When I caught up on my news reading over the long holiday weekend in the U.S., I felt like I should find shelter somewhere, or at least go back to bed, pull up the covers, and cover my head with a pillow. 

It seems the hurricane season in the Atlantic is about to pick up again, after a quiet few weeks, and could produce a perilous September. Meanwhile, scientists said climate change could produce baseball-sized hail, as convective storms in the U.S. continue to get worse, and is causing a wave of damage and death from lightning strikes in India. And wildfires continue to rage, to the point that Allstate just got approval to raise homeowners insurance rates 34% in California. 

Yikes.

But there were also glimmers of hope, at least on the wildfire front. As one article said, in a reference to Silicon Valley, "What happens if you set a region full of technology entrepreneurs and investors on fire? They start companies. Dozens of start-ups, backed by climate-minded investors with more than $200 million in capital, are developing technology designed to tackle a fundamental challenge of the warming world."

Some of those efforts seem to me to have real promise. 

Let's have a look.

The New York Times article I quoted, which carries the clever headline, "Silicon Valley Wants to Fight Fires With Fire," describes two promising ideas, in particular.

The first, from Kodama Systems, is a way to accelerate the thinning of forests to reduce the amount of fuel that can ignite in a wildfire. 

The article says:

"In 2022, the U.S. Forest Service set a target for 50 million acres to be treated — thinned, pruned or burned — on public and private lands over the next decade. In 2023, 4.3 million were treated, including two million acres of prescribed burning — and that was a record. To keep pace, treatment would need to grow by a third this year."

To help with that, Kodama is automating work done by skidders, which are massive machines with a bulldozer blade on the front and a grapple on the back that can grab and drag trees after they're cut or knocked down. 

Currently, a driver operates a skidder from the cab for a 12-hour shift, but Kodama's AI handles enough of the work that a remote operator can run two skidders at the same time. The skidders also can operate in the dark, using lidar and other sensors to map their surroundings.

Having one person run two skidders at the same time, working around the clock, obviously improves the productivity of both the workers and the equipment. The ability to operate remotely is also key, given that it's hard to find enough people who want to do the hard, hot work of thinning forests. Kodama has operated skidders in the U.S. from London, so the remote operators wouldn't even have to work through the night; they can be many time zones away, working from the comforts of home during normal, daylight hours. 

For now, any limbs and brush that can't be used for commercial purposes at a sawmill are piled up and burned, but Komada is experimenting with the possibility of burying it to keep carbon dioxide from being released into the atmosphere when the material is burned.

The other startup that really struck me in the TImes article is BurnBot, which is used to create firebreaks that can protect a community from a wildfire. This concept could be especially powerful because it won't just be used for routine maintenance, like Komada's thinning of forests, but could be used in an emergency if a fire breaks out nearby. 

The Times describes the BurnBot as basically an upside-down propane grill. The device, which looks to me a bit like a Zamboni, trundles over the ground, incinerating everything beneath it in a five-foot-wide swath and then putting out the fire with water. 

The article says:

"Alongside a highway, this protective line could prevent ignition caused by passing cars; checkerboarding a large stretch of land, it could allow for controlled burns that normally require dozens or hundreds of people and ideal weather conditions.

"The CalFire chief Jim McDougald, who works on fuel reduction efforts across the state, said firebreaks like these gave his firefighters time to protect the community of Shaver Lake during the rampaging 2020 Creek fire."

I can picture the work being done with the BurnBot because it's being tested near "the Dish," the giant satellite dish on a hilltop next to the Stanford campus. I used to cycle by it all the time when we lived in the area and can still see all the dead grass on the hillside that is there this time of year.

There has also been some good news about drones that can detect wildfires sooner than happens now. From last week, here is an article about swarms of drones that use "AI technology—incorporating thermal and optical imaging—... to automatically detect and investigate fires, and relay all the information to the fire team. Under the supervision of fire and rescue teams and using swarm technology,... the drones can then intelligently self-coordinate as first responders to rapidly deploy fire retardant onto the fire, monitor the situation and return to base."

The Times mentions some other startups that show promise for early detection: Pano, which sets up monitoring stations and uses AI to spot wildfires quickly; Overstory, which uses satellite and aerial imagery to help utilities reduce the risk that vegetation causes to their power lines; and Treeswift, which creates "digital twins" that let utilities model their environments and reduce the risk to their power lines. Rain uses autonomous helicopters to put out blazes.

None of these startups will help much in what figures to be a brutal September, as hurricane activity picks up in the Atlantic, as lightning lashes India, and as baseball-sized hail becomes more common. But maybe they can make a difference by a September or three from now.

Cheers,

Paul

P.S. As long as I'm on the subject of drones, here are two video clips that show how ubiquitous and versatile they have become. 

One is deadly serious. This clip shows a Ukrainian "dragon fire" drone setting ablaze a stand of trees that Russian soldiers are using to hide their emplacement. 

The second is just a goof, from a clever person with too much time on his hands who decides to scare the pants off someone. 

The landscape of technology adoption, with artificial intelligence (AI) at the forefront, is rapidly changing industries and economies across the globe. The meteoric rise of ChatGPT to 100 million users in 2023 is a testament to the rapid integration of AI technologies into daily life. This whitepaper analyzes the required evolution of the data strategy in organizations aiming to harness the full potential of AI.

1. Background

The rapid growth and widespread adoption of AI technologies across industries underscore the urgency for organizations to adapt. Grasping the scale of AI's economic influence and its transformative effects on various business functions can help us appreciate the critical role that data plays in driving AI success.

The Economic Impact of AI

Generative AI, a branch of artificial intelligence, is poised to create substantial economic benefits globally, estimated to range between $2.6 trillion and $4.4 trillion annually. This technology is expected to notably affect higher-wage knowledge workers, accelerating productivity growth globally. Approximately 40% of working hours could be influenced by AI, leading to significant job transformations, particularly in advanced economies where around 60% of jobs could be affected. While North America and China are projected to reap the most benefits, Europe and developing countries may experience more moderate increases.

Generative AI's economic impact isn't confined to improving productivity. It extends to reshaping market dynamics, altering competitive landscapes, and allowing the creation of new business models. In sectors like healthcare, generative AI can enhance diagnostic accuracy, personalize treatment plans, and streamline administrative tasks. Similarly, in finance, it can optimize trading strategies, improve risk management, and enhance customer service through intelligent chatbots. The ripple effects of these changes will require successful businesses worldwide to adapt and evolve much faster than their competitors.

Impact Across Business Functions

According to McKinsey, the potential of generative AI will extend across various business functions. In the short term, it is expected that 75% of its value will concentrate in customer operations, marketing and sales, software engineering, and research and development (R&D). Industries such as banking, high-tech, and life sciences are anticipated to witness the most substantial revenue impacts from generative AI.

See also: 'Data as a Product' Strategy

Customer Operations

In customer operations, AI can automate routine inquiries, enhance customer satisfaction through personalized interactions, and provide predictive insights that help anticipate customer needs. Advanced AI systems can analyze customer behavior and preferences, enabling businesses to offer tailored products and services, thus driving customer loyalty and increasing lifetime value.

Marketing and Sales

For marketing and sales, AI-driven analytics can optimize targeting, streamline lead generation processes, and improve conversion rates. AI algorithms can analyze vast amounts of data to identify patterns and trends much faster than human analysts. This capability allows marketers to craft highly personalized campaigns that resonate with individual customers, thereby maximizing the return on marketing investments.

Software Engineering and R&D

In software engineering and R&D, generative AI is accelerating the development process by automating coding tasks, identifying bugs, and suggesting improvements. A prime example of this is GitHub Copilot, an AI-powered code completion tool developed by GitHub and OpenAI. Copilot assists developers by suggesting code snippets, entire functions, and even complex algorithms based on the context of their work. It can significantly speed up coding processes, reduce repetitive tasks, and help developers explore new coding patterns. For instance, a developer working on a sorting algorithm might receive suggestions for efficient implementations like quicksort or mergesort, complete with explanations of their time complexity. AI-driven simulations can also enhance R&D efforts by predicting outcomes of various experiments and guiding researchers toward the most promising avenues. This acceleration not only reduces time-to-market but also fosters innovation and enhances competitive advantage.

2. Challenges Organizations Are Facing With AI

Despite significant investments in data and AI, many organizations grapple with deriving measurable value. Vantage Partners and Harvard Business Review Analytic Services find that 97% of organizations are investing in data initiatives. Ninety-two percent are working with AI/ML in either pilot phases or production. Despite this, 68% fail to realize measurable value from AI. A disconnect exists: While 74% have appointed chief data or analytics officers, 61% lack a data strategy to support machine learning and data science.

The primary reasons for this disconnect include inadequate data quality, lack of skilled personnel, and insufficient integration of AI initiatives with business strategies. Many organizations collect vast amounts of data but struggle with ensuring its accuracy, consistency, and relevance. Additionally, the shortage of AI and data science talent hampers the effective implementation of AI projects. Lastly, without a clear strategy that aligns AI efforts with business objectives, organizations find it challenging to translate AI capabilities into tangible business outcomes.

3. AI Models and Related Learning Processes

Understanding AI models and their learning processes is fundamental to developing an effective data strategy. This knowledge illuminates the specific data requirements for various AI applications and informs how organizations should structure their data pipelines. By first exploring these technical foundations, we establish a clear context for the subsequent discussion on data strategy, ensuring that proposed approaches are well-aligned with the underlying AI technologies they aim to support.

Supervised vs. Unsupervised Learning

In the context of AI, supervised and unsupervised learning represent two primary methodologies of models.

• Supervised Learning

Supervised learning relies on labeled data to train models, where the input-output pairs are explicitly provided. This method is highly effective for tasks where large, accurately labeled datasets are available, such as image classification, speech recognition, and natural language processing. The effectiveness of supervised learning is heavily contingent on the quality and accuracy of the labeled data, which guides the model in learning the correct associations. For enterprises, creating and maintaining such high-quality labeled datasets can be resource-intensive but is crucial for the success of AI initiatives.

The vast majority of enterprise AI use cases fall under supervised learning due to the direct applicability of labeled data to business problems. Tasks such as predictive maintenance, customer sentiment analysis, and sales forecasting all benefit from supervised learning models that leverage historical labeled data to predict future outcomes.

• Unsupervised Learning

Unsupervised learning, on the other hand, involves training models on data without explicit labels. This approach is useful for uncovering hidden patterns and structures within data, such as clustering and anomaly detection. While unsupervised learning can be powerful, it often requires large volumes of data to achieve meaningful results. Enterprises may find it challenging to gather sufficient amounts of unlabeled data, especially in niche or specialized industries where data is not as abundant.

The lack of large datasets within many enterprises to train unsupervised models underscores the importance of data-centric approaches. High-quality, well-labeled datasets not only enhance supervised learning models but also provide a foundation for semi-supervised or transfer learning techniques, which can leverage smaller amounts of labeled data in combination with larger unlabeled datasets.

See also: Data Mesh: What It Is and Why It Matters

The AI Training Loop

AI systems need to be performant and reliable. The AI training loop is a critical process that underpins the development and deployment of such models. Each AI training loop consists of key stages that include data collection and preparation, model training, evaluation and validation, model improvement through tuning and optimization, and monitoring. Each phase is crucial for building robust and reliable AI systems, with a strong emphasis on data quality and iterative improvement to achieve optimal performance.

• Data Collection and Preparation

The first step in the AI training loop is the collection and preparation of data. This phase involves gathering raw data from various sources, which may include structured data from databases, unstructured data from text and images, and streaming data from real-time sources. The quality and relevance of the data collected are paramount, as they directly influence the effectiveness of the AI model.

Data preparation includes cleaning the data to remove inconsistencies and errors, normalizing data formats, and labeling data for supervised learning tasks. This process ensures that the data is of high quality and suitable for training AI models. Given that the success of AI largely depends on the quality of the data, this phase is often the most time-consuming and resource-intensive.

• Model Training

Once the data is prepared, the next step is model training. This involves selecting an appropriate algorithm and using the prepared data to train the model. In supervised learning, the model learns to map input data to the correct output based on the labeled examples provided. In unsupervised learning, the model identifies patterns and relationships within the data without the need for labeled outputs.

The training process involves feeding the data into the model in batches, adjusting the model parameters to minimize errors, and iterating through the dataset multiple times (epochs) until the model achieves the desired level of accuracy. This phase requires substantial computational resources and can benefit from specialized hardware such as GPUs and TPUs to accelerate the training process.

• Evaluation and Validation

After training, the model undergoes evaluation and validation to assess its performance. This step involves testing the model on a separate validation dataset that was not used during training. Key metrics such as accuracy, precision, recall, and F1-score are calculated to measure the model's performance and ensure it generalizes well to new, unseen data.

Validation also includes checking for overfitting, where the model performs well on training data but poorly on validation data, indicating it has learned noise rather than the underlying patterns. Techniques such as cross-validation, where the data is split into multiple folds and the model is trained and validated on each fold, help in providing a more robust assessment of model performance.

• Model Tuning and Optimization

Based on the evaluation results, the model may require tuning and optimization. This phase involves adjusting hyperparameters, such as learning rate, batch size, and the number of layers in a neural network, to improve the model's performance. Hyperparameter tuning can be performed manually or using automated techniques like grid or random searches.

Optimization also includes refining the model architecture, experimenting with different algorithms, and employing techniques like regularization to prevent overfitting. The goal is to achieve a balance between model complexity and performance, ensuring the model is both accurate and efficient.

• Deployment and Monitoring

Once the model is trained, validated, and optimized, it is deployed into a production environment where it can be used to make predictions on new data. Deployment involves integrating the model into existing systems and ensuring it operates seamlessly with other software components.

Continuous monitoring of the deployed model is essential to maintain its performance. Monitoring involves tracking key performance metrics, detecting drifts in data distribution, and updating the model as needed to adapt to changing data patterns. This phase ensures the AI system remains reliable and effective in real-world applications.

• Feedback Loop and Iteration

The AI training loop is an iterative process. Feedback from the deployment phase, including user interactions and performance metrics, is fed back into the system to inform subsequent rounds of data collection, model training, and tuning. This continuous improvement cycle allows the AI model to evolve and improve over time, adapting to new data and changing requirements.

4. Why Data Is as Important as Model Tuning

Data quality and quantity directly affect model performance, often surpassing the effects of algorithmic refinements. A robust data strategy achieves a critical balance between data and model optimization, essential for optimal AI outcomes and sustainable competitive advantage. This shift from a model-centric to a data-centric paradigm is crucial for organizations aiming to maximize the value of their AI initiatives.

The Pivotal Role of Data in AI

Andrew Ng from Stanford emphasizes that AI systems are composed of code and data, with data quality as crucial as the model itself. This realization requires a shift toward data-centric AI, focusing on improving data consistency and quality to enhance model performance. Data is the fuel that powers AI, and its quality directly affects the outcomes.

Data-Centric vs. Model-Centric AI

Historically, the majority of AI research and investment has focused on developing and improving models. This model-centric approach emphasizes algorithmic advancements and complex architectures, often overlooking the quality and consistency of the data fed into these models. While sophisticated models can achieve impressive results, they are highly dependent on the quality of the data they process.

In contrast, the data-centric approach prioritizes the quality, consistency, and accuracy of data. This paradigm shift is driven by the understanding that high-quality data can significantly enhance model performance, even with simpler algorithms. Data-centric AI involves iterative improvements to the data, such as cleaning, labeling, and augmenting datasets, to enhance model performance. By focusing on data quality, organizations can achieve better results with simpler models, reducing complexity and increasing interpretability.

The Need for Accurately Labeled Data

The need for accurately labeled data is particularly critical in supervised learning. High-quality labeled data ensures that models learn the correct associations and can generalize well to new, unseen data. However, obtaining and maintaining such datasets can be challenging and resource-intensive, underscoring the importance of robust data management practices.

For enterprises, this means investing in data labeling tools, employing data augmentation techniques to increase the diversity and quantity of labeled data, and implementing rigorous data quality assurance processes. Additionally, leveraging automated data labeling and machine learning operations (MLOps) can streamline the data preparation process, reducing the burden on data scientists and ensuring that high-quality data is consistently available for model training.

See also: How External Data Is Revolutionizing Underwriting

The Enterprise Data Strategy

A data-centric approach is essential for enterprises aiming to leverage AI effectively. By prioritizing data quality and adopting robust data management practices, organizations can enhance the performance of AI models, regardless of whether they employ supervised or unsupervised learning techniques. This shift from model-centric to data-centric AI reflects a broader understanding that in the realm of AI, quality data is often more important than sophisticated algorithms.

If data quality is not given equal importance to model quality, organizations risk falling into a trap of perpetually compensating for model noise. When input data is noisy, inconsistent, or of poor quality, even the most advanced AI models will struggle to extract meaningful patterns. In such cases, data scientists often find themselves fine-tuning models or increasing model complexity to overcome the limitations of the data. This approach is not only inefficient but can lead to overfitting, where models perform well on training data but fail to generalize to new, unseen data. By focusing on improving data quality, enterprises can reduce noise at the source, allowing for simpler, more interpretable models that generalize better and require less computational resources. This data-first strategy ensures that AI efforts are built on a solid foundation, rather than constantly trying to overcome the limitations of poor-quality data.

Ensuring that enterprises have access to high-quality, accurately labeled data is a critical step toward realizing the full potential of AI technologies.

5. Potential Generative AI Approaches

Organizations can adopt different approaches to AI based on their strategic goals and technological capabilities. Each approach has distinct data implications that chief data officers (CDOs) must address to ensure successful AI implementations. The three primary approaches are: Taker, Shaper, and Maker.

Taker

The "Taker" approach involves consuming pre-existing AI services through basic interfaces such as application programming interfaces (APIs). This approach allows organizations to leverage AI capabilities without investing heavily in developing or fine-tuning models.

Data Implications:

• Data Quality: CDOs must ensure that the data fed into these pre-existing AI services is of high quality. Poor data quality can lead to inaccurate outputs, even if the underlying model is robust.
• Validation: It is crucial to validate the outputs of these AI services to ensure they meet business requirements. Continuous monitoring and validation processes should be established to maintain output reliability.
• Integration: Seamless integration of these AI services into existing workflows is essential. This involves aligning data formats and structures to be compatible with the API requirements.

Shaper

The "Shaper" approach involves accessing AI models and fine-tuning them with the organization’s own data. This approach offers more customization and can provide better alignment with specific business needs.

Data Implications:

• Data Management Evolution: CDOs need to assess how the business’s data management practices must evolve to support fine-tuning AI models. This includes improving data quality, consistency, and accessibility.
• Data Architecture: Changes to data architecture may be required to accommodate the specific needs of fine-tuning AI models. This involves ensuring that data storage, processing, and retrieval systems are optimized for AI workloads.
• Data Governance: Implementing strong data governance policies to manage data access, privacy, and security is essential when fine-tuning models with proprietary data.

Maker

The "Maker" approach involves building foundational AI models from scratch. This approach requires significant investment in data science capabilities and infrastructure but offers the highest level of customization and control.

Data Implications:

• Data Labeling and Tagging: Developing a sophisticated data labeling and tagging strategy is crucial. High-quality labeled data is the foundation of effective AI models. CDOs must invest in tools and processes for accurate data annotation.
• Data Infrastructure: Robust data infrastructure is needed to support large-scale data collection, storage, and processing. This includes scalable databases, high-performance computing resources, and advanced data pipelines.
• Continuous Improvement: Building foundational models requires continuous data collection and model iteration. Feedback loops should be established to incorporate new data and improve model accuracy over time.

The approach an organization takes toward AI—whether as a Taker, Shaper, or Maker—has significant implications for data management practices. CDOs play a critical role in ensuring that the data strategies align with the chosen AI approach, facilitating successful AI deployment and maximizing business value.

6. Core Components of an AI Data Strategy

An effective AI-driven data strategy encompasses several key components.

Vision and Strategy

Aligning data strategy with organizational goals is paramount. The absence of a coherent data strategy is a significant barrier to expanding AI capabilities. A robust data strategy provides a clear framework and timeline for successful AI deployment. This strategy should be revisited regularly to ensure alignment with evolving business goals and technological advancements.

Data Quality and Management

Data quality remains a persistent challenge for AI. Organizations must prioritize sourcing and preparing high-quality data. Employing methodologies like outlier detection, error correction, and data augmentation ensures the data used in AI models is accurate and reliable. Establishing data governance frameworks, including data stewardship and quality control processes, is essential for maintaining data integrity over time.

AI Integration and Governance

Integrating AI into business functions necessitates a comprehensive approach. This involves addressing technical aspects (architecture, data, skills) and strategic elements (business alignment, governance, leadership, and culture). Ensuring robust governance frameworks for data quality, privacy, and model transparency is essential. These frameworks should include clear policies for data access, usage, and protection, as well as mechanisms for monitoring and mitigating biases in AI models.

See also: Can AI Solve Underlying Data Problems?

7. Evolution of Data Architectures in the Context of AI

Future Trends and Considerations

The rise of generative AI introduces new requirements for data architectures. These include seamless data ingestion, diverse data storage solutions, tailored data processing techniques, and robust governance frameworks. As AI technologies evolve, the value of traditional degree credentials may shift toward a skills-based approach, fostering more equitable and efficient job training and placement.

Data Architectures

Modern data architectures must support the diverse and dynamic needs of AI applications. This includes integrating real-time data streams, enabling scalable data storage solutions, and supporting advanced data processing techniques such as parallel processing and distributed computing. Furthermore, robust data governance frameworks are essential to ensure data quality, privacy, and compliance with regulatory standards.

The Evolving Skill Set for the AI Era

As AI becomes more prevalent, the skills required in the workforce will evolve. While technical skills in AI and data science will remain crucial, there will be an increased demand for "human" skills. These include critical thinking, creativity, emotional intelligence, and the ability to work effectively with AI systems. Lifelong learning and adaptability will be essential in this ever-changing landscape.

Workforce Transformation

Organizations must invest in continuous learning and development programs to equip their workforce with the necessary skills. This includes offering training in AI and data science, as well as fostering a culture of innovation and adaptability. By nurturing a diverse skill set, organizations can better leverage AI technologies and drive sustainable growth.

8. Methodologies for Data-Centric AI

To effectively harness the potential of AI, organizations must adopt a data-centric approach, employing methodologies that enhance data quality and utility:

• Outlier Detection and Removal: Identifying and handling abnormal examples in datasets to maintain data integrity.
• Error Detection and Correction: Addressing incorrect values and labels to ensure accuracy.
• Establishing Consensus: Determining the truth from crowdsourced annotations to enhance data reliability.
• Data Augmentation: Adding examples to datasets to encode prior knowledge and improve model robustness.
• Feature Engineering and Selection: Manipulating data representations to optimize model performance.
• Active Learning: Selecting the most informative data to label next, thereby improving model training efficiency.
• Curriculum Learning: Ordering examples from easiest to hardest to facilitate better model training.

The evolution of data strategy is not just a trend; it's a necessity for organizations aiming to leverage AI for competitive advantage. By prioritizing data quality, aligning AI initiatives with business goals, and establishing strong governance frameworks, organizations can unlock the transformative potential of AI, driving productivity, innovation, and economic growth in the years to come.

For every business, customer satisfaction isn't just a goal—it's the foundation for building long-term relationships and ensuring success. Merchants are finding new ways to satisfy their customers, and embedded insurance is one of them. It can delight customers by offering insurance right alongside merchants' products. 

But while businesses are excited over the growth potential of embedded insurance, there's one industry that's feeling the pressure: insurance. For insurers and insurtechs, the opportunities and challenges of embedded insurance are neck and neck. 

To embrace the shift, they must face these hurdles head-on.

Personalized Customer Experience

Merchants can ace customer experience with embedded insurance, but for insurers, it's not so easy. Here's what's holding them back:

1. Outdated UI/CX

While the world is busy building modern apps, many insurers are still stuck with complex systems due to a lack of resources and complicated regulations. Consumers will have a smooth UI experience until they are redirected to the insurance part. Jargon-filled insurance policies and complex claim processes often frustrate customers. Though insurers have started to make a shift, there's still a lot to be done. Until then, they can use the following strategies to ensure worthy customer experiences:

• Minimal Integration: Use the merchant's platform to show basic policy information and claim status. For anything complex, redirect to the insurer's website.
• Dedicated Customer Support: Support customers in navigating the complex platform through email and chat.
• Third-party Integration: Integrate with third-party digital tools to help overcome the outdated UI/CX hurdle.

Consider a customer renting a car online. The process is quick and seamless, but when the insurance part hits, the experience often becomes fragmented. Insurers can make a difference by letting the rental company offer only basic insurance details online and assisting customers with complex claim processes. Insurers like Geico and Lemonade have already started to improve their customer service with advanced support systems and third-party AI chatbots. Similar solutions can help customers navigate complex processes and enhance their experience.

2. Experience Through Merchants

When insurance products are bundled with the merchant's, ensuring a seamless customer experience falls on insurers. They need to establish clear communication with the merchant to avoid misalignment while framing the terms and conditions for the product. Insurers can improve the customer experience by involving merchants in all their processes:

• Merchant Support: Have the merchant handle customer inquiries and limit direct customer interaction. For instance, Tesla supports its insurance provider, State National Insurance, by handling most of the insurance queries on Tesla's platform.
• Co-Branded Marketing: Initiate joint marketing efforts to build brand awareness and trust.
• Data-Sharing Agreements: Maintain customer insights by creating data-sharing agreements with the merchants.

If you are a travel insurer, you can better use embedded insurance by partnering with merchants like online booking platforms. Imagine a customer booking a flight and being presented with a personalized insurance offer that covers everything from trip cancellations to lost luggage, all without leaving the booking site. This not only enhances the customer experience but also increases the likelihood of a purchase. Additionally, promoting products together with the booking platform makes the insurance offering an extension of the travel booking process, which can help drive more sales.

3. Data Privacy and Customer Knowledge

As embedded insurance involves data-sharing between merchant and insurer, it's the insurer's responsibility to safeguard customer data and educate customers about the terms and conditions. This can help avoid discrepancies in their claim journey. Here's how insurers can do it:

• Regulatory Compliance: Adhere to data privacy regulations such as GDPR and CCPA.
• Standard Privacy Policy: Provide clear privacy policies to customers that outline data collection and usage practices.
• Necessary Customer Education: Offer all the must-know information about the insurance products on the merchant's platform.

Transparency and trust are crucial to customer satisfaction. Imagine the impact of a seamless integrated experience where a customer is provided with all the necessary data about privacy policies and the risks associated with it while booking a flight ticket or getting employee-provided health insurance. This level of transparency not only builds customer loyalty but also strengthens the brand's reputation in an increasingly competitive market. Insurers can enhance the customer experience by keeping them informed, protected, and engaged about how their data is being used.

Enhancing CX With New Insurance Models

The advent of connected devices such as wearables, IoT, and smart home devices has opened a new era of possibilities for the insurance industry. It has led to the introduction of new insurance models that attract users to move from the old way of paying annual premiums. Data generated through connected devices further helps insurers cater to more specific and personalized products.

1. On-Demand Insurance

Is it possible to have insurance only when you need it, no matter how brief its usage? On-demand insurance offers this flexible and cost-effective solution by allowing you to activate coverage exactly when you require it. Consider a taxi service where the service provider offers insurance coverage along with the rent for that specific trip rather than requiring payment for an annual policy.

This model provides the convenience of using insurance for immediate needs and helps avoid unnecessary costs for coverage you don't use. Uber offers its customers and drivers the option to purchase insurance coverage on-demand. This means that both parties can use insurance protection only during the ride. Uber leverages connected devices like GPS and telematics to accurately track their vehicles, ensuring that both riders and drivers have protection precisely when they need it.

2. Contextual Insurance

Ever thought about insurance that adapts to your real-time needs? Contextual insurance makes this a reality by providing coverage based on the user's activities. Imagine you've just installed a smart home system that can analyze data like occupancy patterns, appliance use, and even environmental conditions. This data offers a clearer picture of potential risks in your home, allowing your insurance coverage to adjust.

This approach ensures that you're not paying for unnecessary coverage but are instead getting it precisely when you need it. Google Nest's extended warranty is a prime example of contextual insurance. At the time of purchase, you're offered a plan that not only covers accidental damage but also uses data from your thermostat to offer protection to your home.

3. Pay-Per-Use Model

Why should consumers pay heavy premiums for products they use occasionally? The pay-per-use model offers the flexibility to pay for insurance only when they use the product. It integrates insurance into existing services or products and provides the flexibility to use it only when needed.

Consider buying high-tech products. Merchants offer a standard warranty period for the product, and if customers wish, they can opt for an extended warranty. This extended insurance coverage can also be applied to specific parts of the product, perhaps only for the motor of a washing machine. 

The First Step

Customer experience is not just about keeping customers happy—it's about acquiring and retaining them with minimal costs. That's why many businesses are shifting toward embedded insurance. To stay in line, insurers are relentlessly working to overcome challenges in embedded insurance and achieve the goal of delivering personalized customer experiences. The emergence of new insurance models is a clear sign that they are on the right track.

The future of insurance is undoubtedly embedded, and current progress will benefit insurers, merchants, and, most importantly, customers.

With capabilities to revolutionize risk assessment, customer experience, and operational efficiency, artificial intelligence is set to unlock significant economic value in the insurance industry.

Fears about using AI ethically can hold insurers back, and the costs associated with building scalable AI solutions may seem daunting, but insurers should rest assured there are several ways to monetize these services.

The insights below explore how AI will enhance industry practices and how insurers can balance development costs and navigate regulatory challenges to ultimately shape the future of insurance.

How AI can create significant value in coming decades

AI technology will revolutionize the insurance industry by enhancing risk assessment, improving fraud detection, and automating claims processing, leading to more precise pricing and reduced costs. It will enable personalized customer experiences through chatbots and predictive analytics, fostering better engagement and loyalty. AI will also streamline operations through robotic process automation, freeing resources for strategic tasks and driving efficiency. Moreover, AI will facilitate new business models like usage-based insurance and peer-to-peer platforms, catering to evolving consumer preferences and opening new revenue streams. These advancements will generate significant economic value and drive industry growth. Some examples include:

• Allstate's AI Chatbot "ABIE": Allstate uses an AI chatbot named ABIE (Allstate Business Insurance Expert) to assist small business owners in selecting appropriate coverage. The chatbot provides instant, personalized insurance quotes based on user inputs and real-time data analysis.
• Lemonade's Fraud Detection: Lemonade, a digital insurance company, employs AI to detect fraudulent claims. Their AI system, "Jim," reviews and processes claims in seconds, cross-referencing data points and flagging suspicious activity, leading to lower fraud rates and faster claim resolutions.
• Progressive's Snapshot Program: Progressive Insurance's Snapshot program uses AI and telematics to monitor driving behavior. Policyholders receive personalized discounts based on their driving patterns, promoting safer driving habits and reducing the likelihood of accidents.

How AI service providers can monetize services

GenAI costs are concentrated in foundational model development, which involves significant investments in R&D, computational resources, and talent. These models are then integrated into platforms requiring robust infrastructure and API development. Service providers customize and fine-tune these models for specific industries, incurring additional costs for scalability solutions. Businesses access AI capabilities through subscription or licensing, incorporating AI into their products and improving operational efficiency. This value is ultimately passed on to end-users through enhanced products and services, ensuring cost recovery and profitability for AI service providers. AI service providers can balance infrastructure and development costs by:

• Subscription Models: Offering tiered plans for different business sizes.
• Usage-Based Pricing: Charging based on actual usage, similar to cloud services.
• Value-Based Pricing: Charging a percentage of savings or earnings generated by AI solutions.
• Partnerships: Integrating AI into broader platforms through revenue-sharing agreements.
• Industry-Specific Solutions: Creating tailored AI applications for specific industries.
• Data Monetization: Selling anonymized data and providing market analytics.
• Consulting Services: Offering implementation and support services for AI integration.
• Proprietary Tools: Developing advanced, proprietary AI platforms with premium features.

How regulatory and legal challenges will affect AI

Regulatory and legal considerations will increase compliance costs and slow innovation. Companies will face stricter data privacy and security requirements, accountability demands for transparent and fair algorithms, and potential intellectual property disputes. Liability concerns for AI system failures will necessitate comprehensive insurance, while ethical considerations will require careful navigation. Regulatory hurdles can create market entry barriers, especially for startups, and differing regulations across countries can complicate international operations.

We must strike a balance between compliance and innovation to harness the benefits of AI while mitigating potential risks. The insurance industry should continue to collaborate with AI experts and regulators to establish a code of conduct specific to the sector. By implementing an industry-specific code of conduct, insurance companies can have greater assurance that they are not subject to flawed decision-making processes resulting from unethical AI practices, and greater confidence in applying this technology to routine processes.

The integration of AI in insurance reflects a significant shift that will redefine industry standards and consumer expectations. From enhancing fraud detection to personalizing customer interactions, the impact will be far-reaching. As regulatory frameworks develop, companies must navigate complexities to harness AI's full potential. Embracing AI will drive growth and ensure competitive advantage in this increasingly digital and fast-paced environment.

The insurance industry is being driven by technological advancements that promise to enhance efficiency, security, and transparency. At the forefront is blockchain technology, a decentralized and secure system that has the potential to reshape the way insurance processes are conducted. 

Understanding Blockchain in Insurance

Blockchain, the distributed ledger technology that underlies cryptocurrencies like Bitcoin, has several features that make it particularly well-suited for the insurance industry. Its decentralized nature eliminates the need for intermediaries, reducing administrative costs and increasing efficiency. The immutability of blockchain ensures that once data is recorded, it cannot be altered, providing a transparent and tamper-proof record of transactions.

1. Enhanced Security and Fraud Prevention: Blockchain's cryptographic features ensure secure data transmission and storage. In the insurance industry, this translates to a significant reduction in fraud. Through the use of smart contracts, which automatically execute and enforce the terms of an agreement, blockchain minimizes the risk of fraudulent claims. Insurers can verify the authenticity of claims in real time, streamlining the claims process and reducing the overall cost of fraud detection.
2. Improved Transparency and Trust: Transparency is a cornerstone of blockchain technology. In insurance, this translates to an accessible record of policy details, premiums, and claims. This increased transparency fosters trust among stakeholders, including policyholders, insurers, and regulators. By providing a shared view of transactions, blockchain reduces disputes and enhances the credibility of the insurance industry.
3. Streamlined and Efficient Processes: Traditional insurance processes are often marred by cumbersome paperwork, delays, and manual errors. Blockchain's decentralized ledger simplifies and automates these processes. Smart contracts can automate underwriting, policy issuance, and claims processing, reducing the time and resources required for these tasks. This streamlined approach not only enhances efficiency but also improves the overall customer experience.

Challenges and Considerations

While the potential benefits of blockchain in insurance are substantial, the technology is not without its challenges. Integration with existing systems, regulatory concerns, and the need for industry-wide collaboration are among the hurdles that insurers must navigate. However, as the technology matures and regulatory frameworks evolve, these challenges are increasingly being addressed.

Future-Ready Insurers – Embracing Mobility Tech and Trends

In addition to blockchain, future-ready insurers are embracing mobility tech and trends to stay ahead in a rapidly evolving landscape. The integration of mobile technology, data analytics, and emerging trends such as the Internet of Things (IoT) are reshaping the way insurance is underwritten, sold, and serviced.

1. Mobile Apps and Customer Engagement: Mobile apps have become a powerful tool for insurers to use to engage with their customers. Insurers are developing user-friendly apps that enable policyholders to manage their policies, submit claims, and access important information seamlessly. The convenience offered by mobile apps enhances customer satisfaction and loyalty.
2. Data Analytics for Risk Assessment: The abundance of data in today's digital age is a gold mine for insurers. Advanced data analytics tools allow insurers to analyze vast amounts of data to assess risks more accurately. Machine learning algorithms can identify patterns and predict potential risks, enabling insurers to make informed underwriting decisions and set more precise premiums.
3. IoT Integration: The proliferation of IoT devices is transforming risk assessment and claims processing. Insurers can leverage data from connected devices such as smart home sensors, wearable devices, and telematics in vehicles to gather real-time information. This not only enables personalized pricing based on individual behavior but also facilitates proactive risk mitigation.
4. Artificial Intelligence in Underwriting and Claims Processing: AI is playing a pivotal role in automating underwriting and claims processing. Machine learning algorithms can analyze vast datasets to assess risks, while natural language processing facilitates faster and more accurate claims adjudication. This not only improves efficiency but also reduces the likelihood of errors.

Shaping the future

Blockchain technology is a game-changer for the insurance industry, offering enhanced security, transparency, and efficiency. As insurers navigate the challenges of integration, those who successfully implement blockchain stand to gain a competitive edge. Moreover, the synergy between blockchain and mobility tech is propelling insurers into a future where customer-centricity, data-driven insights, and automation are paramount. 

The future-ready insurer is not merely an adopter of technology but an innovator, embracing the transformative power of blockchain and mobility tech to redefine the insurance landscape. As we move forward, the collaboration among insurtechs, regulators, and industry stakeholders will be crucial in shaping a future where insurance is not just a protective measure but a seamless and intelligent part of our daily lives.

This summer has brought on a slew of severe weather events that have challenged our insurance defense lines with increasing intensity. In July, the National Oceanic and Atmospheric Administration (NOAA) confirmed four new billion-dollar disasters, ranging from wildfires in the West to unprecedented tornadoes in the Midwest and Category 5 Hurricane Beryl in the South. These events brought July’s totals to 19 declared disasters, four more than in July 2023.

The evolving nature of disasters calls for a reevaluation of traditional insurance strategies and a sincere consideration of how innovation in supplemental disaster insurance solutions can ensure dynamic coverage that protects consumer finances in today’s changing weather landscape.

Disasters are striking in unprecedented ways

No community is entirely immune to severe weather, with at least one weather-related disaster declared in all 50 states over the last five years. We are familiar with the widespread need for severe weather considerations when building comprehensive insurance portfolios; however, this summer has heightened the urgency of adaptation with a series of unexpected and unusual weather events.

Most recently, Hurricane Ernesto and Hurricane Debby tested the East Coast with flash floods, power outages, rip currents, and extensive damage in August, which was closely preceded by the devastating Hurricane Beryl in July. The storms have been unusual this hurricane season, as Debby formed nearly a month earlier than the average second Atlantic hurricane, and Hurricane Beryl was the earliest Category 5 hurricane in Atlantic history. These historic events are only kicking off the year’s above-average hurricane season, with the National Hurricane Center (NHC) forecasting 17 to 24 named storms and four to seven major hurricanes before the end of November, surpassing the 1991-2020 seasonal averages of 14 named storms and 3 major hurricanes.

In addition to record-setting hurricanes, tornado patterns for 2024 have called for concern. July saw the second-highest number of tornadoes since 2010 and a 45% higher-than-average pace. Last month set a Chicago-area record for most tornadoes in a day after a derecho suddenly spawned 32 tornadoes across the region.

In the West, the Park Fire has taken its place as the fourth-largest wildfire in California history, devastating more than 401,000 acres. As extreme weather affects states with a new level of intensity, it is increasingly imperative that homeowners assess their preparedness against natural disasters with an openness to new insurance considerations.

Catching the consumer up to speed

While it has always been key for homeowners and renters to consider their financial vulnerabilities when it comes to looming disasters, insurance literacy has taken on heightened importance in the face of today’s increasingly frequent and intense weather events. 

Few policyholders are aware of what their current insurance covers and what it doesn’t. A 2020 survey by Policygenius found that 53% of homeowners were unaware that home insurance policies don’t typically cover flood damage and 80% were unaware that earthquakes aren’t typically covered. Despite these gaps in insurance knowledge, 74% of respondents said they felt confident in their coverage to fully replace their home in the event of a disaster. 

This disconnect between consumer understanding and confidence is concerning and calls on us as insurance professionals to help policyholders get honest about their emergency savings and where severe weather patterns can wreak havoc on their lives and finances.

While the insurance industry is keyed into these evolving weather trends and constantly reacting, “set it and forget it” consumers need more support in staying informed. They might be aware that these new weather trends are causing insurers to raise deductibles in disaster-prone areas and even exit states entirely, but are they familiar with the spreading nature of coverage stipulations like named storm deductibles? With the NHC’s prediction of an above-average number of hurricanes this year, consumers need real-time education on such nuances to get a truer understanding of how they will be financially affected by a disaster.

Coverage needs are changing, and clients need tailored support

As severe weather disrupts lives around the country with a new sense of vigor, consumers need to be made aware of the massive out-of-pocket expenses that can result from coverage gaps in their insurance policies. 

A recent Bankrate report found that only 44% of U.S. adults would be able to afford an emergency expense of $1,000 with savings and 27% lack emergency funds altogether. If you set this against the context that out-of-pocket costs for a disaster can reach $10,000 per household, it is clear that people need additional support, which is why we at Recoop offer up to $25,000 in fast and flexible recovery cash.

When it comes to tailoring coverage to homeowners’ specific needs, exploring relevant multi-peril insurance products is a way to address the ever-evolving climate landscape, as policyholders may face a wide array of unexpected natural disaster risks. For example, in the last 10 years, New York state had declared disasters in six peril areas: hurricanes, tornadoes, winter storms, earthquakes, wildfires, and dust storms.

This kind of peril diversity highlights the need for agents to be on top of regional and national disaster trends to provide meaningful advice to protect their clients’ interests in more dynamic ways. Furthermore, the degree of supplemental disaster coverage can be adapted to suit customers’ needs. Residents living in high-risk areas or far away from family support may require greater backing in the face of a disaster and need guidance from trusted agents to identify more supportive coverage plans.

When natural disasters rank as the fifth most common cause of bankruptcies in the U.S., we must evaluate how to secure more comprehensive insurance coverage to better protect our clients, especially as the changing climate patterns present new challenges. The severe weather trends experienced this summer position supplemental disaster insurance as not a mere add-on but a vital component in delivering comprehensive protection against a range of perils in an increasingly unpredictable weather landscape. The more we stay informed and innovate alongside these trends, the better we can serve consumers with a combination of insurance coverage.

Wildfires have become a relentless threat across the U.S., with the 2024 Texas Panhandle wildfires being one of the most recent and severe examples. The Texas Panhandle wildfires—including the Smokehouse Creek fire, the largest in Texas history—caused $123 million in preliminary agricultural losses, making it the costliest wildfire on record. 

As these disasters grow in frequency and intensity, U.S. insurers face increasingly complex challenges in responding effectively. Here, we’ll explore the key challenges insurers encounter during wildfires and discuss potential solutions, drawing recent insights from the Texas Panhandle wildfires and global wildfire trends.

See also: Big Tech Tackles Wildfires

1. Escalating risk exposure

One of the most significant challenges insurers face is the increasing risk exposure due to the expanding wildland-urban interface (WUI). The WUI is the area where human development and undeveloped wildland meet, and development in this zone has grown significantly, especially in Southern and Western areas of the U.S. As more homes and businesses are built in wildfire-prone areas, the potential for massive financial losses surges. 

The Texas Panhandle fires alone devastated over a million acres, destroying hundreds of properties and causing significant economic disruption. With such widespread devastation, insurers are struggling to balance the risks associated with providing coverage in these high-risk areas.

To address this, insurers must invest in advanced risk modeling and analytics that factor in climate change, historical fire data, and future land use patterns. This can help in accurately pricing policies and setting aside adequate reserves to cover potential claims. Leveraging satellite imagery and AI-driven models can enhance risk assessments and enable insurers to offer more tailored coverage options, possibly encouraging homeowners to adopt fire-resistant building practices.

2. Underinsurance and coverage gaps

Another major issue highlighted by recent wildfires is the prevalence of underinsurance. Many property owners underestimate the replacement cost of their homes or do not update their policies to reflect current values, leaving them inadequately covered when disaster strikes. The extensive losses in the Texas Panhandle emphasized this point, as many victims discovered that their coverage was insufficient to rebuild their properties.

Insurers should engage with policyholders to regularly review and update coverage limits. Educating consumers on the true cost of rebuilding and the importance of updating their policies can reduce the incidence of underinsurance. Additionally, offering extended replacement cost coverage or automatic policy updates linked to inflation or local building costs can provide better protection.

3. Claims handling and customer service bottlenecks

The surge in claims following a major wildfire can overwhelm insurers and their loss adjuster resources, leading to delays and customer dissatisfaction. The aftermath of the 2024 Texas Panhandle wildfires saw a deluge of claims that tested the capacity of many insurers. Efficiently managing this influx while maintaining high customer service standards is a persistent challenge.

Insurers can mitigate this issue by investing in solutions that provide them with immediate intelligence around their affected portfolios, policies, and properties in the aftermath of a fire. These solutions use a combination of data sources such as satellites, aerial imagery, sensors, and specific imaging formats that can see through the smoke created by wildfires, like infrared imagery or synthetic aperture radar (SAR).

This gives insurers an accurate, detailed digital representation of an event's impact, ensuring they can accurately reserve for losses and more quickly respond to support their affected policyholders, even before first notice of loss (FNOL) on occasion.

See also: Technology Can Prevent 4 of 5 Electrical Fires

4. Climate change and the uncertainty of future events

The increasing unpredictability of wildfires due to climate change adds another layer of complexity for insurers. With “weather whiplash” events becoming more common, such as sudden shifts from drought to intense rain or heat waves, traditional risk models are often insufficient. This was evident in the 2024 wildfires, where unusual weather patterns contributed to the rapid spread and intensity of fires.

Insurers must integrate climate science into their risk modeling processes. This includes collaborating with climate experts and investing in scenario planning that accounts for extreme weather variations. Developing partnerships with government agencies and environmental organizations can also provide insurers with valuable data and insights to refine their risk assessments.

5. Regulatory and community pressures

In the wake of catastrophic wildfires, insurers often face scrutiny from regulators and pressure from communities to continue providing affordable coverage. The balance between maintaining solvency and meeting regulatory requirements can be delicate, particularly when there is public outcry over rising premiums or coverage denials in high-risk areas.

Insurers should engage with regulators and community leaders to find sustainable solutions that protect both their financial health and the community’s need for coverage. For example, the Texas Panhandle fires may have been caused by a decayed utility pole that broke, causing live wires to fall on dry grass. Incidents like this require advocating for the creation of state-backed insurance pools or promoting community-based fire prevention initiatives that reduce overall risk.

Responding to these challenges

The increasing frequency and severity of wildfires in the U.S. present significant challenges for insurers. However, by leveraging advanced loss assessment technologies, improving customer education, and collaborating with stakeholders, insurers can better manage these risks and provide essential protection to communities at risk. 

As we move forward, it’s imperative that the insurance industry continues to adapt to the evolving landscape of wildfire risk, ensuring resilience and stability in the face of these natural disasters.

Let’s first agree on what we mean by transformation: “a dramatic change in form or function; a transformation is an extreme, radical change.” Let’s not confuse transformation with mere improvement. 

See also: Let's Stop With the Gibberish

Offering brief context with pros and cons in ascending order of difficulty, which is not to say efficacy, here are ways you can improve your organization, with the long-term goal of transformation in mind:  

1. Outsourcing: Lifting and shifting entire operations (e.g., claims, systems support) from in-house to a vendor partner, typically overseas or in any geography with a substantially lower cost structure. An excellent tool for cost reduction and reallocating in-house resources, but control can be an issue. Also, out of sight can also mean out of mind. Who’s responsible for innovation and continuous improvement? Metrics are key, and they must evolve as circumstances change.     
2. Systems: Implementing a new system or replacing many systems with one system. This has historically been a risky endeavor, with whiff rates approaching 50% for in-house, on-prem implementations, but the risks decrease with cloud-based subscription models, where risks are typically reduced to integration and data migration. Motivated by a desire for efficiency and sometimes new capabilities, people typically confuse systems transformation with cultural transformation, and that can be a mistake. Are you changing the way your people think and work, or are you just changing the method through which transactions are processed? The good news is you replaced a legacy system with something new, but do legacy attitudes, mindsets, and habits remain?   
3. Process Optimization: Here we’re talking about aggressive programs of Lean, Six Sigma, or my favorite, Knowledge Work Standardization, typically led by external consultants. Starting with standard operating procedure (SOP) documents, critical processes are mapped with work products and cycle times, and people are interviewed to eliminate wasted motion and variance. While these engagements invariably spike urgency and can often lead to a new culture of continuous improvement, things can also go the other way, with process efficiencies lapsing once the consultants leave. Establishing good “control” metrics helps ensure this doesn’t happen. Process optimization is rightly viewed as the necessary first step toward process automation, but automation is its own thing, with, to date, mixed results at best. Intended as a cost-out center, automation (e.g., robotic process automation, or RPA) often becomes just another cost center.     
4. Operational Intelligence: COVID-era activity-monitoring tools have evolved into valuable metrics foundries based on both quantitative and qualitative measurement of humans and systems at work. The transformation that comes with operational intelligence is more subtle. Imagine one source of operational truth. Imagine publishing performance metrics by person, by team (e.g., submission turnaround), to all team members in real time. Imagine making personnel decisions based on numbers everyone, including workers themselves, can agree on. Operational intelligence also informs systems decisions. Before springing on ops intelligence, look yourself in the mirror and ask two questions: Do you really want to know? Are you prepared to act?  
5. Artificial Intelligence: This one is a big question mark for all of us. At present, AI, like blockchain, is a solution in search of problems. The technology ethos of “move fast and break things” is clashing with the enterprise ethos of “move slowly and keep things.” Given the risks associated with AI--mainly operational, reputational, and financial--carriers are nonetheless experimenting with it, quarantined to R&D, as a hedge against market/competition risk. It’s possible that AI fizzles out amid legal, financial, and technical issues such as model collapse. It’s also possible AI subsumes the other four vectors of transformation, becoming THE vector for how insurance is processed, with extreme automation, high efficiency, flexibility, and dynamic policy pricing. It’s possible AI-enabled mega-processors emerge, like Visa and Mastercard in card payments, processing, for example, auto and homeowners insurance policies, with carriers maintaining an affinity relationship. Or not. We’ll see.           

See also: 5 Keys to Transforming Underwriting

Anyway, we operate in a relentless cost-out reality. This is our lot in life. Transformation is more aspirational. It’s about finding better ways to work and connect with customers to the good of shareholders. Remember, in digital products such as insurance, operational excellence is marketing excellence. The two converge. And we can’t just cut our way there.  

Our inbox has become inundated with solicitations to participate in a wide variety of North American insurance industry conference as attendees, presenters, moderators, or sponsors.  We suspect yours has, too. 

In addition to the increased number of conference and media production companies entering the fray – inexplicably, mostly U.K.-based – conference themes are increasingly specialized (insurtech, climatetech, innovation, data and analytics, sustainability, GenAI, cyber etc.), which account for this growth in events.

Background

When we entered the insurance industry decades ago, the majority of conferences were long-standing and produced by insurance associations. A few others were relevant to the P&C insurance and technology sectors – IASA being a prime example. Since 1928, the Insurance Accounting & Systems Association (IASA) conference had been a trusted source of knowledge and education, connecting the community of insurance professionals in accounting, finance, systems, and technology. In 2023, IASA became affiliated with The Institutes. The next IASA Xchange conference will be held in St. Louis, June 8–10, 2025.

Beginning in the '90s, the insurance media, seeking to supplement shrinking print revenues, began sponsoring conferences with themes relevant to their readership and advertisers. Many of these continue to thrive today (e.g., DigIn and PropertyCasualty Claims).

In addition, industry associations (e.g., RIMS, PLRB, SIR, APCIA, Global Insurance Forum/III Joint Industry Forum/The Institutes) SEMA,IBIS, CIECA, and CIC) produce large, successful industry and member-specific conferences and still draw large crowds today. PLRB’s continuing education credits are attractive for the various state licensing requirements. Numerous private and public organizations serving the auto and home damage/repair segments have expanded and thrive, as well (CoreLogic, Auto Insurance Report, Property Insurance Report, and others). The regulatory space is well represented by NAIC (National Association of Insurance Commissioners) through a series of state and national quarterly meetings. Finally, the industry’s larger solution providers sponsor promotional, customer-centered events – several are invitation-only.

In 2015, global conference producers began to enter the U.S. market with more highly targeted conferences focused on emerging technologies and insurance industry transformation coinciding with the rise of insurtech. Despite the two-year disruption caused by the pandemic, this expansion has not only resumed but has flourished. One of the more successful of these was the U.K.-based Insurance Nexus (since acquired by Reuters Events), whose first U.S. event was Connected Claims. It debuted in Chicago in 2015 and has evolved to become the world’s largest insurance executive claims event. It will host an estimated 900 attendees on Nov. 12-13 in Austin, TX. Reuters Events has also established other well-attended annual North American industry events, including The Future of Insurance USA and The Future of Insurance Canada. 

We now count well over 50 major insurance-centric conferences scheduled in 2024 and some already announced for 2025.

The first InsureTech Connect (ITC) Vegas was held in 2016 and almost immediately established itself as the world’s largest gathering conference for insurance innovation. The ITC Vegas 2024 event, scheduled for this Oct. 15-17 and presented by McKinsey, is expected to have over 9,000 attendees from the insurance and related industries. It combines extensive networking with what is new and next and facilitates meeting large numbers of people, sourcing more solutions, and creating valuable relationships and partnerships.

In addition to these “national” and “global” conferences, there are dozens more annual and quarterly regional association events, some of which are very well attended, from coast to coast.

The economics of a successful conference can be impressive, but it typically takes multiple events to build the needed awareness and attendance loyalty. The conference and participant data that is obtained is valuable and can feed other year-round informational and educational activities. Of course, this depends on the various stakeholder interests, depending on whether you are an event organizer, sponsor (mainly industry solution providers), insurance carrier attendee, speaker, or investor. Insurtech startups and incumbent solution providers are constantly balancing cost/benefits and seek an ROI in terms of eventual deal flow with carriers and weigh many of the following factors.

See also: Riding the Insurance Roller Coaster

General Selection Criteria

Most of us are unable and unwilling to attend all these events, so it is important to develop some criteria to choose based on our available resources (time and money)

• Keep in mind that, based on conference location and travel options, your commitment may be a day or two longer than the conference itself. And based on time of year and location, consider the potential for travel disruption. 
• Before committing, find out who is attending (at least by company and title). Depending on your responsibilities, ensure that enough of your prospects will be available. Conference attendance numbers can be misleading; find out the breakdown among solution providers, insurers, investors, media, academics, and others. 

Insurance Company Staff

• Consider the opportunity to hold on-site company team meetings, if appropriate - this can be very cost-efficient. Most conferences offer attractive group rate discounts.
• Identify opportunities to gain competitive intelligence. 
• Meeting privately with selected solution providers can save time and control the meeting length.
• If you have a speaking role, conferences can be good for career management and self- promotion.
• Check to see if continuing education (CE) credits are available.

Solution Providers

• Booth location can matter; try to get positioned near hall entrances, close to refreshments. The uniqueness of your premiums or giveaways can also make a difference.
• Take full advantage of the event network apps to search for, identify, and schedule introductions to your highest-priority targets.
• Identify prospects and schedule appointments in advance; reconfirm them one week in advance.
• Exhibitor maps can help you identify and plan your time on the show floor.
• Arrive early and document the exhibit hall floor with your video camera for future reference. 
• Establish an offsite meeting location, which could include a hotel suite.
• Organize and sponsor informational sessions (meet-and-greets) with selected targeted attendees; ask the conference organizer to assist you with the invitation process.
• Schedule offsite social/dinner/sports outings; invitation-only can be considered to keep things manageable.
• Do not expect to see an immediate sales result; your objectives for participation should include network development, visibility, brand awareness, and thought leadership.
• If your company is early stage or seeking funding, keep in mind that most of these events include investors with special interest in insurance technologies.

See also: A Guide to Legacy Modernization

Maximize Your Investment

Whether you are an insurer, solution provider, or “other,” these tips are equally relevant and valuable. The average investment in attending a single conference today is $3,000 - $4,000 all in for insurers and much more for sponsors and exhibitors. 

These strategies may appear to be plain common sense, but most attendees do not practice them.

• In addition to the conference networking app (which is very valuable), use social media before, during, and after the event to share thoughts and let people know where you are and what you are interested in.
• If you are attending with colleagues, resist the natural temptation of sticking with them in sessions and especially during networking breaks and meals. Networking is one of the most important benefits of attending.
• Post-conference, be sure to share your learnings, including your notes, with your colleagues. You are more likely to get approval for your next conference attendance request.  

Our View

While main stage presentations and panel discussions can be informative, we find that smaller, topic-specific group breakouts of between eight and12 individuals, curated by industry experts, allow for more focused attendee participation and informational exchange. Attendee satisfaction survey results are improved, and we hope to see more time allocated to workshop and roundtable sessions.   

The three-day event structure is being supplanted by shorter, 1.5-2-day sessions. Longer events can consume nearly a whole week when including travel and time for the vendors to arrive early/stay late for setup and breakdown of expo areas.

We hope to see you out there in conference land soon.  

One last bit of advice – wear comfortable shoes, especially for supersized events like ITC Vegas.

Paul Carroll

I’ve been a fan of Planck since I met the CEO a couple of years ago, so your acquisition of Planck caught my interest. I’m intrigued about how you intend to integrate their impressive AI capabilities into your software for agencies. Could you start us off with the background for the deal?

Taylor Rhodes

Sure. Applied Systems is an insurtech that actually has been around for 41 years now. Our founder started with an agency management system and has branched out from the core back office into front office sales and marketing, digital payments, etc. Our clients number in the thousands of agencies, with over 700 insurers on the carrier side of the market.

I’ve been in tech now 25-plus years, including at an early cloud company called Rackspace in the early 2000s. I remember going out into the world in the early 2000s and trying to sell cloud computing to CIOs. We used to get told, “No, we're never going to run that kind of data outside of our data center. It’s too critical.” The unlock was the global financial crisis in 2008. People had to cut costs and think of new ways to do things.

By 2009, we couldn’t beat cloud customers off with a stick, but it took a cataclysm in the market to get them to think differently.

There had to be some evolution. You can't just take a three-tier web app, throw it on the cloud, and hope it works, even though that's what customers thought initially. Over 2009, 2010, 2011, people learned how to design for the cloud, and you can see the business model explosion that then happened, including with all the apps that were enabled on our phones.

And I got a front seat to all that.

When we started thinking about AI a couple of years ago, it was hard to separate hype from reality. So we started engaging with a lot of agencies and saying, “What are your most pressing problems?”

Historically, the insurance industry has been a bit of a laggard in investing in tech on the agency side, but that started changing with COVID. When you sent everybody home, there were a whole bunch of processes that didn’t work. That lit a fire under this industry to digitize more and more.

What I think the industry hopes for out of AI is that it can truly be an intelligent automation capability. We have an aging workforce, a lot of whom will retire over the next decade, and unemployment is exceptionally low within the insurance industry. The last thing an agency wants to do is lose a valuable producer or a CSR [customer service representative] or have them so burdened with inefficient systems that they often can’t work on the most valuable tasks.

AI holds the promise of intelligent automation across key workflows. That might be conversations at the point of renewing with a client; the AI can go out and look at all the data points around the company. Has the risk changed because something has changed about the business? If this business, for example, is a restaurant but takes digital payments, do they have a cyber policy? If they don't have a cyber policy, that's a great sales opportunity.

And the Planck guys have built a very clever, proprietary platform that takes advantage of all the LLMs [large language models]. They can go out and discover a lot of valuable information about a target company, about the risk profile of that company, and offer very clear and powerful insights.

We also have unique datasets here at Applied because we see a lot of information within our agency management system and in our carrier connectivity system, and we can leverage those datasets to help train AI models to be much more insightful for application to an insurance agency.

Paul Carroll

Do you have a favorite example or two of how AI is being used?

Taylor Rhodes

Let's start on the productivity side. CSRs at an insurance agency spent an awful lot of time reading emails, trying to understand what the email is actually asking for, searching for documents, opening documents and reading them, comparing them against a different document, and doing all of this in preparation for what actually matters: the conversation with the client.

That conversation might be around a claim, a servicing issue, a renewal, or a new piece of business. What AI can do is summarize emails and tell you in a very punchy way what they are actually about. That sounds simple, but it’s very powerful when you're handling hundreds of emails a day.

The second thing is, AI can understand documents and can distill what’s in them. It can then help you classify what's in that document and compare it to another one so you have a before-and-after view of what's happening. People spend a lot of time on that sort of work.

The person is still in charge. You still get to review and see if AI has made a summary or a recommendation that seems right to you. But instead of doing that all day long, you have an AI that fits right into your workflow and provides you with the right insights at the right moment and cuts hours of work out of your day.

Now let's talk about the revenue side. The number one job in an insurance agency is managing renewals. It is by far the most time-consuming and highest-volume thing that folks do. And the difference between having a renewal rate at, let's say, 90% versus 93% or 94% really matters to the bottom line.

So what do you do when you're getting ready for renewal? Well, again, you're pulling up old emails from the client. You're going to the current policy. You're looking at what happened. You're trying to go out onto the internet and find information about that insured client and their business. Maybe they’re a barber shop, but they’ve started to serve liquor. Are they still a barber shop, or are they actually in a different NAICS code that has a different risk profile?

What Planck and Applied together can do is really create an intelligent renewal capability. We can go out and find all that information and summarize it into key risk factors with ratings, with transparency about why we assigned the ratings we did. This can’t be a black box.

We can also do the sort of thing I mentioned before in that simple example about a company that has started accepting digital payments. That’s a high-fraud area, so if the business doesn’t have a cyber policy we can flag the opportunity to add a line of business.

Paul Carroll

If you project out three to five years, are there particular areas for development that you think could be especially fertile?

Taylor Rhodes

In our point of view, AI isn’t meant to replace human beings. It's meant to help them do the highest and best work.

But there are many ways AI agents could help. Think of a CSR answering phone calls, trying to understand what the client is looking for., connecting the client to the right information so they can self-serve if they want to, finding the right information and presenting it to the human who then will pick up the interaction. ”Now that we’ve agreed on what we're really talking about here, let me get a human involved so the human can do the things that only humans can do.”

You can think about all of these interaction points that happen between an agency and a client or an agency and the insurer, and you can think about AI being a source for the first interaction.

Those are the types of things we see taking place over all the different workflows in jobs within the agency. And they probably aren’t too far down the road. They just take focused effort based on understanding the peculiarities of an agency. And they take having feedback loops and cycles with your clients to help you shape the product in a way that's going to be most valuable for them.

Paul Carroll

That’s great. Thanks, Taylor.

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About Taylor Rhodes

Taylor Rhodes, Chief Executive Officer, leads Team Applied and is responsible for the company's overall strategy and operational execution. Rhodes joined Applied in 2019 after serving as chief executive officer of SMS Assist, the leading cloud-based software platform for multi-site property management. Previously, he was CEO of Rackspace, where he led the Company’s growth from a cloud pioneer to an industry leader with more than two billion dollars in revenue, while establishing the company as a mainstay on the Fortune 100 Best Companies to Work For®. Prior to Rackspace, he served as a leader in enterprise, financial and corporate strategy roles at Electronic Data Systems Corporation. Mr. Rhodes is a former United States Marine Corps infantry officer and holds a MBA from the University of North Carolina at Chapel Hill. He serves on the board of directors for Applied, Zenoss and Liquid Web, LLC.