True Life Statement
Bob retired (Cornell Nuclear Engineer, Navy Captain, etc) recently retired (12/31) from work as a project manager ( BIG projects).
Bob writes “I started working on an AI effort as it relates to Project Management a year ago. I pursued this because I am finding that PMs today provide qualitative status updates, not quantitative, and this leads to poor performance on projects. Most projects are delivered late, over cost, or scope is reduced, or some combination of all the above is experienced by the owner/client.
I entered some quantitative ‘dummy project data’ into ChatGPT and received misleading replies as it relates to project status. However, with further queries, I obtained results that were impressive. I have that transcript.
I drafted a paper for an initiative to team with a company to develop a product. I have a very extensive project background, fairly extensive math background, but no programming background except for one course in college using PL/C. The effort didn't gain traction, but I know there is a market for this type of product.”
Analysis
Bob’s statement shares some common themes and goals with QSLS Engineering, particularly in terms of leveraging AI and quantitative analysis to improve project management and systems engineering outcomes. Here are a few key points of alignment:
1. Quantitative vs. Qualitative: Both the statement and QSLS emphasize the importance of quantitative measurements over purely qualitative assessments. The statement notes that project managers often provide qualitative status updates, which can lead to poor project performance. Similarly, QSLS aims to provide quantitative measurements of system attributes to enable data-driven decision making, rather than relying on "gut feel" or past experience alone.
2. Improving Project Outcomes: The statement highlights common project challenges, such as delays, cost overruns, and scope reductions. QSLS aims to mitigate these risks by providing early visibility into system characteristics, quality attributes, and alignment with business drivers. By quantifying these factors during architecture and design phases, QSLS can help identify potential issues and optimize system development before significant resources are invested.
3. AI-Powered Solutions: Both the statement and QSLS recognize the potential for AI to transform project management and engineering practices. The statement mentions using ChatGPT to analyze project data and generate insights, while QSLS leverages AI and natural language processing to quantify relationships between system elements and compute levels of support and risk.
4. Market Opportunity: The statement acknowledges a market need for AI-powered project management tools. Similarly, QSLS has identified a significant market opportunity in industries developing complex systems, where quantitative analysis and data-driven decision making can provide a competitive advantage.
While the specific focus and implementation details may differ, both the statement and QSLS Engineering share a vision of leveraging AI and quantitative methods to improve outcomes in project management and systems engineering. The challenges and opportunities identified in the statement align well with the value proposition and target market for the QSLS technology.
As QSLS Engineering brings QSLS tools to the market, we will be attacking this specific problem that exists in Program Management and tie PMs closer to the Architects and development teams to reduce cost over-runs, provide higher insights into the development process. This will be another great step forward in Systems Development.
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