Organizations across many industries are harnessing the power of foundation models (FMs) and large language models (LLMs) to build generative AI applications to deliver new customer experiences, boost employee productivity, and drive innovation.
Amazon Bedrock, a fully managed service that offers a choice of high-performing FMs from leading AI companies, provides the easiest way to build and scale generative AI applications with FMs.
Some of the most widely used and successful generative AI use cases on Amazon Bedrock include summarizing documents, answering questions, translating languages, and understanding and generating brand new multimodal content.
Business challenge
Problem-solving, logical reasoning, and critical thinking are critical competencies for achieving business success, accelerating decision-making, and fostering innovation. Although strategy consultants have honed these skills, many knowledge workers lack them due to inadequate training and limited access to appropriate tools. Developing these skills not only enhances individual productivity but also drives significant benefits for the organization.
Business use cases
In this post, we want to demonstrate some additional generative AI use cases on Amazon Bedrock. We show how Anthropic’s Claude 3.5 Sonnet in Amazon Bedrock can be used for a variety of business-related cognitive tasks, such as problem-solving, critical thinking and ideation—to help augment human thinking and improve decision-making among knowledge workers to accelerate innovation. For this, we are using several frameworks and tools widely used by the management consulting community, such as mutually exclusively collectively exhaustive (MECE); strengths, weakness, opportunities, threats (SWOT) analysis, issue tree, value chain analysis and value driver tree analysis.
Solution overview
To demonstrate these five use cases, we used the Amazon Bedrock playground with Anthropic’s Claude Sonnet 3.5 LLM. Where necessary, in addition to text prompts, we also used Anthropic’s Claude Sonnet 3.5’s image-to-text capability to improve the accuracy of the responses generated.
Explanation of the five use cases—together with the prompts and images used to feed the LLM and the responses generated—are shown in the following sections. To improve the explicability of text responses generated by the LLM, we’ve provided additional diagrams, where necessary, to complement each LLM response (for example, the tree diagram corresponding to the LLM generated response).
The following sections explain the solution flow for each use case.
MECE
MECE is a widely used framework for business problem-solving. MECE helps break down a problem into well-defined, nonoverlapping parts (mutually exclusive), but when all the parts are taken together, they’re comprehensive and exhaustive to address the entirety of the problem (collectively exhaustive).
In this example, we want to demonstrate the use of the MECE principle by asking the LLM to arrange food items (in this case, various fried rice options) listed in a restaurant menu, adhering to the MECE guidelines.
We provided the following prompt to the LLM:
Here are some fried rice options: chicken fried rice, vegetable fried rice, lamb fried rice, beef cried rice, egg fried rice, prawn fried rice, squid fried rice
Can you please arrange them using MECE (Mutually Exclusive Collectively Exhaustive) principle?
We get the following response from the LLM:
We arranged the LLM response in tree format for visual clarity, as shown in the following diagram.
Suppose the restaurant wants to add three more varieties to the menu: egg and prawn fried rice, lamb and prawn fried rice, and vegan fried rice. We sought the help of the LLM with the following prompt to rearrange the menu while preserving the MECE principle:
I want to add 3 more varieties to the menu: egg and prawn fried rice, lamb and prawn fried rice, vegan fried rice. Can you please rearrange the list in MECE?
We get the following modified response from the LLM:
We rearranged the LLM response in tree format for visual clarity. As shown in the following diagram, the LLM has preserved the MECE principle, intelligently adding new categories as needed to accommodate the menu changes.
Issue tree
An issue tree, also known as a logic tree or problem-solving tree, is a strategic analytical tool used to deconstruct complex problems into their constituent elements. This hierarchical framework facilitates a systematic approach to problem-solving by:
- Disaggregating the primary issue into discrete, manageable subcomponents
- Organizing these elements in a structured, top-down format
- Providing comprehensive coverage through the application of the MECE principle
The visual representation afforded by an issue tree enables stakeholders to:
- Identify key drivers and root causes
- Prioritize areas for further investigation or resource allocation
- Maintain a holistic view of the problem while focusing on specific aspects
By employing this methodology, organizations can enhance their decision-making processes, streamline strategic planning, and improve the efficiency of their problem-solving endeavors.
To demonstrate the LLM’s ability to solve problems using an issue tree, we used a fictitious company—AnyCompany Tile Factory—whose profits are down by 30%. AnyCompany’s management wants to use an issue tree to identify the main issues and subordinate issues, and then use it to analyze reasons for declining profits. To give additional context to the LLM, we provided the following diagram with a skeleton issue tree structure.
To prompt the LLM, we attached the preceding diagram and used the following text:
Problem = profits at the AnyCompany Tile Factory is down 30%. Using the diagram as a guide, can you develop an issue tree identifying the main issues, sub issues and then help with the corresponding analysis against each sub-issue to find the reasons for profit decline?
We get the following response from the LLM:
And we populated the issue tree with the response from the LLM for additional visual clarity, as shown in the following diagram.
As shown in the diagram, the LLM has intelligently identified the two main top-level issues contributing to profit decline at AnyCompany (revenue decline and cost increases) and under each category identified the secondary issues, together with a high-level analysis for the management to pursue.
Next, we asked the LLM to elaborate “facility overhead costs” using the prompt:
Please elaborate “facility overhead costs”
We get the following response from the LLM:
SWOT
A SWOT analysis is a strategic management tool that can be used to evaluate the strengths, weaknesses, opportunities, and threats of an organization, industry, or project. SWOT helps in decision-making and strategy formulation by identifying internal factors (strengths and weaknesses) and external factors (opportunities and threats) that can impact success. Management can then use the analysis to develop way forward strategies, using strengths, addressing weaknesses, capitalizing on opportunities, and mitigating threats, as identified in the SWOT.
In this example, we ask the LLM to develop a way forward strategy for the Australian higher education sector using the SWOT analysis diagram provided. We ask it to identify four key strategic themes for the sector, making sure the approach uses inherent strengths, addresses weaknesses, capitalizes on opportunities, and mitigates threats, as identified in the SWOT diagram and illustrated in the following graphic. We also ask the LLM to list critical activities to be pursued by the sector under each strategic theme.
To prompt the LLM, we attached the preceding diagram and used the following text:
Using the SWOT analysis for the Australian higher education sector, we want your expertise to help develop the way forward strategy. Please identify 4 key strategic themes for the sector, ensuring your approach leverages strengths, addresses weaknesses, capitalizes on opportunities, mitigates threats as identified in the SWOT diagram. Under each strategic theme, list critical activities to be pursued.
We get the following response from the LLM, which includes four strategic themes and activities to be pursued:
We constructed the following diagram based on the LLM response for visual clarity.
Value chain analysis
Value chain analysis is a strategic management tool that helps organizations evaluate each value-creating activity in their value chain, such as inbound logistics or operations, to identify opportunities to build completive advantage, reduce costs, and increase efficiencies.
In this example, we want the LLM to perform a value chain analysis for the AnyCompany Tile Factory and make recommendations to improve profitability. As additional context to the LLM, we provided the following end-to-end value chain diagram for AnyCompany.
To prompt the LLM, we used the following text:
Profits at the AnyCompany Tile Factory are down 30%. The diagram shows their end-to-end value chain. Please perform a value chain analysis and make recommendations to improve profitability at AnyCompany.
We get the following response from the LLM, with recommendations for improving profitability across the five main areas:
We updated the value chain diagram with the recommendations supplied by the LLM under each category, as shown in the following diagram.
Value driver tree
A value driver tree is a framework that maps out key factors influencing an organization’s value or specific metrics such as revenue, profit, or customer satisfaction. This framework breaks down high-level business objectives and drivers into smaller, measurable components. By doing so, it reveals the cause-and-effect relationships between these elements, providing insights into how various factors contribute to overall business performance. Value driver trees are used for business performance improvement, strategic planning, and decision-making.
In this example, we want the LLM to define a value driver tree for the AnyCompany Tile Factory so the management team can analyze revenue, cost, and efficiency drivers contributing to low profitability and take action to remediate issues.
To prompt the LLM we used the following:
Profits at the AnyCompany Tile Factory are down 30%. Please help develop a value driver tree for the AnyCompany’s management to analyze the problem and take remedial action. Consider revenue, cost and efficiency drivers
We get the following response from the LLM, with a breakdown of major components—revenue, costs, and efficiency— affecting profitability at AnyCompany. It has also provided a five-step action plan for the management to consider.
We constructed the following value driver diagram for AnyCompany Tile Factory in tree format, based on the responses provided by the LLM.
Conclusion
Problem-solving, critical thinking, and logical reasoning are cognitive processes that use the brain to find a solution to a problem or reach an end goal, especially when the answer isn’t immediately obvious. As we’ve shown in the examples in this post, LLMs such as Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock can be used to improve your cognitive skills, especially in the areas of problem-solving, creative thinking, and ideation. This in turn will help improve team collaboration, cut decision times, and drive innovation. The examples we used are basic to showcase the art of the possible. To improve LLM responses in complex problem-solving use cases, we recommend using RAG sources that are relevant to the problem, chain-of-thought prompting, and giving additional problem-specific context through prompt engineering.
We encourage you to begin exploring these capabilities through the Amazon Bedrock chat playground, a tool in the AWS Management Console that provides a visual interface to experiment with running inference on different LLMs and using different configurations.
About the Authors
Senaka Ariyasinghe is a Senior Partner Solutions Architect working with Global Systems Integrators at Amazon Web Services (AWS). In his role, Senaka guides AWS Partners in the APJ region to design and scale well-architected solutions, focusing on generative AI, machine learning, cloud migrations, and application modernization initiatives.
Deependra Shekhawat is a Senior Energy and Utilities Industry Specialist Solutions Architect based in Sydney, Australia. In his role, Deependra helps energy companies across the APJ region use cloud technologies to drive sustainability and operational efficiency. He specializes in creating robust data foundations and advanced workflows that enable organizations to harness the power of big data, analytics, and machine learning for solving critical industry challenges.