Why India’s AI Mission Is a Critical Step for the Country’s Future

I’ve been reading up on the Government of India’s AI Mission over the past few days. It’s genuinely interesting and, honestly, quite heartening. You can see the level of focus, effort, and thought going into it, along with serious intent to invest. Every rupee put into AI today is likely to pay back many times over. Just as India took an early lead in IT decades ago, we now need to build a similar moat in AI.

The AI Mission is especially important given how complex the global geopolitical situation is becoming for India. We need to be self-sufficient, with our own distinct AI capabilities and offerings. Our markets are massive, which is why foreign players are doing everything they can to enter them. There’s no reason Indian, homegrown companies shouldn’t get strong support too, just as other countries actively protect and nurture their own ecosystems.

In October 2025, the Government of India, through the Press Information Bureau, published an article titled “Transforming India with AI” (https://www.pib.gov.in/PressReleasePage.aspx?PRID=2178092&reg=3&lang=2). It’s a good read. Earlier in this series, I had written about the seven sutras of India’s AI strategy. Taken together, it’s encouraging to see that India does seem to be moving in the right direction.

Source: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2178092&reg=3&lang=2

Using AI to Strengthen Election Integrity and Voter Verification

AI can play a meaningful role in election data processing, voter impersonation prevention, and in protecting the integrity of the entire election chain. If we focus just on voter impersonation, today’s AI systems are already accurate enough to reliably detect and match faces against large databases.

At the polling booth, a voter’s photograph can be captured and processed by an AI system that converts facial features into a numerical “vector.” This vector is then compared with the vector created from an existing photo in a trusted database, such as the UIDAI record. If the difference between the two vectors crosses a defined threshold, the voter is rejected. If it falls within acceptable limits, the voter is allowed to proceed.

In India, a version of this process already exists. The returning officer manually compares the person standing at the booth with the photograph on the Aadhaar card. However, this step has always carried the risk of human error or deliberate compromise. An AI-based system removes that vulnerability. There is no scope for human bypass, except in clearly defined and auditable exceptions.

This is just one example of how AI can help. In a system as large, complex, and data-rich as a national election, there are many other areas where AI can add value. We will look at some of those possibilities in the future.

AI Slop, Algorithms, and the Erosion of Online Trust

I read an interesting piece in The Guardian yesterday. It claimed that nearly 20% of all content shown to YouTube viewers is now AI slop.

By AI slop, they mean content generated at scale using AI tools, not to inform or entertain, but to farm views by gaming algorithms that reward frequent uploads.

These accounts are exploiting one of the core business pillars of social media, especially YouTube: viewer stickiness. Platform revenue depends on how often people upload, how much users engage, and how many views they rack up. Entry barriers are almost nonexistent. For $20–50 a month, anyone can access industrial-grade AI software on ordinary hardware. Anyone can open a Google, OpenAI, or Nano Banana account and start uploading.

It gets worse. In the race to shock and grab attention every single time, AI-generated content is pushing into territory that, in any other era, would have been considered offensive at best and illegal at worst. The irony is that most of it remains perfectly legal until it triggers actual civil or criminal unrest.

The biggest danger of AI slop isn’t volume. It’s believability. The software is getting uncomfortably close to real. Telling AI from reality is no longer easy. There are already enough reports from credible news outlets showing how hyper-real AI content can shape narratives and sway perception. We need to pause and ask whether what we’re seeing is real before reacting to it. And definitely before forwarding it. Just because you can share something doesn’t mean you should.

source: media reports

So how do we stay alert to AI slop? It isn’t easy, especially when most of us won’t spend more than three to five seconds actually thinking about what we’re watching. Still, there are signs. Skin that’s too smooth. Lip sync that’s almost perfect. Motion that feels oddly slow or floaty. Backgrounds that are too clean, too tidy, too dust-free. In real life, especially in India, nothing looks that pristine. Stories that feel overly sentimental or oddly polished.

Legal guardrails and disclaimers are one option- and they will come in. Typically the law takes a bit of a time to catch up to technology. In the end, trust the human eye. The brain is good at spotting patterns and sensing when something feels off. We just have to give it a moment to work.

Which Business Functions Are Adopting Agentic AI the Fastest?

Agentic AI is the latest buzzword, pitched as a fix for enterprise headaches like data overload, lack of actionable insight, time spent in dats / presentations, messy workflows, leaks, and more. It does deliver real value. The question is where it’s taking hold fastest and what teams are actually getting out of it so far.

A review of recent literature, media coverage, and studies from BCG, McKinsey, and others points to a clear pattern. Based on that analysis, we’ve put together Jetmetaphy’s list of the top five functions already using agentic AI at scale (see infographic). You could have a different listing; this is from our understanding at this point. 

AI Agents Explained: How Agentic AI Goes Beyond Bots and Assistants

At Jetmetaphy, we’re seeing a growing number of questions around agentic AI. That interest reflects our core strength: applying AI in ways that directly support real business functions.

So what are AI agents, exactly? Traditional assistants and bots are largely reactive. They pull from predefined data sources and respond to prompts or rules. Useful, but limited.

AI agents are different. When properly trained and governed with clear guardrails, they can learn, adapt, and act autonomously. Instead of responding once, they work iteratively toward a goal, adjusting their approach until the best possible outcome is reached.

In simple terms, bots follow instructions. Agents pursue objectives. Think of an AI agent as an intelligent, always-on manager for your business, continuously learning, optimizing, and delivering results without constant human intervention.

AI Acquisitions by Indian Companies: Strategy, Talent, and the Future of Jobs

There has been a clear increase in AI-focused acquisitions by Indian companies over the past two years. The acquisition data points to a strategic recalibration rather than a shift in risk appetite. 

Indian companies seem to be balancing internal capability building with selective inorganic growth, using acquisitions to shorten time-to-market and access specialized talent or platforms. The pattern suggests a deliberate, phased approach to AI adoption rather than an aggressive land grab, as illustrated in the accompanying infographic (below). 

source: Media reports, TCS / Wipro/ HCL press releases; infographic made using my own prompt. 

What this implies is that while certain categories of software roles will shrink or disappear, a different set of requirements is emerging. Demand is shifting toward professionals who can work fluently with AI tools as well as software, and toward techno-consultants who can bridge technology and business rather than operating in either silo. The pace of change in AI-driven roles is likely to be rapid. That should be interesting, if also somewhat unsettling.

AI in Medicine: Connecting the Dots Across Medical Data

One of the areas where AI can genuinely help is medical science.

The modern medical industry generates massive volumes of data in many different forms: text, PDFs, structured data, scans, X-rays, videos, EEGs, ECGs, and more. Traditionally, each of these data sources contributed only a part of the overall picture, often leading to separate conclusions and individual courses of action.

Today, AI in medicine can bring all these diverse data types together into a single, integrated view. Instead of treating each input in isolation, AI can analyze them collectively and suggest a course of treatment that balances all available information for the best possible outcome. This is something that may not be achievable even for a panel of experienced doctors.

Not because doctors are incompetent or inexperienced, but because of the sheer volume of data involved and the difficulty of analyzing it all at once to identify subtle patterns that may point to an emerging crisis.

Listen to this podcast, based on verified sources. These is strictly informational and not meant to be any medical advice/ guidance/treatment. 

Listen to AI in Cardiac

Sources: 

https://www.foreseemed.com/artificial-intelligence-in-healthcare

https://www.scirp.org/journal/paperinformation?paperid=148222

https://pmc.ncbi.nlm.nih.gov/articles/PMC11374272/

https://iris.who.int/server/api/core/bitstreams/f780d926-4ae3-42ce-a6d6-e898a5562621/content

https://amwa-doc.org/wp-content/uploads/2025/10/Power-of-AI-in-Improving-Early-Diagnosis-and-Care-for-Alzheimers-Disease-and-Dementia.pdf

https://arxiv.org/pdf/2307.00067

Why Is Secrets Sprawl a Growing Security Risk and How Can AI Help?

We’ve all done this, and more. We create accounts everywhere: OTT platforms, banks, NBFCs, gaming apps, e-commerce, quick commerce, telecom, and social media. Then we forget about them. Worse, we reuse the same passwords across platforms. In security terms, this expands the attack surface and creates easy openings for hackers.

The same problem exists inside enterprises. It’s often called “secret sprawl.” Credentials, passwords, API keys, tokens—they spread quietly across codebases, documents, and tools. 

The problem begins small. A developer hard-codes a key into source code to meet a deadline. The code is pushed to a repository, copied into logs, shared over email, pasted into tickets or fed into AI coding tools for debugging. Each step creates a new, unmanaged copy. Over time, the secret travels—across teams, tools and platforms—without oversight.

This is what makes secrets crawl dangerous. The attack surface expands quietly. Even when a key is rotated, older versions often remain active elsewhere. According to industry estimates, exposed secrets are now among the leading causes of cloud breaches, API abuse and supply-chain attacks.

The rise of generative AI has accelerated the risk. Employees routinely paste production code—and sometimes live credentials—into chatbots, pushing secrets beyond enterprise security perimeters. Its now big enough an issue that CISO/ Cyber security and IT policies take note ( see infographic) .

source: Gitguardian; Gitlab, Media Reports, NIST

AI plays a dual role in managing secrets sprawl. It is both a powerful tool for detection and, paradoxically, a contributor to the problem itself. On the defensive side, AI can help in in real time monitoring at scale, potentially expanding the ability of teams to detect and action;  real time risk based assessments, triggering alerts/ defensive action much faster than ever; Ai could also be crafted for automated key rotation at scale, thereby reducing one attack vector. 

AI can also be the problem! It could suggest insecure patterns or detection. And it may generate keys- compounding the very problem it was created to solve! 

Clearly AI is not a one-stop solution. It still needs a lot of human intelligence and final control. 

What Are Non-Human Identities (NHIs) and Why They Matter in Cyber Security (and AI)

What are Non-Human Identities and why are these an urgent focus these days in cybersecurity?  

Non-Human Identities (NHIs) are digital identities used by machines, not people.They allow applications, bots, scripts and systems to access data, APIs and infrastructure.

Put simply: humans log in with usernames and passwords; machines log in with keys and tokens.

source: microsoft, Amazon, Reco.ai, media reports. Infographic created by my own prompt.

NHIs are everywhere in modern tech stacks:

• API keys used by apps to talk to each other

• Service accounts running background jobs

• Automation scripts and bots

• Cloud workloads such as VMs, containers and serverless functions

• DevOps tools like CI/CD pipelines

• IoT devices and sensors

In most enterprises today, non-human identities vastly outnumber human users! 

Unlike humans, NHIs don’t use passwords or MFA. They rely on:

• API tokens

• OAuth tokens

• Certificates

• SSH keys

• Cloud IAM roles

These credentials are often long-lived, shared and rarely rotated. 

NHI can be a risk for four reasons: increased attack surfaces because of high use of cloud / IOT. Failure of authentication which is designed for humans, and not faceless bots; bots gain access where they shouldn't without human review, and by virtue of their omnipresence, a prime target for hackers, as these are non-traditional surfaces not easily monitored. 

This is the simple summary of HI vs NHI: 

source: microsoft, Amazon, Reco.ai,  IBM, media reports. Infographic created by my own prompt.

AI agents have a deep impact- even transformational- on NHI and security. We will explore this in next posts. 

India AI Governance: Balancing Innovation, Safety and Trust in the Age of Artificial Intelligence

The Indian government published the India AI Governance Guidelines on November 25 2025. 

Source: India AI Governance Guidelines : Enabling Safe and Trusted AI Innovation (PDF) on PIB website. Infographic generated by own prompt. 

Source: India AI Governance Guidelines : Enabling Safe and Trusted AI Innovation (PDF) on PIB website; infographic generated by own prompt. 

My take : India’s AI governance approach is pragmatic, pro-innovation and risk-aware. The focus is not on banning or tightly controlling AI, but on using existing laws, voluntary frameworks and digital public infrastructure to guide safe adoption. 

The government recognises real risks—deepfakes, bias, data misuse and national security—but believes these can be managed through graded accountability, techno-legal tools and strong institutions, not blanket regulation.

Source: India AI Governance Guidelines : Enabling Safe and Trusted AI Innovation (PDF) on PIB website. Infographic generated by own prompt. 

The big message is clear: build trust, expand access, skill people, and regulate only where harm is proven. AI is seen as a growth engine for healthcare, education, agriculture and governance—especially for Bharat, not just India’s metros.

Shadow AI Explained: The Hidden Risk Inside Your Organisation

Shadow AI refers to the use of artificial intelligence tools by employees without formal approval, oversight, or governance from their organisation.

In practice, it means staff using public or unsanctioned AI systems to generate reports, presentations, analyses, code, or insights using internal company data. This use is often well-intentioned, not malicious. Employees are usually trying to save time, meet deadlines, or improve productivity. The risk comes from how the tools are used, not why.

To get useful output, AI systems need input. That input often includes:

• Internal reports

• Pricing or inventory spreadsheets

• Strategy decks

• Customer or partner information

• Operational or financial data

Once this information is entered into an external AI system, control over that data is effectively lost. Even when providers claim privacy or non-retention, the organisation has no practical way to verify how data is stored, reused, logged, or incorporated into future models.

Shadow AI can show up anywhere:

• A rushed manager generating a board deck

• An analyst uploading a spreadsheet for faster insights

• A salesperson polishing a proposal with sensitive client data

• A junior employee using AI because it feels natural and efficient

• A hospital employee uploading reports or researching using patient data. 

Each instance may seem harmless, even noble in the pursuit of efficiency and commitment. But at scale, it becomes a serious data exposure risk.

Shadow AI is expanding faster than traditional IT controls can keep up with. AI tools are:

• Easy to access

• Cheap or free

• Familiar to younger, AI-native employees

• Useful even to non-technical staff

As new generations enter the workforce, AI usage becomes instinctive. Policy, firewalls, and monitoring often lag behind real-world behaviour.

The answer is not banning AI. That rarely works.

Instead:

• Define a clear AI policy: what tools are allowed, which are not, and why.

• Specify data boundaries: what can never be uploaded, even to approved tools.

• Be explicit about monitoring: what is logged, tracked, and audited.

• Apply rules consistently: including to senior management.

• Educate employees: most Shadow AI happens through ignorance, not intent.

source: reco.ai, media reports, my own prompt to generate infographic 

AI is a powerful efficiency tool. But when data control becomes fragmented, the risk of leaks, competitive loss, regulatory exposure, and carelessness rises sharply.

Risk Type	What It Means	Real-World Example
Data Security & IP Leaks	Employees accidentally upload sensitive, confidential, or proprietary information (like source code, financial data, or future product plans) to public AI tools.	Samsung engineers leaking proprietary source code into ChatGPT.
Legal & Compliance Violations	Using unapproved AI can violate data privacy laws (like HIPAA for patient data) or lead to professionals relying on inaccurate, AI-generated information.	Lawyers getting sanctioned for using fake legal cases created by an AI in a court filing.
Expanded Attack Surface	Unvetted AI tools, especially browser extensions, can contain malware or have security flaws, creating new ways for cybercriminals to attack a company's network.	A Chrome extension named "Quick access to Chat GPT" was found to be malware that hacked users' Facebook accounts.

source : Media reports, reco.ai,  Forbes (https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/) 

Regardless of privacy statements or assurances, nothing shared with external AI systems should be assumed private. Data always leaves your control in some form.

With AI, the rule is simple: user beware.

How AI Is Used in Cybersecurity

Cybersecurity has moved beyond firewalls and antivirus software. It is now about behaviour, patterns and speed. This is where artificial intelligence has become critical (see infographic).  At Jetmetaphy Labs, our product WardenAI is at the forefront of leveraging AI for forensics, detection, prevention and diagnosis. 

Across Indian banks, insurers, telecom companies and digital platforms, AI continuously studies what “normal” activity looks like—user logins, network traffic and data access. When behaviour shifts, risk is flagged early. Most cyberattacks do not announce themselves. They creep in. AI catches the early signals.

Threat detection is no longer signature-led. AI watches how files behave. If a program starts encrypting data, altering systems or contacting unknown servers, it is blocked—even if the attack is new. This has become essential as ransomware incidents rise across hospitals, manufacturing units and government systems.

Email fraud remains a major weakness. AI scans language patterns, sender behaviour and links to stop phishing, fake invoices and CEO fraud—common attack routes for Indian enterprises.

AI also protects digital identities. It learns how users normally log in—device, location and timing. Unusual access triggers additional checks or blocks accounts, reducing fraud in banking, UPI and insurance platforms.

When attacks do occur, AI speeds up response. Systems are isolated, access is shut down and alerts are triggered in seconds. What once took hours now happens automatically.

The shift is structural. Cybersecurity has moved from rule-based defence to behaviour-led intelligence. Attackers use automation. Defenders now have little choice.

Put simply: AI gives cybersecurity teams speed, scale and foresight—now essential, not optional.

How Underwriting Works—and How AI Is Improving It

While researching the insurance industry, I took a closer look at underwriting and the balance of art and science behind it. I found that underwriting blends data analysis, professional judgment, corporate strategy, personality, and risk management. One insight that stood out was the idea that weak underwriting amplifies risk, while strong underwriting multiplies profits.

Underwriting is the process through which companies decide whether to take on risk, how much to charge for it, and under what conditions. 

In insurance, this means evaluating details like age, health, driving history, location and past claims before issuing a policy. 

The same logic exists in other industries too. 

Banks underwrite loans by checking income and credit history before deciding interest rates and limits. Investment banks underwrite IPOs and bond issues by pricing risk before selling securities to investors. 

BNPL (buy now pay later) firms do instant underwriting at checkout, approving or rejecting customers in seconds. 

Leasing and asset finance companies underwrite based on asset value and usage risk, while reinsurance involves large-scale underwriting across geographies and climate exposure. 

Even in startups, the investor underwrites tech, people costs, and other costs in the hope of disproportionate returns, or at worst, capped losses. 

In every case, the principle is identical: price risk correctly or pay for it later.

Traditionally, underwriting has been manual, slow and rule-based. Decisions depended heavily on individual judgement, limited data and fixed risk slabs. 

AI changes this completely (see infographic) . 

source: myself; infographic created using my own prompt 

AI-led underwriting systems analyse large volumes of data in real time, identify patterns humans often miss, and score risk in seconds. 

Low-risk cases are approved automatically, while complex cases are escalated to human underwriters. 

Pricing becomes more precise, fraud is flagged early at the proposal stage, and risk assessment shifts from a one-time activity to a continuous process based on behaviour, usage and external factors. 

The key to always remember : AI does not replace underwriters; it supports them with clearer insights, consistency and speed. The "gut feel", the indefinable experience, the institutional memory of events. circumstances, analysis and outcomes should never be under estimated or belittled. Nothing can replace the HUMINT- Human Intelligence. 

SBI Life’s AI Playbook: Automation First, Scale Always

SBI Life’s AI is about automation at scale, better risk selection, cleaner growth and quieter efficiency gains. This analysis is based on SBI Life's H1 FY26 analyst call and investor's presentation. 

The most visible impact of AI is in underwriting.

SBI claims that around 59% of individual policy proposals are now processed through automated underwriting systems. This materially reduces human intervention, compresses decision timelines and improves consistency in risk assessment — a critical advantage at SBI Life’s scale.

Near-complete digitisation at the front end supports this shift. 99% of individual proposals are submitted digitally, ensuring clean, structured data flows into underwriting engines and analytics systems.

source: SBI H1FY26 analyst call, investors presentation ; own prompt for infographic. 

In other words, the underwriting stack is no longer people-first with tech support. It is increasingly machine-first, with human oversight.

Individual premium generated through the company’s own digital platforms grew 34% year-on-year, while online protection business expanded 55%. Importantly, this growth is largely driven through SBI Life’s proprietary channels rather than third-party aggregators.

AI-enabled workflows support instant underwriting decisions, pricing alignment and smoother onboarding — essential for protection products where customer patience is limited and drop-offs are high.

Rider attachment on eligible ULIP policies has reached ~38%, supported by automated eligibility checks and recommendation logic embedded in the sales journey. Longer premium-paying terms and smarter rider bundling are being used to improve margins without resorting to headline price hikes.

The strategy is clear: use data and automation to improve quality of business, not just volume.

Despite branch expansion and headcount additions in H1 FY26, SBI Life continues to rely on automation to manage operating leverage.Efficiency gains are being driven by process automation, digital workflows and scale effects, not organisational shock therapy.

One of the less discussed — but most material — benefits of AI-led systems is trust.

SBI Life stated a mis-selling ratio of just 0.02% and a death claim settlement ratio of 99%. Automated proposal checks, underwriting rules and streamlined claims processing reduce subjectivity, error and post-sale disputes.

This is where AI really delivers! 

Inside Meesho’s AI Strategy: How Artificial Intelligence Powers Its E-commerce Platform

Meesho is going all in on artificial intelligence. It sits at the core of how the platform works, from shopping and deliveries to ads and fraud checks.

According to its latest IPO filing, AI and machine learning now power almost every interaction on Meesho. When users search for products, browse their feeds, place an order or contact support, there’s an algorithm working in the background. The same applies to sellers listing products, running ads or managing prices.

To drive this, Meesho has set up Meesho AI Labs, its in-house AI team. The focus here is on building models for Indian users, including small and large language models, agent-based AI for shopping and post-order journeys, stronger risk systems and more automated ad optimisation (see infographic). The company follows a simple approach: test new AI ideas, measure impact, and scale only what works.

source: Meesho.com updated Red Herring prospectus ; Infographic generated using my own prompt. 

Behind the scenes, Meesho runs its own machine learning platform called BharatMLStack. It’s built to handle India’s scale while keeping costs low and response times fast. In FY25 alone, the platform processed close to 2 petabytes of data every day. These systems power real-time recommendations, pricing decisions, geo-location mapping and fraud detection.

One of the more practical uses of AI is in solving India’s address problem. Meesho has built a custom GeoIndia language model that turns messy, unstructured addresses into accurate map coordinates. This has helped improve last-mile delivery and cut fulfilment costs.

For shoppers, AI shows up as more personalised feeds and better search. Users can search using text, images or voice, even in local languages or with spelling mistakes. AI-powered chat and voice bots now handle a large share of customer queries. In FY25, they resolved about half of all support requests, and this crossed 60% in the June 2025 quarter, reducing wait times and support costs.

Sellers also benefit from AI tools. These help with onboarding, catalogue creation, pricing insights and targeted ads. Meesho says its AI-driven ad systems delivered an 8.6x return on ad spend in FY25, which jumped to 18.3x in the June quarter, as targeting became more precise.

Logistics is another area where AI plays a big role. Meesho’s logistics arm, Valmo, uses dynamic routing models instead of fixed shipping routes. These systems look at the network in real time, select the best delivery partners, predict disruptions and reroute shipments during capacity or weather issues. AI also helps read complex delivery addresses and improve delivery success ( see infographic below)

The Logistics System

source: Meesho.com updated Red Herring prospectus ; Infographic generated using my own prompt. 

Trust and safety remain critical. Under Project Vishwas, AI systems track account misuse, fake GPS signals, bot activity and transaction fraud. Project Suraksha uses computer vision and language models to spot counterfeit or infringing listings, backed by continuous monitoring of product quality through customer reviews.

Meesho is also using generative AI internally. Engineers use it to speed up coding and product releases, while marketing and product teams rely on AI to create images and videos for campaigns. The company is even testing whether some AI-powered support tools can be offered to external partners.

All of this runs on massive data. Meesho’s AI systems process over 4.3 billion data points every day. Even as transaction volumes grow, the company says its infrastructure costs are rising much more slowly, showing how AI is helping improve efficiency.

People remain a big part of the plan. As of June 2025, Meesho had 155 AI and ML specialists, and more than half its workforce is in tech roles. A portion of the IPO funds will go towards hiring and retaining AI talent and strengthening long-term AI capabilities.

In summary, it should be interesting how the company ramps up and integrates AI as it scales up. A lot rides on successful implementation and adoption! 

AI Sentiment Meter : AI Headlines Reveal India’s Jobs, Innovation, and Policy Sentiment (2024–2025)

It’s that time of year when we step back and take stock. With AI dominating headlines, boardrooms, and policy debates, I wanted to look at how the media portrayed AI and related developments across the 2025 calendar year, and compare that coverage with what we saw in 2024. The goal was to understand how narratives, priorities, and concerns shifted over time.

The underlying assumption is simple: media coverage, while imperfect, tends to mirror the broader socio-economic, policy, and political mood of the moment.

What follows is a summary of the methodology I used, the process I followed, and the results that emerged.

The AI Sentiment Meter  

"A headline-based index tracking how Indian newspapers frame AI each month—scored using weighted positives (jobs, pay, healthcare, policy) and negatives (job losses, energy, water, fraud) to capture shifting media sentiment." 

Disclaimer

This index reflects media sentiment, not economic impact or employment outcomes.

Headline selection and classification involve editorial judgement based on my understanding and alternative interpretations are possible. The dataset is indicative, based on verified headlines, and is intended to capture narrative direction rather than provide an exhaustive census of all AI-related coverage.

Sentiment Calculation

$$\text{Monthly Sentiment Score}=\frac{\sum (\text{Weighted Headline Scores})}{\text{Total Headlines in the Month}}$$

Weightages Used

Positive Headlines

Theme	Weight
New job roles created due to AI	+2.0
Increased pay / wage premium for AI roles	+2.0
Government initiatives to expand AI usage	+1.5
Detection or prevention of fraud using AI	+1.5
New medical diagnosis or healthcare benefits due to AI	+1.5

and other similar 

Negative Headlines

Theme	Weight
Job losses due to AI	–2.0
Increased water consumption by AI or data centres	–1.75
Increased energy consumption by AI or data centres	–1.5
Fraud or crime enabled using AI	–1.5

and other similar 

Neutral Headlines

• Assigned a score of 0

Methodology

This AI Media Sentiment Meter is based on a headline-only analysis of five national newspapers: Times of India, Economic Times, Hindustan Times, The Hindu, and Business Standard. Headlines referring to Artificial Intelligence or data centres were identified month-wise for the period January 2024 to December 2025.

Each headline was manually classified using a predefined rule-based framework and assigned a sentiment score based on its dominant theme (noted in weightages used section). Only headlines were analysed; article bodies, opinion pieces, and duplicates were excluded.

Monthly sentiment values represent the arithmetic average of weighted headline scores for that month. All headlines carry equal weight within a month. The meter value will most certainly be more accurate and reflective of sentiment with greater number of headlines analysed- but current constraints on time,effort and analysis restrict the number of headlines checked. 

As I emphasise, this is not a detailed, mathematical, comprehensive index or meter. I am merely trying to track AI sentiment subjectively based on media headlines. Use at your own risk and discretion. 

The AI Sentiment Meter for 2024 and 2025 (Jan- Dec) 

source: headlines, weightages assigned per my own judgement. 

Interpretation 

In both 2024 and 2025, media coverage in India stayed broadly cautious. Most reporting centred on announcements, commentary, and international developments, rather than concrete domestic data on job displacement, resource use, or environmental impact.

This is partly because India is still on the upside of the AI adoption curve. With benefits more visible than costs, there is little hard evidence for either industry or government to anchor definitive conclusions. For now, the media reflects a wait-and-watch mood, shaped more by global signals than by local statistics.

Assessing AI Maturity Across India’s Listed Insurance Companies

I analysed four public sector insurance companies in India through the lens of AI maturity. I’m not a professional AI or ML engineer, but I closely track how AI is being adopted in data-rich industries like insurance. Drawing on my own reading, analysis using AI tools, and conversations with people deeply involved in AI implementation, I put together this AI maturity infographic.

I’m fully aware that there can be differing views—and that’s inevitable in a space as fast-moving as AI. What feels accurate today may well change tomorrow.

Inside HDFC Life: How AI Powers Modern Insurance

 

AI in Insurance: LIC's Practical Uses and Real Impact

AI is already reshaping financial services in India, and insurance is no exception. The gains from AI adoption at lenders like L&T Finance and Bajaj Finance are well documented. In insurance too, AI has the potential to be genuinely business-altering rather than just incremental.

source : LIC annual report 2024-25; infographic using my own AI prompt

What makes insurance especially fertile ground for AI is the sheer breadth of processes involved. From customer acquisition to underwriting, claims, fraud detection, operations, and compliance, most workflows are data-heavy, repetitive, and rule-driven. These are exactly the conditions where AI and automation deliver real value.

AI can be applied across almost the entire insurance value chain: customer onboarding, document processing, underwriting risk assessment, detection of incomplete or incorrect data, fraud analytics, robotic process automation in operations, customer service, advanced data analytics, and even cyber security. The opportunity is not confined to cost reduction alone. It also spans speed, accuracy, scalability, and customer experience.

LIC’s recent AI initiatives offer a useful lens into how a large, legacy insurer is approaching this shift. Drawn from LIC’s 2024–25 annual report, the examples below point to a largely pragmatic, business-first adoption rather than experimental or headline-driven use of AI.

At the customer interface, AI-powered chatbots are being deployed across digital channels to handle policy servicing and routine queries. The intent is straightforward: faster response times, lower call-centre load, and a more consistent customer experience at scale.

In claims processing, AI-based analytics and rule-driven systems are being used to flag anomalies and reduce manual scrutiny. The outcome here is quicker settlements and lower fraud leakage, both critical in a high-volume insurer.

Underwriting is seeing the use of machine learning models to assess risk using historical and demographic data. While not radical, this improves pricing accuracy and reduces subjectivity in underwriting decisions.

Fraud detection remains a core use case. Advanced analytics and AI tools are being applied to identify suspicious patterns across policies and claims, helping reduce losses and strengthen compliance.

On the operations side, LIC is combining robotic process automation with AI to automate repetitive backend tasks such as policy issuance and servicing. This directly improves turnaround times and operational efficiency.

AI is also being applied to data analytics through predictive platforms that generate insights on persistency, customer behaviour, and renewal trends. These inputs support better decision-making and help improve policy retention.

Customer engagement is being addressed through intelligent CRM and personalisation tools, using AI-driven analysis of customer data to tailor communications and service journeys ( the LIC digital app and MITRA chatbot). This creates scope for higher engagement and cross-sell over time.

At an infrastructure level, LIC is investing in centralised data lakes and advanced analytics platforms. While less visible, this is foundational. Without clean, unified data, AI initiatives cannot scale across departments.

Governance and compliance are another focus area, with AI-assisted monitoring tools supporting regulatory reporting, internal controls, and audit readiness. For a systemically important insurer, this is as much about risk management as efficiency.

Finally, LIC is investing in ongoing AI capability building, including analytics platforms and digital skills, as part of a longer-term digital strategy. This signals an understanding that AI adoption is not a one-off project but a continuous capability.

Taken together, these initiatives may not look flashy, but that is precisely the point. Given LIC’s size and outsized influence in India’s insurance ecosystem, even incremental improvements in speed, accuracy, and protection have outsized impact. Every deployment that simplifies processes, reduces risk, or improves service quality moves the industry forward.

Travel Portals Bet on Branded AI Chatbots

A scan of India’s leading travel portals shows a clear pattern. The digital-first players have already rolled out branded chatbots—most offering similar services, but wrapped in stronger positioning. The larger play is branding. By putting an AI face on routine customer journeys, these companies are making their bots easier to recall, easier to track, and potentially, the front door of the business in the years ahead.

Check out this list, compiled from respective websites: 

Chatbots Take Over the Front Desk in Travel Booking

Continuing my scan of how India Inc is using AI, today I’m looking at the online travel portals. EaseMyTrip lists several AI initiatives, which I’ve summed up below. The immediate focus — for this company and for most customer-facing firms — is the interaction layer for booking queries, details and data. 

The goal is simple: shorten resolution time, cut manpower and costs, and make the process feel smoother. In practice, most of them are pushing more of the detailed back-and-forth onto the customer. The chatbot becomes the first stop, and you’re left navigating a mixed bag of responses.

I’ve used the Emirates and IndiGo chatbots myself, for details on booking, PNR, additions and so on. Both have their strengths. It’s nice not to hear the endless “your call is important to us” loop. But the moment you ask anything even slightly off-track, the bot’s rigid logic shows up- a "non linear" query still suggest you talk to the travel consultant. 

Still, there’s a lot of AI being deployed across the sector — including on our side. One of our expertise, our Jetmetaphy chatbot can now handle several layers of queries with reasonable depth.