Why Just Using AI Isn’t Enough: The Secret Ingredient Companies are Missing
Hi everyone, John here. It feels like you can’t go a day without hearing about some new, mind-blowing AI, right? Every company, from the corner store to the biggest media giants, seems to be in a frantic race to use it. But here’s a little secret: just grabbing the latest AI tool off the shelf and plugging it in is a recipe for disappointment. It’s like buying the most expensive, professional-grade camera and expecting to win a photography award without knowing who or what you want to shoot.
Today, I want to talk about a much smarter way to think about AI, especially for businesses like news and media companies. It’s not about having the flashiest technology; it’s about using a powerful ingredient they already have in abundance: a deep understanding of you, their audience.
The Big Rush to AI and a Common Mistake
There’s a lot of excitement, and frankly, a bit of panic, driving companies to adopt AI. The main star of the show right now is something called Generative AI.
“Hang on, John,” Lila, my assistant, chimed in. “That sounds a bit technical. What exactly is ‘Generative AI’ in simple terms?”
That’s a great question, Lila! Imagine you have a super-smart assistant. You can ask it to write an email, summarize a long report, or even create a picture of a purple elephant surfing. Generative AI is like that assistant—it doesn’t just find information, it creates something new (text, images, code) based on your instructions. It’s a powerful creator.
The mistake many are making is that they’re so dazzled by what this AI can do that they forget to ask who they’re doing it for. They build a generic AI tool, and it ends up being a solution looking for a problem. The real power isn’t in the AI itself, but in how it’s tailored to solve a specific person’s specific problem.
Your Data is the Key: Understanding the ‘Who, What, and Why’
So, how do companies tailor their AI? They use something often called “audience data.” This isn’t as scary as it sounds. It’s simply the information a company has that helps them understand its readers better. The best strategies look at three layers of this data:
- WHO the audience is: This is the basic stuff. What’s their age, where do they live, what kind of job do they have? It gives a general picture of the person on the other side of the screen.
- WHAT the audience does: This is about behavior. What articles do they click on? How much time do they spend reading about sports versus business? Do they watch videos or prefer to read long articles? This shows what they’re interested in.
- WHY the audience is here: This is the gold mine. Why are they visiting the site in the first place? Are they a busy executive who needs quick, scannable news to stay ahead? Are they a student doing research for a project? Or are they just looking for a fun read to unwind after a long day? Understanding this “need” is the most important piece of the puzzle.
When a company combines these three layers, they get a crystal-clear picture of their audience. And with that picture, they can start building AI tools that are genuinely, incredibly useful.
Three Smart Ways Companies Can Use AI
Once a company understands its audience, it can create a plan. This plan, or AI product strategy, usually involves putting resources into one of three main areas.
“John, what do you mean by an ‘AI product strategy’?” Lila asked. “Is that just a fancy term for a to-do list?”
You’re on the right track, Lila! Think of it as a game plan. Before a team takes the field, the coach has a strategy based on their players’ strengths and the other team’s weaknesses. Similarly, an AI product strategy is a company’s game plan for how they’ll use AI. It helps them decide where to focus their time and money to get the best results for their specific audience.
Here are the three main plays in their playbook:
1. Making the Company Work Better on the Inside (Internal Efficiency)
This is about using AI to help the company’s own employees. For example, an AI could instantly summarize a long government report for a journalist, saving them hours of reading. Or it could help writers by suggesting headlines for their articles. You, the reader, might not see this AI directly, but you benefit from it because the journalists have more time for in-depth investigation and writing.
2. Making Existing Products Better (Enhancing Current Products)
This is where AI is added to a product you already use to make it better. Imagine the news website you visit adds an AI-powered assistant. You could ask it, “Can you give me a 2-minute summary of the top business stories today?” and it would do it instantly. It’s not a brand-new product, but a powerful upgrade to one you’re familiar with.
3. Creating Brand-New AI Tools (New Product Offerings)
This is the most ambitious approach. It involves building a completely new product powered by AI that the company can offer to its audience, often for a fee. For example, a financial news company might create a special AI tool for its subscribers that analyzes market trends and gives personalized insights. This is riskier but can create a powerful new reason for people to become loyal customers.
A Tale of Two Chatbots: Why ‘How’ You Build It Matters
Let’s make this crystal clear with an example. Imagine a news company wants to build a chatbot to answer reader questions.
The Generic, Useless Chatbot:
This company just takes a general-purpose AI and puts it on their site. A reader asks, “What is the company’s view on the latest interest rate hike?” The chatbot, having no specific knowledge, gives a generic, Wikipedia-style answer about what interest rates are. It’s not helpful because it doesn’t know anything about the news company’s unique articles or expert analysis. It’s like asking a random person on the street for expert advice—you won’t get very far.
The Smart, Data-Driven Chatbot:
This company first looks at its audience data. It sees that a large portion of its readers are small business owners who are worried about the economy. So, it builds a chatbot specifically for them. It trains the AI on all of its own published articles and analysis about business and finance. Now, when a business owner asks, “How might this interest rate hike affect my business?” the chatbot can provide a summary based on the expert opinions and articles the company has published. It can even point to specific articles for a deeper dive. This is a valuable tool.
See the difference? The technology might be similar, but the strategy of using audience data makes the second chatbot infinitely more useful.
My Final Thoughts
For me, this approach is a breath of fresh air. It’s a powerful reminder that technology, no matter how advanced, is at its best when it serves a human purpose. Instead of getting lost in the hype of what AI can do, the real goal should be to figure out what people need and then directing this amazing technology to meet that need. It puts the focus back on people, not just processors.
Lila added her thoughts: “As someone who is still learning, this makes AI feel much less intimidating. Knowing that the best AI products are the ones designed with real people like me in mind is really comforting. It changes the picture from ‘scary, complex technology’ to ‘a helpful tool that’s built for me’.”
Ultimately, companies that use their unique understanding of their audience to guide their AI strategy won’t just be surviving in this new era—they’ll be the ones who truly lead the way.
This article is based on the following original source, summarized from the author’s perspective:
Audience data should factor in when creating AI product
strategies