Something interesting is happening in media technology. Publishers aren’t just using AI tools—they’re building them. And selling them.

Over the past year, I’ve watched several media organisations launch AI products aimed at other publishers, businesses, or consumers. It’s a significant strategic shift worth examining.

The Trend

The examples are accumulating:

Axel Springer has built AI tools for newsroom operations that they’re now licensing to other publishers. Their headline optimization and translation systems are being sold as standalone products.

Bloomberg has commercialised aspects of their AI fact-checking and data analysis infrastructure, offering it to financial services clients beyond their core media business.

The New York Times has created internal tools for everything from summarization to image analysis that could (though they haven’t yet) be productized.

Schibsted in Scandinavia has spun out AI products from their newsroom experiments that now generate meaningful revenue.

In Australia, the activity is smaller scale but present. Several publishers are exploring similar paths.

Why This Is Happening

The logic is straightforward: media companies have been forced to invest in AI capabilities. Those investments create assets that could generate revenue beyond their original purpose.

Consider what a sophisticated media company has built:

• Systems for processing large volumes of text
• Tools for summarization and headline optimization
• Fact-checking and verification workflows
• Content management with AI integration
• Audience analytics with predictive elements

These capabilities took significant investment to develop. Why not recoup some of that investment by selling to others?

The additional motivation: revenue diversification. Media companies desperately need new revenue streams. Technology licensing can provide high-margin income that doesn’t depend on advertising or subscription.

The Opportunities

For publishers with genuine AI capabilities, the opportunities include:

Licensing to other publishers. Smaller newsrooms can’t afford to build sophisticated AI tools. Buying them from larger publishers who’ve already invested makes sense.

Serving adjacent industries. Media AI tools often have applications in corporate communications, marketing, public relations, and other content-intensive businesses.

Platform plays. Some publishers are building infrastructure others can build on—not just tools but platforms.

Data assets. Publisher archives represent valuable training data. Licensing that data to AI companies is already generating significant revenue for some.

The potential is real. Technology products can scale in ways media products can’t. And publishers have domain expertise that pure technology companies lack.

The Risks

But there are significant risks too.

Distraction from core mission. Running a technology business requires different capabilities than running a media business. The skills, culture, and focus differ. Publishers who chase technology revenue may neglect their core journalism.

Competitive exposure. Selling tools to competitors strengthens them. A smaller publisher using your AI might become more competitive with you.

Technical debt accumulation. Productizing internal tools requires ongoing investment. Support, updates, security—these aren’t trivial. Many internal tools work well enough for internal use but aren’t ready for commercialization.

Brand confusion. Are you a media company or a technology company? The market values these differently, and confusion can hurt both identities.

Who Should Consider This

Not every publisher should build AI products. The opportunity is real for organisations with:

Genuine technical depth. Superficial AI implementation doesn’t create saleable assets. You need real capabilities developed over time.

Scale to amortize investment. Building products for external sale requires investment beyond internal needs. Smaller publishers likely can’t justify this.

Distinctive capabilities. Generic AI tools won’t sell—too much competition. You need something unique, often rooted in domain expertise.

Business development capacity. Selling technology is different from selling advertising or subscriptions. Do you have the commercial capabilities?

For most Australian publishers, the honest answer is probably no. The investment required to build saleable AI products is substantial, and the competition from pure technology companies is fierce.

But for larger organizations with established technical capabilities, it’s worth considering.

Case Study: What Works

I looked closely at Axel Springer’s approach, which seems relatively successful.

They focused on products that solve specific problems they’d already solved for themselves—headline optimization, translation, content tagging. The products were mature because they’d been used internally for years.

They priced aggressively to gain market share, recognizing that licensing revenue is incremental to their core business.

They maintained clear separation between the product business and the newsroom, avoiding confusion about priorities.

And they were realistic about scale. This isn’t going to replace advertising revenue. It’s a meaningful diversification, not a transformation.

Implementation Considerations

If you’re seriously considering building AI products from newsroom tools, here’s what I’d think about:

Start with what’s already working. The best products come from tools that have proven themselves internally. Don’t build products speculatively.

Validate demand before investing. Talk to potential customers before productizing. Would they actually pay for this?

Plan for ongoing investment. Products require maintenance, support, and development. Budget for years, not months.

Consider partnerships. Building a product business from scratch is hard. Partnering with technology companies or engaging an AI consultancy for productization support can accelerate the process.

Maintain focus. Don’t let the technology opportunity distract from journalism. Set clear boundaries on investment and attention.

The Bigger Picture

This trend reflects a broader reality: media companies are becoming technology companies, whether they intended to or not.

The digital transformation of publishing has forced investment in technology capabilities that didn’t exist a decade ago. Publishers now employ engineers, data scientists, and product managers in numbers that would have been unimaginable in the print era.

Those capabilities represent assets. Using them only for internal purposes may be leaving value on the table.

But there’s a tension here. Media companies exist to serve audiences with journalism. Technology is a means to that end, not an end itself. Losing sight of that priority has destroyed media companies before.

The publishers who navigate this best will be those who see technology products as supporting their journalism mission, not replacing it. Revenue diversification that strengthens the core business. Not a pivot away from what makes media companies valuable.

Looking Forward

I expect this trend to accelerate. More publishers will productize AI tools. Some will succeed; many will struggle.

The winners will be those who bring genuine domain expertise—understanding of content, audiences, and journalism—to AI products. Pure technology companies can build generic tools. Publishers can build tools that understand media.

For those exploring this path, the advice I’d offer: be honest about your capabilities, validate demand rigorously, and never forget that technology products exist to support your core mission.

If you’re considering this path, getting expert guidance early makes sense. AI consultants in Sydney and similar firms specializing in AI productization can help assess whether your internal tools have commercial potential.

The opportunity is real. So are the risks. Proceed thoughtfully.

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Are you seeing publishers in your network building AI products? I’m tracking this trend and would love to hear about examples.