Over the years a number of companies have approached me with a seemingly simple question: "How should we use AI (or ML, or data science)?"
Do you see the problem there? That question treats "using AI" as a foregone conclusion. That way lies tears. Tears and lost money.
I've found it more helpful to first ask whether AI will address the problem at hand.
And that's why I've assembled this guide.
If you're curious about whether AI can help you, or if your CEO has declared that the company will "do AI" and then left it up to you to make that a reality, these six questions should help you on your way.
These are same questions I ask as I suss out whether a company has an AI-shaped problem.
(Just a note: while I think what I've written here is solid, you'll want to refer to the disclaimer at the end of this page.)
My main website has more details.
Tap or click each question to see the explanation:
AI is hot (again) and investors are throwing serious money at AI startups. So if your goal is to convince some VCs or potential customers that you're all about the hip technology … Be my guest. Spray-paint "AI-powered" onto your product. Cash is king and I respect your hustle.
You can also skip the rest of this guide, then.
If you're actually trying to build something for real -- something that needs to function properly and yield business value -- read on:
AI is all about automation. Specifically, it is automation based on finding patterns in data. That makes it good for:
If your problem fits one of those shapes, you're in luck! AI may just help you. Continue reading.
If this kind of automation won't solve your problem, though, AI is not for you. You can skip the rest of this guide and save your money.
Are you trying to automate firm, unambiguous business rules? Skip the AI and build some custom software. Those if/then statements and for() loops power just about every business out there, with good reason.
On the other hand, do you have records of past activity? And do you want to mine those records for patterns in order to uncover what those business rules might be? You're probably looking at AI.
Emphasis on "probably":
While AI beats software as far as automating fuzzy concepts, it's not perfect. Some situations simply call for the expertise and nuance of an experienced person.
In those cases, AI is a liability. It will give you answers, sure … but those answers will be wrong. Probably a lot more often than you'd like.
So if your problem requires too much of a human touch, AI won't do the entire job. At best you'll be able to build something to assist your human team members. Which is great. But if your plan was to develop AI in order to replace people on your team … no.
AI has developed a weird reputation. Sometimes people love it. Sometimes they think it's creepy. And once in a while, they love it until they find out it's being used in creepy ways.
(Remember when Target got called out for predicting customers' pregnancies? Or when insurance company Lemonade claimed it was analyzing customers' "non-verbal cues" to detect fraud? That sort of thing.)
Even if AI has looked like a good solution thus far, ask yourself how people will feel when – not "if," but "when" – they find out what you're doing. Is the answer "not good"? Then it's time for you to hit the off-ramp.
If your use of AI is above-board, read on:
AI works by finding patterns in data. You'll need access to a fair amount of data related to whatever you're trying to automate or predict. This can be past customer transactions (if you're in retail), flight arrival times (airlines), call records (telcos), whatever.
If you have a dream but no historical data, then building a custom AI solution simply won't work.
If you do have a decent amount of data, though … you're all set:
If you've made it this far, then you're ready to give AI a try.
What's next on your AI journey?
Your company's first AI project.
You can head over to "How Do I AI?" for my ideas on how to approach your company's first AI projects.
Additional reading material