In the mosaic of Artificial Intelligence (AI), generative AI subtly emerges as an increasingly significant component. Rather than making loud strides, it quietly integrates into the operational structures of tech companies, amplifying efficiencies, and innovating solutions. This article will shed light on the spectrum of opportunities generative AI presents and its influence on shaping industry dynamics.

Understanding the Invisible Artist

Let’s begin by demystifying generative AI. It’s a technological field that leverages machine learning to generate new data, modeled after the input it’s been trained on. From crafting emails to creating realistic human portraits, generative AI applications are multifold.

Re-imagining Content Creation

“Content is king,” Bill Gates famously remarked in 1996. Fast forward to today, and generative AI has taken the throne as the kingmaker. Trained on a myriad of data, AI models can generate diverse content forms from textual to audio-visual. As reported in 2020, GPT-3, developed by OpenAI, could draft contextually relevant textual content indistinguishable from human-created text. This capacity alleviates the burden of producing routine content from tech companies, allowing them to allocate resources more strategically.

Case Study: The Associated Press and Automated Insights have used AI to automate the generation of news stories, enabling the production of over 3,700 earning reports stories per quarter, a tenfold increase from the manual capacity.

Streamlining Software Development

Software development is another domain that generatively AI has been quietly revolutionizing. AI-powered tools like Codota and Tabnine suggest code completions by learning from billions of code lines, reducing debugging time and enhancing productivity.

For instance, GitHub’s pilot project, Copilot, uses AI to suggest code as you type, accelerating the development process and improving code quality.

Power of Data Augmentation

When real data is scarce, expensive, or privacy-sensitive, generative AI steps in to synthesize data that mirrors real-world attributes. This data synthesis capability has the potential to enhance machine learning model training, thus improving models’ robustness and precision.

Fact: A 2020 report by Gartner predicts that 60% of the data used for the development of AI and analytics projects will be synthetically generated by 2024.

We’ll now delve deeper into this technology’s transformative potential in user experience personalization, design prototyping, conversational systems, and anomaly detection.

Beyond the Visible Horizon – Unveiling More Potential

Unraveling the broader horizon of generative AI, let’s delve into the impact this transformative technology has on shaping user experiences, expediting prototyping, powering conversational systems, and bolstering anomaly detection in tech companies.

Tailoring Experiences: The Personalization Paradigm

“Personalization – it is not a trend, it’s a marketing tsunami,” remarked Avi Dan, a veteran marketing executive. Tech companies are riding this tsunami using generative AI. Based on a user’s behavior, preferences, and past interactions, AI systems can generate personalized content, creating a tailor-made user experience.

Netflix, for instance, is an industry leader in utilizing AI for personalized content recommendations, contributing to its substantial user engagement rates.

Prototyping: Painting with a Broader Palette

Generative AI offers a broader palette to paint from when it comes to design prototyping. It can generate numerous design prototypes based on specific parameters or criteria, speeding up the prototyping process, and fostering innovation.

A prominent example of this is Airbnb’s use of AI in their design process. They leverage generative models to rapidly create multiple design layouts, enhancing user experience and expediting the design process.

Conversational Systems: Enhancing Interactions

Generative AI’s role in powering advanced conversational agents exemplifies its quiet efficiency. Capable of generating human-like responses, AI-powered chatbots like Hitee developed by product engineering firm Mantra Labs and virtual assistants make interactions more engaging and natural.

Use Case: Mantra Labs’ Hitee, Google’s Meena, and OpenAI’s GPT-3 are advanced conversational AI models that can generate contextual and meaningful responses, significantly improving user engagement.

Anomaly Detection: The Hidden Watchman

In the realm of cybersecurity, fraud detection, and quality control, generative AI serves as an unsung hero. Trained to understand ‘normal’ patterns within a dataset, it raises alerts when data deviates from this norm.

In 2021, MasterCard integrated AI into its systems to detect and predict fraud before the user notices it, saving millions of dollars annually.

Conclusion

The integration of generative AI in the operational fabric of tech companies is subtly ushering in a transformative era. It has proven to be an instrumental tool in optimizing tasks and innovating solutions, all the while being unobtrusive.

However, the true prowess of generative AI lies not in what it has achieved, but in its potential. With continuous advancements, generative AI holds promising prospects for tech companies, offering a wider canvas for them to explore, experiment, and innovate.

As we step into the future, it’s clear that the quiet symphony of generative AI will continue to play a harmonious tune, enhancing the rhythm of the tech industry’s dance with progress.

NPS in Insurance Claims: What Insurance Leaders Are Doing Differently

By : Divyanjali Gupta

Published on Jul 24, 2025

Customer Experience CX Insurance NPI In Insurance

Claims are the moment of truth. Are you turning them into moments of loyalty?

In insurance, your app interface might win you downloads. Your pricing might drive conversions.
But it’s the claims experience that decides whether a customer stays—or leaves for good.

According to a survey by NPS Prism, promoters are 2.3 times more likely to renew their insurance policies than passives or detractors—highlighting the strong link between customer advocacy and retention.

NPS in insurance industry is a strong predictor of customer retention. Many insurers are now prioritizing NPS to improve their claims experience.

So, what are today’s high-NPS insurers doing differently? Spoiler: it’s not just about faster payouts.

We’ve worked with claims teams that had best-in-class automation—but still had low NPS. Why? Because the process felt like a black box.
Customers didn’t know where their claim stood. They weren’t sure what to do next. And when money was at stake, silence created anxiety and dissatisfaction.

Great customer experience (CX) in claims isn’t just about speed—it’s about giving customers a sense of control through clear communication and clarity.

The Traditional Claims Journey

Forms → Uploads → Phone calls → Waiting
No real-time updates
No guidance after claim initiation
Paper documents and email ping-pong

The result? Frustrated customers and overwhelmed call centers.

The CX Gap: It’s Not Just Speed—It’s Transparency

Customers don’t always expect instant decisions. What they want:

To know what’s happening with their claim
To understand what’s expected of them
To feel heard and supported during the process

How NPS Leaders Are Winning Loyalty with CX-Driven Claims and High NPS

1. Real-Time Status Updates

Transparency to the customer via mobile app, email, or WhatsApp—keeping them in the loop with clear milestones.

2. Proactive Nudges

Auto-reminders, such as “upload your medical bill” or “submit police report,” help close matters much faster and avoid back-and-forth.

3. AI-Powered Document Uploads

Single-click scans with OCR + AI pull data instantly—no typing, no errors.

4. In-the-Moment Feedback Loops

Simple post-resolution surveys collect sentiment and alert on issues in real time.

For e.g., Lemonade uses emotional AI to detect customer sentiment during the claims process, enabling empathetic responses that boost satisfaction and trust.

Smart Nudges from Real-Time Journey Tracking

For a leading insurance firm, we mapped the entire in-app user journey—from buying or renewing a policy to initiating a claim or checking discounts. This helped identify exactly where users dropped off. Based on real-time activity, we triggered personalized notifications and offers—driving better engagement and claim completion rates.

Tech Enablement

Claims Orchestration Layer: Incorporates legacy systems, third-party tools, and front-end apps for a unified experience.

AI & ML Models: For document validation, fraud detection, and claim routing, sentiment analysis is used. Businesses utilizing emotional AI report a 25% increase in customer satisfaction and a 30% decrease in complaints, resulting in more personalized and empathetic interactions.

Self-Service Portals: Customers can check their status, update documents, and track payouts—all without making a phone call.

Business Impact

What do insurers gain from investing in CX?

A faster claim is good. But a fair, clear, and human one wins loyalty.

And companies that consistently track and act on CX metrics are better positioned to retain customers and build long-term loyalty.

At Mantra Labs, we help insurers build end-to-end, tech-enabled claims journeys that delight customers and drive operational efficiency.
From intelligent document processing to AI-led nudges, we design for empathy at scale.

Want a faster and more transparent claims experience?

Let’s design it together.
Talk to our insurance transformation team today.

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Generative AI: Quietly Powering Innovation in Tech