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InsurTalks Podcast with Dr. Robin Kiera: Nurturing Sales in the Global Shutdown

9 minutes, 45 seconds read

Today, we are facing a crisis unlike ever before. The outbreak of COVID-19 has shaken the very foundation of the world economy. Social distancing and lockdowns have disrupted supply chains and international trade. 

We are seeing an unprecedented surge in lay-offs since the last economic recession in 2008. Amongst other industries that have been affected, Insurance and InsurTech are also finding it difficult to deal with the crisis. To get deeper insights on the new strategy for Insurance Sales, we interviewed Dr. Robin Kiera, founder of  Digitalscouting and renowned Insurtech & Fintech Influencer.

Digitalscouting is a platform with over 70,000 followers for thought leaders, entrepreneurs, and senior managers in finance and insurance to share best practices, lessons learned, and up-to-date views on tech and business trends around the world. 

Dr. Robin is a Co-author of The InsurTECH Book which offers essential updates, critical thinking, and actionable insight globally from startups, incumbents, investors, tech companies, advisors, and other partners in this evolving ecosystem, in one volume. He also advises and mentors many start-ups and corporations in various industries. His other areas of expertise include Brand & Marketing, Public Relations, Business, and Management. 

Connect with Dr. Robin Keira- LinkedIn

Complete podcast:

Excerpt from the interview-

Impact of COVID-19 on Insurance in Europe

Europe’s Insurance markets are facing a severe crisis? How are carriers, agencies, and insurance professionals in Europe developing measures to survive the global shutdown?

Dr. Robin: We have several live shows that we do in English and German where we interview several CXOs in the insurance industry. Mr. Christopher Lohmann from Gothaer Insurance said that only 10% of sales are doing well. 

So the question is, what do they do well? 

The answer to that is, they talk to clients, change their value propositions and product portfolios. 

One agent from Allianz said he is doing well because in the initial weeks of the lockdown he reached out to small and mid-sized businesses and helped them cope with the situation — displaying humility on their part. One of them helped his client in restaurant business sign-up for an online delivery service during lockdown. It wasn’t his job, however, if his client does good business, he will remain his client. Some agents and brokers took this opportunity to invest time into their client’s businesses and flourishing them. 

Other areas of distribution such as bank insurance i.e. selling insurance via banks are facing losses as most retail banks are closed and a lot of them have not found ways to effectively communicate with their clients.  

On the claims side, we haven’t seen any disastrous news yet. Also, it is interesting to see from the Life Insurance segment which did not see a surge in claims for term life or whole life insurance. However, we saw general insurance lines such as car insurance going down as not many people are driving. It’s a mixed picture. While in US insurers are paying the claims, in Germany insurers have decided to wait till the end of the year to assess the state of claims. 

The holding company of AXA announced it’s revenue numbers for the 1st quarter which was (-)10%. The negative figure means that new businesses are slaughtered. On the other hand, there’s been a boost for agencies. Our clients ask us how they can help their sales team to sell insurance policies since they cannot meet during this time. There’s been a dramatic change in the way insurance sales works.

The Reinforced Insurance Sales Strategy

How can Insurers equip their agents to generate sales in this pandemic?

Dr. Robin: It depends on the philosophy of an insurance company. Many insurance companies here are very old and go as far back as the 19th century. They have a very authoritarian culture and the agent-insurer relationship is very complex. There are some trust issues due to the unethical practices. 

For example, one of the big insurers promised young people to get on board and provide them with customer portfolios. But on the day of business, they see that only half of the portfolios were provided. If there is such a toxic relationship in an organization, then it gets difficult to deliver good output. 

However, small and mid-sized insurance companies have a very nice working culture where they willingly help their sales people. They have a very effective way of communicating with each other. 

Insurance Sales in The New Normal 

What are some Attention hacking lessons for Insurers operating in ‘The New Normal’?

Dr. Robin: Let’s divide and focus on two groups. 

First is the End Customer. For example, we have a client which is a big insurer with a distribution model to banking i.e bank insurance. We looked at every single touch-point in the process and noticed that they were not present in any. So we started placing them in every touch-point. Therefore, when a user logs into his bank account, the insurer can extract their data with the demographics, individualize and present relevant content/product.

[Related: How Technology is Transforming Insurance Distribution Channels]

Second is Sales. It doesn’t make sense to spend millions of euros and dollars in advertising with celebrities. Instead, they should produce content with people they know in the insurance community such as bank managers, insurance agents, etc. However, what’s more important is how insurers communicate and manage their sales against brokers, retail employees, etc. 

One of the most unestimated channels of communication is the messenger system. For example, WhatsApp, which is very popular in the west. We started experimenting with WhatsApp two years ago and the response was amazing. 

Sales managers use this distribution channel to share content with their agents. It brings 10x or sometimes 50x better engagement than a newsletter coming from headquarters. It is very necessary to focus on the attention of the end customer but also of the sales team. There are many simple ways to engage with sales guys such as interviewing top 2-3 performers with a simple smartphone. The key is to produce massive content and share with customers and sales that they are zoomed into the company. 

[Product: Lead Generation Chatbot]

Prevailing Technological Challenges

What are some of the technological challenges faced by Insurers operating in the New Normal?

Dr. Robin: The insurance industry needs to change culturally. They need to be a part of the daily lives of its customers and agents. They need to win their hearts, minds, and home screens. Insurers need to be on the apps, on platforms, and help them provide value. For that, we need technology. 

[Related: Four New Consumer-centric Business Models in Insurance]

Technology helps in faster claims and application processes which is a given but there are still many companies in the world that do not have these standard processes automated. I believe that the true game-changer will be AI, data science, data analytics in claims or underwriting departments. 

[Report: The State of AI in Insurance 2020]

Insurance is ripe for AI. Still, why are some Insurers still hesitant to invest in AI?

Dr. Robin: Traditional Insurers overlook the capabilities of AI and other technologies. Having people both — from inside and outside of the industry can give a broader perspective on applications of technology to bring innovative products and solutions. 

Product Innovations in Insurance

What product innovations in insurance are going to take place for short-term, mid-term, and long-term?

Dr. Robin:  In the short-term, adaptation of certain payment methods like pausing the payments and lowering risks could be done. 

In the mid-term, you will see more flexible products, more lean underwriting, or more lean claim management. What we need to think is about our role in society. Our mind-set should not be of pushing insurance products down the throats of the customers. What our role is to pool risks and stand by those who have been hit by these risks. We should not just help them during claims but before as well. 

For example, why should we have to replace a car if we could send them a push notification intimating them of the hailstorm and advising to put their car inside the garage? 

Another example is, why to sponsor super expensive healthcare when we can help the people to live healthier lives. Having customers call you portrays that the insurer is the solution. And that is what I believe the long-term goal should be. 

COVID-19: An Impetus to Digital Processes?

Insurers are taking the distribution process online. How are the Insurers adjusting to this new model? Is the ongoing pandemic, going to be the impetus for insurance to move completely digital? 

Dr. Robin:  Having come so far to 2020, I think it is very rude to do time-consuming tasks. What is needed here is to educate the people that their manual tasks can be done by machines. Today, there is so much knowledge available out there. One of my clients asked me how much we should invest in educating employees. To which I said, zero. You don’t have to hire so many consultants to educate them, just send them relevant YouTube videos. 

Way Forward for InsurTech

InsurTech largely facilitates technology in terms of scale, distribution, and market fit. How will New Normal InsurTech create market attractiveness?

Dr. Robin: The first thing that InsurTech or any other company needs to understand that all the plans made earlier have now become irrelevant. It’s time to research and build value propositions for existing clients and prospects. This would include a new pricing and pilot structure as well. 

For example, we can create a small package of services which an insurer can easily download and try out. I don’t think they should aim for $100-$200 million deals but rather give this small package demo to insurers trying to get them hooked on it. This will pave the way for getting more business. I have seen some software companies slashing prices to 70%-80% in some cases. It’s not always the question of pricing, but when InsurTechs say that they’re not able to sell, it ‘s time to re-evaluate their pricing models.   

Road to Recovery for General Insurance

Many General Insurance lines are hit- Travel, Motor, Home – what will be the road to recovery for these Insurance lines?

Dr. Robin: It’s going to be difficult. I was talking to Chris Skinner, an author in the finance space where he said that it’ll take two years to recover. This is a significant paradigm shift in Insurance Sales. Maybe we will never go back to normal. I used to travel a lot for business. Right now, I am very happy to do remote workshops. It is unpredictable how it’s going to pan out.  

The Expert Advice

First, it is time to bypass the moment of shock. Most insurers have done that. They are past the crisis mode and are addressing the issues at hand. 

Second, avoid watching too much news about the coverage on COVID-19. It’ll just bring the morale down.

Third, Stay Zero! and re-evaluate everything- what kind of products we should make, what do clients need, which clients have money to invest, what additional products can we launch, what should be the pricing model, what kind of value proposition we should provide and

Fourth, is to take action rather than Netflix. There will be massive layoffs, so it’s important to ensure you bring value to the company. 

Last but most important, is to focus on the attention of the end customer and provide value to them. Make your customer come to you.


AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com.

Podcasts in this series:

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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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