Imagine a project where the sponsor uses AI to find gaps in the business case, the project team uses it to forecast costs and completion dates, the contractor uses another AI to assess schedule performance, and the customer uses AI to interpret progress reports and contracts.
The problem is that their AI systems may not agree. For the completion date, one predicts a six-week delay, and another predicts three weeks. One team member uses AI to conclude that a requested change is within scope, but your AI doesn't agree. Also, the customer interprets AI-forecast deliverables differently than the reports you produced.
The focus is no longer how to use AI, but how to manage a project when everyone is using it.
Project leaders can prepare for this by establishing a simple AI information protocol:
o Identify the project data considered authoritative.
o Require important AI-generated conclusions to state their data sources and assumptions.
o Compare conflicting AI outputs rather than automatically choosing one.
o Clarify who has authority to make the final decision.
o Record significant decisions and the information used to make them.
o Keep accountability with the person responsible for the decision, not the AI that produced the recommendation.
As AI becomes easier to use, generating analysis becomes easier too. The challenge is determining which information to trust and how to resolve competing conclusions.
Welcome to a new project leadership responsibility: managing an environment in which almost everyone can bring their own AI-generated analysis to the table.
The problem is that their AI systems may not agree. For the completion date, one predicts a six-week delay, and another predicts three weeks. One team member uses AI to conclude that a requested change is within scope, but your AI doesn't agree. Also, the customer interprets AI-forecast deliverables differently than the reports you produced.
The focus is no longer how to use AI, but how to manage a project when everyone is using it.
Project leaders can prepare for this by establishing a simple AI information protocol:
o Identify the project data considered authoritative.
o Require important AI-generated conclusions to state their data sources and assumptions.
o Compare conflicting AI outputs rather than automatically choosing one.
o Clarify who has authority to make the final decision.
o Record significant decisions and the information used to make them.
o Keep accountability with the person responsible for the decision, not the AI that produced the recommendation.
As AI becomes easier to use, generating analysis becomes easier too. The challenge is determining which information to trust and how to resolve competing conclusions.
Welcome to a new project leadership responsibility: managing an environment in which almost everyone can bring their own AI-generated analysis to the table.
Posted on: October 12, 2026 08:00 AM |
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