Why is AI so useless for business?

You don't need to solve every problem to make AI worth the investment, just a large enough handful of billion-dollar problems.

This is clickbait (1) to promote his startup, Proda.

ML is used in business workflows all the time - to date, I have built several solutions that are being used for 53 clients, internal and external.

Here is what makes B2B ML hard: People have to trust it.

This isn't some movie-recommendation engine, which spams you with more bank heist movies after you watch one. B2C ML systems can get it wrong, and customers are generally forgiving, because it's a low stakes game. B2B applications are generally higher-stakes, because they impact business workflows, and if someone has decided to automate it, it's probably a high-volume, critical workflow. It has to be extremely accurate, and demonstrably better than the equivalent human system.

The problem has to be well-defined enough that an ML system can act with high-accuracy, but not well-defined enough that a rule-system could replace it. Don't use ML if a rule-system will do a better job. (For those scenarios, you can still put an ML anomaly-detection system to make sure the rule-system is still valid, and to guard against data input changes.) As just mentioned, the problem also has to be important enough and high-volume enough to warrant an ML solution. The percentage of problems that fulfill these criteria is not very large.

Now to actual ML development and deployment - the model is the tip of the iceberg. The rest of the iceberg is data acquisition, feature selection, data/feature versioning, automated training, CI/CD, model performance monitoring, et cetera. If ML is being developed inside a software development organization, this isn't a problem, most people will understand this. If it is being developed within an embedded BI team inside a business unit - they will generally not have support/runway needed to build the full system. The ML model might make it to production, but it will probably run naked, be brittle, and hard to retrain. A dramatic failure with business impact is just a matter of time.

There are a lot of low-code, no-code ML solutions that have been developed, or are being developed, and some of the supporting infrastructure as well, but, at the risk of sounding parochial/protectionist, you need a rock-solid, end-to-end, integrated, data management system that is fully understood by whomever needs to pick up the phone at 2AM. It's the interfaces that are hard, and chaining together a bunch of third-party black-box systems just means more interfaces and behavior you don't control. Choose and use these systems wisely.

So yeah, B2B ML is hard. But it's generally not due to lack of data, and transfer learning is generally not necessary. Understanding business processes is important, I agree, but that's comparatively easy. It's what consultants have been doing for decades. The hard part is choosing a problem where ML can add value, and then executing on it with enough integrity that people will actually trust it.

(1) Ok, clickbait might be harsh. But it is self-promotion, and the article itself is a collection of generic banalities. I feel it falls on the wrong side of the line.

> 99% isn’t good enough for truly critical applications, especially when you don’t know for sure that it’s actually 99%; there’s no way to detect which 1% might be wrong; there’s no real path to 100%; and critically: there’s no one to hold responsible for getting it wrong.

AI also exposes the possibility of systemic error where humans would be stochastic.

A human might only identify the right number of rentable units from a spreadsheet (to pick an example from this article) 97% of the time when an AI might do it 99% of the time, but even the same human will have a different 3% error on each day. The consequences of failure are more limited and more dilute.

On the other hand, the AI may work perfectly right up until a holding company redesigns their data tables for the 100th time, whereupon it misreads every financial report with much more concentrated ill effect.

I mean, part of the problem is that no one can seem to agree about what "AI" even is anymore; you described a control theory model that "calculate[s] the envelope" which I'm interpreting to mean some sort of confidence interval/posterior, but to me that's exactly what you should be doing whenever possible in an "AI" model, as in, control theory (it sounds like with Gaussian Processes or something) is the right "AI" approach to that problem.

To me, linear/logistic regression is AI, as are neural nets, as are pretty much any statistical model. These days "AI" seems to be roughly interpreted as "whatever a DS person deems as a solution to our business problem" at least in my very limited experience. It's all a bit silly since AI has come to mean so many different things that may not fit the traditional definition of AI from e.g. the 90's, but as long as people aren't pidgeon-holeing themselves into particular methods, I'm happy to go along with slapping an "AI" label on pretty much anything.