Are we making some of the same mistakes with AI that we made with Agile? That question came up during my conversation with Patty Aluskewicz, and it stayed with me. Patty has had one of those careers that does not fit neatly into a single box. She started as a science teacher, became an entrepreneur, moved into Agile and enterprise transformation, and now spends a lot of time thinking about AI, learning, organizational change, and the future of work. One of the things I appreciated most about this conversation was Patty’s willingness to sit with contradictions instead of rushing toward simple answers. Companies say they want people to experiment, but experimentation comes with real professional and financial risk. We ask employees to become fluent with AI, but often give them a short training session and assume the capability will follow. We talk about transformation, but the structures, incentives, hierarchies, and ways of measuring work often remain exactly where they were. That led us into a really interesting comparison with Agile. Agile started with ideas around adaptability, collaboration, experimentation, and continuous improvement. But in many organizations, those ideas eventually became Jira tickets, certifications, burndown charts, and metrics. Patty made the point that AI could easily go down the same path. We can measure prompts, tokens, licenses, and usage, but none of those things necessarily tell us whether people are doing better work or whether the organization itself is getting better. The second half of our conversation moved into Patty’s life as a creator, and that may have been my favorite part of the episode. We talked about storytelling, LinkedIn, creator burnout, the pressure to keep feeding algorithms, and the growing amount of generic AI-generated content online. Patty talked about how she tries to connect ideas from work to things she has actually experienced in her own life. That lived experience is what gives a story texture. AI can help polish the words, but it cannot have the experience for you. That opened up a much harder conversation about creativity and AI. I use AI heavily in the business world and actively encourage my team to use it for things like coding. But AI feels very different when you enter creative communities. Why does AI-assisted software development often feel like progress, while AI-assisted art can feel like a moral issue? Is software engineering somehow less creative? Is the struggle involved in making art part of its value? Or are we simply more aware of the human craft behind some kinds of work than others? We did not arrive at a clean answer, and I think that was appropriate. The more I think about AI, the more I find myself holding two ideas at once. It can be incredibly useful while also creating real disruption. It can expand what people are capable of while also changing the meaning of expertise, effort, authorship, and creativity. This was a thoughtful conversation about AI, Agile, work, creativity, storytelling, and what happens when technology changes faster than the organizations and social norms around it. Welcome to Data Democracy.
