I’m currently in the second semester of my MBA and recently started working alongside my studies. Earlier this year I had a few offers a management consultant role at a boutique consulting firm, a Market Analyst role at Frost & Sullivan, and a management consultant role at my previous company. I ended up returning to my previous company because the role and compensation made the most sense for me. Alongside my MBA, I’ve also been going deep into experimenting with AI tools and building things for my own workflows. Not just using mainstream tools like ChatGPT or Gemini, but actually building small systems using APIs, web scraping pipelines, Google AI Studio, open-source tooling, and automation frameworks. Initially it started as a way to make my coursework and research easier. I began building small applications, dashboards, and research pipelines to help with things like company analysis, financial analysis, investment research, M&A landscape reviews, consulting case simulations, and competitive intelligence. During breaks between semesters, I also took several AI-related courses (from sources like Google, Oxford Saïd online programs, and other technical courses) just to understand the underlying concepts better things like ML basics, LLM architecture, and how retrieval systems work. Because of that mix of MBA frameworks and technical experimentation, I gradually started building tools that could automate parts of strategy and research work, such as: Competitive intelligence pipelines, Market and product landscape analysis, Financial and company analysis, GTM and product strategy preparation, Macro and industry trend analysis, IP and patent landscape research. The moment where things got interesting happened recently on a project at work. My first assignment was a GTM strategy project that required market analysis and business strategy. I initially created a six-week execution plan for the project, which the company approved. Then during spring break, I spent a lot of time experimenting with more advanced workflows learning more about ML concepts, LLMs, and building better pipelines. I rebuilt my research workflow using a combination of APIs, scraping tools, and a RAG-based system where I integrated structured datasets (things like Statista data, product catalogs, research sheets, etc.). When I came back to the project and ran the new workflow, the results honestly surprised me. The work I had planned to complete in six weeks was largely done in about four days and the depth of the analysis was significantly better than what I had originally planned. The workflow I built basically combined: Multi-source data collection (scraping, structured datasets, APIs) A RAG pipeline so outputs were grounded in real datasets A validation layer where outputs were checked against sources Competitive intelligence mapping across incumbents, new entrants, and AI-enabled products For the strategy side, I structured the outputs using common consulting frameworks like structured problem solving, Blue Ocean Strategy concepts, and JTBD (Jobs-to-Be-Done). The topic itself was extremely niche, which made the depth of the output even more surprising. It was pulling together competitive positioning, product intelligence, market signals, and strategic recommendations in a way that normally would require a research team. That’s when it hit me that what I had built wasn’t just a one-off workflow. At this point I can realistically see these systems becoming tools for things like: Market intelligence, Competitive monitoring, Product landscape analysis, financial benchmarking, Strategy preparation for GTM or expansion, IP/patent landscape analysis, Early signals for industry shifts. The key difference is that these systems aren’t just generating generic AI outputs they’re grounded in datasets with verification layers and structured reasoning. Now I’m in a weird spot. Part of me thinks I should just keep improving these tools, expand the integrations (more datasets, APIs, analysis modules, etc.), and eventually turn it into some sort of AI-driven consulting or intelligence platform. Another part of me thinks I might just be overestimating how useful this actually is in the real world. So I’m curious to hear from people who work in consulting, AI engineering, startups, or strategy roles: Where do you see the real value in tools like this for companies? What would need to be true for something like this to become a viable product or consulting service? What are the biggest blind spots or limitations in AI-driven strategy analysis? If you were building something like this, what would you focus on next? I’m still early in my career and figuring things out but building this and seeing what it can do has been a pretty eye-opening experience. Curious to hear some honest perspectives. submitted by /u/Unusual_Host2134
Originally posted by u/Unusual_Host2134 on r/ArtificialInteligence