Annika asks, "What are your recommendations to begin with and how would one proceed in a small or medium sized company or start up in utilizing AI in their marketing processes?"
First, you have to be ready for AI. That means taking the AI journey.
Assuming you've made the steps up through data science, the place to start looking for AI application is based on the types of problems you have that AI excels at solving. For that, take a look at the AI lifecycle.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode, Anika asks, What are your recommendations to begin with? And how would one proceed in the small or medium sized company or startup in utilizing AI and their marketing processes? Okay, so there's two different angles to this question number one, the first thing to do is that a company has to be ready for AI.
There's a seven part journey, and I'll put a link in the show notes to to a chart that we have to trust and sense website about this is a seven part journey to becoming ready for AI.
Number one, you have to be data ready, which means your data has to be in good condition after you'll find it, use it etc.
Without without having your data, you can't do AI period.
Second, you have to be measurement focused in analytics driven, that means developing a culture of being data driven, having things like KPIs and such because if you don't know what to do with your data, or you don't understand the value of your data, AI is not going to help you.
Third, you have to have qualitative research capabilities.
Because no matter how good your AI is, it cannot crawl inside the heads of your customers and ask them questions.
Right about why they made the decisions they made.
Did Why did you abandon that shopping cart? Why did you buy this sweater? Why did you vote for that person.
The data will never tell you that.
You have to ask people for that.
So I mean, qualitative research capabilities forth.
You need to have process automation in place throughout your company to free up time, because AI consumes a lot of time and resources, it can deliver multiples of that back.
But to get started requires time, money expertise.
So you need to free up as much as possible with traditional process automation.
Fifth, you need to have data science capabilities, whether it's through a vendor, whether it's through a higher, whatever the case may be, you need to have somebody who can help you.
design experiments, process data, develop really interesting insights use modern statistical techniques, because at the end of the day, AI is nothing more than math.
That's when you get to machine learning.
And then deep learning, you have to have those, you have to take that journey.
So a company has to have those steps in place.
Not necessarily throughout the entire company, but at least in the department where you want it to occur.
So that all the pieces are in place to do AI.
Well, if you think about think about AI is like cooking, right? You say I want to use AI? See, I want to use a honey, I want to make omelets.
Do you have a frying pan? Do you have eggs? Do you have a whisk? Do you have a bowl, the assault, jabbing? filling? If you don't have all the pieces and you don't know how to do those things, you can't make an omelet.
And so someone saying I want to use AI for this, which you hear a lot of people in the scenes would say, let's let's make sure we use AI here we need to get some AI into this product.
Because we need to, we need to have some omelets.
But But chef we're a Japanese restaurant we know.
But All right, we'll find a way to work it in.
doesn't, it doesn't quite work.
And that brings me to the second point, which is look at the AI life cycle.
Again, we'll have a link in the show notes to wait and get this chart because it's a life cycle of AI.
The first step is business requirements.
Do you have a business problem that AI excels at solving? So we talked in a previous episode on the types of problems AI is good at solving continuous versus non continuous data, supervised versus unsupervised learning? Do you have a problem like that? Do you have a problem? Where you need to understand what happened? Do you need to be able to predict what's going to happen in a numerical capacity? Do you have a lot of data that needs to be classified or categorized? If you have a problem that is important to your business, that doesn't fit in those categories, AI is not going to help.
Right? In fact, it'll just make things worse because you'll spend a lot of time and money on something that won't deliver any returns.
So the key to understanding how to get started with AI in small businesses start up a medium sized business and enterprise doesn't matter where is do you have the business requirements down and documented and clear.
This is what the problem is this is why it's important.
This is we've added that we have identified this as a problem that AI in some capacity is likely to be able to partially solve.
Remember that the core benefits of AI are our acceleration, accuracy and automation, right.
That's what we call the three days of AI.
faster, better, cheaper.
It's not really good at innovation.
So if you have an innovation problem, it's not going to help if you're not really good today at strategy.
So if you have a strategy problem, it's not going to help you there.
It's going to help you at better faster cheaper, if you have a problem where you are processing data to slowly or inaccurately or using too many human resources to do it.
That is a great question candidate for using AI.
And so you can see there's business requirements and an analytic approach that are well suited for AI.
So that's the starting point.
Do you have a problem that AI can help you solve? If you're not sure, don't embark on a massive AI project.
The other thing that I will add in that's not on this chart is that treat aI think and we mentioned this in the in previous episodes, treat AI as a form of software development, because it is the only difference is that instead of a human writing all the code, the machines writing a good chunk of the code treat like a form of software development.
Which means you'll want to do things like run a pilot, right? Do a proof of concept, build a minimum viable product.
So that you can see yes, there's a there there.
And this problem was worth solving at a larger scale, right? Just don't go all in and it's like, we're gonna automate, we're gonna use AI for everything.
Know, find a problem, do a do a pilot, test out your idea.
See if there's a they're there.
And only then do you move on to making bigger iterating it growing it moving into production, things like that treat it like software development, because it is.
And you'll be much more likely to succeed when it comes to using AI in a practical way.
The final thing I'll say, especially for small businesses and startups is that your vendors are going to be adding AI capabilities to their products, particularly for marketing anyway.
Google Analytics has it built in already? My favorite social media monitoring tool talk Walker has it built in already.
So it's not like you have to go out and build something net new, unless you have a problem that those tools don't already solve.
But if you're using Google Analytics, and you're using instant insights, and you're using the ask Google feature, you can credibly say, yes, we're currently using some form of AI in some capacity in our marketing.
Is it yours? No.
Did you build it? No.
But are you using it to get the benefits of better faster, cheaper? Yes.
And that's the point.
Right? That's the goal is get to better, faster, cheaper, whether you build it or not, doesn't matter.
Just get to better, faster, cheaper.
So great question.
Again, check out the links to those diagrams in the show notes over at Christopher penn.com has the full transcript in the notes and everything.
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