I challenge you to repeat the experiment with LLM's accessible to you, and let me know if you got anything usable. Feel free to modify the AI capability axis to better suit your own industry's issues.

1. Challenge: How to come up
with different scenarios
An experiment, February 2024
CC-BY Tuija Aalto Yle 2-2024

2. Two key uncertainties discovered by a recent workshop
At a recent gathering hosted by the Reuters
Institute, 33 media executives imagined how AI
would impact their industry in the near future.
According to David Caswell’s workshop
summary, “there was a rough consensus that
the scenarios for what AI might do to news
depended on two significant questions.”
How media managers think AI might transform
the news ecosystem | Reuters Institute for the
Study of Journalism
The two key uncertainties were
1. How audiences react to AI-generated
content – boring, “cold [and]
unreliable” or “super convenient”?
2. How AI models handle the recency of
data, assessing, analysing,
contextualising and narrating recent,
fast-changing information, possibly
from multiple sources?
CC-BY Tuija Aalto Yle 2-2024

3. My question: How fruitful is it to
have large language models
come up with scenarios based
on these two axes?
CC-BY Tuija Aalto Yle 2-2024

4. What I did and what I ended up with
I scripted the following prompt, and gave it to
three LLM’s accessible to me: my employer´s
inhouse YleGPT, my private paid OpenAI
ChatGPT4, and Microsoft Copilot.
I then manually compiled the visual based on
how I understood the scenarios to situate
relative to the two axis.
You are senior expert within a media company, responsible for
creating plausible as well as wild-card - black swan -type of
future scenarios
Please create four very different scenarios based on two key
uncertainties about AI in relation to news media business.
What might happen regarding AI and news media? Use the
following key uncertainties as scenario axes
Axis 1. How audiences would eventually react to AI-generated
content – whether they would find it to be boring, “cold [and]
unreliable” or “super convenient”? What does an informed
public choose regarding its access to and use of information?
Axis 2. How AI models can handle the recency of data,
assessing, analysing, contextualising and narrating recent,
fast-changing information, possibly from multiple sources?
CC-BY Tuija Aalto Yle 2-2024

5. Audience finds AI Boring,
Cold, and Unreliable
AI’s Recency Handling:
Exceptionally Capable
AI’s Recency Handling:
Inconsistent, Struggling
Audience Embraces AI
as Super Convenient
The AI News Utopia,
The AI News Nirvana,
Digital Renaissance,
The Enlightened Garden
The Fragmentation Era
The Fragmented Reality
Audience Skeptical and Varied
Return to Human Essence
The AI News Overload
AI News Nightmare:
Audiences hate AI-generated
content, AI models generate
dull and incoherent narratives
The Echo
Chamber
Cascade
AI News Hybrid: Traditional news
media maintain their relevance and
influence
AI becomes a complementary
source of news production and
consumption.
AI News Wildcard
audiences find AI-generated
content surprising,
entertaining, and influential. AI
becomes a disruptive source
of news production and
consumption, and traditional
news media lose their control
and authority
CC-BY Tuija Aalto Yle 2-2024
AI and news scenarios

6. What was learned from this experiment?
The three LLM’s together gave me quickly
and efficiently a basic output; various
scenario titles and descriptions.
This was a small one-off experiment,
making use of relevant and timely
thought-work made available by human
industry experts (the media managers
gathered in January by the Oxford
Institute). By opportunitively tapping
into the thought-work made by others I
was able to cut right into this
methodologic experimentation.
To capture to value of this experiment for my own
organization I would next invite internal experts to
continue the thinking: Such a workshop should
address the company’s options of relevant action
in each of the imagined scenario fields.
AI might give us a head start and cut, depending
on the method of facilitation, maybe two hours of
workshopping time. However, human minds do
need time to think slowly, discussing the
scenarios’ implications. Although using LLM’s
might cut time workshopping from an entire
day to, say, half a day, cutting the time to
reflect to a minimum may not be advisable.
CC-BY Tuija Aalto Yle 2-2024

7. Tuija Aalto, Yle
https://www.linkedin.com/in/tuija/