Is Your Company Big Enough to Sell or License Its Data to AI?
AITrainingDataBroker.com
Is Your Company
Big Enough to Sell
Its Data to AI?
The size test in plain words: staff, years of data, and systems
Jimmy Lin
Is Your Company Big Enough to Sell Its Data to AI?
Is your company big enough to sell or license its data to AI?
Owners ask me this before anything else, usually with a number in mind, like 12 people or 40.
In this video I will give you the real size test, and I will also tell you why the answer can change.
Stay to the end, because the check that owners forget most often is how many years your data covers.
The video
Full script
Is your company big enough to sell or license its data to AI?
Owners ask me this before anything else, usually with a number in mind, like 12 people or 40.
In this video I will give you the real size test, and I will also tell you why the answer can change.
Stay to the end, because the check that owners forget most often is how many years your data covers.
The first thing an AI company checks is the size of your company, and it checks it before it looks at a single record.
A small company produces a small amount of everyday work, and AI companies need a lot of it.
Many owners think size is the whole answer, and it is actually only the first filter, as you will see in a moment.
First, you should know who is telling you this.
I'm Jimmy. I have worked in data at Fortune 500 companies, as a data analyst, a data engineer and a software engineer, and I am trained in data science. I am also a Canadian military veteran, where I specialized in data analysis and reporting.
Are you selling your data? We are your broker and technical advisor. We get you the best price, and we guide your team through the technical side, so your data stays private.
Are you buying data for an AI lab? We find the right data for you and see it through to delivery, so you can focus on advancing AI research.
At this time, our floor is 30 or more full-time desk staff.
Other AI companies set their own floors, some at 20, some at 30 and some at 50 or more, so the number depends on who is looking.
In general, the bigger the company, the more often AI companies show interest.
That is why we start at 30, and why a company below it needs something unusual, which I will cover later.
Here is how to count.
We count full-time desk staff, which means the people who do their work in email, chat, the CRM and shared files.
Drivers, warehouse workers and people on a job site matter a lot to your company, but they leave little written trail, so they do not count toward the 30.
A company with 25 desk staff and 80 people in the field is still below the floor.
Next is time.
We look for 3 or more years of data, because AI companies learn from how work changes over time, so a long record of the same kind of work is worth more than one busy year.
The data should also sit on the same systems, since a record split across many tools is hard to follow.
If you switched tools along the way, the older years may still count, but they are harder to use.
Then comes what your data is stored in.
Most data deals start with 5 core systems: email, shared files, chat, your CRM, and accounting.
Together they show how a piece of work got done, from the first message to the final invoice.
You do not need all 5 to be considered, but each one you have makes the picture fuller, and a fuller picture draws more interest.
Putting it together, here is the checklist.
You want 30 or more full-time desk staff, 3 or more years of data, as many of the 5 core systems as you have, and data that is mostly in English.
If you tick all 4, you are in the range where AI companies take a look.
If you tick 3, it depends on which one is missing and how strong the rest is.
Now I need to give you an important warning.
Everything I just said applies at this time.
These numbers are not fixed rules, because AI companies change what they want, and the supply of data changes too.
When many companies offer the same kind of data, the bar goes up, and when a kind of data is scarce, the bar comes down.
Some industries are in more demand than others right now, so a smaller company in a wanted industry can get interest that a larger one in a crowded industry does not.
Treat this as a guide, and check again before you decide.
If you are below 30, you usually do not get offers, unless there is something unusual about your company, and there are 3 things to look at.
First, some data is scarce, such as source code or recorded calls, and that can get attention even from a small company.
Second, if you own related companies on the same systems, their data can sometimes be combined into one larger package.
Third, you can check again later, because both your staff count and the market change.
None of these is a promise, but each one is worth a look.
Now let us make this about you.
Take a few minutes and write down 6 things: how many full-time desk staff you have, how many years your data covers, which of the 5 core systems you use, what language the data is in, whether you hold anything unusual, and whether you own related companies.
That list is exactly what we ask for first, and it is the fastest way to know where you stand.
That is where a broker and technical advisor comes in.
As your broker, I bring your data to several AI companies and AI labs, get competing offers for you to compare, and work through the terms with you, and you make the final call.
As your technical advisor, I guide your team through the technical side, which means what to export, in what format, and how your data is cleaned so personal information stays private.
Nothing moves until your team signs off, and the terms are agreed case by case.
And you do not pay anything for the broker work: AI companies pay my fee.
So, is your company big enough to sell or license its data to AI? Now you can check it yourself.
You do not sign anything to start.
If you run a company with 30 or more people, the data you already have could become a new revenue stream.
Want to find out?
You can check if yours qualifies, for free, at AITrainingDataBroker.com.
If you have further questions, leave them in the comments, and I will answer them in the next video.
Thank you for watching.
I will see you next time.
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