No matter how simple or complex a project presents itself, getting back to the fundamentals will help with the business value creation. Define, follow, tweak/ refine simple processes while undertaking projects to ensure project context, dependencies, stakeholder and end user's goals and objectives are very well understood. Let us review the PMI case to understand the players, process or lack of it, and we can help improve to mitigate the situation.

Note: Please review the attached Mini-Case Study before the event.

Takeaways:

• Taking a Project centric approach increases interaction and meeting organizational goals and objectives
• Understanding context, players, and processes helps in planning the delivery
• More importantly, having a clear process framework with a
  well-defined engagement model with stakeholders will minimize project failure
• How essential is to customize and templatize frequently
  used tools to support the well-established project processes and interactions
• Understand the difference between a simple installation to a scaled implementation

While working in venture capital, Ms. Surio leveraged generative AI to support her creation of a research and discovery process that would inform knowledge and content creation for digital health startups. She provided a framework to review and analyze existing content for its relevance to the venture firm's startup companies and founders. Outcomes included supporting a new startup success team, gathering hundreds of business needs across funded startups, implementing a standardized approach to research and discovery across the company and venture capital ecosystem to gather data on past startup successes and failures, and authoring solutions that would be delivered as knowledge products (i.e., templates, articles, or presentations during workshops). She applied agile software development principles to prioritize and develop these solutions, which would be included on a searchable web-platform for founders. In her time, she developed 25 scalable solutions to enable 50 founders in go-to-market, product build, investor fundraising, and founding team construction.

Takeaways:

• To establish a research and discovery process that gathers data and information across the venture or company ecosystem.
• To determine which best practices, content, data, and ultimately solutions, yield the best outcomes for startup companies.
• 3) To identify patterns, business needs, and preferences from historical data to understand what works vs. what does not for startups.
• To test, validate, and evaluate the utility of the knowledge products that are developed with support from AI tools.

While working in venture capital, Ms. Surio leveraged generative AI to support her creation of a research and discovery process that would inform knowledge and content creation for digital health startups. She provided a framework to review and analyze existing content for its relevance to the venture firm's startup companies and founders. Outcomes included supporting a new startup success team, gathering hundreds of business needs across funded startups, implementing a standardized approach to research and discovery across the company and venture capital ecosystem to gather data on past startup successes and failures, and authoring solutions that would be delivered as knowledge products (i.e., templates, articles, or presentations during workshops). She applied agile software development principles to prioritize and develop these solutions, which would be included on a searchable web-platform for founders. In her time, she developed 25 scalable solutions to enable 50 founders in go-to-market, product build, investor fundraising, and founding team construction.

Takeaways:

• To establish a research and discovery process that gathers data and information across the venture or company ecosystem.
• To determine which best practices, content, data, and ultimately solutions, yield the best outcomes for startup companies.
• 3) To identify patterns, business needs, and preferences from historical data to understand what works vs. what does not for startups.
• To test, validate, and evaluate the utility of the knowledge products that are developed with support from AI tools.