1. The future is
already here
– how to use AI in Discovery
Anna Haverinen, PhD
Design Anthropologist & Insight Strategist
anna.haverinen@reaktor.com
AI was being heavily used to create this presentation

2. Design Anthropologist & Insight Strategist, PhD
Since 2009 have helped major brands, start-ups, teams, and individuals to have a better
understanding of their industry, market, and customers.
I specialize in providing strategic human insight that has an actual impact in product,
business and strategy development.
Anna
Haverinen
NICE TO MEETYOU

3. ABOUT
Reaktor is a digital agency
passionate about
technology, design and
doing things right.
80 M€
Revenue in 2022
2000
Year founded
750+
Team members
We are based in Helsinki, Amsterdam, Lisbon, New York,
Seinäjoki, Stockholm, Tampere, Tokyo & Turku
30+
Ecosystem companies

4. 4
ABOUT
Full digital powerhouse with
end-to-end capabilities at your service.
20
BUSINESS ‘
ADVISORY
90
DESIGN
40
DATA & AI
40
CREATIVE
20
GROWTH &
CONTENT
500
SOFTWARE
ENGINEERING
20
AGILE
COACHING
20
TECH
ADVISORY

5. 01
02
03
04
Using AI in Discovery and Research
Tips & tricks
Case example
Final thoughts
5
INDEX

6. 6
Using AI in
Discovery and
Research

7. The future is already here
– it’s just not very evenly distributed.
- William Gibson

8. Double diamond has typically
two phases

9. In our session today let’s talk about the first part

10. 10
The benefits of using generative
AI in Discovery are..
1. Forming hypotheses from hunches
2. Analysing complex data sets with various methods
3. Evaluating project plans
4. Desktop research
5. Avoiding bias(?)
6. Saving time in multiple areas
What I said:

11. 11
What ChatGPTsaid
1. Forming hypotheses from hunches
2. Analysing complex data sets with various methods
3. Evaluating project plans
4. Desktop research
5. Avoiding bias(?)
6. Saving time in multiple areas
What I said:
The benefits of using generative
AI in Discovery are..

12. 12
What ChatGPTsaid
The benefits of using
generative AI in Discovery
1. Forming hypotheses from hunches
2. Analysing complex data sets with various methods
3. Evaluating project plans
4. Desktop research
5. Avoiding bias(?)
6. Saving time in multiple areas
What I said:
When preparing for this
presentation in early May 2023,
this ethics section wasn’t there.
This is a very recent addition.

13. There’s two ways to approach research in Discovery

14. We might be validating our findings or discovering entirely
new areas
Research is being used
to validate/invalidate
presumptions or
hypotheses.
RISKS
You begin with incorrect
premises or flawed data.
Example:
All birds have feathers,
thus all animals that
have feathers are birds.
(False, some dinosaurs
had feathers)
Research is being used
to form assumptions or
hypotheses.
RISKS
Conclusions are being
made without the
correct information.
For example:
There’s a white ball. A
red light is shining on
the ball making is look
red. What color is the
ball?

15. Tips and tricks
15

16. 16
Install a Plugin –
Where’s the AI
deriving it’s
information?
ChatGPT is not up to date!
It’s information is not updated since September 2021.

17. 17
Plugin – Where’s
the AI deriving it’s
information?
Create better prompts and assess the sources of
ChatGPTwith UseChatGPT.AI: ChatGPTAI Copilot
for the Web.

18. 18
Plugin – Where’s
the AI deriving it’s
information?
Create better prompts and assess the sources of
ChatGPTwith UseChatGPT.AI: ChatGPTAI Copilot
for the Web.
Sources are extremely important when you review the
AI’s reliability and the usability of it’s responses.

19. 19
Perplexity provides also sources, better UI
and no need to login
Microsoft Bing also automatically lists the sources!

20. 20
Ask from the AI how to use it better
– work smarter, not harder
• "How can AI assist in gathering and analyzing large volumes of qualitative and quantitative data for design research?"
• "In what ways can AI algorithms help identify patterns, trends, and insights from research data more efficiently and accurately?"
• "How might AI be utilized to uncover latent user needs or preferences that may not be immediately apparent?"
• "What AI-powered tools or techniques can be employed to enhance the synthesis and interpretation of research findings?"
• "How can AI support the creation of user personas or archetypes based on collected data and observed behaviors?"
• "What opportunities exist for utilizing AI-generated simulations or prototypes to evaluate and refine design concepts?"
• …
• Ask for Long Lists, Better Lists, and Weirder Lists (Wired Magazine)

21. Case example
(Now it’s time to take out your laptop)
21

22. CASE
22
Project/client
Let’s try it out!
Let’s pretend we are collecting customer
insights for Oda.
We need to understand:
- The role of Oda in people’s everyday lives?
- What is the current customer experience?
- What value does Oda have for their customers?
- Who should we talk to?
- What methods to use and why?
Bonus point!
Oda wants you to make an ethnographic research about their customer
experience (if you’re not familiar with ethnography, you can ask about
it!)

23. CASE
23
Let’s try it out!
We need to understand:
- The role of Oda in people’s everyday lives?
- What is the current customer experience?
- What value does Oda have for their customers?
- Who should we talk to?
- What methods to use and why?
Bonus point!
Oda wants you to make an ethnographic research about their customer experience.
Try this prompt:
“Create an example of interview questions where we want to understand the role of Oda online
food store in people's daily lives?“
(Wait for the answer)
“Use the perspective above but make the questions more ethnographic.“
Compare the results and discuss!
Let’s pretend we are collecting customer
insights for Oda. Try for example these

24. CASE
24
Project/client
What did we learn?

25. Final thoughts
25

26. 26
Project/client
Things to consider when using AI in
Discovery
1. Identify suitable use cases
2. Better understanding of limitations
3. May speed up expertise and skill development
4. Can help with continuous evaluation and improvement
5. Speed up tedious tasks
6. Enhance creative thinking
7. Mitigating risks by exploring different options
8. Enhanced data analysis
9. Faster modelling of results
1. Ethical considerations (may impact privacy, data security, and
potential bias in the design process)
2. Transparency and interpretability (black boxes)
3. Your ability to avoid risks needs to be based on both
experience and literature
4. Ethical and legal risks
5. Unintended consequences
6. Human-AI collaboration challenges
7. Adoption barriers
8. User acceptance and collaboration (mindset within the team
and the client)

27. The way we learn our craft (and about ourselves)
is also through failure and experiment.
Failure is essential in growth.
Is it possible with AI assisted services?

28. Thank you
www.reaktor.com
Anna Haverinen, PhD
Design Anthropologist & Insight Strategist
anna.haverinen@reaktor.com
Let’s connect on LinkedIn and Instagram!
@anna_haverinen