Why is AI so useless for business?

The hardest things to automate are always the things that are so easy for us they don't even register consciously

In my opinion, most of the issues leading about AI "failing" in traditional organizations are due to the following:

(1) Inflated expectations from higher/middle management which trickle down the organization. AI is seen as a high-profile case which has to lead to success (and a larger budget next year for my dept.)

(2) Data quality issues. The data itself has issues, but the key issue is lack of metadata and dispersed sources. Lack of historical labels (or them being stuck in Excel or on paper) is part of this as well. Big data without any labels is mostly useless, contrary to expectations

(3) Most AI or ML projects are not about ML. In fact, they're mostly about automation or rethinking an internal or customer-facing process. In many cases, such projects could be solved much better without a predictive component at all, or by simply sourcing a 1 cent per call API. AI is somehow seen as necessary, however, without which our CX can never be improved. ("We need a chatbot" vs. "No, you just need to think about your process flow")

(4) Deployment issues and no clean ways to measure ROI leads to projects being in development indefinitely without someone daring to stop them early. This is also related to orgs starting 30 projects in parallel (2m lead times with one to two data scientists for each), which end up all doing kind of the same preprocessing and all lead to kind of the same propensity model. No one dares to invest in long-term deeply-impacting projects as "we want to go for the low hanging fruit first"

The article is just vaguely complaining about undefined problems which makes it very difficult to defend or argue against. Are we talking about ML? Optimization problems? Operational research in general? Automation? And which tasks in which businesses? There's tonnes of useful stuff in each of those categories and vaguely saying that it's all useless doesn't get you anywhere.

While true for some things, it's not for others.

In our application, one key operation is that the user is required to classify a line item based on the text description. There's a huge code list of possible classifications, and the user has to pick one that is the most correct.

This is definitely a task that registers consciously. And, while most of the time it's fairly easy for trained users, there are often cases which require extra thought or research.

For example, a T-shirt of mostly cotton vs a T-shirt of mostly synthetic fibers should be classified differently. How would you know based on the description "Small Womens V neck Short"?

Because "Artificial Intelligence" is a label forever applied to the effort of replicating some human cognitive ability on machines. A well-known lament goes something like: "once it's possible, it's no longer AI".

Business is about exploiting what exists. This is why the buzzword is "innovation", not "invention". Incremental improvements, not qualitative jumps. So nothing will ever be really considered "Artificial Intelligence" once it is boring enough for business.

Scheduling algorithms are incredibly useful for business. There was a time when this was considered AI, but that was the time when they didn't work well enough to be useful.