How Kent Online Wired AI Into the Daily Newsroom Routine
~75%
2–4 months
Not disclosed
Kent Online's journalists were spending significant time on repetitive, routine tasks — transcription, research and administration — that ate into time for discovering new stories and digging into the facts. The newsroom wanted to use AI to speed up and improve reporting without compromising editorial standards, but needed practical, responsible ways of working rather than abstract AI theory.
GenFutures Lab delivered a hands-on AI training programme for 25 journalists, tailored to real newsroom workflows. Sessions combined live demonstrations, guided exercises and collaborative labs across the full reporting cycle: foundations on how LLMs (ChatGPT, Claude) work and their limits; high-impact use cases in research, transcription, summarisation and headline optimisation; practical prompting frameworks (RTF, RISEN, RODES); rapid content extraction of quotes, angles and data points from dense documents; responsible use of AI-generated images; and NUJ-aligned ethics and compliance embedded throughout. Each participant defined a personal next-week experiment to apply learning immediately.
GenFutures Lab started from the newsroom, not the technology. The programme was built around the full reporting cycle — story discovery, research, editing, administration and responsible use — so every session mapped to work the 25 journalists were already doing. Delivery combined live demonstrations, guided exercises and collaborative labs, giving reporters hands-on time rather than abstract theory. Foundations covered how large language models such as ChatGPT and Claude actually work, including their limitations and risks. From there, the team identified high-impact use cases across research, transcription, summarisation and headline optimisation, and taught practical prompting frameworks — RTF, RISEN and RODES — to make outputs consistent and reliable. Content-extraction techniques helped reporters pull quotes, angles and data points from dense documents quickly, while guidance on AI-generated images addressed responsible visual storytelling. Throughout, NUJ-aligned principles and clear guardrails were embedded so AI use never compromised editorial standards. To anchor adoption, each participant defined a personal next-week experiment to apply immediately, turning a training event into sustained day-to-day practice.
Regional and independent newsrooms whose journalists need practical, responsible AI for research, transcription and editing — without compromising editorial or NUJ standards.
25 journalists
Less admin time
Responsible by default
Mel Moeller
GenFutures Lab ran a hands-on training programme mapped to real newsroom workflows — live demos, guided exercises and collaborative labs across story discovery, research, transcription, summarisation, editing and responsible use. Each of the 25 journalists left with a personal next-week experiment to apply it immediately.
The programme centred on general-purpose assistants — ChatGPT and Claude — applied through practical prompting frameworks (RTF, RISEN and RODES) rather than any custom build. The focus was responsible, workflow-level use, not a single tool.
Outcomes were qualitative: faster story discovery and research, reduced administrative burden through automated transcription and routine tasks, improved content quality from AI-supported editing and headline testing, and sustained day-to-day adoption — all within NUJ-aligned editorial standards. (No quantified metrics were reported.)
It was delivered as a focused, hands-on training programme; the source does not state a fixed duration. Adoption was designed to continue beyond the sessions via each journalist's personal next-week experiment.
Regional and independent newsrooms whose journalists need practical, responsible AI for research, transcription and editing without compromising editorial or NUJ standards.
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