Are we managing AI, or is AI managing us?

AI reminds me of the quote by G. Michael Hopf - "Hard times create strong men. Strong men create good times. Good times create weak men. And weak men create hard times."

My biggest short-term concern is data privacy - shadow AI, user error, and API & third-party app hacks (a plug-in is an easier target than a data center). Long-term, I'm more concerned about the loss of knowledge and tenacity that can come from people getting used to having the answers handed to them.

Instead of recommending specific tools, I recommend that you start by finding and using tools that fit/enhance your workflow and help you solve the problems you are facing. It's possible that there's a game-changer out there that will disrupt your workflow and lead to new patterns of success, but that's almost never as simple as just using a new tool.

Interesting question.

The Excitement: The AI capability I'm most excited about isn't automated status reporting or meeting summaries. Those save time, but they're incremental improvements.

What interests me most is AI's ability to synthesize information across systems, teams, and workstreams to surface signals that might otherwise remain fragmented. In large organizations, the challenge is often not a lack of data but a lack of visibility. AI has the potential to help identify emerging risks, conflicting priorities, dependency bottlenecks, and decision delays much earlier than traditional reporting mechanisms.

The Toolkit: ChatGPT has become a regular part of my workflow, particularly for research and analysis. On the delivery side, I've also been experimenting with Gemini, Cursor, and MCP-based integrations that allow AI to interact with systems such as Jira. Those use cases are becoming increasingly interesting because they move beyond content generation and into operational intelligence.

The Fear: My biggest concern isn't job displacement. It's the possibility that organizations become more efficient at generating information without becoming better at interpreting it. AI can produce reports, summaries, dashboards, and recommendations at incredible speed, but judgment, context, prioritization, and decision-making still matter. The risk is that people begin accepting outputs without developing the expertise required to challenge them.

Data privacy and governance are also legitimate concerns, particularly as organizations adopt AI faster than they establish appropriate controls.

Tools I Recommend: Rather than recommending a specific tool, I'd recommend starting with a specific problem. The most successful AI adoption I've seen starts with questions like:

• How do we reduce manual reporting effort?
• How do we improve visibility across portfolios?
• How do we identify risks earlier?
• How do we make better decisions faster?

Once the problem is clear, the right tool usually becomes much easier to identify.

For project and portfolio professionals, I think the future value of AI will be less about automation and more about interpretation—helping organizations understand what's happening across increasingly complex systems.