Understand the Criteria Before You Invest
Much of the industry still frames Ai in fundamentally magical terms, so the question of how to deliver on the promise of this new technology remains unclear for many leaders. To unlock the power of Ai, you must understand its real potential, the challenges you may face when implementing it, and how engineering can be applied to overcome those challenges.
How do you know if your problem is ready for Ai?
Existing solutions aren't good enough.
It may seem obvious — why would something still be a problem if a solution already exists? — but increased attention to Ai has caused many to reconsider the way they have long performed a task. In many cases, this is a good thing … but not always.
The end goal is well understood.
If the end goal can be clearly articulated, and trade-offs between the priorities within that goal can be clearly defined, then Ai may be the right way to achieve it. If not, the Ai will solve a different problem altogether, which may do nothing to advance the mission.
Good data can be found or made.
For a problem to be Ai ready, the available data — whether real or simulated — should mirror the properties of the problem domain closely, so that an Ai trained on them will have a viable answer on hand across a broad range of mission needs.
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