The Problem with Project
One of the hardest challenges in project management is accurately
forecasting future outcomes (project completion date, total cost, etc.,) of very
complicated and highly uncertain endeavors (projects) we call this "planning".
an industry, we have developed some pretty well tried and trusted techniques such
as Critical Path Method (CPM) to try and help model project outcomes. But these
models are only as good as the inputs we feed into them. Any worthy planning tool
today uses CPM as its underlying forecasting engine but as the planner we are
still left with the onerous task of knowing which activities to include in our
plan. But worse yet, we must decide the activity durations, their cost and even
However, CPM does little more than convert durations and sequences
of durations into a series of dates. It doesn't help one bit with:
scope should I focus on when building my plan?
- What activities should
- What should our durations be?
- What is the true sequence
and logic between our activities?
If CPM were the be-all and end-all
solution, then we wouldn't continue to experience project cost and schedule overruns.
The problem isn't CPM though. The problem is our inability to accurately model
what we think will happen during project execution because of
number of variables (tasks and sequence) and
The huge number of uncertainties
associated with those variables (duration or scope uncertainty).
risk analysis tools help tell us how bad our forecast may be, but they do nothing
in terms of telling us what the inputs to our schedule should have been in the
This is why I believe AI can massively help project planning.
If AI can help the planner by making suggestions that are sound, then the immense
challenge described above starts to become surmountable. Added to that, if our
planning tool can also start to make better suggestions by observational or associative
learning, then we are headed down a seriously valuable and exciting path.