SlideShare a Scribd company logo
info@uman.ai - www.uman.ai
A Story About
Product Management
at uman.ai
Hi,
I’m Jasper
‣ Civil Engineering - Computer Science
@Ghent University
‣ Started in Information Management
Consulting
‣ Earlier: digital strategist and UX
architect @Wijs
‣ Co-founder and Head of Product
@uman.aijasper@uman.ai
www.uman.ai
Today
1. About uman.ai
2. Setting the scene
3. AI product challenges & how to tackle them
4. Takeaways
About uman.ai
Knowledge as your
company’s
collective intelligence
The ideal scenario
Knowledge Sharing Utopia
The actual scenario
Knowledge Sharing Reality
❌
Time: “I don’t have time to read this right now”
The actual scenario
Knowledge Sharing Reality
Interest: “Why would I read this, it
doesn’t seem interesting to me”❌
❌
Time: “I don’t have time to read this right now”
The actual scenario
Knowledge Sharing Reality
Interest: “Why would I read this, it
doesn’t seem interesting to me”❌
❌
Time: “I don’t have time to read this right now”
Relevance: “This is too heavy for me
now, it might be relevant later though.”
❌
The actual scenario
Knowledge Sharing Reality
Interest: “Why would I read this, it
doesn’t seem interesting to me”❌
❌
Time: “I don’t have time to read this right now”
Relevance: “This is too heavy for me
now, it might be relevant later though.”
❌
Transparency: “I don’t know who and
what can help me with this subject.”
❓
❓
❓
Where we want to bring you
1. Where & when you need it In the flow of work
2. To your skills and level Personalised & dynamic
3. Better project matching Real-time talent insights
Connected knowledge automated for your teams
AI layer
Knowledge
sharing platform
Talent insights
module
In The Flow
of Work
Intelligent knowledge sharing, in the flow of work
Knowledge Efficiency
Knowledge Retention
Competency Mapping
knowledge,
content &
resources
Setting the scene
UX at an early-stage startup
It’s not pretty.
Unlearn “methodologies”
Unlearn “processes”
Unlearn “best practices”
Setting new standards
There’s very cost-sensitive trade-off between efficiency and flexibility
Design Systems
Hi-Fi
Prototyping
Fine-grained
product backlog
Adjusting standards
There’s very cost-sensitive trade-off between efficiency and flexibility
Design Systems
Hi-Fi
Prototyping
Fine-grained
product backlog
Challenges
User-testing AI experiences
challenge #1
High-level process
Identify needs First concept Initial usertest
Iterate on high
fidelity prototype
etc.
High-level process
Identify needs First concept Initial usertest
Iterate on high
fidelity prototype
etc.
The experience is highly dynamic
How can you prototype a highly dynamic experience?
Prototyping dynamic experiences with static designs is a huge investment.
How we work today
Identify needs Qualify need Draft UX flow
Develop first
version
Test it in early
adopter program
PRODUCT STRATEGY EARLY ENGINEERING
The trade-off
Design & test
static designs
Develop
dynamic
experience
Draft static
experience
Develop & test
dynamic
experience
Why?
Engineering early is a risk.
But, currently, it’s the only way we can test AI thoroughly.
You test in ‘production’, but only open to a few
Our early adopter program
invite
(still running 😉)
Prototyping AI experiences
challenge #2
Testing anticipatory design
The main challenge is that we’re testing anticipatory cues:
▸ Suggestions for new learning resources
▸ Suggesting tags for learnings
▸ Showing a notification “this might be interesting” when visiting a relevant URL
▸ etc.
How to test anticipatory experiences
Step 1
Based on:
● Earlier user feedback
● Usage data
● Strategic product decisions
Define an assumption and draft a UX flow that may validate this.
How to test anticipatory experiences
Step 2
Build it, so that it ‘kinda works’
Example:
● Start with a very easy to implement algorithm, rather than a fully fledged graph-based recommender
system
Most of all, reuse as much components as possible.
This is crucial to allow fast iteration in development
How to test anticipatory experiences
Step 3
Be mindful of the ‘type of experience feedback’
● Case #1 - the proposed solution isn’t adequate
● The suggestions aren’t that personal to me → Iterate on the algorithm
● Case #2 - there’s no need fulfillment
● “I don’t get why I need suggestions, I know what I want” → Iterate on the flow
Example
“I want to fill this in before I
complete an article.”
“The skill I want isn’t in there.”
“I want to edit the skills before
and after I have completed it.”
How we prototype for AI is different
(even as a designtool-nerd)
It became clear that static design tools are lacking for AI products
Conclusion
Spend more time upstream, stay flexible downstream
Validating problem / AI-solution fit
challenge #3
How to fit a solution to a problem
Show the product
Ask if they want it
New strategy
Learn the business
Identify the challenges
Isolate a possible use-case
New strategy
Mini “knowledge jam”:
▸ How Might We
▸ Can-We
▸ User Journey mapping
▸ Crazy 8s
▸ Concept Sketching
▸ etc.
Takeaways for AI
products
Try iterating faster during engineering, spending less time in
high-fidelity design tools
1
Try iterating faster during engineering, spending less time in
high-fidelity design tools
Just like wireframes in UX, you have similar ‘early prototyping
methods’ during development
1
2
Try iterating faster during engineering, spending less time in
high-fidelity design tools
Just like wireframes in UX, you have similar ‘early prototyping
methods’ during development
Experiment with design thinking techniques to identify actual
needs as a solution validation method
1
2
3
Try iterating faster during engineering, spending less time in
high-fidelity design tools
Just like wireframes in UX, you have similar ‘early prototyping
methods’ during development
Experiment with design thinking techniques to identify actual
needs as a solution validation method
1
2
3
talk to one of our experts
Thank you
Charles Boutens
charles@uman.ai
Thomas Verschuere
thomas@uman.ai
Jasper Verplanken
jasper@uman.ai
The Product
AI layer
Knowledge
sharing platform
Talent insights
module
In The Flow
of Work
Intelligent knowledge sharing, in the flow of work
Knowledge Efficiency
Knowledge Retention
Competency Mapping
knowledge,
content &
resources
In the flow of work
● Your knowledge communicates w/ you
● You push knowledge to uman.ai
🌍Learn and share while working
Personalized learning journey
● Only content relevant to you
● Content curated by people you know
As approved by
Hyper intelligent
● Attaches skills to knowledge resource
● Measures expertise level of knowledge
resource
🧠Knows relation between +10.000 skills
As approved by
Real time talent insights
📊Real time competency data
● Uncover employee and team skill
profiles
● Match innovative projects with
people’s growth paths
As approved by

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UX Beers - A Story about product management at uman.ai - Jasper Verplanken

  • 1. info@uman.ai - www.uman.ai A Story About Product Management at uman.ai
  • 2. Hi, I’m Jasper ‣ Civil Engineering - Computer Science @Ghent University ‣ Started in Information Management Consulting ‣ Earlier: digital strategist and UX architect @Wijs ‣ Co-founder and Head of Product @uman.aijasper@uman.ai www.uman.ai
  • 3. Today 1. About uman.ai 2. Setting the scene 3. AI product challenges & how to tackle them 4. Takeaways
  • 5. Knowledge as your company’s collective intelligence The ideal scenario Knowledge Sharing Utopia
  • 6. The actual scenario Knowledge Sharing Reality ❌ Time: “I don’t have time to read this right now”
  • 7. The actual scenario Knowledge Sharing Reality Interest: “Why would I read this, it doesn’t seem interesting to me”❌ ❌ Time: “I don’t have time to read this right now”
  • 8. The actual scenario Knowledge Sharing Reality Interest: “Why would I read this, it doesn’t seem interesting to me”❌ ❌ Time: “I don’t have time to read this right now” Relevance: “This is too heavy for me now, it might be relevant later though.” ❌
  • 9. The actual scenario Knowledge Sharing Reality Interest: “Why would I read this, it doesn’t seem interesting to me”❌ ❌ Time: “I don’t have time to read this right now” Relevance: “This is too heavy for me now, it might be relevant later though.” ❌ Transparency: “I don’t know who and what can help me with this subject.” ❓ ❓ ❓
  • 10. Where we want to bring you 1. Where & when you need it In the flow of work 2. To your skills and level Personalised & dynamic 3. Better project matching Real-time talent insights Connected knowledge automated for your teams
  • 11. AI layer Knowledge sharing platform Talent insights module In The Flow of Work Intelligent knowledge sharing, in the flow of work Knowledge Efficiency Knowledge Retention Competency Mapping knowledge, content & resources
  • 13. UX at an early-stage startup It’s not pretty. Unlearn “methodologies” Unlearn “processes” Unlearn “best practices”
  • 14. Setting new standards There’s very cost-sensitive trade-off between efficiency and flexibility Design Systems Hi-Fi Prototyping Fine-grained product backlog
  • 15. Adjusting standards There’s very cost-sensitive trade-off between efficiency and flexibility Design Systems Hi-Fi Prototyping Fine-grained product backlog
  • 18. High-level process Identify needs First concept Initial usertest Iterate on high fidelity prototype etc.
  • 19. High-level process Identify needs First concept Initial usertest Iterate on high fidelity prototype etc.
  • 20. The experience is highly dynamic How can you prototype a highly dynamic experience? Prototyping dynamic experiences with static designs is a huge investment.
  • 21. How we work today Identify needs Qualify need Draft UX flow Develop first version Test it in early adopter program PRODUCT STRATEGY EARLY ENGINEERING
  • 22. The trade-off Design & test static designs Develop dynamic experience Draft static experience Develop & test dynamic experience
  • 23. Why? Engineering early is a risk. But, currently, it’s the only way we can test AI thoroughly.
  • 24. You test in ‘production’, but only open to a few Our early adopter program invite (still running 😉)
  • 26. Testing anticipatory design The main challenge is that we’re testing anticipatory cues: ▸ Suggestions for new learning resources ▸ Suggesting tags for learnings ▸ Showing a notification “this might be interesting” when visiting a relevant URL ▸ etc.
  • 27. How to test anticipatory experiences Step 1 Based on: ● Earlier user feedback ● Usage data ● Strategic product decisions Define an assumption and draft a UX flow that may validate this.
  • 28. How to test anticipatory experiences Step 2 Build it, so that it ‘kinda works’ Example: ● Start with a very easy to implement algorithm, rather than a fully fledged graph-based recommender system Most of all, reuse as much components as possible. This is crucial to allow fast iteration in development
  • 29. How to test anticipatory experiences Step 3 Be mindful of the ‘type of experience feedback’ ● Case #1 - the proposed solution isn’t adequate ● The suggestions aren’t that personal to me → Iterate on the algorithm ● Case #2 - there’s no need fulfillment ● “I don’t get why I need suggestions, I know what I want” → Iterate on the flow
  • 30. Example “I want to fill this in before I complete an article.” “The skill I want isn’t in there.” “I want to edit the skills before and after I have completed it.”
  • 31. How we prototype for AI is different (even as a designtool-nerd) It became clear that static design tools are lacking for AI products Conclusion Spend more time upstream, stay flexible downstream
  • 32. Validating problem / AI-solution fit challenge #3
  • 33. How to fit a solution to a problem Show the product Ask if they want it
  • 34. New strategy Learn the business Identify the challenges Isolate a possible use-case
  • 35. New strategy Mini “knowledge jam”: ▸ How Might We ▸ Can-We ▸ User Journey mapping ▸ Crazy 8s ▸ Concept Sketching ▸ etc.
  • 37. Try iterating faster during engineering, spending less time in high-fidelity design tools 1
  • 38. Try iterating faster during engineering, spending less time in high-fidelity design tools Just like wireframes in UX, you have similar ‘early prototyping methods’ during development 1 2
  • 39. Try iterating faster during engineering, spending less time in high-fidelity design tools Just like wireframes in UX, you have similar ‘early prototyping methods’ during development Experiment with design thinking techniques to identify actual needs as a solution validation method 1 2 3
  • 40. Try iterating faster during engineering, spending less time in high-fidelity design tools Just like wireframes in UX, you have similar ‘early prototyping methods’ during development Experiment with design thinking techniques to identify actual needs as a solution validation method 1 2 3
  • 41. talk to one of our experts Thank you Charles Boutens charles@uman.ai Thomas Verschuere thomas@uman.ai Jasper Verplanken jasper@uman.ai
  • 43. AI layer Knowledge sharing platform Talent insights module In The Flow of Work Intelligent knowledge sharing, in the flow of work Knowledge Efficiency Knowledge Retention Competency Mapping knowledge, content & resources
  • 44. In the flow of work ● Your knowledge communicates w/ you ● You push knowledge to uman.ai 🌍Learn and share while working
  • 45. Personalized learning journey ● Only content relevant to you ● Content curated by people you know As approved by
  • 46. Hyper intelligent ● Attaches skills to knowledge resource ● Measures expertise level of knowledge resource 🧠Knows relation between +10.000 skills As approved by
  • 47. Real time talent insights 📊Real time competency data ● Uncover employee and team skill profiles ● Match innovative projects with people’s growth paths As approved by