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Insights
Short, practical pieces on running automation with the right controls.
The quiet problem behind every AI success story
Every AI success story has a quiet problem underneath it. Not if a system gets something wrong, but when. The difference is what happens next.
Before an AI project goes live, ask three questions out loud. Who notices, so that a person or alert catches a mistake before an unhappy customer does? Who can stop it, with a tested way to pause it and someone who knows they may use it? And who can show what happened, so you can reconstruct what it did, what it used and who approved it?
If any answer is that you would find out later, you are not finished yet. Build oversight at the same time as speed, not afterward.
Completed is not the same as solved
A count of tasks completed tells you almost nothing. There are four rungs. The task was completed. A person accepted the output. The output was used. The business result happened.
The first rung is activity and the last is value. A system that completes work confidently but wrongly looks good on the first rung and costs you on the last. When you report savings, ask which rung the number comes from.
Five questions to ask any AI vendor
What exactly happens to our data? Can you show me how a decision was made, with a real record? Where is a human required, and can we add more? How do I stop or roll back the system? What evidence do you have beyond promises?
Ask for the answers in writing, and notice how plainly they are given. This is general guidance; have your own advisers review any contract before you sign.
Put the thinking to work.
Request a demo to see how these ideas show up in the product, or send us a note about what you would like us to cover.
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