Why are you leaving money on the table without AI-powered content? 99% of marketers are already regularly using AI in content creation in some way.
Screenshot showing 99% of marketers use AI in some way
Source: Singular
AI is no longer a future.
Content marketers, like you and me, need to adopt it to stay ahead of the curve.
And I believe in keeping things interesting!
So, let’s calculate how AI works in a marketer’s day-to-day tasks, with a clear breakdown of its impact.
Ready, set, and off we go!
You already know that content creation is at the heart of any solid marketing strategy.
And it involves way more than just writing a few blog posts.
Between research, optimization, scheduling, and reporting, a huge chunk of time is spent on repetitive tasks.
If you’re a content marketer putting in 100% effort, AI can take over at least 85% of those time-sucking activities.
Think about it…
AI handles research (20%), content creation (35%), scheduling (10%), and analytical reporting (20%) for you.
This makes 85% in total. Now, what does this mean?
Pie chart showing AI vs human involvement in content creation
Your to-do list just got shorter!
But, is that it?
Your smart use of AI has also saved you considerable time, effort, and mental energy in developing an effective AI content strategy.
Here’s the scoop!
Out of all these essential tasks, only content creation requires human input to cross-check factual info and add a personal touch.
You’re freed up to focus on strategic tasks, e.g. creative strategy and audience engagement.
That’s how AI helps you get hours back every week.
How AI in Content Creation Works
How AI works in content creation
By now, we know why using AI for content creation is smart.
But is just incorporating AI without knowing its potential enough?
Yes, but no!
See, if you’re willing to leverage only 30-40% AI capabilities for your content, that’s fine. Go ahead.
But if you really want to take 100% advantage of AI’s potential, you need to understand how it works on a deeper level.
Without a basic grasp of how it works, you’ll miss out on pushing it to its full potential. The real magic happens when you’re not just using AI to create content, but mastering it.
Don’t worry, you don’t need to be an ML engineer!
I’ll break down the essentials so you can see how exactly AI functions.
But before taking you through AI algorithms, I’ll introduce you to datasets.
What Are Datasets?
Datasets are rich text sources, e.g. books, articles, and web pages, that are used to train AI models.
This training helps AI produce high-quality content that closely mimics human writing styles and meets specific user needs.
To achieve optimal performance, datasets must be diverse, representative of the target audience, and especially, free from bias.
It’s a simple phenomenon, the richer the dataset, the better the AI’s output.
Understanding AI Algorithms
AI algorithms in one frame
At the core of AI content creation are algorithms that process and understand information.
Natural Language Processing, Machine Learning, and Deep Learning algorithms form the base of modern AI.
These foundational algorithms are the starting point for creating intelligent systems. They build on further concepts, creating increasingly complex, advanced algorithms.
Natural Language Processing Algorithms
Natural Language Processing (NLP) is what allows AI to understand and process existing text.
NLP algorithms power essential tasks like sentiment analysis, summarizing content, and extracting keywords.
Put simply, they ensure AI can optimize and categorize content, providing a tailored response based on the context.
For instance, NLP can analyze user comments or reviews to gauge sentiment. Moreover, it can extract critical points from long-form or bulk content for quick summaries.
Machine Learning Algorithms
Machine learning (ML) is key in enabling AI to adapt to various content types, from formal articles to friendly social media posts.
ML algorithms learn patterns and trends within datasets and improve over time. They rely on statistical techniques, e.g. decision trees to make informed decisions.
They’re used for tasks like text generation, summarization, and translation.
In content creation, these algorithms help AI systems refine their output, ensuring better accuracy in grammar, tone, and context.
Did you know personalizing content to match user preferences and suggesting relevant articles or topics is made possible by ML algorithms?
