Strategic approaches to audience engagement in journalism

Audience development evolved fastest where the direct relationship was challenged.

Ten organisations, four countries

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2025 · Interviews and industry reports

2026 · An insights pipeline that follows the industry

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Three questions under every element

• Every element
  • Why are we doing it?
  • What are we doing?
  • How is it organized?
• Audience
  • Who do we serve, and why?
  • What do they need, and can we serve them?
  • How do we learn who they are and what they need, instead of guessing?
• Engagement
  • Why is there engagement between us and the audience?
  • What is it in us, and in what we make, that the audience wants to engage with?
  • How is that engagement built and sustained?
• Measuring success
  • Why are we counting, and what decision depends on the answer?
  • What counts as success?
  • How are the goals set, and who keeps the definitions honest?
• Audience development
  • Why are we doing audience development?
  • What does the craft cover, and where does it stop?
  • How is the work organized, and who does it?

Part 3

The arc

Five eras, the same questions

• Pre-digital era
  • Through 1994
  • Print and broadcast
• Early web era
  • 1995–2005
  • Portals and homepages
• Platform era
  • 2006–2018
  • Search and social feeds
• Post-platform era
  • 2018–2023
  • Direct relationships
• AI-discovery era
  • 2024–now
  • Answers instead of links

Five eras

What the arc shows

• Audience
  • The unit of audience keeps shrinking
  • Circulation area → household → person with a phone → audience of one
• Engagement
  • Every turn begins outside the newsroom
  • Search → social feeds → AI answers
• Measuring success
  • Metrics move from the article to the user
  • Copies → pageviews → known users
• Audience development
  • The work keeps moving closer to the journalism
  • Separate building → teams → everyone's job

Part 4

Future of <element>

From signals to scenarios

Engagement

Are these the questions for the AI era?

• Why is there engagement between us and the audience?

  • Why do we still hold on to engagement between us and the audience, when platforms disconnect the content from who made it and the organic top of the funnel dries up?

• What is it in us, and in what we make, that the audience wants to engage with?

  • When the article as we know it is challenged, what is left for people to attach to? A brand, a reporter, a habit, a format?

• How is that engagement built and sustained?

  • How do we build the relationship? Inside a product we own or by making our journalism legible inside interfaces we never will?

Audience

Are these the questions for the AI era?

• Who do we serve, and why?

  • Who do we serve, and why, when AI can build a news service around a single person?

• What do they need, and can we serve them?

  • What does the audience need that AI cannot give them, and can we serve that?

• How do we learn who they are and what they need, instead of guessing?

  • How could we learn more from real people, when a synthetic panel answers in seconds?

Measuring success

Are these the questions for the AI era?

• Why are we counting, and what decision depends on the answer?

  • Why are we following dashboards, now that a model can do the deciding?

• What counts as success?

  • What should the behaviour of the user decide? Paywall, price, format, or content?

• How are the goals set, and who keeps the definitions honest?

  • How do we check that the goals and the guardrails are still right, when AI acts on them without anyone reviewing each decision?

Audience development

Are these the questions for the AI era?

• Why are we doing audience development?

  • Why are we doing audience development, when agents can read the data and draft the distribution?

• What does the craft cover, and where does it stop?

  • What does the craft cover, when discovery, product and data run through the same agents?

• How is the work organized, and who does it?

  • How is the verification done, and whose job is it, when AI is the interface?

Twelve questions for the AI era

• Every element
  • Why
  • What
  • How
• Audience
  • Who do we serve, and why, when AI can build a news service around a single person?
  • What does the audience need that AI cannot give them, and can we serve that?
  • How could we learn more from real people, when a synthetic panel answers in seconds?
• Engagement
  • Why do we still hold on to engagement, when platforms disconnect the content from who made it and the organic top of the funnel dries up?
  • When the article as we know it is challenged, what is left for people to attach to?
  • How do we build the relationship? Inside a product we own or by making our journalism legible inside interfaces we never will?
• Measuring success
  • Why are we following dashboards, now that a model can do the deciding?
  • What should the behaviour of the user decide?
  • How do we check that the goals and the guardrails are still right, when AI acts on them without anyone reviewing each decision?
• Audience development
  • Why are we doing audience development, when agents can read the data and draft the distribution?
  • What does the craft cover, when discovery, product and data run through the same agents?
  • How is the verification done, and whose job is it, when AI is the interface?

Scenario work in six steps

1. Gather the questions that matter
2. Sort them by impact and uncertainty
3. Cross the two most important uncertainties
4. Write a short story for each world
5. Trace what each world does to your newsroom
6. Find the no-regret moves and the signposts

Steps 1 and 2

Close to twenty questions, sorted

• Fairly certain
  • AI answers more questions and sends fewer visits
  • A news service built around one person gets cheap
  • Agents take over routine work on audience data
• Truly uncertain
  • Shared news, or news tailored to me?
  • A relationship with a news brand, or an answer that is enough?
  • The article, or liquid journalism?
  • How much of what readers need can AI give?
• Choices
  • Our own product, or legible in interfaces we will never own?
  • Should the depth of the relationship set the price?
  • Who owns verification and trust?

• The shared voice

  In 2029 most people still start the day with a news brand they know. AI search answers their quick questions, but for what matters today they open a front page or an app that editors have put together, and they read or listen to the same stories as their neighbours. AI has become a natural part of the newsroom workflow for creating versions, so they can choose a listen on the bike or a read on the bus from the same article, with exactly the same script. The picture of the day remains shared. Trust stays with the brands that show their work. The money still comes mostly from readers, who pay because they want to be part of the same conversation, and from advertisers who want to reach them there.

• The personal voice

  In 2029 people still choose a news brand, but each of them gets a different version of it. The brand's own AI knows what they read and how much time they have, and builds their news around that, a morning briefing on the way to work or a few longer stories in the evening. The journalism still comes from the newsroom, and the brand's voice holds it together, so readers trust the masthead more than any single story. That trust is also the risk. When the AI gets a quote or a fact wrong, the brand carries the mistake. The money comes from readers who pay for a service that knows them, and the price may follow how deep the relationship goes.

• The common answer

  In 2029 most people get their news by asking an AI, or from one of the few popular aggregators that sum up the big stories, such as Particle. Both give everyone roughly the same answer, so a shared picture of the day survives, but the platform is the one putting it together. Few people ask who reported it, and even fewer click through to find out. News brands still do the reporting, yet to most readers they are a name in a list of sources, if they are named at all. Trust goes to the answer. The money goes to the platform, and newsrooms earn what they can from licensing deals with the few AI companies that pay for journalism.

• The private feed

  In 2029 each person's AI assistant knows them well enough to put together their news without being asked. Overnight it reads dozens of outlets, and in the morning it delivers a briefing in the length and language its owner prefers, next to their calendar and their messages. No two people get the same picture of the day, and few could say where a story came from. Trust belongs to the assistant. The money goes to the companies that make the assistants, and newsrooms are paid per use, if at all, by the ones that choose to license journalism. A newsroom's job becomes supplying material that a machine can read and cite.

Scenario work

Three no-regret moves

• Make every story provable

  • Verification and a record of where the journalism came from

• Write once, publish in any form

  • One checked script becomes any version

• Keep a direct line to real people

  • Newsletters and events where readers talk back

How to prepare for the futures of engagement by AEFJ

1. Define the role of audience and engagement for you
2. Get familiar with the history
3. In a changing ecosystem, are you asking the right questions?
4. Prepare for multiple scenarios

The guide is a mindset, an introduction and a toolkit