Architecting and Delivering Value Using Generative AI in the Insurance Industry- the Lemonade Insurance Example
by Daniel Lambert (book a 30-minute meeting)
The job of business and enterprise architects is changing significantly right now. They are still designing and aligning an organization's business strategies to business capabilities, information, and IT infrastructure, ensuring that their technology and processes support their overarching goals, but generative AI is changing how their output is made. They are generated at a much rapid pace. Planning in the organization can now handle many more scenarios and iterations in a shorter time than a year ago because of generative AI. With the willingness of top management, it has become simpler to plan and architect from strategy to execution in an agile setting. In this article, I intend to show you what is now possible to do for a business and enterprise architecture team at Lemonade Insurance with generative AI using strictly publicly available information[i].
Business Context
To enhance the quality and relevance of outputs generated using AI, establishing a clear business context is essential. Figures 1 and 2 below present the business context for Lemonade Insurance, compiled from publicly available information. This context includes in Figure 1 the company's mission, vision, and primary strategies, and in Figure 2 its customer segments, and product lines. This draft took only a few minutes to create, offering a significant time-saving advantage over starting from scratch.
This generative AI output can be created using platforms like OpenAI, Co-Pilot, Gemini, Claude, Le Chat Mistral, and others. While these platforms allow you to set a business context for prompts related to Lemonade Insurance, you may need to re-establish this context frequently. For this reason, using a more specialized generative AI tool tailored to your specific workspace or project—such as ‘Lemonade Insurance’ in this case—can be beneficial.
The Importance of Prompts in Generative AI
Prompts are crucial in generative AI as they guide the model's response, directly impacting the quality, relevance, and accuracy of the output. Every word matters in a well-crafted prompt. A good prompt can help narrow focus, align responses with specific contexts, and produce insightful results tailored to the architect’s needs. Prompts can also generate significantly better results using confidential information and documents. For example, in complex projects like business architecture or strategic planning, providing context through preliminary prompts allows AI to generate content that resonates with project goals and organizational specifics, as explained earlier. Additionally, prompts help manage tone, complexity, and style, making them key to achieving useful, actionable, and consistent results. Thus, effective prompt design is essential for maximizing AI’s potential.
Providing Value
Business and enterprise architecture can enhance SAFe® project delivery a lot using generative AI. When planning and architecting from strategy to execution in an agile environment, I prefer to start with value streams, which are the cornerstone of the Scaled Agile Framework® (SAFe®). Figure 3 below illustrates the top 7 strategic client-driven value streams for Lemonade Insurance, as suggested by generative AI—although many more could have been identified.
This output was generated by ChatGPT from Open AI, though similar results could be produced by other AI platforms. This table is a draft that was generated again in just a few minutes.
Next, let's use generative AI to outline and describe the value stages of the 7 value streams mentioned in Figure 3. The results are presented in Figure 4 below.
Again, these value stages of the 7 value streams listed in Figure 3 were generated in a few minutes with strictly public content. This output would have been significantly enhanced using confidential information and documents.
It now needs to be validated by subject matter experts, which can be anyone listed in the next section.
For the rest of this article, we’ll focus on the “Provide Seamless Digital Insurance Onboarding” value stream, where all value stages are described in Figure 5 below.
Participating Stakeholders
Using generative AI, identifying the internal and external stakeholders involved in each value stage of the value stream “Provide Seamless Digital Insurance Onboarding” becomes a straightforward task. The results from my prompt are displayed in Figure 6 below.
This draft includes 42 internal and external stakeholders, with 6 stakeholders listed per stage. It’s likely that more stakeholders are involved, and some may contribute across multiple value stages, though none are repeated here. Created in just minutes, this draft demonstrates a significant time-saving advantage over starting from scratch, though it remains a preliminary version open to further refinement.
Enabling Business Capabilities
Using generative AI to generate enabling capabilities for a value stream is challenging, as results often require more refinement. Substantial work is typically needed to polish these capabilities before presenting a draft to subject matter experts and business stakeholders. I used generative AI to identify enabling capabilities for the ‘Provide Seamless Digital Insurance Onboarding’ value stream, resulting in a prompt with 42 capabilities listed in Figure 7.
Significant improvements are needed before finalizing this draft for publication. The capabilities listed likely represent level-2, level-3, and level-4 business capabilities, which need to be accurately mapped to the organization’s current capability map. Some enabling capabilities may be missing, and others may require more precise naming. Additionally, certain capabilities may duplicate or closely resemble existing ones. Completing this task efficiently would benefit from a robust EA tool.
Furthermore, each of the 42 enabling capabilities in the 'Collaborate on Design' value stream, excluding duplicates, must be aligned with the organization's current applications, modules, or micro-services using APIs, where applicable. Advanced EA tools can now leverage generative AI to automate this alignment, achieving reasonable accuracy. Additionally, these enabling capabilities may need alignment with other important domains within the business architecture of the organization.
Required Information
Information and data architecture are vital to the success of any artificial intelligence project. Traditional information mapping is often too complex to effectively support digital transformation delivery teams. Identifying the required information types for a value stream offers a more accessible and practical alternative. Figure 8 illustrates 42 information types for the ‘Provide Seamless Digital Insurance Onboarding’ value stream.
Substantial refinement is necessary before this draft is ready for publication. The information types identified likely correspond to level-2, level-3, and level-4 information types, which must be accurately aligned with the organization’s existing information map—a structure often less detailed than the organization’s capability map. Some required information types may be missing, while others may need more precise naming. Additionally, certain information types may overlap or closely resemble existing ones. Using a robust EA tool would streamline this process significantly.
Strategy to Roadmap
Specialized generative AI tools are now advancing the pace of digital transformation by enabling the rapid creation of multiple detailed iterative scenarios. These tools can support capability-based roadmap output drafts that can efficiently outline strategic requirements, paving the way for thorough planning in complex projects. The ability to generate such scenarios quickly allows organizations to adapt their digital transformation strategies with greater precision and responsiveness.
Beyond just strategic planning, specialized generative AI tools streamline the creation of user stories, which are often time-consuming but essential for guiding project execution. By automating in part this aspect, organizations can focus on delivering high-quality digital transformation initiatives, ensuring that roadmap requirements align seamlessly with user needs and overall business objectives.
In conclusion, generative AI is transforming the work of business and enterprise architects by enabling faster output and expanding the scope of scenario planning and iterations. With support from top management, planning from strategy to execution is now more agile and efficient. This article explores how Lemonade Insurance’s architecture team can leverage generative AI, using only publicly available information, to achieve impactful, rapid results.
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[1] Lemonade Insurance is not a client of Business Architecture Info.