Considering that the average business is unlikely to be writing any sort of software, other than perhaps basic Excel formulas... I wouldn't hold my breath waiting for them to use advanced software development techniques such as ML.

I would suggest that not writing any sort of software is a good proxy for going out of business soon.

I also think you would be surprised at how many businesses both do, and really want and value, software - but don't have anyone convincing to do it for them

I increasingly feel that almost every business would benefit from having someone with programming experience on-board. This comes from observing that businesses tend to have a lot of ad-hoc processes and unique requirements, for which there is no software package to buy - not without throwing away huge parts of the processes and rebuilding them in a "standard" way. However, a lot of those unique areas could be improved with a help of someone who's capable of writing small programs and scripts to glue things together.

A random example: my SO often works with high-res photos of their products, and sometimes this involves modifying them to suit the random requirements of other businesses. Something I currently solve for her by running ImageMagick one-liners if she needs more than a few photos modified. A person with basic familiarity of shell scripting and CLI tools could improve their workflow and keep improving it on-demand, as the requirements change.

I agree with your sentiment, but I think most business would already fare very well with having someone structured with project management skills in significant leading position.

Without that, working as a programmer in such disorganized team, you will mostly spend your time working "against" them. It's like you are trying to glue things together and people will just go and and rip those glued together things apart constantly.

> I also think you would be surprised

Unless you have some sort of evidence supporting your claim, I guess we'll never know how surprised I would be by the reality you're describing.

I work for an actual tech company, which actually told me to look into a couple of ML projects to improve our operating efficiency, which actually produced good results and had an obvious and short path to being put into production... and which were promptly shelved.

Business people make no sense.

Can you go into why they were shelved? Were they normal technology and business reasons - or related to ML directly? I ask so that potentially others can learn to either navigate around those in the future.

I wish I could. I have very little insight. I presume it's some sort of "normal business reasons", but the from my point of view the decision process went something like this:

Me: "It's finished, here are the areas of strength and weakness, and here's where we can deploy the system for maximum effectiveness."

Business: "We're thinking about the best way to deploy this."

Me: "This is how you deploy it."

Business: "We'll think about and get back to you. Don't do anything until we tell you."

Me: "..."

Business: "..."

Perhaps they needed you to elucidate the risks or possible downsides?

That's the "weaknesses" part. And I did in fact spend a good bit of time with my manager going over exactly what the model does not mean, and what you should not conclude from it or use it for.

I would also be interested in knowing which projects / applications of ML seemed easy wins - I generally get stumped on "use CV and facial recognition" for a business that has no need of facial recognition.

1. Market prediction -- given basic demographic data, and publicly-available or easily acquirable information like when the person's house was purchased, what their credit score is, etc., how likely is this person to want / need your product, and is it worth spending a salesman's time on them?

2. Data entry. We took a picture of this customer's utility bill / bank statement / receipt / whatever. Now do we give it to human to identify relevant fields and manually type them into a spreadsheet, or do we have a computer automatically extract the business-relevant data? Or, heck, maybe that's too complex, but can we at least have a computer help--automatically filter out bad images, do perspective correction, highlight areas of interest, etc.? (This sort of thing is actually used in, e.g., digitizing census records; we don't trust handwriting OCR to be good enough on its own, but we trust it to automatically highlight relevant fields, in order, and provide a first-draft guess at the transcription to assist the human transcribers).

I could probably come up with a few more if I thought about it for a while, but those are the areas I've actually worked on recently.

thank you

The answer is, indeed, to "hire some AI guys". Many of your challenges are likely shared with your competitors, which means there's a market that some specialized "AI guys" can serve.

My employer does supply chain optimization, because many "average" businesses have supply chains that need to be optimized. We have specialized AI tools based on deep learning and differential programming, and we have human supply chain experts that can fit those tools to the specifics of a customer's situation.

I would say that despite our best marketing efforts, many of our customers don't know that we're doing this "AI" thing they've been hearing about.

The anti-pattern with "Do we hire some AI guys?" is that, if you have to teach those guys how a supply chain works, you've already lost. Pick someone who is already specialized in the problem you're trying to solve.

> The biggest challenge yet unconquered is getting your "average" business ($1-100m revenue, zero AI knowledge) using ML to help with literally anything they do.

Does the AI which can help with literally everything a business does exist? That sounds rather general purpose and extremely open-ended.

No. But the parent said 'anything' not 'everything' and that seems less ambitious. I would think that ML which could find patterns in what your employees do day to day and make suggestions about how to make improvements (like virtual efficiency audits) would benefit many businesses. Even for individual users, just having their OS be able to pipe up like a latter-day Clippy and say "hey, I notice you copying data from This.app and pasting it into That.app, want me to take a stab at repeating that until all the fields are populated?" would be potentially a huge improvement (and the OS is already gathering enough telemetry that's barely more intrusive).

ML is not magic. If you can't run controlled experiments then it isn't actually very easy to make confident recommendations about improvements to business processes.

Sure, I was exaggerating for effect. Maybe not proper ML, but something more like the OS automatically generating (and discarding if unused) macros in the background, and using CV / OCR on the windows to try to interpret what the user is doing - I am sure v1.0 would be laughable, but eventually...

I don't think that will happen until AI becomes a commodity, ie some thing that you can buy off the shelf and instruct it to do things for you. I think for that to happen it needs to be actually intelligent and not simple ML tricks.

Your average business is probably using ML indirectly via Google and Facebook without knowing it. I they want to find new customers or new suppliers and use those services ML will quite likely be used under the hood.

can you give some examples? i don't think the average medium sized business has many obvious use cases for ML.

this article by evan miller does a good job of explaining why: https://www.evanmiller.org/predictive-analytics.html