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The Next Big Thing for Big Tech: AI as a Service

4 minutes, 9 seconds read

The biggest challenge with AI practitioners (so far) is to find a considerable volume of relevant data to feed machine learning algorithms. And nobody ever thought that this problem would be resolved in the blink of an eye. 

With huge data repositories, the crowned tech giants —  Amazon, Google, Microsoft, Apple, IBM, Salesforce, SAP, Oracle, Alibaba and Baidu have become the AI leaders of today. Their next venture into AI as a Service (AIaaS), adequately powered by Data as a Service is, yet again, prone to disrupt Digital. 

How will AI as a Service impact businesses?

Organizations may have centuries-old data with them, and they might even invest in digitizing all historic data to generate volume. But, is this data a good fodder for machine learning models? Certainly not. Consumers today are way different from yesterday. What the world needs is real-time data. And who has it? The aforementioned AI leaders, who not only made efforts to collect data but also made arrangements to organize them and use them wherever, whenever. 

Today, Google Home has over half a billion users; meaning — there’s no scarcity of data. With this, Google cloud offers a range of AIaaS products like AI Hub — a repository of plug-and-play AI components; AI building blocks — to make developers utilize structured data into their applications; and an AI platform — a development environment to let data scientists and ML developers quickly take projects from ideation to deployment. 

The point is, the quest for quality data to train ML models is nearly over. The hunt for Machine Learning experts is seeing an end. Because with Google Cloud AutoML developers with limited ML expertise will be able to train their specific ML models. Similarly, Amazon SageMaker provides Managed Spot Training, which can reduce ML models’ training cost by 90%. This drastic cost reduction will encourage businesses to adopt AI at a larger scale; thus opening new avenues for innovations.

Is AIaaS different from MLaaS (Machine Learning as a Service)?

MLaaS is a set of services that offer ready-made Machine Learning tools. Organizations can utilize this as a part of their working needs. The popular MLaaS services available today are (mostly from Amazon, Google, Microsoft, and IBM)-

1. Natural language processing

2. Speech recognition

3. Computer vision

4. Video and image analysis

While ML corresponds to making machines learn by themselves, AI focuses on both the acquisition and application of information. AI is the process of simulation of natural intelligence to solve complex problems. AIaaS, thus, broadens the scope of MLaaS by enabling machines with cognitive capabilities.

We’re rapidly moving beyond the algorithms that were designed to deliver experiences based on predefined rules. “AI… is a group of algorithms that can modify its algorithms to create new algorithms in response to learned inputs…” (Kaya Ismail, CMSWire)

How will AI as a Service disrupt digital products and experiences?

  • With the commercialization of AI, most of the digital products will be equipped with AI.
  • The time-to-market for AI and ML-based products will reduce drastically.
  • AI-enabled products comply with connected data ecosystems, which enables effective organization and utilization of huge volumes of data.
  • AIaaS will deliver multi-layer insights to humans at a moment’s notice. 
  • It will smartly integrate different technologies (like AR) on-need basis.
  • Making sense of regional language data will be no more challenging.
  • Delivering intuitive experiences will become much simpler. For instance, the Google Translate app automatically takes input from the user’s device language settings and applies augmented reality experience to scanned images. 
  • It will connect the dots — IoT, Driverless cars, drones, hyperloop, and even space technologies.

[Related: The State of AI in Insurance 2020]

Getting the edge over operations for the next era of tech

Cloud is changing the AI marketplace and serverless computing is making it possible for developers to quickly get AI applications up and running. Also, the prime enabler of AI as a Service business is information services. The biggest change that serverless computing has brought is — it has eliminated the need to scale physical database hardware. For instance, Amazon Aurora extends the performance and availability of commercial-grade databases at 1/10th of the cost. To mention, Netflix moved its database to AWS to leverage the benefits of serverless computing. Another example of distributed infrastructure for data is that of Microsoft Azure Data Lake. It dynamically allocates or deallocates resources, enabling a pay-per-use model. 

While business benefits from AI as a Service are immense, the competition among AI Leaders is not less. Tech giants pour billions of dollars in AI research to shape the business of the future. What we see today is the outcome of decades of hardship and the quest to get the best minds to execute their AI strategy. 

Read the story – The Big Five of Tech are winning more often with AI — The AI race so far.

We are helping leading Insurers like Aditya Birla Health Insurance, Religare, DHFL Pramerica, and many more in their AI initiatives. Please feel free to talk to us for your AI strategy roadmap and implementation. Drop us a line at hello@mantralabsglobal.com


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Retention playbook for Insurance firms in the backdrop of financial crises

4 minutes read

Belonging to one of the oldest industries in the world, Insurance companies have weathered multiple calamities over the years and have proven themselves to be resilient entities that can truly stand the test of time. Today, however, the industry faces some of its toughest trials yet. Technology has fundamentally changed what it means to be an insurer and the cumulative effects of the pandemic coupled with a weak global economic output have impacted the industry in ways both good and bad.

Chart, line chart

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Source: Deloitte Services LP Economic Analysis

For instance, the U.S market recorded a sharp dip in GDP in the wake of the pandemic and it was expected that the economy would bounce back bringing with it a resurgent demand for all products (including insurance) across the board. It must be noted that the outlook toward insurance products changed as a result of the pandemic. Life insurance products were no longer an afterthought, although profitability in this segment declined over the years. Property-and-Casualty (P&C) insurance, especially motor insurance, continued to be a strong driver, while health insurance proved to be the fastest-growing segment with robust demand from different geographies

Simultaneously, the insurance industry finds itself on the cusp of an industry-wide shift as technology is starting to play a greater role in core operations. In particular, technologies such as AI, AR, and VR are being deployed extensively to retain customers amidst this technological and economic upheaval.

Double down on digital

For insurance firms, IT budgets were almost exclusively dedicated to maintaining legacy systems, but with the rise of InsurTech, it is imperative that firms start dedicating more of their budgets towards developing advanced capabilities such as predictive analytics, AI-driven offerings, etc. Insurance has long been an industry that makes extensive use of complex statistical and mathematical models to guide pricing and product development strategies. By incorporating the latest technological advances with the rich data they have accumulated over the years, insurance firms are poised to emerge stronger and more competitive than ever.

Using AI to curate a bespoke customer experience

Insurance has always been a low-margin affair and success in the business is primarily a function of selling the right products to the right people and reducing churn as much as possible. This is particularly important as customer retention is normally conceived as an afterthought in most industries, as evidenced in the following chart.

Chart, sunburst chart

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        Source: econconusltancy.com

AI-powered tools (even with narrow capabilities) can do wonders for the insurance industry at large. When architected in the right manner, they can be used to automate a bulk of the standardized and automated processes that insurance companies have. AI can be used to automate and accelerate claims, assess homeowner policies via drones, and facilitate richer customer experiences through sophisticated chatbots. Such advances have a domino effect of increasing CSAT scores, boosting retention rates, reducing CACs, and ultimately improving profitability by as much as 95%.

Crafting immersive products through AR/VR

Customer retention is largely a function of how good a product is, and how effective it is in solving the customers’ pain points. In the face of increasing commodification, insurance companies that go the extra mile to make the buying process more immersive and engaging can gain a definite edge over competitors.

Globally, companies are flocking to implement AR/VR into their customer engagement strategies as it allows them to better several aspects of the customer journey in one fell swoop. Relationship building, product visualization, and highly personalized products are some of the benefits that AR/VR confers to its wielders.  

By honoring the customer sentiments of today and applying a slick AR/VR-powered veneer over its existing product layer, insurance companies can cater to a younger audience (Gen Z) by educating them about insurance products and tailoring digital delivery experiences. This could pay off in the long run by building a large customer base that could be retained and served for a much longer period.

The way forward

The Insurance industry is undergoing a shift of tectonic proportions as an older generation makes way for a new and younger one that has little to no perceptions about the industry. By investing in next-generation technologies such as AR/VR, firms can build new products to capture this new market and catapult themselves to leadership positions simply by way of keeping up with the times.

We have already seen how AR is a potential game-changer for the insurance industry. It is only a matter of time before it becomes commonplace.


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