Hi Claudia, great question!
In our current project environment, we’ve started embedding a Gen AI readiness checklist to guide our integration. It starts by assessing the sensitivity and structure of project data—we filter for what’s safe to use with AI models, especially regarding client confidentiality and proprietary constraints.
Once cleared, we leverage Gen AI for instant decision support during high-pressure phases—like compressing risk logs into actionable insights or proposing micro-schedules during sprint planning. Tools like ChatGPT and Azure OpenAI help us dissect complex dependencies and refine resource allocations.
However, we remain cautious—AI-generated outputs still require critical human review. We’ve seen instances where suggestions are contextually misaligned, so we pair AI insights with domain SME validation. To manage this, we created a two-step protocol: AI drafts > human refinement > decision checkpoint.
Supervision-wise, AI plays a role in real-time anomaly detection across project dashboards, flagging lagging KPIs and scope drifts early. But we don’t let automation override team judgment—we see it more as a co-pilot than a commander.
We’re also working on embedding ethical AI use training into our onboarding—so teams don’t over-rely or unknowingly introduce bias.
Gen AI doesn’t solve everything, but when used smartly and critically, it amplifies our project agility, sharpens foresight, and makes complex work more manageable.
Thanks for opening up this space to share!
PMI Team Member
