Kenneth Lo

By "poetry" I was referring to creatively tasks that have no definite right/wrong answers. There's no "wrong" joke. Only unfunny ones :) Data analytics fundamentally is arithmetic and divide-and-conquer. Computers are known to be good at those. Now add a LLM that understands natural language which frees us from "scripting" or even basic order of operations.

The "chairperson", public or not, has an inappropriate weight on the success of the initiative. Winning the alignment of this party is the crux of the project.

Dr. Strange goes through all permutations exhaustively. AI does the same, given enough computing! Find the choke point and attack it. 1/ Identify Bottlenecks AI algorithms analyzes your workflow to find bottlenecks. By pinpointing these critical points, you can resolve the most pressing issues first, streamlining your process. 2/ Find the Critical Path Use AI to determine your project’s dependencies. This identifies the sequence of tasks that directly impact completion time, ensuring you address the most crucial elements. 3/ Risk Mitigation AI calculates risks and suggests mitigation strategies. By evaluating task dependencies and historical data, AI provides insights to prevent delays and ensure smoother project execution.

Know who your "true" masters are. 1/ Recognize Key Stakeholders: Identify the primary stakeholders who will be most affected by the project’s outcome. Understand their needs and expectations to ensure that their priorities are addressed first. 2/ Ethical Considerations: Prioritize tasks based on the ethical impact on stakeholders. Ensure that decisions benefit the most critical stakeholders without compromising integrity or quality. This approach fosters trust and aligns with professional standards. 3/ Align Priorities Professionally: Balance stakeholder demands with professional best practices. Prioritize tasks that align with both stakeholder needs and industry standards, ensuring timely and high-quality delivery of AI projects.

AI aces some tasks and fail others. Use the sharp edge. Avoid the dull one. 1. Acknowledge AI’s Limitations: Understand and accept that AI tools have limitations. Recognizing these boundaries helps manage expectations and allows your team to focus on realistic outcomes. 2/ Identify AI’s Strengths: Focus on what AI does well, such as data processing, pattern recognition, and predictive analytics. Encourage your team to utilize these strengths to maximize productivity and efficiency. 3/ Strategically Apply AI: Guide your team to lean on AI for tasks where it excels and supplement its weaknesses with human expertise. This balanced approach ensures that AI enhances your team’s performance without causing frustration.

Old habits die hard. Appeal to the emotional hesitance, which hides itself deeper below the surface. 1/ Show Empathy Acknowledge the concerns. Express understanding that new technology can be daunting and that their apprehensions are valid. This empathetic approach sets a collaborative tone. 2/ Validate Their Concerns: Address common fears like AI disrupting workflows or potential job losses. Discuss how these issues have been resolved elsewhere and reassure them that their input is crucial in the process. 3/ Offer Support and Resources Assure vendors the support being received throughout the integration. Training, technical support, and clear communication channels. Highlight available resources to reduce anxiety and build confidence.