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Published 9 November 2025

Choosing the Right AI: Why Not All Tools Are a Match (And How to Spot the Threats)

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AI has moved from a buzzword to a boardroom priority for news organisations. Yet as publishers navigate these new territories, a quieter challenge has emerged: AI choice fatigue. There are now hundreds of tools promising to fix workflows, drive engagement, streamline production, reinvent storytelling, and save money. Some do. Many don’t. And a significant number introduce risks that newsrooms haven’t fully anticipated.

Across Europe, including the UK, editors and digital leads are asking a similar question: 

Do we really need AI for this, or are we creating a bigger threat by adopting the wrong tools?

This article breaks down what’s behind the mismatch, how to evaluate AI without falling into the hype cycle, and why ultimately the people behind the tools will shape the industry’s future more than any model.

The Problem: Abundance Without Alignment

Studies from the Reuters Institute and WAN-IFRA highlight a consistent pattern: publishers are interested in AI primarily for efficiency, content creation, and audience development. But newsrooms that rushed into early tools often discovered:

  • Misaligned output quality (e.g., generic video, off-brand writing)
  • High editorial overhead due to poor accuracy
  • Black-box systems that undermined trust with journalists
  • Hidden costs, both financial and operational
  • Ethical and legal concerns (copyright, hallucinations, bias)

A 2024 INMA report noted that over 60 percent of publishers now feel overwhelmed by “the volume of tools vs. the clarity of value.” This is the moment when careful evaluation matters more than speed. 

The issue isn’t that AI lacks potential. It's that many tools weren’t built for newsroom realities.

Why Not All AI Tools Are a Match

Evidence from recent case studies shows that misaligned AI tools typically fall into one of four categories:

1. Tools that automate the wrong thing

Some AI replaces tasks that don’t need replacing, while ignoring the processes where newsrooms actually lose time. As it was mentioned during one of the industry events:  A Scandinavian publisher found that an auto-writing tool created more editing work than it saved.

2. Tools that can’t explain their logic

Black-box AI creates risk. If editors can’t understand how a system produced a story summary or video script, they can’t verify its accuracy. This becomes an integrity threat, not an efficiency boost.

3. Tools that don’t fit newsroom realities

Newsrooms run on deadlines, handoffs, CMS constraints, and editorial rules. Tools designed for marketing teams often fail. WAN-IFRA research shows that newsroom adoption rates improve only when tools adapt to the newsroom, not the other way around.

4. Tools that introduce new dependencies

Some AI platforms lock publishers into proprietary formats, limited export options, or inconsistent output that requires manual rework. Rather than reducing workload, they shift it.

The Framework: How to Evaluate AI Without Getting Lost in Hype

Here are the practical decision clues editors can use, drawn from documented best practices, real newsroom deployments and the commercial realities publishers face:

Clue 1: Start with the pain, not the promise

If a tool solves a real bottleneck (slow video production, repetitive formatting, multilingual distribution), it’s worth exploring. If it solves a hypothetical problem, walk away.

Clue 2: Ask: “Does this increase accuracy or create new errors?”

A strong AI system should reduce editorial risk, not introduce hallucinations, copyright concerns, or unverifiable claims.

Clue 3: Check for contextual awareness

Does the tool understand journalism formats, story structures, editorial tone, and local context? This is where many generic AI tools fail.

Clue 4: Demand explainability

Responsible AI in news must show its logic: sources, process, and confidence levels. If it can’t, it’s not newsroom-safe.

Clue 5: Assess the overhead

Does it genuinely save time? Tools that require heavy editing or multiple steps often cost more than traditional workflows.

Clue 6: Evaluate data governance

Is content stored securely? Are models trained on proprietary data? Are licensing rights clear?

Clue 7: Look for fit-for-purpose design

Tools built specifically for publishers tend to respect editorial workflows, compliance requirements, and quality control.

Clue 8: Tie the tool to a business goal

Every AI adoption should map to a measurable commercial outcome. Does the tool help shorten production time, increase time on page, grow pageviews, expand video inventory for advertising, or enable new revenue streams like branded content? If the business impact is unclear, the value is questionable.

Clue 9: Check whether it strengthens sustainability

AI isn’t just about efficiency. It should support the broader business model: subscription growth, audience engagement, cost reduction or new content formats. If the tool can’t earn its place commercially, it's not solving the real challenge.

Why This Matters Right Now

Publishers are entering a new phase of AI adoption where speed is no longer the competitive advantage; clarity is. A misaligned AI choice can damage credibility, waste resources, or even erode audience trust. Meanwhile, correctly chosen tools can meaningfully support journalism, freeing talent to focus on reporting, investigation, and storytelling.

This is why some of the most forward-thinking media organisations from Germany through the UK and  Nordics are now taking an outcome-first, tool-second approach. They’re evaluating AI not by novelty but by whether it strengthens the core mission: informing the public responsibly and efficiently.

A Realistic Perspective

No AI tool will transform a newsroom alone. But the right one, applied to the right problem, can shift the balance between what teams must do and what they want to do. Whether it’s generating story videos faster, localising content, summarising long reports, or supporting desk editors during peak hours, AI works best when it respects the craft rather than trying to replace it.

As publishers face tighter budgets, rising competition, and the push for scale, the biggest threat isn’t AI itself. It’s choosing AI that pulls the newsroom in the wrong direction.

With the right framework, that’s avoidable.

The Human Layer: Why the People Behind the Tool Matter More Than the Tool Itself

Among all the conversations about “the right AI,” one truth consistently emerges from newsroom case studies: technology alone never drives transformation. It’s the people who design it, implement it, question it, and guide others through it.

This is something we see every day at Flipatic. Yes, we build an AI tool. But more importantly, we work side-by-side with publishers to ask the deeper questions that surface once the tool is introduced: What does this change for your team? Where are the hidden workflow gaps? What should be automated and what should stay human?
Those conversations often reveal problems newsrooms didn’t even realise existed,  bottlenecks buried in legacy processes, invisible handoffs, recurring editorial friction that has simply become “the way things are.”

Workshops and strategy sessions frequently become just as valuable as the product itself. Choosing the right AI isn’t only about the technology’s capabilities. It’s about whether the people behind it understand editorial realities, respect journalistic values, and support teams through the “now what?” stage.

Across the industry, the most successful AI projects share a pattern: they’re built on partnership, not procurement. Tools don’t drive meaningful change unless humans guide them, challenge them, and integrate them responsibly. The same applies to journalism. It has always been people-to-people. Editors to reporters. Newsrooms to audiences. AI should sit alongside that relationship, not in front of it.

And there’s another principle people often forget: humans should teach AI, not the other way around. Being present at industry events, watching trends unfold, talking to peers, listening to audiences that’s where real understanding is formed. AI should help you summarise what you’ve observed, wrap up what you’ve learned and scale the insights you’ve already gained. But it should never become the source of truth by itself. Don’t let AI teach you. Be the teacher.

Every AI system is only as strong as the people who shape it, question it, and use it with purpose. Newsrooms don’t need tools that try to overwrite human judgment. They need tools that support it, enhance it, and give teams more room to do the work only people can do.

by Monika Kaminska


Photo by cottonbro studio

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Choosing the Right AI: Why Not All Tools Are a Match (And How to Spot the Threats) | Flipatic Blog