AI’s role in publishing: where is it really adding value?

Management

Sponsored by FT Strategies

Joanna Levesque, Managing Director, FT Strategies

Sam Gould, AI Lead, FT Strategies

AI is no longer a future trend. It is already embedded in newsrooms, integrated into news products and is prompting strategic questions about value, trust and differentiation. Yet many organisations still find themselves asking: what is AI really good for?

The answer depends not on the technology itself, but on how clearly organisations define their problems and structure their responses. Where AI is genuinely delivering value, it is not replacing journalism: it is enabling humans to tell important stories more effectively.

Where AI is truly helping: depth, reach and engagement

The most promising uses of AI in publishing are not about speed or volume of content production, but about insight, clarity and access.

For example, publishers are using AI to extend the reach of their content to new and more diverse audiences. Translation tools are evolving from word-for-word engines into systems that can adapt tone, style and local references – or at least create a draft for human review. This allows publishers to localise stories for specific markets or diaspora communities, without the need to scale their newsroom footprint linearly.

Publishers are also applying AI to external or unstructured datasets – provided they can be brought safely in-house – opening up new possibilities for deep reporting. Some of the Financial Times’s most impactful stories have involved applying AI to satellite imagery, or building the right tech infrastructure to wrangle heavy datasets related to blockchain transactions. The human layer remains essential for interpreting the results, but AI can speed up the analysis, as well as the acquisition, of this data.

New formats are another area of innovation. Whether it’s dynamic visualisations, voice synthesis or real-time explainers making use of conversational AI, AI can support more interactive and accessible storytelling. These reflect changing expectations about how news is consumed, particularly among younger or more mobile-first audiences.

Less glamorous but high-value use cases

Some of the most impactful uses of AI are internal and often overlooked. At FT Strategies, we use AI for day-to-day tasks such as summarising meeting notes and sharing knowledge. These are not groundbreaking innovations, but they reduce friction and free up valuable time across teams.

This kind of enablement is often where media companies see the clearest, measurable return. Rather than focusing solely on audience-facing tools, they benefit from improving workflows, documentation and access to insight. This also builds confidence and capability across the organisation.

Investing in the right foundational skills and infrastructure is essential. At the Financial Times, building in-house prompting fluency and strengthening AI procurement processes (for example, identifying opportunities for different teams to share common tools) helped the team win Generative AI Initiative of the Year at the 2025 British Data Awards.

Capabilities over hype: what readiness really looks like

AI adoption is not only a technical challenge. It is a leadership and culture question. The FT Strategies Attitudinal Survey – covering around 2,000 professionals across 20 media companies in EMEA – revealed consistent gaps between the amount of time available for training with AI tools and the vision set by senior management. Many journalists see the potential but lack the support to use it meaningfully.

Leaders do not need to solve everything at once. But they do need to foster experimentation, empower their teams and prioritise reusable capabilities even when the immediate payoff is not obvious. For instance, investments in technologies such as vectorisation can unlock a whole class of AI tools later on. Ask FT – the FT’s generative AI tool for answering reader queries, which combines newer large language models with older vectorisation techniques (in a so-called “RAG” approach) – benefits from this kind of forward planning.

It is also important to know when not to scale. Early experiments can fail for perfectly good reasons: the technology is not ready; the business model does not support it; or the real problem is different to how it first appeared. Teams that define clear goals and evaluation methods tend to recover value even from missteps.

Rethinking value in an AI-shaped landscape

AI forces media organisations to revisit fundamental strategic questions. What makes your journalism worth paying for? What sets your newsroom apart in a world where content can be generated instantly? The answers will vary. Some may evolve into trusted brands focused on curation and verification. Others may specialise in formats, niches or services that AI cannot easily replicate.

Whether you are experimenting with content formats, integrating LLMs into your workflow, or reassessing your data capabilities, the goal is not to automate journalism. It is to protect its value while extending its impact.

For all publishers, the emerging question is not just “how do we use AI”, but “how do we stay valuable and trusted in a world that uses AI?”

As publishers navigate the promises and pitfalls of AI, the challenge is not only to adopt new tools but to do so with purpose. At FT Strategies, we help organisations build the strategies, skills and structures to use AI responsibly, and in ways that strengthen their journalistic and commercial value.

Discover how FT Strategies can support your AI journey

by Joanna Levesque, Managing Director, FT Strategies