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Consumer-Centric Design in Insurance

Insurance instinctively feels old. It’s as though, the fast-moving parts of the digital age can’t seem to permeate its an archaic blueprint. Sure enough, it looks and feels that way too. One look at the spasm of choices to avail insurance online will leave you feeling dull and permanently bored. Consumers often don’t buy insurance, (even when they need it) because they are turned off by the complexity involved in understanding the product itself, and in the way, it is typically packaged & sold. 

In the Internet 2.0 era, users switch lightning quick between a dozen websites in tandem. The insurance industry, like most, is largely affected by the seeming lack of leverage they have in understanding what appeals to the consumer’s buying nature, instincts and experience.

The power of ‘choice’ lies in the hands of the insured, while the ability to ‘influence’ choice is a matter of design thinking. So if a user doesn’t get the price, product, service, communication and/or experience — they quickly move on

Insurers need next-gen customer engagement solutions that enable them to deliver the right interaction or experience at every customer touchpoint across the lifecycle, in order to maximize real customer lifetime value.

A detailed UX audit reveals many lacklustre areas in traditional insurance websites. In my experience (from having performed countless such audits) — insurance pages create limited awareness of the product, incomplete product understanding, confusion about features, low trust in delivery, frustration about lack of transparency, limited access to easy self-service tools and often a feeling of being overwhelmed leading to a tendency to put-off the purchase.

The inability to correct low engagement among Gen Y and Z users will hurt the long term stability for product innovation. According to a recent McKinsey analysis, the average number of interactions among banks and big tech cos with their customers (above the age of 20) is between 2100 to 2500 interactions per year. The same for health insurers average only around 270 to 300 interactions each year, perhaps indicating a strong disconnect between the need for insurance as a product/ service and its perception otherwise.

The transition from a ‘policy-centric’ to a ‘customer-first’ approach for up-selling, cross-selling and retention requires designing for three needs — ease of use, choice and (access to) support.

As customer expectations continue to evolve and lower tolerances are built for needlessly long and drawn-out customer journeys, the need for consistently delivering a superior experience stands out.

Let’s take a look at how insurers can improve some key areas of engagement:

  1. Omnichannel

    Nearly half
    of all life insurance customers prefer an omnichannel journey. This means that they expect the same superior experience today through search, social, website, app and in-person interactions with the company, and tomorrow. For creating the ideal ‘target customer journey’, basic pain-points are critical to addressing. For instance, a simple call before a routine health check-up to reassure the customer for any assistance post-appointment can go a long way in reassuring the brand’s commitment to even the finer details. These out-of-the-box experiences facilitate the creation of ‘Signature moments’ for the customer, driving loyalty.



    Today, most buying journeys begin with mobile — as people explore their insurance options in their free time, and on the move. Insurers will have to reinvent multichannel experiences like any other consumer product, say designer clothing or high-end electronics. While basic hygiene factors such as a mobile-responsive website equipped with a home-page wizard that seamlessly engages and assists the user are mandatory for companies who wish to increase their conversions, especially among younger demographics.

  2. Straight-to-Quote

    Getting to a product quote is one of the first interactions a user engages in. A potential customer checks on average, 4-5 websites before coming to any serious buying decision.

    The majority of insurers still use a plain design approach to displaying products — the method of asking the same bundle of questions in a ‘tick-box’ format. Asking less but relevant questions to offer quotes should be seen as a prerequisite in order to let go of outdated buying flows.



    A redesigned process can manufacture simple operational improvements. A prospective buyer who is looking for a quote on an insurer’s website is already spending time researching a multitude of different products with varying features. Insurers can save these users time spent on extensive research, through quick outreach that delivers a sensible buying rationale that feels personal to the user (using data & analytics).

    From here, a human agent (who is monitoring the journey thus far) can quickly take over and interactions can move beyond the jargon to address real needs. The user can be led to a more personalized interaction site (instead of being forced to download an app) and can get all account information, policy summaries and main headlines straight to phone or email — without having to re-enter any data.

    An overhaul of the journey (such as the one above) can unlock 50% or more increase in new premiums, simply because the customer and the insurer got off on the right page together.
  3. Policy Details

    Even in the age of digitalization, prospective customers still prefer to talk to people when it comes to getting information about the cost and quality of insurance products. Hardly anyone reads the 200-page brochure explaining every minute detail of an insurance policy. Users expect a simple, easy-to-understand summary of the policy, it’s pricing, its beneficial features and how it fares better than other policies in the market offered by other insurers.

Aggregators typically overcome this well, because they have to pit multiple policies against each other. In order to achieve this, a streamlined UI needs to be placed at the forefront of the interaction. This can easily navigate users through the buying journey and gather the relevant information along the way.

Lemonade and Insurify are great examples of new-age insurtechs already doing this — by using extensively user-tested pages with simple, clean CTAs strategically positioned along with the page, drawing the users scroll to each next step.

Lemonade Insurance



Another approach to disseminating the right policy information at the right time is to demonstrate the utility of the product through simple and effective storytelling. This way, the policy is broken down into easily digestible chunks that are always accessible to the user at any stage of their lifecycle with an insurer and avoids their dependency on legal confusing jargon. Insurers can also allow the user to craft their own policy (eg: lemonade insurance), which allows the user to experience exactly how their coverage works in-and-out.

  1. Quick Support & Advice
    Buying insurance protection is often unplanned and can be an emotional decision — since customers are looking to protect their life, health, home, family, or possessions. The process is usually mired with the hassle of navigating poorly designed experiences that don’t pay attention to an individual’s immediate or future needs but rather focuses on selling a generic product with no unique features. This makes the very idea of designing personalized user experiences extremely modern and a conscious path to the future of ‘individualized selling’. Insurers will have to present an uncluttered, clean, and straight-to-the-point visual website with simple & memorable messaging, and a conversational wizard that gives every user the freedom to explore freely and transition fluently across each stage in the buying process.
Customer Journey

Design thinking is all about product innovation for the best customer experience. A customer-first approach has been proven to create better business ROI, that demonstrably improves the customer-company dynamic. The right UX expert can bring an unbiased view into what your customer feels, and point out where the relationship, for insurers, can finally begin to improve.
To know more about how our customer-first design approach is solving insurer challenges across their customer journeys, reach out to us at hello@mantralabsglobal.com.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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