Scrum Master Toolbox Podcast: Agile storytelling from the trenchesBONUS From AI Curiosity to Practical Project Tools With William Davis
BONUS: From AI Curiosity to Practical Project Tools With William Davis AI becomes useful when it moves from generic answers into the daily work of solving real problems. In this BONUS episode, William Davis shares how he started building his own AI-assisted project management tools, what went wrong when the code grew too fast, and how Scrum Masters can use AI more carefully at individual, team, and project levels. When AI Stops Being a Curiosity "I really need to get a handle on how to productively use AI in my job." William's shift started with a familiar problem: release plans are full of uncertainty, but stakeholders still need to understand what might happen and when. Instead of handcrafting uncertain delivery ranges in Excel, William used AI to build a desktop application that created probabilistic Gantt charts. In his work, these were not traditional command-and-control schedules. They were release plans that showed stakeholders a realistic range of possible delivery dates, making uncertainty visible instead of hiding it behind a false promise. The Euphoria and the Crash "Anybody can prompt an app into existence, but if you want an app that is actually enterprise-worthy, it does take a little bit of software engineering knowledge to know what questions to ask." The first experience felt almost magical: ask questions, get code, assemble the pieces, and see a working application appear. But the magic faded when William kept extending the tool and the code started breaking in familiar ways. The AI forgot previous decisions, repeated mistakes, and produced a growing mass of tangled code. The lesson was direct: AI can move fast, but it still needs architecture, tests, and software engineering judgment. Without that, teams can build quickly and still end up with something hard to use, hard to maintain, and hard to trust. AI as a Partner Inside the Tool "The collaboration has a third partner, the AI." William's work expanded from one application to several tools for forecasting, story mapping, release planning, and budgeting. The more interesting change was not only using AI to build tools, but building tools that could connect to AI while people used them. Through Model Context Protocol, William's tools can work with an LLM partner to help teams turn rough product ideas, emails, and scattered artifacts into structured story maps. The team still edits, challenges, moves, splits, and reframes the result. AI helps create a first model faster, but the team keeps responsibility for meaning and decisions. Security Starts With Where the Data Lives "Start with the easy sell: I'm building a tool, and the data that I'm creating is stored locally on your employer-managed device." William is clear that AI adoption in organizations cannot ignore infrastructure and cybersecurity concerns. His first approval path came from designing tools that default to local storage in the browser, on an employer-owned and managed device. That made experimentation easier because sensitive project data did not need to leave the company environment. For teams using MCP or company AI platforms, the same question matters: where is the data going, who governs it, and what agreements protect it from being used for model training? Scrum Masters and software leaders need to treat security as part of the coaching conversation, not as an afterthought. Better Questions, Earlier in the Work "My goal is to solve the problems that I have at the moment that I'm having them." For William, AI changed the work by removing delays between seeing a problem and trying a solution. A release forecast that once took 30 minutes to handcraft can now be updated in a few minutes during the team conversation. In a cloud ERP evaluation, AI allowed him to ask vendor-specific timeline questions much earlier than before. Instead of waiting deep into an RFP process to discover how different solutions would change the implementation plan, he could compare likely timelines upfront and make the trade-offs visible sooner. Go Slow to Go Fast With AI "Ask three different sessions the same question." One of William's strongest warnings is that a single LLM answer can feel more certain than it really is. LLMs are probabilistic, and the same prompt can produce different answers across models or sessions. His workaround is to slow down the design step: ask multiple sessions or models to analyze the same problem, then use an orchestrator session to compare the answers and improve the design. For architecture questions, he may use Claude, ChatGPT, Grok, and Gemini. For smaller product improvements, he uses multiple sessions inside one LLM ecosystem. This is not a return to big upfront design. It is short-cycle research, planning, and implementation, repeated in small increments. A Practical First Step for Scrum Masters "Rather than just read about how to use AI, just start using it." William's practical advice is to choose one real problem and ask your LLM how to approach it. If you are not familiar with MCP, start there: ask your preferred model how to connect to a tool through MCP, and experiment with a low-risk use case. William also invites listeners to try the free tools at SPERT Suite, where the default local mode keeps data on your own device. His broader point is simple: AI becomes useful when it is connected to a specific work problem, a clear feedback loop, and a human who still owns the judgment. About William Davis William Davis is a seasoned IT professional with four decades of experience as a software developer, project manager, and agile advocate. A certified Scrum expert and PMP, he promotes personal and organizational agility, delivers customized training, and mentors agile practitioners. Creator of Statistical PERT® and SPERT® Suite, William innovates with free, AI-powered project management tools for today's agile teams. You can link with William Davis on LinkedIn and explore William's free tools at SPERT Suite.
