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How is generative AI disrupting the insurance sector?

Generative AI in insurance

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In the rapidly evolving landscape of the insurance industry, where data-driven decisions and personalized experiences reign supreme, generative AI, a subset of Artificial Intelligence (AI), emerges as a formidable catalyst for innovation. Given this dynamic setting, insurance providers must devise innovative solutions to fulfill customer demands and enhance operational efficiency. Generative AI, with its distinct capabilities, is actively influencing the insurance sector, reshaping traditional practices and redefining how insurers conduct their operations.

Generative AI empowers insurers to harness the power of advanced ML models, facilitating the creation of personalized recommendations and customized products for customers as well as the precise determination of individualized pricing while maintaining high levels of customer satisfaction. This data-driven approach not only enhances insurers’ decision-making capabilities but also paves the way for a faster and more seamless digital buying experience for policyholders.

Further, the success of an insurance business heavily relies on its operational efficiency, and generative AI plays a central role in helping insurers achieve this goal. Through AI-enabled task automation, they can achieve significant improvements in their operational efficiency, enable insurers to respond faster, reduce manual interventions, and deliver superior customer experiences.

Generative AI’s ability to generate fresh and synthetic data is another game-changer. This unique capability empowers insurers to make faster and more informed decisions, leading to better risk assessments, more accurate underwriting, and streamlined claims processing. With generative AI, insurers can stay ahead of the curve, adapting rapidly to the ever-evolving insurance landscape.

In the article, we will delve into a comprehensive exploration of generative AI’s impact on the insurance sector, uncovering its diverse applications, tangible benefits, and real-world examples that showcase its disruptive influence.

Generative AI in insurance: An overview

Generative AI is the subset of AI technology that enables machines to generate new content, data, or information similar to that produced by humans. Unlike traditional AI systems that rely on pre-defined rules and patterns, generative AI leverages advanced algorithms and deep learning models to create original and dynamic outputs. In the insurance industry context, generative AI plays a crucial role in redefining various aspects, from customer interactions to risk assessment and fraud detection. Generative AI introduces a new paradigm in the insurance landscape, offering unparalleled opportunities for innovation and growth. The ability of generative AI to create original content and derive insights from data opens doors to novel applications pertinent to this industry. It facilitates predictive modeling, enabling the creation of risk scenarios that empower insurers to formulate preemptive strategies for proactive risk management. Additionally, generative AI’s capability to create personalized content enables insurers to offer tailor-made insurance policies and experiences, fostering stronger relationships with customers.

Growth projections for generative AI in insurance

The global market size for generative AI in the insurance sector is set for remarkable expansion, with projections showing growth from USD 346.3 million in 2022 to a substantial USD 5,543.1 million by 2032. This substantial increase reflects a robust growth rate of 32.9% from 2023 to 2032, as reported by Market.Biz.

The factors driving this growth are:

  1. Automation and efficiency: Generative AI’s ability to automate insurance tasks leads to heightened operational efficiency. It streamlines operations and enhances overall effectiveness by accelerating processes and reducing human errors.
  2. Rising complexity and data volume: The insurance industry deals with an escalating volume of data. Generative AI helps handle this surge and manage complex datasets, extracting valuable insights. This capacity to utilize vast data sets is a major driving force behind generative AI’s growth in the insurance market.

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Comparing traditional and generative AI in insurance operations: What sets them apart?

Generative AI and traditional AI are distinct approaches to artificial intelligence, each with unique capabilities and applications in the insurance sector. Understanding how generative AI differs from traditional AI is essential for insurers to harness the full potential of these technologies and make informed decisions about their implementation.

Traditional AI, also known as rule-based AI or narrow AI, relies on predefined rules and patterns to perform specific tasks. It follows a deterministic approach, where the output is directly derived from the input and predefined algorithms. In contrast, generative AI operates through deep learning models and advanced algorithms, allowing it to generate new content and data. Unlike traditional AI, generative AI is not bound by fixed rules and can create original and dynamic outputs.

When it comes to data and training, traditional AI algorithms require labeled data for training and rely heavily on human-crafted features. The performance of traditional AI models is limited to the quality and quantity of the labeled data available during training. On the other hand, generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can generate new data without direct supervision. They learn from unlabelled data and can produce meaningful outputs that go beyond the training data.

Traditional AI is widely used in the insurance sector for specific tasks like data analysis, risk scoring, and fraud detection. It can provide valuable insights and automate routine processes, improving operational efficiency. On the other hand, generative AI opens up new possibilities. It can create synthetic data for training, augmenting limited datasets, and enhancing the performance of AI models. Generative AI can also generate personalized insurance policies, simulate risk scenarios, and assist in predictive modeling.

Traditional AI models excel at analyzing structured data and detecting known patterns of fraudulent activities based on predefined rules regarding risk assessment and fraud detection. In contrast, generative AI can enhance risk assessment by generating diverse risk scenarios and detecting novel patterns of fraud that may not be explicitly defined in traditional rule-based systems. Furthermore, generative AI enables insurers to offer truly personalized insurance policies, customizing coverage, pricing, and terms based on individual customer profiles and preferences. While traditional AI can support personalized recommendations based on historical data, it may be limited in creating highly individualized content.

However, generative AI, being more complex and capable of generating new content, raises challenges related to ethical use, fairness, and bias, requiring greater attention to ensure responsible implementation. Traditional AI systems are more transparent and easier to explain, which can be crucial for regulatory compliance and ethical considerations.

Types of generative AI models used in the insurance sector

Generative models in AI are designed to learn and replicate patterns and structures found within their training data, allowing them to generate new samples that resemble the original data. In the context of generative AI in insurance, three prominent types of generative models stand out: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models.

Generative Adversarial Networks (GANs)

GANs are a class of generative models introduced by Ian Goodfellow and his colleagues in 2014. They consist of two neural networks, the generator and the discriminator, engaged in a competitive game. The generator’s role is to generate fake data samples, while the discriminator’s task is to distinguish between real and fake samples. During training, the generator learns to generate data that is increasingly difficult for the discriminator to differentiate from real data. This back-and-forth training process makes the generator proficient at generating highly realistic and coherent data samples.

In the context of insurance, GANs can be employed to generate synthetic but realistic insurance-related data, such as policyholder demographics, claims records, or risk assessment data. These generated samples can augment the existing data for training and improve the performance of various AI models used in insurance applications. For instance, insurers have used GANs to generate synthetic insurance data, which helps in training AI models for fraud detection, customer segmentation, and personalized pricing. By generating realistic synthetic data, GANs not only enhance data quality but also enable insurers to develop more accurate and reliable predictive models, ultimately improving insurance operations’ overall efficiency and accuracy.

Variational Autoencoders (VAEs)

VAEs are another type of generative model that combines elements of both generative and inference models. VAEs consist of two main components: a decoder and an encoder. The encoder is responsible for transforming the input data into a latent space representation, while the decoder reconstructs the data from the latent space back into the original data space.

VAEs differ from GANs in that they use probabilistic methods to generate new samples. By sampling from the learned latent space, VAEs generate data with inherent uncertainty, allowing for more diverse samples compared to GANs. In insurance, VAEs can be utilized to generate novel and diverse risk scenarios, which can be valuable for risk assessment, portfolio optimization, and developing innovative insurance products.

Autoregressive models

Autoregressive models are generative models known for their sequential data generation process, one element at a time, based on the probability distribution of each element given the previous elements. In other words, an autoregressive model predicts each data point based on the values of the previous data points. These models are often used for generating sequences or time-series data.

In insurance, autoregressive models can be applied to generate sequential data, such as time-series data on insurance premiums, claims, or customer interactions. These models can help insurers predict future trends, identify anomalies within the data, and make data-driven decisions for business strategies. For example, autoregressive models can predict future claim frequencies and severities, allowing insurers to allocate resources and proactively prepare for potential claim surges. Additionally, these models can be used for anomaly detection, flagging unusual patterns in claims data that may indicate fraudulent activities. By leveraging autoregressive models, insurers can gain valuable insights from sequential data, optimize operations, and enhance risk management strategies.

All three types of generative models, GANs, VAEs, and autoregressive models, offer unique capabilities for generating new data in the insurance industry. GANs excel at producing highly realistic samples, VAEs provide diverse and probabilistic samples, while autoregressive models are well-suited for generating sequential data. By leveraging these powerful generative models, insurers can enhance their data analysis, risk assessment, and product development, ultimately redefining how the insurance industry operates.

The benefits of generative AI in insurance

Generative AI is significantly impacting the insurance industry, offering numerous advantages that redefine how insurers operate, interact with customers, and manage risks. Here are some key benefits of implementing generative AI in the insurance industry:

Benefits of generative AI in insurance

Enhanced customer experience

Generative AI enables insurers to offer personalized experiences to their customers. By processing extensive volumes of customer data, AI algorithms have the capability to tailor insurance products to meet individual needs and preferences. Virtual assistants powered by generative AI engage in real-time interactions, guiding customers through policy inquiries and claims processing, leading to higher satisfaction and increased customer loyalty.

Improved risk assessment and underwriting

Generative AI models can assess risks and underwrite policies more accurately and efficiently. Through the analysis of historical data and pattern recognition, AI algorithms can predict potential risks with greater precision. This enables insurers to optimize underwriting decisions, offer tailored coverage options, and reduce the risk of adverse selection.

Streamlined claims processing

Generative AI automates claims processing, extracting and validating data from claim documents. This streamlines the entire claims settlement process, reducing turnaround time and minimizing errors. Faster and more accurate claims settlements lead to higher customer satisfaction and improved operational efficiency for insurers.

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Our custom genAI solutions and services will empower your insurance business to unlock fresh avenues for innovation and expansion

Advanced fraud detection and prevention

Advanced generative AI algorithms can detect patterns and anomalies associated with fraudulent behavior, enabling early detection and prevention of fraudulent claims. By analyzing vast datasets, generative AI enhances fraud detection capabilities, safeguarding insurers from potential financial losses and maintaining their credibility.

Proactive risk management

Generative AI’s predictive modeling capabilities allow insurers to simulate and forecast various risk scenarios. By identifying potential risks in advance, insurers can develop proactive risk management strategies, mitigate losses, and optimize their risk portfolios effectively.

Data-driven insights

Generative AI-driven customer analytics provides valuable insights into customer behavior, market trends, and emerging risks. This data-driven approach empowers insurers to develop innovative services and products that cater to changing customer needs and preferences, leading to a competitive advantage.

Cost savings and operational efficiency

By automating various processes, generative AI reduces the need for manual intervention, leading to cost savings and improved operational efficiency for insurers. Automated claims processing, underwriting, and customer interactions free up resources and enable insurers to focus on higher-value tasks.

Regulatory compliance and transparency

Generative AI can incorporate explainable AI (XAI) techniques, ensuring transparency and regulatory compliance. Insurers can understand the reasoning behind AI-generated decisions, facilitating compliance with regulatory standards and building customer trust in AI-driven processes.

Put data control back in the consumer’s hands

Generative AI makes it efficient for insurers to digitally activate a zero-party data strategy—a data-gathering approach proving successful for many other industries. The zero-party advantage leverages responses that consumers willingly provide an insurer to a set of simple, personalized questions posed to them, helping sales and marketing agents collect response data in a noninvasive and transparent way. Insurers receive actionable data insights from consumers, while consumers receive more customized insurance that better protects them.

Generative AI holds immense potential to reshape the insurance industry. By enhancing customer experiences, improving risk assessment and underwriting, streamlining claims processing, detecting and preventing fraud, enabling proactive risk management, providing data-driven insights, optimizing operations, fostering innovation, and ensuring regulatory compliance, generative AI empowers insurers to thrive in an ever-changing market.

Generative AI use cases in the insurance industry

Generative AI has a wide range of use cases in the insurance industry, offering innovative solutions to longstanding challenges and driving disruptive changes. Here are some notable generative AI use cases in the insurance industry:

Personalized insurance policies

Generative AI enables insurers to create personalized insurance policies tailored to individual customers’ needs and risk profiles. By analyzing vast datasets and customer information, AI algorithms generate customized coverage options, pricing, and terms, enhancing the overall customer experience and satisfaction. For instance, an auto insurer can utilize generative AI to analyze a customer’s driving history, vehicle details, and personal characteristics to offer a customized car insurance policy that aligns with the individual’s specific requirements.

Automated underwriting

Generative AI streamlines the underwriting process by automating risk assessment and decision-making. AI models can analyze historical data, identify patterns, and predict risks, enabling insurers to make more accurate and efficient underwriting decisions. For instance, a life insurance company can use generative AI algorithms to analyze medical records, lifestyle data, and family history to make efficient underwriting decisions, enabling faster policy approvals and enhancing customer experiences.

Claims processing automation

Generative AI automates claims processing by extracting and validating data from claim documents, reducing manual efforts and processing time. Automated claims processing ensures faster and more accurate claim settlements, improving customer satisfaction and operational efficiency. For example, property insurers can utilize generative AI to automatically process claims for damages caused by natural disasters, automating the assessment and settlement for affected policyholders.

Fraud detection and prevention

Generative AI helps combat insurance fraud by analyzing vast amounts of data and detecting patterns indicative of fraudulent behavior. AI-powered algorithms can identify suspicious claims in real-time, enabling insurers to take proactive measures to prevent fraud and reduce financial losses. For instance, health insurers can identify anomalies in medical billing data, uncovering potential fraudulent claims and saving costs.

Virtual assistants and customer support

Generative AI-powered virtual assistants provide real-time support to customers, addressing policy inquiries, claims status updates, and general insurance-related questions. Virtual assistants enhance customer interactions and reduce the burden on customer support teams. For example, a property insurance company can implement a virtual assistant on their website, guiding customers through the claims process and providing helpful information.

Risk modeling and predictive analytics

Generative AI models can simulate various risk scenarios and predict potential future risks, helping insurers optimize risk management strategies and make informed decisions. Predictive analytics powered by generative AI provides valuable insights into emerging risks and market trends. For instance, a property and casualty insurer can use generative AI to forecast weather-related risks in different regions, enabling proactive measures to minimize losses.

Product development and innovation

Generative AI facilitates product development and innovation by generating new ideas and identifying gaps in the insurance market. AI-driven insights help insurers design new insurance products that cater to changing customer requirements and preferences. For example, a travel insurance company can utilize generative AI to analyze travel trends and customer preferences, leading to the creation of tailored insurance plans for specific travel destinations.

Natural Language Processing (NLP) applications

Generative AI leverages NLP to process unstructured data, such as customer feedback, social media interactions, and medical records, to gain valuable insights. NLP-powered sentiment analysis helps insurers understand customer sentiments and improve services accordingly. For instance, an insurer can use NLP-powered sentiment analysis to understand customer sentiments towards their products and services, enabling improvements based on customer feedback.

Anomaly detection

Generative AI’s anomaly detection capabilities allow insurers to identify irregular patterns in data, such as unusual customer behavior or suspicious claims. Early detection of anomalies helps mitigate risks and ensures more accurate decision-making. For example, an auto insurer can use generative AI to detect unusual claims patterns, such as a sudden surge in accident claims in a specific region, leading to the identification of potential fraud or emerging risks.

Image and video analysis

Generative AI can analyze images and videos to assess damages in insurance claims, such as vehicle accidents or property damage. This visual analysis aids in faster claims processing and accurate assessment of losses. For example, a car insurance company can use image analysis to estimate repair costs after a car accident, facilitating quicker and more accurate claims settlements for policyholders.

Real-world examples: Insurance organizations using generative AI

Generative AI is rapidly reshaping the insurance industry, offering major companies opportunities for operational improvements, informed decision-making, and enhanced customer experiences. Among the industry’s key players, five insurance companies stand out for their strides in adopting generative AI:


GEICO, an auto insurance company, has developed a user-friendly virtual assistant to assist the company’s prospects and customers with insurance and policy questions. The virtual assistant engages in conversations and provides essential information, leveraging message intent recognition to understand custom queries and offer relevant links. Although the virtual assistant does not generate quotes directly, it redirects users to appropriate sales pages for actions like obtaining a quote. GEICO’s innovative use of generative AI in their virtual assistant enhances customer engagement and improves their overall user experience.


Chubb, headquartered in Zurich, Switzerland, is a prominent global insurance company offering a diverse range of insurance services. Embracing the potential of generative AI, Chubb is prepared to implement these cutting-edge tools on a large scale. Their strategic focus revolves around leveraging AI-driven advancements to enhance crucial operations, such as underwriting and claims processing. By embracing generative AI technology, Chubb aims to optimize efficiency and further elevate their services, positioning themselves at the forefront of innovation in the insurance industry.

Liberty Mutual

Headquartered in Boston, USA, Liberty Mutual is a leading diversified global insurer with a wide range of offerings, including General Insurance, Health Insurance, and Life Insurance. The company is actively exploring the potential of AI and machine learning through its innovative initiative, Solaria Labs. As part of their AI endeavors, Liberty Mutual has successfully developed an AI-powered auto damage estimator, which significantly streamlines the process of assessing vehicle damage. By embracing AI-driven technologies, Liberty Mutual aims to enhance customer experiences and drive efficiency in its insurance operations, solidifying its position as a forward-thinking industry player.


Based in Chicago, USA, Clearcover is a digital car insurance provider known for its key offerings in Auto Insurance. With an impressive investment raised amounting to approximately $329.5 million, the company stands at the forefront of insurance innovation. Clearcover harnesses advanced technology to deliver a unique proposition of lower prices and a seamless customer experience. Central to their approach is ClearAI, an AI-powered tool that automates the claims process. Through ClearAI, customers benefit from swift and efficient responses, streamlining the claims settlement process and ultimately enhancing overall customer satisfaction. As a pioneer in digital insurance, Clearcover continues to demonstrate its commitment to leveraging technology to redefine the car insurance landscape.

Ping An Insurance

Headquartered in Shenzhen, China, Ping An Insurance is a prominent global player offering a comprehensive range of services, including life insurance, property and casualty insurance, banking, and financial services. The company is a dedicated proponent of cutting-edge technologies, including AI, big data, and cloud technologies. Notably, Ping An’s AI-driven initiatives feature generative models for crucial aspects of their operations, such as underwriting, risk assessment, and automated customer service. By embracing such advanced AI technologies, Ping An Insurance positions itself at the forefront of innovation in the insurance and financial services sector, aiming to provide efficient and tailored solutions to their diverse customer base.

SWICA (Switzerland)

SWICA, a health insurance company, has developed a sophisticated chatbot for customer service. The chatbot not only answers frequently asked questions but also handles policy changes seamlessly within the chat window. Customers can perform various actions, such as changing franchises, updating addresses, ordering insurance cards, including accident cover, and registering new family members, all without being redirected to a different page. SWICA’s chatbot exemplifies the use of generative AI to enhance customer experience and streamline policy management processes.

Insurify (USA)

Insurify, an insurance comparison website, has been an early adopter of chatbots in the insurance industry. Their chatbot utilizes natural language processing and machine learning technologies to ask users clarifying questions and assist them in finding the right insurance policy based on their needs. Remarkably, the bot can even process pictures, such as photos of license plates, in addition to text messages. Operating within the Facebook Messenger app, Insurify’s chatbot demonstrates the effective integration of generative AI to facilitate seamless communication and personalized policy recommendations.

Lemonade (USA)

Lemonade, an innovative AI-powered insurance company, offers a chatbot that seamlessly guides policyholders through their entire customer journey. Users can conveniently apply for policies, make payments, file claims, and receive real-time updates without the need for phone calls. Notably, Lemonade’s chatbot, Maya, achieved a world record by processing and paying a $979 claim in under 3 seconds. This remarkable feat showcases Lemonade’s innovative use of generative AI to provide efficient, real-time customer support and claims processing.

By adopting generative AI, these companies anticipate numerous benefits, including personalized offerings, efficient claim settlements, and objective risk assessments, leading to higher customer satisfaction.

The future of generative AI in insurance

As the insurance industry continues to evolve, generative AI has already showcased its potential to redefine various processes by seamlessly integrating itself into these processes. Generative AI has left a significant mark on the industry, from risk assessment and fraud detection to customer service and product development. However, the future of generative AI in insurance promises to be even more dynamic and disruptive, ushering in new advancements and opportunities.

Autonomous claims processing: The future of generative AI in insurance will see a significant shift toward autonomous claims processing. With the integration of advanced computer vision and natural language processing capabilities, AI-powered systems will efficiently process and validate claims without human intervention. Customers will benefit from quicker and more accurate settlements, reducing the time and effort involved in claim filing and processing.

Improved fraud detection and prevention: The future of generative AI in insurance will further enhance fraud detection and prevention capabilities. AI algorithms, coupled with anomaly detection techniques, will be able to identify fraudulent patterns and activities with greater precision, significantly reducing losses due to insurance fraud. Real-time monitoring and intelligent algorithms will act as a robust defense against ever-evolving fraudulent schemes.

Integration of IoT and generative AI: The Internet of Things (IoT) will intersect with generative AI to create a seamless ecosystem of interconnected devices and data. Insurers will leverage data from smart devices such as connected cars, wearables, and home sensors to assess risks accurately, tailor policies based on real-time data, and prevent losses by offering personalized safety recommendations.

Explainable AI (XAI) for transparency: As the adoption of generative AI in insurance grows, the demand for Explainable AI (XAI) will intensify. XAI techniques will be crucial in ensuring transparency, compliance, and trust in AI-generated decisions. Insurers will need to explain how AI algorithms arrive at specific recommendations or decisions, enabling better customer understanding and regulatory compliance.

Automated customer interaction: Generative AI will redefine customer interactions with insurers through advanced chatbots and virtual assistants. These AI-powered assistants will handle routine queries and engage in sophisticated conversations, understanding complex customer needs and offering personalized recommendations for policies and coverage options.

Risk modeling and underwriting advancements: The future of generative AI in insurance will witness significant advancements in risk modeling and underwriting. Insurers will harness the power of generative models to simulate and forecast various risk scenarios, improving the accuracy of underwriting decisions and optimizing their portfolios to achieve better profitability.

Partnerships and collaborations: To harness the full potential of generative AI, insurers will increasingly collaborate with technology companies, data providers, and insurance tech startups. These partnerships will facilitate access to cutting-edge technologies, extensive data sources, and expertise, enabling insurers to accelerate innovation and stay ahead in a competitive market.

Ethical AI and regulatory compliance: As generative AI becomes more pervasive in the insurance industry, ensuring ethical AI practices and adherence to regulatory guidelines will be paramount. Insurers will be required to adopt robust governance frameworks to address bias, fairness, and privacy concerns associated with AI algorithms.

Health and well-being convergence: The trend of health and well-being convergence complements the application of generative AI in promoting holistic and personalized care. Insurers can leverage AI-generated insights to offer customized health insurance plans that incentivize healthy lifestyles, reducing claims costs and improving customer retention. Generative AI’s ability to analyze health data and predict risk factors will enable insurers to design wellness programs tailored to individual policyholders’ needs.

Inclusive insurance: Generative AI in insurance is well-aligned with the trend of inclusive insurance. As AI models process vast amounts of data, insurers can identify underserved markets and demographics, including low-income households, migrants, and small businesses. By expanding into these underserved segments, insurers bridge the protection gap and contribute to the financial well-being and stability of the broader population.

The future of generative AI in insurance holds immense promise, redefining the industry’s landscape and reshaping how insurers operate, interact with customers, and manage risks. Embracing generative AI with a balanced approach, insurers can unlock unprecedented levels of efficiency, customer satisfaction, and profitability in the dynamic world of insurance.

Final words

The emergence of generative AI has significantly impacted the insurance industry, delivering a multitude of advantages for insurers and customers alike. From automating business processes and enhancing operational efficiency to providing personalized customer experiences and improving risk assessment, generative AI has proven its potential to redefine the insurance landscape. Leading insurance organizations like Lemonade, SWICA, and GEICO have demonstrated how leveraging generative AI can streamline customer interactions, enable seamless policy management, and expedite claims processing. As the technology continues to advance, insurers are poised to unlock new levels of innovation, offering tailored insurance solutions, proactive risk management, and improved fraud detection. However, the adoption of generative AI also demands attention to data privacy, regulatory compliance, and ethical considerations. With a balanced approach, the future of generative AI in insurance holds immense promise, ushering in a new era of efficiency, customer satisfaction, and profitability in the dynamic and ever-evolving insurance landscape.

Want to leverage the power of generative AI in your insurance operations? Connect with LeewayHertz’s team of AI experts to explore tailored solutions that enhance efficiency, streamline processes, and elevate customer experiences.

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What is Chainlink VRF

Author’s Bio

Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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