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: