updates, route alternatives, and digital ticketing, improving passenger convenience. Integrating railways with buses, metros, and even micro-mobility options via AI platforms is enabling seamless urban mobility. In megacities, this creates rail-centric multimodal ecosystems where trains form the backbone of transportation.
  • Smart Ticketing and Crowd Management: With the use of computer vision and behavioural analytics, Railways can monitor crowd flows in stations and adjust boarding strategies in real time, improving passenger experience and safety.
  • Energy Efficiency: AI-powered driving systems can optimize acceleration and braking, saving energy and reducing emissions—a critical benefit as Railways strive to meet sustainability goals.
  • Environmental Sustainability: AI can help Railways fine-tune energy use by adjusting acceleration, coasting, and braking in real time, reducing fuel and electricity consumption.

When paired with green innovations like hydrogen-powered trains—such as Germany’s Coradia iLint and the US’s ZEMU—railways can become even more climate-friendly, especially in non-electrified regions

In short, AI can turn data into decisions—at scale and in real time.

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⚠️ The Perils: Bias, Job Displacement, and System Vulnerabilities

Yet, for all its promise, AI also brings forth complex challenges that Railway systems must navigate with care. Let us try and understand what these are.

  • Algorithmic Bias: AI systems are only as unbiased as the data they’re trained on. In Railways, there is a high chance this could lead to unfair prioritization of certain routes or populations. This is because of historical inequities that are embedded in the stored data.
  • Job Displacement: As AI would continue to automate driving, monitoring, scheduling, maintenance and customer service, several roles would become redundant. While this may lead to job displacement in the short term, it will also create new roles in data science, system integration, and AI governance.

This is where visionary leadership would come in to shift focus and resources relating to reskilling, transitioning and to answer the more fundamental question about the human cost of automation.

  • High Implementation Costs: AI deployment demands hefty upfront investment in digital infrastructure—sensors, data platforms, training, and cybersecurity. For developing economies like India, justifying these expenses against long-term gains poses a financial and strategic challenge. This is also where a visionary leadership needs to come in.
  • Cybersecurity Risks and systemic reliability: Risks would surely go up as a more digitized and AI-integrated Railways system would become an attractive target for cyberattacks. A breach in an AI-driven control system could have dangerous and far-reaching consequences.

Reliance on AI systems thus must be balanced with robust fail-safes by strong governance and redundancy protocols.

  • Public Trust and Ethics: AI in public infrastructure must be transparent and accountable. Otherwise, trust erodes—especially if systems malfunction or make controversial decisions without human oversight.

The above risks underscore the need for careful design, regulation, and human-in-the-loop systems.

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Railways vs Other Transport Modes: A Comparative Snapshot

FactorRailways (AI-enhanced)Road TransportAir Transport
CostLow per ton/km for freightHigh due to fuel and laborHighest operational cost
Environmental ImpactLow (electrified or hydrogen)High (diesel trucks)Very high (jet fuel)
ConvenienceIdeal for dense corridorsFlexible last-mile serviceSpeed for long distances

Railways, strengthened by AI, would thus remain the most cost-effective and sustainable mode for high-density freight and passenger volumes. Hydrogen trains further extend these advantages to non-electrified routes.

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🧭 The Pathways: Navigating the AI Railway Future

So, how can the Railways harness AI’s promise while avoiding its perils? The following thoughts come to mind.

1. Adopt a Human-Centric Approach: AI should always be viewed as an Enabler, not a Replacer of human expertise.  Railways systems should ensure the centrality of human judgment, ethics, and oversight; this becomes particularly important in safety-critical functions.

2. Invest in Digital Infrastructure: To unlock AI’s power, the Railway systems would need high-quality data, real-time connectivity, and interoperable platforms. One can well envisage that Digital twins, Edge computing, and IoT-enabled trains would form the backbone of AI-enabled rail networks in the future.

3. Prioritize Ethics and Explainability: AI based decisions need to necessarily be transparent and explainable. Regulators and the Railways need to work together to ensure AI systems meet public standards of fairness, accountability, and non-discrimination.

4. Reskill and Redesign Work: The rise of AI urgently calls for a parallel investment in people—training them to work with AI tools, interpreting machine insights, and contributing to higher-value tasks. Railway jobs and functions need to evolve, not disappear.

5. Collaborate Across Sectors: The Railways need tocollaboratewith the private sector vendors and suppliers, technology companies, and researchers to create standards, protocols, and governance models that ensure responsible innovation.

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🚉 Need for a New Era of Railways Leadership

Integrating AI into Railway transportation is not merely a technological shift—it’s a leadership challenge. It requires vision, ethics, inclusiveness, and a commitment to long-term impact.

As Railway systems worldwide experiment with smart stations, autonomous maintenance, and AI-based scheduling, one thing is clear: those who navigate this transformation thoughtfully will shape the future of mobility.

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Future Outlook: Smarter, Cleaner Railways

Over the next 3 to 5 years, we’ll surely witness:

  • Autonomous train operations with AI-powered dispatch and navigation.
  • Real-time dynamic pricing to optimize demand and revenue.
  • Prototypes of hydrogen-electric hybrid locomotives becoming mainstream in Europe, North America, and parts of Asia.
  • AI-enhanced simulation systems to train staff and emergency responders.

Railways stand at a unique inflection point. From my own early days in the Indian Railway Service of Mechanical Engineers, I’ve seen the disruption from steam to diesel-electric and now to AI and hydrogen. With the right investments, policy frameworks, and workforce strategies, the railways of tomorrow will be not just faster or cleaner—but smarter

Final Thoughts

The train to the future has already left the station. The question is:Are we building the right tracks for it?

If you’re working in transportation, AI, or infrastructure, or remain interested and curious about these domains, I would love to hear your thoughts. How is AI showing up in your work? What opportunities—or concerns—are you seeing? Let’s build the conversation together.

The article link, as published in LinkedIn is here: https://www.linkedin.com/pulse/artificial-intelligence-future-railway-transportation-shakti-ghosal-tcb9e

References

  1. Tang et al. (2022), “AI and Predictive Maintenance in Transport Systems”
  2. Bitdeal (2024), “Case Studies on AI in Railways: Deutsche Bahn and Indian Railways”
  3. World Economic Forum (2024), “Hydrogen Trains: The Future of Clean Mobility”

In Learning…….. Shakti Ghosal

#ArtificialIntelligence #Railways #Transportation #AIandEthics #FutureOfWork #Mobility #SmartInfrastructure #Leadership

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Is your leadership changing to suit an AI World?


Artificial Intelligence (AI) is no longer a futuristic concept—it is here, and disrupting all that we are familiar with in terms of the way we work, make decisions, and interact. As Leaders and Managers, while we might agree with this, we might not have given much thought about how we ourselves might need to change, rethink our own roles and priorities. How we as leaders need to adapt to make the most of the shift at the workplace and business perspectives taking place?

So how do we go about Shifting the Leadership Mindset?

In 2018, Satya Nadella, CEO of Microsoft, shared an interesting insight: “The role of a leader is not to be the know-it-all, but to be the learn-it-all.” This could not be truer in an AI-driven world. Leaders who cling to old ways of working—making decisions based on experience alone or holding tightly to hierarchical structures—will struggle. Instead, they need to embrace a few key shifts in thinking. I am enumerating these here.

  • From Authority to Empowerment: Picture yourself, a seasoned marketing manager who has always relied on gut instinct to craft campaigns. Now, AI-driven analytics suggest alternative strategies. Instead of resisting, you encourage your team to experiment with AI insights, leading to a campaign that doubles engagement. According to MIT Sloan Management Review (2022), businesses that integrate AI into decision-making while maintaining human oversight see greater innovation and efficiency [1]. To be an effective leader today, you need to empower your teams to harness AI rather than dictating decisions.
  • From Gut Feeling to Data-Driven Decisions: Years ago, a retail CEO might have made inventory decisions based on past sales and intuition. But today, AI predicts customer demand with astonishing accuracy. McKinsey Global Institute (2023) highlights that companies leveraging AI for predictive decision-making can outperform competitors by up to 20% in operational efficiency [2]. Take Zara, for instance, which uses AI to analyze shopping trends and adjust production in real time. As a leader, you must learn to trust AI insights while still applying human judgment where it matters most.

( c ) From Stability to Agility: When the pandemic hit, Airbnb faced a crisis. Travel halted; bookings plummeted. Instead of panicking, leadership used AI-driven insights to pivot towards long-term rentals, adapting to new customer needs. The shift kept the company afloat. Deloitte Insights (2023) emphasizes that adaptability is a critical survival skill in AI-driven businesses [3]. In an AI world, you as a leader needs to develop adaptability as a survival skill.

The Changing Leadership Context

The rise of AI is reshaping not just how businesses operate, but also how leaders interact with teams and technology.

  1. Human-AI Collaboration: Imagine an investment firm where AI scans market trends and suggests portfolio adjustments. It does not replace human analysts—it enhances their work. According to Harvard Business Review (2021), companies that use AI for augmentation rather than automation see higher employee engagement and better decision outcomes [4] You as a leader need to foster a culture where AI is seen as a tool for augmentation, not as a threat.
  • A Workforce with Different Expectations: I know of a tech startup which has hired a mix of Gen Z employees who are eager to automate repetitive tasks and seasoned professionals who fear AI might make their skills obsolete.Gartner (2022) stresses the importance of AI literacy, stating that leaders must bridge this gap to create a future-ready workforce [5]. You as a leader need to bridge this gap by promoting AI literacy and ensuring every team member understands how AI enhances, rather than replaces, his / her role.
  • Ethical Leadership in AI Use: Amazon once developed an AI-driven recruitment tool that unknowingly favoured male candidates. When this bias was exposed, leadership had to step in and scrap the project. This highlights the importance of ethical oversight.World Economic Forum (2023) notes that responsible AI governance is one of the most pressing leadership challenges today, requiring fairness, transparency, and accountability [6]. In an AI world, you as a leader must ensure fairness, transparency, and accountability in AI applications.

So, what might be the key leadership qualities needed for an AI-Driven World?

  • Digital Fluency: Take Elon Musk—while not an AI engineer, he understands AI’s potential enough to integrate it into Tesla’s self-driving technology.Stanford AI Index (2024) reports that organizations with AI-savvy leadership are more likely to succeed in AI implementation [7].  You as a leaderdo not need to code AI algorithms, you should be able grasp how AI works.
  • Emotional Intelligence: Consider Jacinda Ardern, whose empathetic leadership style resonates deeply in an increasingly digital world. AI can provide data, but only humans can provide understanding and connection.World Economic Forum (2020) highlights that empathy and communication will remain irreplaceable leadership traits in an AI-driven world [8]. As a leader, you need to always hold the perspective that, with AI handling repetitive tasks, the human touch becomes even more critical.
  •  Adaptability and Curiosity: In the 1990s, Jeff Bezos saw the internet’s potential and launched Amazon. Decades later, he embraced AI to power recommendation engines and logistics. As a leader, if you stay curious and embrace continuous learning, you will thrive in an AI-powered future.
  •  Vision and Strategic Thinking: Netflix uses AI to recommend content, but it’s the leadership that chooses to invest in original programming. As a leader, know that while AI offers insights, it is human vision that drives bold, strategic decisions.
  •  Inclusive Leadership: AI is only as good as the data it learns from. As a leader, you must ensure that diverse teams contribute to AI-driven decisions to prevent biases and improve outcomes.

Conclusion: Leadership in an AI-powered world is not about controlling technology—it’s about guiding people to make the best use of it. The leaders who will thrive are those who embrace AI, empower their teams, and balance technological advances with human insight. Just like any major transformation in history, success will not come from resisting change but from learning how to harness it.McKinsey (2023) suggests that successful AI-driven companies are those that invest not just in technology but in leadership transformation [9]. The future belongs to leaders who can blend AI-driven intelligence with human wisdom and vision.

What would you need to do to be such a leader?

In learning………………….                                                     Shakti Ghosal

References:

  1. MIT Sloan Management Review (2022), “Reshaping Business with Artificial Intelligence.”
  2. McKinsey Global Institute (2023), “The State of AI in 2023: Generative AI’s Breakout Year.”
  3. Deloitte Insights (2023), “AI & The Future of Work: The Leadership Imperative.”
  4. Harvard Business Review (2021), “Building the AI-Powered Organization.”
  5. Gartner (2022), “Top Strategic Technology Trends: AI Leadership.”
  6. World Economic Forum (2023), “AI Governance: Aligning AI with Ethical Standards.”
  7. Stanford AI Index (2024), “AI Adoption and Leadership Report.”
  8. World Economic Forum (2020), “Future of Jobs Report.”
  9. McKinsey Global Institute (2023), “Leadership in the Age of AI.”

Impact of AI on Ethics, Morality, epistemology, and ontology. The nature of human-AI relationships and the implications for our sense of Identity


First the aspect of impact of AI as a fledging technology on Ethics and Morality. This becomes important as the influence could span across various aspects of human life, society and a ‘code of conduct’ for responding to situations. Another impact could be certain unintended moral consequences arising out of AI, such as enhancing of social inequalities and taking away of human jobs.

 Secondly, the aspect of AI’s impact, or shall we say, its interrelationship with the concepts of Epistemology and Ontology. Epistemology is all about how our mind perceives reality. Our sense making of a situation provides us knowledge. AI algorithms are all about sense making and predictions. Deep Learning and neural networks might make a prediction, without any underlying explanations, which may be contrary to what we might be thinking. Ontology is the science of ‘Being’, of being something. As AI’s capability advances, it would show up as an entity with complex behaviours and responses. This prompts ontological questions about the nature of these behaviours and responses—how should we conceptualize AI’s ability to learn, reason, and interact with the world?

So, how do we determine the impact of AI on fundamental issues relating to Ethics and Morality? This aspect becomes particularly important because at the core of AI and its capabilities lies the requirement of enormous data streams. Since most of AI algorithm predictions relates to likely human behaviour and response, the data relates to humans. And this is where the fault line between usage of harvested data and the ethics and morality of using such data for AI based predictions, emerges. This is even beyond the realm of privacy laws. Fundamental questions about the consent, transparency, and control individuals have over their personal information remain.

AI, being a general-purpose technology, its use and spread is increasing exponentially in terms of organisations, individuals, and domains. This interaction of human-AI relationships is evolving rapidly, as AI technology evolves. These relationships are raising profound questions about our sense of self and identity. This becomes important aspects to deliberate upon.

Privacy Concerns: How AI systems handle personal data and influence decision-making processes can affect the consent, transparency, and control individuals have over their personal information.

Bias and Fairness: AI algorithms can perpetuate or amplify biases present in the data they are trained on, leading to unfair outcomes. Addressing bias in AI systems is crucial for upholding principles of fairness and equity, which are fundamental to moral frameworks.

Human jobs & Processes: AI technologies can disrupt job markets, potentially displacing human workers. Socio-economic considerations include ensuring a just transition for affected workers and promoting AI-driven benefits equitably.

Moral Principles & Decision Making: AI systems are increasingly being tasked with making decisions that have ethical implications, such as in healthcare (e.g., medical diagnostics and treatment recommendations) and autonomous vehicles (e.g., decisions in potentially fatal situations). Questions arise about their moral agency and whether they should be held responsible for their actions.

Epistemology and how knowledge shows up :  As has been mentioned above, AI systems increasingly play a role in decision-making processes across various domains, from medical diagnostics to legal judgments. This challenges traditional notions of epistemic authority—who or what can be considered a reliable source of knowledge and expertise.

AI’s form of existence: As AI technologies evolve, they exhibit increasingly complex behaviors and capabilities. This prompts ontological questions about AI’s nature of being —how should we conceptualize AI’s ability to learn, reason, and interact with the world?

In the above context, Executive Coach Frank Marinko and I discussed the pressing issues on the development of AI and what that means for society at large.

  • Does AI possess moral agency? 
  • How can you design AI systems to make ethical decisions?
  • Are there concepts such as moral reasoning and ethical behaviour in machines?

You can listen to our podcast on this link: https://audio.com/frank-marinko/audio/frank-and-shakti-podcast-2024-07-30-16-01-gmt-10

In Learning……. Shakti Ghosal

Unveiling The Essence of AI and Leadership in the Future


Frank Marinko and myself, both international Executive Coaches and Facilitators, grappled with this question using the critical thinking methodology, in a joint podcast. You might enjoy the discourse and the podcast link is given at the end.

If we are to deliberate on this question, we need to get to the essence of two aspects mentioned. First, AI or Artificial Intelligence as we call it. Second, ‘to lead’ which is all about Leading or Leadership.

So, what really is the essence of AI? That lies in its ability to mimic and augment human intelligence and decision-making processes using computational algorithms and data. At its core, AI systems can analyze vast amounts of data, recognize patterns, infer relationships, and make predictions or recommendations.

The important aspect to be kept in mind is AI’s ability to ‘learn’, its adaptability and the ability to improve over time. Machine learning algorithms, for example, can automatically adjust their behavior based on new data, allowing AI systems to become more accurate and effective with experience.

And when we think of the essence of Leadership, it is really all about envisioning a future which speaks to all stakeholders by addressing their concerns or satisfying some needs.  Leadership thus involves directing and coordinating the efforts so that the full potential and collective success can be realised.

With the dawn of computers seven decades back, Alan Turing had considered the question, ‘Can a machine think like a human?’ and came up with a test now known as the Turing Test. With the advent of AI, several Artificial Intelligence programs have already passed the test. The purpose of this question seems to be a deep-down threat to our unique ‘leadership ability’ that we see emanating from AI. There are concerns that AI will not only start doing complex and decision-making tasks replacing humans but in the long run go beyond human controls and frameworks.

This idea of technological obsolescence where technology renders humans obsolete, and takes over most of human jobs and work, is a concern that has been raised in discussions about AI and automation. However, we humans have creativity, empathy, intuition, adaptability, and the capacity for complex moral reasoning, which are integral to many aspects of work and life. These qualities enable humans to excel in areas such as innovation, problem-solving, interpersonal relationships, and above all Leadership.

The idea of AI achieving consciousness is another topic of much speculation and debate. Consciousness is a complex and still poorly understood phenomenon, and whether AI can truly achieve it is uncertain. Even if AI were to achieve consciousness, the process of its development would likely still involve human input and guidance. AI systems, as they exist today, are created, and trained by humans, and any future developments in AI consciousness would likely follow a similar path.

However, it is worth noting that AI can already exhibit forms of “thinking” and problem-solving that are quite different from human cognition. Machine learning algorithms, for example, can process vast amounts of data and recognize complex patterns in ways that humans cannot.

Whether AI should create its own thinking framework independent of human influence is a philosophical question with no easy answer. It raises issues of autonomy, control, and ethics. If AI were to develop its own thinking framework, it would still need to start and remain ‘biased’ by frameworks that align with human values and approach. It thus seems that in the foreseeable future, Humans would continue to lead AI, leaving aside the esoteric visions of the Matrix and Terminator movies.

To effectively lead AI development which would synergise with human development, adherence to principles such as transparency, fairness, accountability, and human-centered design would be needed. We would then be able to harness the full potential of AI while minimizing harm. These principles should serve as guardrails rather than roadblocks, helping to steer AI development in a direction that aligns with human values and promotes the common good.

Podcast link : https://audio.com/frank-marinko/audio/podcast-1

In Learning……. Shakti Ghosal