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Use case I: stakeholder analysis

LLMs enable detailed profiling and analysis of customers, competitors, and other key stakeholders. Profiles can also cover the portfolios and best practices by key competitors. LLMs can provide frequent, cost-effective updates based on AI-assisted research, competitor monitoring, and customer data analysis. This speed and breadth of analysis helps improving the understanding of market expectations, behaviors, and dynamics.

Main requirements:

- Introduce LLM integration: Provide access to a Large Language Model (LLM) like ChatGPT for analyzing anonymized stakeholder data.

- Establish customer data platform: Develop a platform to manage and analyze customer data across relevant data sources, ensuring it is structured and AI-ready.

- Ensure data updates: Implement a system for continuous updating of stakeholder data to keep the analysis relevant.

Use case II: Identification of decision options

AI aids in broadening decision options for portfolio adjustments. It helps in generating new ideas for portfolio changes by using pattern recognition in the available data to identify potential opportunities and optimize strategic choices. Specifically, AI can help identify untapped potential by highlighting discrepancies of the current portfolio with available customer insights, market trends and best practices by comparable competitors.

Main requirements:

- Ensure available strategies are AI-readable: Turn the relevant aspects of current strategies into AI-readable (quantified) key statements.

- Integrate results of stakeholder analysis: Grant AI access to all relevant analysis of the impact and interests of various key stakeholders, especially by customers and competitors. This should include a thorough review of competitor portfolio best practices and strategies with clear assessments of the company-specifics that make each competitor succeed (or fail).

- Describe portfolio for AI Analysis: Submit a comprehensive documentation of the current product and service portfolio, the portfolio performance, and any relevant information about company-specific portfolio enablers. The information about enablers should include the main resources allocated across the portfolio as well as key competitive strengths (or weaknesses). Examples for relevant competitive attributes would be information about the sales and manufacturing model used, describing (e.g.) the resulting supported production speed, variety, flexibility, and cost-efficiency.

Use case III: Assessment of decision options

Latest generation LLMs can assist in qualitative comparison of decision alternatives, for example by suggesting (dis)advantages for each available decision. For quantitative decisions, they can also support in preparing business cases. At the current technological level of LLMs, business cases are mainly manually crafted, with LLMs offering suggestions that require in-depth expert review for accuracy. Over time and as LLMs evolve, their suggestions become more reliable, allowing them to draft business cases based on company data. This evolution will gradually shift the human role towards systematically refining these AI-generated suggestions, leading to more complex and accurate business cases that account for scenarios involving competitor responses, thus enhancing strategic decision-making

Main requirements:

- Integrate results of stakeholder and decision option analysis: Inform the AI about the final set of decision alternatives and the related scenario assumptions. This will be the starting point for the business case. In addition, provide input to the AI with any relevant findings from the stakeholder analysis such as identified customer preferences. This information, for example, can help the AI to suggest meaningful parameters and assumptions for the business case.

- Provide access to operational and financial data: Both internal and market-related operational and financial data must be accessible for a comprehensive AI analysis. Where available, validated forecast data will improve the accuracy of the analysis.

- Involve industry experts: From start to finish, AI prompts should be engineered by industry experts to ensure relevant and accurate AI outputs.

Use case IV: Continuous improvement

AI supports the ongoing refinement of a portfolio strategy. It quickly identifies opportunities for action based on current data and ensures that the portfolio remains aligned with market trends and customer preferences, leading to continuous improvement and strategic agility

Main requirements:

- Establish regular data updates: Ensure AI analyses are based on most current information.

- Implement feedback mechanisms: Integrate customer feedback and performance data into ongoing AI analysis.

- Develop a dynamic strategy adaption process: Allow regular strategy refinements based on AI insights.
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