AI is no longer a tech experiment; it’s a P&L line item that can drive real business impact, but real results depend on far more than selecting the right tools. Data quality, process maturity, security controls, governance and workforce readiness all determine whether AI delivers value or creates unnecessary risk.
Download the GuideDownload this practical AI checklist and self‑assessment to evaluate whether your operational foundation is truly ready to support responsible, scalable AI adoption.
My colleague, Andrea Mondello, recently published two posts worth reading if you’re navigating AI adoption right now. One makes the case that data quality issues, not AI itself, are what’s holding organizations back. The other draws on lessons from enterprise AI deployments to help mid-market companies avoid the costly mistakes that larger organizations have already…
In Part 1, we introduced AI as the first of six critical trends transforming project management and the modern Project Management Offices (PMOs). Now we tackle the practical question every executive faces: how do you actually implement AI in your PMO? Most organizations stumble not at recognizing AI’s potential but at deploying it successfully. This…
You rolled out Copilot to thousands of seats. Adoption looks healthy, and Copilot usage and adoption metrics are up and to the right. Then the CFO asks, “What exactly are we getting from this investment?” You can show them the adoption dashboard. You can show them that 73% of licensed users are actively engaging with…
