Main Takeaways: -AI is just a mean to an end, the end goal need to be extremely clear -Prioritize project where you know you have the data - data access can be the most challenging piece of an AI project -Build your solution for a use case but find ways to make it a shared service

1. www.productschool.com
AI as a Shared Service by Salesforce Senior
Director of Product

2. CERTIFICATES
Your Product Management Certificate Path
Product Leadership
Certificate™
Full Stack Product
Management Certificate™
Product Management
Certificate™

3. Corporate
Training
Level up your team’s Product
Management skills

4. Free Product Management Resources
BOOKS
EVENTS
JOB PORTAL
COMMUNITIES
bit.ly/product_resources
COURSES

5. Artificial Intelligence
As A Shared Service
Tejas Sanghavi
in/tsanghavi/
@teju_sanghavi
Gary Brandeleer
in/garybrandeleer/
@GaryBrandeleer

6. Tejas Sanghavi
Director PM
Salesforce Einstein
Gary Brandeleer
Sr Director PM
Salesforce Field Service

7. Relative to competitors, respondents say their company’s adoption of AI has allowed them to
Source: Deloitte State of AI in the Enterprise, 2nd Edition, 2018
Adoption of AI is Critical to Stay Competitive
16%
Catch Up
20%
Stay On Par
27%
Edge Slightly Ahead
28%
Widen A Lead
28%
Leapfrog Ahead

8. “Seven out of 10 companies surveyed report
minimal or no impact from AI so far. Among
the 90% of companies that have made at least
some investment in AI, fewer than 2 out of 5
report obtaining any business gains from AI
in the past three years. ”
Source: S. Ransbotham, S. Khodabandeh, R. Fehling, B. LaFountain, D. Kiron, “Winning With AI,” MIT Sloan
Management Review and Boston Consulting Group, October 2019.

9. Roadblocks in Success with AI
Talent
Infrastructure
Trust

10. Huge Talent Gap in Meeting Demand
“Overall, 39% of Data Scientists and Advanced Analysts require a master’s
or Ph.D … Therefore, because these roles are already undersupplied and
projected to grow rapidly, the skills shortage is in danger of worsening.”
Source: The Quant Crunch: How The Demand For Data Science Skills Is Disrupting The Job Market, ©Burning
Glass Technologies 2017

11. Supporting an ML Model in Production is Complex
Only a small fraction of real-world ML systems is a composed of ML code, as
shown by the small black box in the middle. The required surrounding
infrastructure is fast and complex.
Source: D. Sculley, et al. Hidden technical debt in machine learning systems. In Neural Information Processing
Systems (NIPS). 2015
Configuration Data Collection
Feature Extraction
Data
Verification
Machine
Resource
Management
Analysis Tools
ML
Code
Process
Management Tools
Serving
Infrastructure
Monitoring

12. AI Needs to be Trusted

13. Roadmap for Success with AI
Data
Trust
Team
MVP
Use Case

14. There is no Data Science without ….Data
Availability
Integration Quality
Data

15. Humans Don’t Trust AI…
...Until They Understand How it Works

16. “When I was a kid, there was no collaboration; it's you with a
camera bossing your friends around.
But as an adult, filmmaking is all
about appreciating the talents of
the people you surround
yourself with and knowing you
could never have made any of
these films by yourself.”
- Steven Spielberg

18. MVP Start with Your Team as Well
Software
Engineer
Data
Scientist
Research
Science
Reach out to other teams who need similar capabilities

19. Success Defines Shared Services
Software
Engineer
Data
Scientist
Research
Science
Shared Service

20. Customers don’t care about your solution.
They care about their problems.

21. Job To Be Done Framework
A Job to Be Done:
● Customer-centric
● Solution agnostic
● Stable over time
● Measurable outcomes
A Job to Be Done doesn't know what products or solutions have existed,
currently exist, or might exist. It's just framing some area of life where your
customer is at the center of a struggle, and they want some help solving
the problem.
https://jobs-to-be-done.com/

22. Bad Job Statement Good Job Statement Why
Organize conference calls using
different phone numbers to ensure
employees talk to each other
Help remote workers engage with
colleagues without in-person
interaction
Original statement includes Solution.
Help me plan a bonding event that my
whole team will enjoy.
Plan a team bonding event.
Original statement includes an
aspirational job and compound jobs.

23. Case Study: First Time Fix Rate
Field Service Job to Be Done:
Improve First Time Fix Rate for the Field Service companies working on
physical devices
Making sure that you have:
● The right person
● With the right skills
● At the right time
● With the right parts
● Assigned with the right tasks

24. Find the right People
Convince with new ideas delivering
true customer value
Start with the beginner mind, you
don’t know what you don’t know
Find trusted customers holding
qualitative data
Negotiate & align Priorities
Leverage your network
Make the value clear and easy to
understand
Leverage your network
Get it done on time
Use the available Technologies
Prioritize trust and security
Don’t reinvent the wheel, think MVP
Manage the cost in terms of time and
money
01 02 03

25. Discover
Focus
Prove
Expand
- Recommend anything on an object
- Recommend parts on a Work Order
- Deliver impact to customers, listen to their needs
- Recommend anything on an object (tasks, etc…)

26. 3 TAKEAWAYS
Technology is just a mean to an end, the end goal need to be extremely clear
Prioritize project where you know you have the data - data access can be the most challenging piece of an AI project
Build your solution for a use case but find ways to make it a shared service
‘Don’t let technology drive your vision, let customer drive it and define which technology can help them, it is all
about sharing ideas, brainstorming, discussing over and over with customers’

27. Thank You
Tejas Sanghavi
Product Lead
Salesforce Einstein
in/tsanghavi/
@teju_sanghavi
Gary Brandeleer
Sr Director PM
Salesforce Field Service
in/garybrandeleer/
@GaryBrandeleer

28. www.productschool.com
Part-time Product Management Training Courses
and
Corporate Training