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AI for Banking Industry in 2026: From Experimentation to Integration

AI for Banking Industry in 2026: From Experimentation to Integration

The financial world is standing on the precipice of a new era. For the past few years, artificial intelligence (AI) has been the subject of countless pilot programs, proof-of-concepts, and boardroom discussions. But as we look into 2026, the narrative is shifting dramatically.

The banking industry is moving from a phase of cautious experimentation to a new reality of deep, systemic AI integration in 2026.

This transformation is not just about technology; it’s about a fundamental rewiring of how financial institutions operate, interact with customers, and manage risk. In 2026, the most successful banks will not be the ones with the largest AI labs, but the ones that have successfully woven AI into the very fabric of their business.

The State of the Industry: The Pivotal Year

By 2026, AI in banking will no longer be considered a competitive edge; it will be a prerequisite for survival. Industry research supports this rapid escalation.

Gartner estimates that in 2026, 90% of finance functions will have deployed at least one AI-enabled technology solution. And more than 80% of banks are likely to use generative AI APIs or deploy AI applications in a production environment, a staggering leap from less than 5% in 2023.

This acceleration is driven by the maturation of Large Language Models (LLMs) and the emergence of Agentic AI. Unlike the chat-based models of years past, which primarily provided information, agentic AI is designed to reason, act, and collaborate across systems to complete complex workflows independently. This means AI is moving from being a passive advisor to an active operator in 2026.

Key Frontiers Shaping Banking in 2026

The integration of AI is making itself felt across every facet of the banking value chain. However, four key areas are undergoing the most significant transformation.

1. Hyper-Personalization: The End of “Segment-of-One”

For decades, banks have strived for a “segment-of-one” approach to marketing. In 2026, AI finally makes this a reality, and goes one step further into predictive engagement.

AI is moving beyond analyzing basic demographics and historical transactions. It now processes real-time behavioral data—including spending velocity, hesitation during digital transfers, and even biometric markers—to create a complete, dynamic picture of a customer’s financial life.

Instead of reactive offers, AI agents will provide prescriptive financial advice. Imagine a customer searching for flights to Italy. Their bank’s AI, working in the background, could automatically complete a travel rewards credit card application and issue a digital card immediately, calibrated to their expected spending. It could then offer a micro-loan for the trip and adjust their automated savings plan, all with a single user-confirmation tap.

The financial impact of this is profound. Financial institutions implementing advanced AI personalization are seeing:

  • Customer engagement rates increase by up to 200%.

  • Improvements in customer lifetime value ranging from 25% to 35%.

2. The New Arms Race in Fraud Detection

As banking becomes more digital and instant, it also becomes more vulnerable to sophisticated criminal networks. Fraudsters in 2026 are already leveraging AI themselves, creating a “threat multiplier” that renders traditional, rule-based security systems obsolete.

The industry is responding with a defensive layer of hybrid AI systems, increasingly enhanced by quantum computing. These systems don’t just flag transactions based on rigid, static rules (e.g., “deny if location is X” or “flag if amount is over Y”). Instead, they analyze millions of data points across the entire ecosystem in real time. This includes network analysis, behavioral biometrics analyzing keystroke patterns and mouse movements, and deepfake detection for document verification.

Early adopters of this behavioral, unified fraud intelligence are reporting remarkable results:

  • Fraud detection accuracy improvements of 25% to 40%.

  • A reduction in false positive rates by up to 60%, significantly improving the customer experience.

3. Intelligent Lending and Credit Scoring

The traditional credit scoring model is exclusive, slow, and often inaccurate for large segments of the population. By 2026, AI-powered lending is rewriting the rules, making credit faster, fairer, and more inclusive.

Predictive models now ingest vast amounts of alternative data, including income patterns, utility payment history, employment behavior, and device-level data. By moving beyond a simple snapshot of a credit report to a dynamic analysis of financial behavior, lenders can expand their reach to previously underserved customers while simultaneously reducing default rates.

For the borrower, the experience is transformed. The loan origination process, notorious for its slowness and complexity, is being streamlined by Agentic AI. These agents ingest and validate financial documents (pay stubs, tax returns), run credit and ID checks, and ensure compliance with underwriting rules, reducing a timeline that once took days or weeks down to minutes.

4. Operational Excellence and the End of Back-Office Drudgery

While customer-facing applications garner the most headlines, some of the most substantial productivity gains are happening in the back office. By 2026, banks are using intelligent process automation (IPA) and generative AI to handle the most manual and inefficient tasks.

Document processing, regulatory workflows, financial close and reconciliation, and compliance reporting are all being automated. Organizations are reporting productivity gains such as:

  • 40% to 60% reductions in document processing times.

  • 30% to 50% improvements in customer service response times through AI-augmented support agents.

  • A 40% increase in software developer productivity using AI copilots.

The Human-in-the-Loop: Navigating the Challenges of 2026

Despite the immense promise of AI, the road to 2026 is not without significant hurdles. For a risk-averse industry, the concept of handing critical decisions to an autonomous system is inherently uncomfortable. This is why Decision Traceability is the most critical issue in 2026.

Ethical AI and Explainability

As AI models increase in complexity, they can become “black boxes,” making it difficult to understand how they arrive at a decision. This poses severe ethical risks, particularly in lending. If an AI denies a loan application, the institution must be able to provide a clear, fair rationale.

Banks are investing heavily in Explainable AI (XAI) frameworks. Successful institutions are maintaining a “human-in-the-loop” approach, where human experts review and validate high-stakes, AI-driven decisions. This ensures that human judgment, empathy, and ethical nuance remain central to the financial process.

The Regulatory Patchwork

The regulatory landscape for AI is complex and rapidly evolving. Different jurisdictions are taking divergent approaches, creating a complex patchwork for global institutions.

  • The EU AI Act sets strict standards for transparency, data governance, and risk management.

  • The US is currently focused on a lighter touch, balancing innovation with consumer protection.

  • The UK, Canada, Hong Kong, and others are pursuing their own, distinct paths, with new legislation coming into force throughout 2026.

For a bank in 2026, compliance is not an afterthought; it is built into the core of their AI architecture.

The Vision: A Trust-Based Transformation

As we move deeper into 2026, a clear differentiation is emerging. The banks that will ultimately succeed are not the ones that applied AIbroadly as a buzzword. The winners are those that applied it purposefully, with the explicit intention of improving consumer outcomes.

Trust will remain the fundamental currency of banking. In the age of AI, trust is built not just by safeguarding data, but by using that data to make customers feel more confident, informed, and financially well. By 2026, AI will be embedded in everyday platforms, making banking invisible and personal, prompting interactions that are proactive, deeply personal, and designed to improve financial well-being.

The journey is just beginning. By embracing AI as a fundamental business transformation, not just a technology upgrade, financial institutions can create a future that is more efficient, secure, inclusive, and user-centric than ever before.

#AIinBanking, #FutureOfFinance, #Fintech2026, #ArtificialIntelligence, #BankingTechnology, #GenerativeAI, #HyperPersonalization, #FraudDetection, #FintechTrends, #MachineLearning, #DigitalBanking, #OpenFinance, #AgenticAI, #IntelligentAutomation, #CreditScoring, #EthicalAI, #ExplainableAI, #EUAIAct, #RegulatoryTechnology, #RiskManagement, #CustomerExperience, #DataAnalytics, #FinancialInclusion, #QuantumComputing, #FinancialServices

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AI for Retail Industry in 2026: The Era of Agentic Commerce and Hyper-Individualization

AI for Retail Industry in 2026: The Era of Agentic Commerce and Hyper-Individualization

In 2026, the retail landscape has undergone a metamorphosis. The “retail apocalypse” narrative of the early 2020s has been replaced by a “retail renaissance,” powered not by square footage, but by silicon and sophisticated algorithms.

We are no longer just talking about “e-commerce” or “brick-and-mortar.” We are living in the age of Unified Commerce, where Artificial Intelligence (AI) serves as the connective tissue between a consumer’s digital intent and the physical fulfillment of their needs.

If 2024 was the year of AI experimentation and 2025 was the year of departmental pilots, 2026 is the year of Systemic Integration. For retailers, AI is no longer a “feature”—it is the operating system.

The Economic Landscape: A Market in Overdrive

The numbers behind this shift are staggering. According to recent industry reports from Fortune Business Insights, the global AI in retail market is projected to reach USD 16.54 billion in 2026, growing at a compound annual growth rate (CAGR) of over 26%.

This growth isn’t just coming from high-end luxury brands. It is being driven by a fundamental shift in consumer expectations. Research indicates that 71% of consumers now expect AI to be integrated into their shopping journey, with Gen Z and Millennial cohorts pushing that number even higher. For the modern retailer, the choice is clear: evolve the infrastructure or face irrelevance.

1. The Rise of Agentic Commerce: From Search to Execution

The most transformative trend of 2026 is the shift from “Conversational AI” to “Agentic AI.” In previous years, AI chatbots were primarily used for “search and suggest”—helping a user find a product or tracking a package. In 2026, we have entered the era of Agentic Commerce. AI agents no longer just provide information; they take autonomous action across systems to complete complex workflows.

The Personal Shopping Concierge

Consumers today often use their own “personal AI agents” to shop on their behalf. These agents understand the user’s budget, style preferences, and even their calendar. Instead of a user searching for “black tie wedding attire,” their AI agent negotiates with various retail APIs, checks stock levels, compares “phygital” fitting options, and presents the user with a curated final choice.

Transactional Autonomy

Salesforce data suggests that by the end of 2026, nearly 45% of online shopping tasks—including returns, subscription management, and basic replenishment—will be handled by AI agents. This “searchless retail” means retailers are shifting their focus from SEO (Search Engine Optimization) to GEO (Generative Engine Optimization), ensuring their products are the ones recommended and selected by these autonomous digital shoppers.

2. Hyper-Individualization: Beyond the Segment of One

In 2026, “personalization” is considered a legacy term. The industry has moved toward Hyper-Individualization.

Traditional personalization relied on static segments (e.g., “Women aged 25–35 who like fitness”). Hyper-individualization uses real-time, high-velocity data to understand a customer’s contextual intent.

  • Predictive Engagement: Using AI-driven “merchandising brains,” retailers can now forecast a customer’s needs before the customer even articulates them. If a customer’s smart fridge signals they are low on oat milk and their wearable device shows an increased heart rate during morning runs, the retail AI might suggest a high-protein recovery smoothie bundle available for 10-minute drone delivery.

  • Dynamic Pricing & Offers: Blanket discounts are a thing of the past. In 2026, AI algorithms calculate “price elasticity” at the individual level. A loyal customer might receive a “VIP perk” that isn’t a discount, but rather early access to a limited-run product, preserving the retailer’s margins while increasing the customer’s emotional loyalty.

3. The “Self-Healing” Supply Chain

While the consumer-facing “magic” gets the headlines, the most significant impact on the bottom line in 2026 is happening in the back office. The retail supply chain has evolved from a reactive chain into a Self-Healing Ecosystem.

Autonomous Merchandising

Inventory misalignment was once the “silent killer” of retail. In 2026, Agentic AI monitors “soft signals”—local weather shifts, viral social media trends on platforms like TikTok or its successors, and competitor price drops—around the clock.

According to McKinsey, retailers using these “Self-Healing” supply chains have seen:

  • Up to a 50% reduction in stockouts.

  • A 30% decrease in manual inventory checks thanks to shelf-scanning robots and IoT-enabled smart shelves.

  • 90% faster inventory redistribution across store networks.

Micro-Fulfillment and Robotics

To meet the “instant gratification” demand, retailers have turned their physical stores into micro-fulfillment hubs. In 2026, it is common to see a “dark” section of a grocery store where autonomous pick-and-place robots (like those showcased by NVIDIA) assemble orders for delivery in under 15 minutes, while the front of the store remains a high-touch “experience center” for humans.

4. “Phygital” Retail: The Store as an Experience Hub

Physical stores are not dying; they are being redesigned as Experience Centers. The goal in 2026 is “Retailtainment.”

  • Virtual Try-Ons & Smart Mirrors: Augmented Reality (AR) has reached a point of high fidelity. Customers can walk up to a smart mirror and see themselves in twenty different outfits in seconds without ever stepping into a changing room. Retailers report that 3D and AR visualization can improve conversion rates by up to 94%.

  • Invisible Checkout: Following the trail blazed by “Just Walk Out” technology, visual AI and sensor fusion now allow for a frictionless exit in most major metropolitan retail outlets. Computer vision monitors interactions in real time, identifying items as they are picked up and automatically charging the user’s digital wallet as they leave.

  • In-Store Guidance: Autonomous shopping robots, equipped with Natural Language Processing (NLP), now navigate aisles alongside customers, answering questions like “Where is the gluten-free flour?” and physically guiding them to the correct location.

5. The Ethical Frontier: Trust as the New Currency

As AI becomes more pervasive, Trust has become the primary differentiator for retail brands in 2026.

Data Privacy and Sovereignty

With AI analyzing everything from browsing history to biometric markers in-store, consumers are rightfully protective of their data. Successful 2026 retailers utilize Privacy-Preserving AI (such as federated learning or synthetic data) to gain insights without compromising individual identities. Transparency is no longer hidden in a 50-page legal document; it is a front-and-center brand promise.

The “Human-in-the-Loop”

Despite the heavy automation, the most successful retailers are those that use AI to augment human staff, not just replace them. In 2026, store associates are “AI-empowered.” They use wearable devices that provide real-time prompts about a customer’s preferences, allowing them to provide a level of service that feels deeply human and informed, rather than robotic.

Conclusion: The Path to Value

As we navigate through 2026, the divide between “leaders” and “laggards” in the retail industry has never been wider. The winners are those who have stopped seeing AI as a series of disconnected “cool tools” and started seeing it as a unified operating model.

By integrating Agentic AI into the supply chain, embracing hyper-individualization for the consumer, and turning physical stores into high-tech experience hubs, the retail industry has finally achieved what was once thought impossible: Scale with Intimacy. The future of retail isn’t just about selling products; it’s about using AI to create a world where every shopping journey feels like it was designed for a party of one.

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Acknowledgements

Welcome! I am a product of India that got refined by experiences in America and Europe. I am grateful to the Defence Forces of India where I grew up; KV schools across India, NIT Jaipur, IIM Lucknow, Stanford University, MIT and Harvard University for giving valuable education; ABB, BP, Citibank, CME Group, Nasdaq, Fidelity, and Infosys for giving valuable work experience, and wonderful alumni for a lifetime. I am grateful to all of you. Thank you!
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Hanuman Ram Jat, Gita, Arup Chakravarty, Manoj Gupta, Manoj Kumar, Manoj Agrawal, Manoj Verma, Manoj Shelar, Srini Peyyalamitta, Deepa Vasudevan, John Prashant, Sonali, Chethan Srikant, Prasoon Rohit, Vipul Dixit, Sandeep Hegde, Sanjay George, Ashish Dixit, Niket Jain, Rajesh Dubey, Shukla, Satyendra Kumar, Sunil Sharma, Kiran Bableshwar, Ajay, Gaurav Karandikar, Manoj Khanvilkar, Abhimanyu Sahoo, G Bhaskar, Kamlesh Bansal, Alok Vashishtha, Alok Kumar, Arjun, Vikas Mishra, Parag Rokde, Pushparaj Mujumdar, Rishi Aharwal, Amrinder Singh, Ram Narayan Shah, Rahul Tandon, Disha, Bhushan Mittal, Samir Rastogi, Ankur Kansal, Pritesh Punjabi, Palani Muthu, Narayan Singhal, Parmar, Tarun Choithani, Ravikumar Iyer, Shibu Matthew, Shobhit Bhargava, Sumeet Singh, Sujeet Singh, Sanjay Sainani, Shyam, Dharmesh Manuja, Dheeraj Sahu, Deep Rahul, Lalit Saraswat, Himanshu Kacker, Shrinivas Bajaj, Harikrishna Pandey, Raja Roy, Yash, Diksha, Sarah, Hanif Shaikh, Nafees Ahmed, Jasdev Singh, Nirbhik Puar, Siddharth Sharma, Sarthak Pattanaik, Raju, Mandaar Barve, Maneesha Barve, Lakshay Khattar, Pronoy Upadhyai, Proasheem Suleebka, Srikanth Tatikonda, Uma, Hemendra Bishth, Hemant Ahire, Sriram Shankar, Sanjay Chaurasia, Ajay Singh, Rohit Sood, Deepak Das,Manoj Sharma, Digvijay Singh, Vikram Saini, Alok Bajpai, RamRoop Pandey, Priya, Ramesh Chandra, Pradeep Joshi, Sandeep Khanna, Vishakha Pushkarna, Deepak, Prashant, Sudhir, RKS,Suman, Sangeeta, Poonam, Renuka Dwivedi Dabral, Anita Dubey, Sheetal Shukla, Nielam Tripathi, Mahua Paul, Kiran Gupta, Amit Agrawal, Ashwini Kumar, Naba Kumar, Mona Bhatia, Shiv Prakash Mishra, Prasanna Rao, Somyasmita Juneja, Rajesh Nair, Samit, Taruna, YS Raju, Yogesh Tiwari, Neeloy Saha, Narayana Bhatta, Aranya Bhattacharya, Bali, Vaneet Arora,Satya Samal, Deepak Agrawal, P Nilakantan, Sudipto Kundu, Tarang Puranik, Chaitanya Gandikota, Ajay, Kajal, Kishalaya Sarangi, Datta Kulkarni, Somnath Basu, Anirban Ghosal, Ujjal Banerjee, Madhu Pulasseri, Balaji Rajam, Ankit Jain, Krish Shankar, Vivek Karunakaran, Bindu Madampilly, Neelam Mohanty, Rahul Shah, Sandeep Prasad, Anand Shah, Samya Ghosh, Paurush Roy, Ramani Ramachandran, Venugopal Ramakrishnan, Surendra Baliga, Jainendra, Vivek Saraogi, Manjush Kumar, Shaleen Mahar, Raman Kataria, Rakesh Mishra, Anoop Jain, Palani Sankar G, Sushil Kedia, Samir Arora, Sanjiv Bhasin, Sanjeev Kapoor, Satwick Tandon,

Ganesh Iyer, Srikanth Iyengar, Atul Pandey, Sudhir Nair, Krishna Gubili, Rambabu Kaipa, Shoubhik Das, Senthil S, Atulya Chandra, Sarat Chandra, Avnish Anand, Saurabh Jauhari, Pankaj Agarwal, Vinod Sankaranarayanan, Balaji Iyer, K Srilatha, Namgyal Basi, Santanu Chari, Joybrata Das, Ranjit Poddar, Anil Khanduja, Udayan Dasgupta, Arvind Saxena, Santanu Debroy, Sandipta Dutta, Sumantu Datta, Binu Balachandran, VineetJain, Neelesh Asati, Keki Mistry, Jairam Panickssery, Anand Giridhar, Sandeep Kaimal, Rajiv Kaka, Aseem Bhatnagar, Rangarajan Iyengar, Alpna Gainda, Devi, Sudha Srinivasan, Saurabh Kunj, Ram Baran, Rohit Dogra, Mukesh Saraswat, Balaji Athreye, Ravi Garg, Saurabh Desai, Ravi Vasudeva, Vijay Mishra, Nitin Ghai, Anindya Ghosh, Dhruv Chadha, Rohit Johri, Krishna Kumar, Sanjay Dhawan, Balasubramanian V, Nitesh Bansal, Eshan Joshi, Saurav DP, Amit Dua, Ramesh Chougule, Jai Venkat, Gopi Krishnan, Ananth Vaidyanathan, Murali Vasudevan, Bharat Pasupathy, Kaushal Mody, Hisham Mundol, Ashish Goel, Sudhir Pai, Rajeev Suri, Sachin Bagla, Arindom Basu, SanjayJayaram, MukeshNakra, Shanthikaran Sharma, Srinivas Kamadi, Ritesh Idnani, Avinash Subramanian, Balram Suguna, Rahul Malhotra, Mellener Coelho, Ketan Ambani, Ashish Ambwani, Atul,Lokesh Chawla, Pankaj Kulkarni, Pankaj Patel, Pankaj Pandey, Suraj Rangashayi, Raju Kadian, Mayoor Tandon, Sukhraj Khassa, Siddharth Sharma, Ali Khan, Brian Dsouza, Priya B, Harikishan Narayanan, Ashish Pathania, Atul Saxena, Manish Sarswat, Vimal, Sheshu Gunisetti, Saurabh Lauria, Ananda Kumar, Aarti Karande, Deepti Bhutani, Richa Varshney, Suraj Kumar, Dharma Rajah, Ravi Bijlani, Vijay Gautam, Amit Singh, Sanket Singhania, Sunil Kumar, Mohamed Anis, Vivek Lodha, Suprio Banerjee, Amit Bhandari, Ankit Bhandari, Aman Behl, Vinu Varghese, Vikas Gupta, Amol Patil, Sunil Sharma, Maya, Sonali Anand, Rakhi Makad, Ravikanth Ganesan, Anurag Nigam, Aneet Shukla, Sridhar Challa, Dinesh Rao, Srinivas Rao, Simhadri Basava, Gayatri Chandrasekharan, Sajith Sankar, Sachin Phansikar, Mayank Manish, Surya Prakash, Sunil Makhija, Siddharth Dwarkani, CB Dubey, Vishal Gupta, Daljeet Singh, Prabh Simran Singh, Rachit Garg, Anand Lunia, Apurva Sharan, Manish Rathi, Pankaj Lal, Prasad Sivalanka, Manish Rathi, Nagmani Mishra, Ankur Kansal, Shishir Manuj, Sandesh Goel, Ritesh Goyal, Surendra Goyal, Suresh Garg,

Suraj Sharma, Rajeev Pandey, Raghav Ranjan, Nagendra Hamsala, Satyajit Samrendra, Chalapathy Tenneti, Aseem Bhatnagar, Shishir Singh, Pavan Goyal, Teginder Kaleka, Sairam Tadigadapa, Ravi Singhania, Pankaj Verma, Rahul Mehrotra, Saket Kakkar, Avinash Thakur, Rajdeep Dua, Prakash Hariharan, Vivek Bajpai, Praveen Rana, Ajay Ohri, Joydeep Bhattacharya, Avinash Kumar, Surbhi Agrawal, Uma, Durga, Dipankar Das, Deepak Wankhade, Sumi Vivek, Maria, Prakash Kamath, Anupama Raghunathan, Arjun Ramdas, Sakshi Bhandari, Shraddha Panara, Puja, Shivani, Bhavani, Ramya, Kaushik, Swarup, Gyanendra, Vaibhav Agarwal, Suramya Gupta, Manish Gupta, Syed Hussain, Deven Shah, Vivek Shah, Noopur Chaturvedi, Pooja Jain, Akhila Palli, Akhil Krishna, Prashant Achanta, Sabaleel Nandy, Vijay Ganti, SidSarangi, Virendra Pratap Singh, Gurpinder Singh, Sameer Taimni, Niraj Pant, Vivek Subramanyam, Rishi Verma, Pankaj Agarwal, Shweta, S Vijayanand, Deepak Maheshwari, Amit Sethi, Nikhil Newalkar, Vishal Anand,Tushar Sud, Sameer Shahapurkar, Ajit Thomas, Amitabh Nandan, Amneet Singh, Rahul Kumar, Siraj Dhanani, Shrikant Vaidyanathan, K Birla, Shomic Saha, DipuKV, CVRao, Ganesh Sankaran, Sivaram Bandhakavi, Amit Bhagat, Lalith Rajan, Subodh Gupta, Ashish Parmar, Sridhar Srinivasan, Sundarrajan B, Rajanikanth C, Rohit Jain, Rhitu, Chirantan Barua, Ramnish Kochgave, Gautam Sharma, Sudhendu Saxena, Ratish Trehan, Ranjit Nambiar, Bhandari, Mayank Saraswat, Saurabh Singhi, Kapadia, Kothari, Laxmi, Sunil Kumar, Manish Mittal, Vinit Chauhan, Nitin Das, Amin, Deepak Chaudhry, Ravi Luthria, Abhijit Maulik, Paul, Rahul D’Souza, Omar Blake Wade, Maneesh Dharmarajan, Vijay, Ravindra Gudapati, Nandkishore, Anil Chauhan, Bal Mukund, Vikas Golechha, J Srinivasan, Hitesh Gossain, Ayush Jain, Anjan Roy, Shankar Prasad Jha, Anil Tanwar, Manishi Varma, Shankar Bhaduri, Lorho Kholi, Lokhi Prasad Deori, Ranjith Thomas, Priyank Singhvi, DigantaDas, Sanjay, Phaneendra, Chintan Kharbanda, Naren Nagpal, Dennis Varghese, Lokesh Prasad, Ashwani Lata, Amandeep Sangha, Gagandeep Kaur, Pratul Chopra, Ashish, Kapil Pawar, Sukhi, Amandeep, Sodhi, Vishal Chadha, Tejbir Singh, Devendra Singh, Gurdeep Singh, Abhishek Singh, Anil, Khera, Bishnoi, Nanak, Randhawa, Wadhwa, Sahni, Bedi, Sandeep, Deshpande, Deshmukh, Gopal, Venugopal, Shastry, Parmar, Kedar Desai, Soni,

Venkat Rao, Nagesh Prasad, Dipendu Saha, V Rajesh, Shiva Kumar, Elizabeth George, Lalitha Kiranmai, Jeba Punitha, K Sudhir, Karuna Annavajjala, Uthara Sibi, Asha Sharma, AshaPande,Samta Baid, Manjari Parashar, Anuja, Shantanu, Teja Lele, Abhimanyu Singh, Vivekanand Vellanki, Manish, Sakshi, Suresh, Dinesh, Kamakhya, Dennis, Winfred Wilson, Madhulika Srivastava, Anamika Sirohi, Parvati Devi, Gita, Sanket Mehta, Nita Mehta, Rahul Bhandari, Pramod, Rangachari, Amit Raje, Mehul Desai, Goswami, RK Jain, Shri, Ram, Vinayak, Rajan, Syed, Nafees, Ahmed, Mohammed, Kafeel, Shaikh, Pushan Verma, Dipika, Deepti, Jasmine, Fatima, Vaishnavi, Garima, Andileep, Divyansha, Priya, Anshima, Sanjana, Sahil Kumar, Shreya Gupta, Shirin Akhtar, Husnal Kaur, Jesus, Lily, Paula, MV Sam, Rinku, Sreya, Asif, Mariam, Ashna, Archana, Seema, Vandana, Vaishali, Neelam, Kavita, Kavitha Rao, Kavita Shenoy, Pallavi, Prachi, Gunjan, Hemlata, Avantika, Smita, Ganguly, Ankita, Mitra, Bhalla, Grover, Keshav, Yadav, Jhadav, Thakur, Sri, Vishnu, Janak, Joshi, Jindal, Patil, Parekh, Rawat, Reddy, Ganapathy, Dr Kulkarni, Dr Talwar, Dr Pradeep Kumar Kakarala, Siya, Vasudev, Payal Khanna, Ankita Singh, Krishnamurthy, Michael, Markus, Goldman, Faith, Sarah, Haliene, Natasha, Eddie, Lee, Marty, Maddy, John, Jeff, Larsen, Rahim, Natalya, Tara, Vicky, Anna, Kelly, Naz, Alex, Milan Dsouza, Charley, David, Jesse, Akira, Sanae, Mizuki, Hana, Hoshi, Nomura, Takaya, Yamada, Zerrin, Anita, Jaiswal, Kaul, Nathan, James, Steve, Luke, Morgan, Jean Luc, Tarique Mansuri, Ethan William, Marc Vergara, Dharani S, Yalda, David, Mark, Warren, Robin, Eldad Sagiv, Daniela Andrich, Tony Mundy, Fernande, Catherine, Jacques Sergent, Monique, Nelly Foque, Isabelle, Ludovic Legrand, Renuka, Ganesh, Mayaank Vadlamani, Sourab Vadlamani, Sailaja, Sudha, V Lalita, VS Laxmi, Padmini Ganti, Sujata, Kalyani, Kalpana, V Raghu, Sai Vedula, Krishna, Narayana, Venkata Sai Dhavala, Peri Sastry, Somes Peri, MD Murthy Peri, PMD Murty, MDA, Madhu Achalla, Satish SVS, Sai Harish, Premnath Ayyalasomayajula, Ydyanath Ayyalasomayajula, A Suryanarayana Murthy, AUNL Rajeswara Rao, Vedula Subrahmanyam, PS Rao, Peri Subbarayan, A Mahadeva Shastry, Peri Surya Narayana Sastry. The list starts with my father, and ends with my grandfathers. I am grateful to all of you. Thank you!

Economy, Leadership, People

Trump is US President Again!

This was the moment Trump won; it was a powerful image that people across America saw.  Donald Trump becomes only the second President to win two non-consecutive terms. The last time it happened, it was way back in 1892.

In Trump 2.0 govt, we can expect non-stop action in US Economic activity and some serious efforts to reduce prices of food and consumer products, and gasoline prices (WTI Crude Oil derivative), to reduce the cost of living for the average American citizens, who constitute the largest voter base for Trump.

In Trump 2.0 govt, we can expect new schemes and benefits for small businesses across America. No wonder, Russell 2000 index is a big gainer today.

In Trump 2.0 govt, we can also expect significant reduction in US Federal Govt spending, based on the findings and recommendations of the Financial and Performance Audit done by the new “Govt Efficiency” department to be led by Elon Musk. No wonder, Tesla stock is up 15% today!

Overall, a new innings will start for America from Jan 2025, and the world is much different than it was in Jan 2017. Wishing all the best to people across America!

Hoping for the wars in Palestine/Middle East and Ukraine to stop and peaceful times to come.

The US equity markets should do well in the next 2-3 years, and all major corrections will be buying opportunities. Investors can use S&P500 ETF (SPY) and Nasdaq100 ETF (QQQ) to gain from the growth in US economy.

5 Questions, Books, MyOrbit Blogs, People, Training

Template or Formula for a Successful Fiction Book

Hello Folks, today I wanted to answer one of the most common questions I get from book writers, and their question is this or some variant of it: “Hi, is there any template or formula for a successful fiction book?”

Well, there is no such template or formula. While there isn’t a one-size-fits-all formula for a successful fiction book, many successful novels follow certain structural templates and storytelling techniques that resonate with readers. Below is a general template (guideline) that can guide you in crafting a compelling fiction story:

1. Concept and Theme

  • Concept: Start with a unique, engaging idea that will captivate readers. This could be a “what if” scenario, an unusual character, or an intriguing setting.
  • Theme: Decide on the central theme or message of your story. This could be love, power, identity, redemption, etc.

2. Character Development

  • Protagonist: Create a relatable main character with clear goals, strengths, and flaws. The protagonist should undergo significant growth or change throughout the story.
  • Antagonist: Introduce an antagonist or obstacle that challenges the protagonist, providing conflict and tension.
  • Supporting Characters: Develop well-rounded secondary characters that serve to complement, challenge, or support the protagonist.

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