Bridging the gap of integrating AI in larger organizations - you can replace AI by any sufficiently complex technology

1. Bridging the gap
of integrating AI
in larger organizations
Dr. Stefan Kühn - XING Marketing Solutions

2. The Gap
„A variety of academic studies argue that a relationship
exists between the structure of an organization and the
design of the products that this organization produces.“
Exploring the duality between product and organizational architectures: A test of the “mirroring” hypothesis - Harvard Business School
Alan MacCormack, Carliss Baldwin, John Rusnak

3. The Gap
„A variety of academic studies argue that a relationship
exists between the structure of an organization and the
design of the products that this organization produces.“
Exploring the duality between product and organizational architectures: A test of the “mirroring” hypothesis - Harvard Business School
Alan MacCormack, Carliss Baldwin, John Rusnak
Product
Product
Product
ProductProduct
Product

4. Data and Information
Data and Information flows horizontally (*)
But management / resources / decisions are vertically aligned
Personal observation, no empirical evidence ;-)

5. Data and Information
Which one looks better?
Which one works better?
Product
Product
Product
ProductProduct
Product

6. Data and AI
Data from here improves product there

7. Data and AI
Data from here improves product there
But no official / efficient way to communicate and align requirements
But no official / efficient way to share costs / benefits between departments

8. Data and AI
Know-how from here improves product there
But no official / efficient way to educate others and share expertise
But no official / efficient way to share people / tools between departments

9. Challenge: Communication
Data flow and decision lines need to be aligned.
Hierarchical companies optimize for top-down
decision-making, not for collaboration or data quality.

10. Challenge: Organization
TODOs
• Sharing of costs and benefits
• Treat Data as a Product - „Data marketplace“
• Know-how transfer
• Know-how is bound to people - and AI Know-how is rare
• Move small expert groups / task forces through the
company - better than solving the same problem multiple times
• Central services don’t work - no product accountability
• Data Governance / Data Quality
• Data Quality limits Product Quality

11. But is this it?

12. Build - Measure - Learn

13. Build - Measure - Learn
There is a simple check for your own company…
Count the number of people in your company that
are paid for
• Building
• Measuring
• Learning
And don’t cheat ;-)

14. There is more but it is
dangerous
to say this in public
;-)

15. Build - Measure - Learn
There is a simple check for your own company…
Count the number of managers in your company
that are qualified / trained for
• Building
• Measuring
• Learning
Or have a professional background with Data

16. Summary
Large organizations have problems leveraging
AI / Machine Learning / Data Science because of
• Organizational structure versus Data flow
• vertical versus horizontal
• Rare Know-how cannot move
• trapped in Product or central service teams
• Management awareness and background
• „Data“ skills underrepresented on
Management Levels

17. Takeaway
In the end it’s about bringing the right people to the
problems they can solve - and I mean it, literally!
Keep your company moving

18. Thanks
And join me on XING
Stefan Kühn