I recently joined Data Alchemy and have found it to be a treasure trove for advancing my career through Dave's Python, LLM, and AI courses. One of the videos, "7 Lessons From Making $100k+ With AI Projects," inspired me some business and sales ideas as Data Alchemist. Here are some ideas I think about: 1. Extend Content to Enterprise Solutions: Incorporate enterprise solutions such as CRM, SCM, ERP, EAM, WMS, MES, and ITIL into the curriculum. These areas often have more BANT-qualified opportunities and greater purchasing power within lines of business (LOB). 2. Associate AI with Leading Enterprise Apps: Integrate AI applications with leading enterprise platforms like Salesforce, ServiceNow, IBM Maximo, and Siemens. This can open up larger opportunities, especially for Managed Service Providers (MSPs) or System Integrators (SIs). 3. Channel Sales or Resales for Aggregated Solutions: Develop a strategy for channel sales or reselling aggregated solutions to enhance market reach and revenue streams.

I thought I would share what we use at work to help identify opportunities for generative AI implementations. Here is the step by step process feel free to ask any questions or provide any feedback. This is less technical its what we put in front of decision makers before collaborating with the teams. So mainly product management, Directors, Architects go through this process with us and then we work with a tiger team for development following Lean, Agile, and DevOps Best Practices. - GenAI Alignment: Align generative AI initiatives with your overall business strategy to ensure they support key objectives. (We get the vision, strategy, and tactics back of the napkin) - Business Context: Understand the current business landscape and identify areas where AI can drive transformation. ( We use Strategyzers business model canvas here primarly with some other self made canvases) - Value Streams: Map out the value streams to pinpoint where AI can enhance efficiency and value delivery. (Use the basic Karin Martin Value Stream Mapping process here. You cant really improve what you can measure and following the "theory of constraints" every value stream is severely suppressed by some major bottleneck and we want to start with those major bottlenecks as improvement items first before smaller less valuable chagnes) - Objectives & Key Results (OKRs): Set clear objectives and key results to measure the success and impact of AI initiatives. (First we collect their current OKRs and KPIs at the company/portfolio level as well as any that are related to their teams.) - Opportunity Identification: Identify specific opportunities within the value streams where AI can provide the most significant benefits. ( Using the business context, OKRs and KPIs we then use the value stream maps to identify where a generative AI solution will give the biggest bang for our buck) - MVP Definition & Prioritization: Define minimum viable products (MVPs) for the identified opportunities and prioritize them based on potential impact and feasibility. (We focus here on leaning out the first iteration functionality based on value measurements and economic prioritization methods. You can also use any of the luke holeman innovation games here as well to get to the right scope for the MVP) - Action Plan: Develop a detailed action plan to implement the prioritized MVPs, ensuring a focused and structured approach to leveraging AI. ( Lastly we focus on the actual tactics of implementation like who will build it, what they need, who they need to collaborate with etc and put everything into the teams backlog. )

Do we still need to buy ChatGPT PLUS to practice the ChatGPT Code Interpreter? I saw it is included as Limited Access now for the free program. Thanks!

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I am not able to see the python environment from the VSC status bar. Anyone has the similar issue as I have? How can I turn on?