Ever Built a Giant Puzzle? Now Imagine Doing 100 of Them… Every Single Day!
Hey everyone, John here! Welcome back to the blog where we make AI easy enough for anyone to grasp. Today, I’ve got my trusty assistant, Lila, with me as always.
“Hi, everyone! Ready to learn,” says Lila.
Great! So, Lila, let me ask you something. Have you ever tried to put together a really big, complicated jigsaw puzzle? Say, one with a thousand pieces?
“Oh, definitely. It takes forever! You have to find all the edge pieces, sort the colors, and figure out what goes where. It’s fun, but it’s a lot of work,” she replies.
Exactly! Now, imagine your job was to put together a new, unique, 1,000-piece puzzle every single day. And not just one, but over one hundred different puzzles, all at the same time. That sounds almost impossible, right? Well, that’s pretty much what it was like for a big news company in Sweden called Bonnier News.
The Daily Newspaper Puzzle
Bonnier News runs more than 100 different local newspapers. Every day, they have to take all the “pieces” – the news stories, the photos, the advertisements – and arrange them perfectly onto the pages of each newspaper. This process is called print production, and for a long time, it was all done by hand.
A team of people called production editors would have to:
- Decide which story is the most important and should go on the front page.
- Figure out how to fit a long article next to a short one.
- Place ads so they don’t sit right next to a competitor’s ad.
- Make sure everything looked nice and was easy to read.
Doing this for just one newspaper is a huge task. Doing it for over 100 local papers, each with its own unique content and ads, was a massive, expensive, and slow-moving challenge. They needed a better way, a smarter way. And that’s where AI stepped in.
A Super-Smart Helper for a Super-Hard Job
Instead of hiring hundreds more people, Bonnier News decided to build a super-smart assistant powered by AI. The goal was to teach this AI how to solve the newspaper puzzle automatically. Think of it as teaching a robot to be an expert puzzle-solver. They did it in a few clever steps.
Step 1: Teaching the AI the “Rules of the Game”
First, you can’t just tell an AI to “make a newspaper.” You have to give it very specific rules. The team at Bonnier News sat down and created a set of “Golden Rules” for how a perfect newspaper page should be built. These rules were like the instruction manual for the puzzle.
Some of the rules were simple, like:
- “An ad for a car dealership can’t be on the same page as an ad for another car dealership.”
- “The most important news story gets the biggest headline at the top of the page.”
- “Every article needs a picture to go with it.”
- “The sports section always starts on page 12.”
By setting up hundreds of these rules, they created a clear blueprint for the AI to follow. This ensured the final newspaper would look professional and make sense to the reader.
Step 2: Creating a “Digital Toy Box” for the AI
Okay, so the AI has the instruction manual. Now it needs the puzzle pieces! Bonnier News created a giant digital storage system, which they call a Content Hub. Imagine a massive, perfectly organized toy box. Every single piece of content – every article, every photo, every ad – was put into this box.
But they didn’t just throw them in. Each item was given a special label, or tag. This is where a fancy-sounding word, metadata, comes in.
Lila chimes in, “Hang on, John. ‘Metadata’ sounds really technical. What on earth is that?”
That’s a fantastic question, Lila! It sounds complicated, but the idea is simple. Think about organizing your music. You have the song itself (the data), but you also have labels like the artist’s name, the album title, and the genre (like ‘Rock’ or ‘Pop’). That extra information – the artist, the album, the genre – is the metadata! It’s just data about the data.
For Bonnier News, an article’s metadata might include:
- Topic: “Local Politics” or “High School Sports”
- Length: “500 words”
- Importance: “Top Story” or “Small Update”
- Photos: “Includes 2 landscape photos”
These labels allow the AI to instantly understand what each piece is and how it can be used, just like you’d look at the picture on a puzzle box to see what you’re building.
Step 3: Letting the AI Solve the Puzzle
Now for the magic moment. With the rules in place and all the labeled content in its digital toy box, the AI gets to work. The system, which they call the “planning engine,” looks at a blank newspaper page and asks itself questions based on the rules:
“Okay, this is the front page of the ‘Smallville Gazette.’ The rules say I need a top story here. Let me check the Content Hub for an article tagged ‘Top Story’ and ‘Smallville.’ Ah, here’s one about the mayor! Let’s place it right at the top. Now, what ad can I place at the bottom that doesn’t conflict with any others?”
It does this for every single space on every single page, for all 100+ newspapers, all at the same time. It can check all the rules and all the content in seconds. A process that took teams of people all day is now 90% finished in just a few minutes.
So, Did It Actually Work? The Amazing Results
You bet it worked! The transformation was incredible. Here are the biggest benefits Bonnier News saw:
- Blazing Fast Speed: The layout for all their papers is now done in minutes, not hours. This means they can spend more time making last-minute improvements or covering breaking news.
- Huge Cost Savings: Because the AI handles the heavy lifting, the company was able to reduce the number of staff focused purely on manual layout from over 100 people down to a small team of about 10 “super-users.”
- Better and More Consistent Newspapers: The AI never gets tired and never forgets a rule. This means the layout quality is very high and consistent across all newspapers, every single day.
- Happier and More Creative Journalists: With the layout taken care of, reporters and editors can focus on what they do best: finding and writing great stories. They no longer have to worry about whether their article will fit on the page.
Are the Robots Taking Over? The New Role for Humans
This is a question that always comes up with AI. If the AI is doing the layout, what happened to all the people who used to do it?
This is the most important part: the humans are still in charge. The AI does about 90% of the work, but the final 10% is all human. The new, smaller team of editors are now like curators in a museum. The AI arranges the exhibit, but the curator comes in to provide the final, expert touch. They review the AI’s work, make small tweaks, approve the pages, and use their human judgment to ensure everything is perfect before it’s sent to the printer.
Their job has shifted from a repetitive, manual task to a more skilled role focused on quality control and strategy. The AI became a powerful tool that helps them do their job better and faster.
A Few Final Thoughts
John’s Take: For me, this is a perfect real-world example of what AI should be. It’s not about replacing humans entirely. It’s about automating the boring, repetitive, and time-consuming parts of a job, which frees up people to be more creative and strategic. It’s a tool that helps us work smarter.
Lila’s Take: “As someone new to all this, I think it’s really cool! I always thought of AI as something for the internet, like chatbots or streaming recommendations. Seeing it used to help make a physical thing like a newspaper is fascinating. It shows that this technology can help almost any industry, even ones that have been around for hundreds of years!”
And she’s absolutely right. This story from Sweden shows us that with a bit of clever thinking, AI can help solve some of the oldest and toughest business puzzles out there. Thanks for reading!
This article is based on the following original source, summarized from the author’s perspective:
Bonnier News transformed its print production through
automation