It used to be that data analysis that should be performed was avoided because it carried too much risk for the stakeholder due to the time and effort required.

Using original BI tools, when an analysis to determine a question, even a valid one, was likely going to take longer than X hours, and tie up limited resources on a quest that may or may not yield a return (even a decent five figure one), it had to be abandoned.

In addition, when an analysis to answer a question that could save six (or more) figures a year required a change to the organizational classification hierarchy to accomplish, which could require weeks to months to obtain (since classic BI systems could support one, and only one, schema that supported one, and only one, cube definition, and such a change would require the approval of all stakeholders), the analysis was abandoned because the effort to get the agreement could cost more than the analysis could return.

When analysis was not quick, easy, and always available, it didn’t get done when it should — and that’s any time someone had an idea that might save time, money, or both.

Today we have the same problem, but instead of a lack of analysis being performed, AI is performing too many and overloading the organization with “opportunities” of all shapes and sizes being pushed to the sourcing and procurement teams to pursue. And they are overloaded, with instructions to pursue and capture as many opportunities as possible.

Since most providers who provide AI Analytics provide hybrid solutions (where the Gen-AI is given access to traditional, deterministic, solutions that don’t make simple math mistakes), except when the LLM retrieves (or hallucinates) bad data or decides to skip using the deterministic solution for the calculation, most of the opportunities are real to some extent. And since most organizations don’t have good manual software, they’ll only have time to check a few, and when those come back good enough, the organization, out of time, will assume the rest are good (enough) as well and send them off to the sourcing and procurement team.

Some will pan out less than expected, some won’t, but since they generally won’t lose money (even though they won’t capture the savings they should), the organizational leaders will assume they’re doing well, that a manual analysis wouldn’t do much better, and that the volume of opportunities will make up for the lack of significant returns on the pursuit of any individual opportunity.

This is a problem for two reasons. First, the biggest opportunities will go undetected because the AI is running off of scripts, doing the same analysis over and over again, and without knowing all the variables, or having human intuition, won’t investigate where the real opportunities lie. It won’t see the downstream effects of a political tension that will result in a border closing, war, or strait closing that will jack some prices sky high unless demand is locked in now. Nor will it see the the impacts of too many data centres coming online at the same time as AI crash hits and not advise you to delay locking in long term data centre contracts until that happens.

Secondly, it will make mistakes, and sometimes the sourcing and procurement teams will spend a lot of time, possibly weeks or months, on exercises that result in new agreements that actually cost more money than the organization is paying (on average now) because they were undertaken at the wrong time, for the wrong demand, with the wrong suppliers … when existing contracts were still in place for partial demand that couldn’t be broken (without penalty).

These false opportunities won’t be caught because the manpower isn’t there to verify every opportunity (without the right software designed for rapid manual spend analysis), and, as a result, more manpower will be wasted verifying opportunities than just pursing what the AI spits out. However, the results won’t match the savings that would be achieved if the team only pursued verified opportunities (where you verified a real, significant, opportunity). But the lack of time and resources to verify (since management froze hiring to pay for the worthless AI) means the opportunities chased don’t get verified. When it takes more time to second guess the AI than to follow it blindly, the psychology of analysis is to not do it, just like 20 years ago the psychology of analysis was not to do it.