Energy project developers face a 70% withdrawal rate after expensive studies because critical information sits scattered across utilities, PDFs, and spreadsheets. A $200 million investment decision shouldn't rely on guesswork. AI solves this by consolidating fragmented data to improve decision-making, not by building better models.
why this is a decision problem. Here's what I learned talking to the developers before we wrote a line of code. The problem is not the information does not exist. The queue is public and the study reports are public too. The network models exist if you are authorized to use them. The problem is that it is scattered across the 7 grid operators, 50 utilities, thousands of PDF study reports, and a consult spreadsheet that was lastly updated by their maybe intern. So, a developer with a 500-megawatt site and a $200 million decision to make Is essentially guessing. They pay for a study, wait 18 months, find out the upgrade cost is unaffordable, so they withdraw and start again somewhere else. This is where the 70% of the withdrawal rate comes from. It is not bad engineering. And this is a very specific kind of problem. It's not a data problem. It's not even a modeling problem, it's a decision problem. And decision problems are exactly where AI can do the most good if you build it right.
