Main takeaways: - It is a tipping point for Enterprise AI products. We have reached phase one of Enterprise AI adoption and are now transitioning an S curve towards adoption phase two. - Understanding AI nativeness and setup complexity can be critical as we look towards the wider adoption of AI products. - There are four key hurdles to implementing and adopting enterprise AI products and the talk will address mitigation strategies around them.

1. www.productschool.com
The Tipping Point of Enterprise AI
Product by Salesforce Sr Dir PM

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6. The Tipping Point of
Enterprise AI Product
Mayukh Bhaowal
Sr. Director of Product Management, Salesforce
Einstein

7. Einstein Customer Momentum
22.5%
Website Revenue
150%
Conversion Rate
2x
Mobile adoption
11B
predictions per day

8. Transitioning S-Curves
The tipping point
Next Phase of Enterprise AI Product

9. Enterprise AI Adoption Phase 2
What have we learnt and where we are going next ...

10. AI Nativeness vs Setup Complexity
Enterprise AI Applications Sliced
High Setup Low Setup
AI Add-On
AI Native
Einstein Bots
Einstein Recommendation Builder
Einstein Search
Einstein Translation
Einstein Marketing Engagement
Scoring

11. Einstein Marketing Engagement Scoring
More
Likely
Less
Likely

12. There are Hurdles to Implementing & Adopting AI
DATA
SETUP
CHANGE
TRUST

13. Key Considerations for Successful Enterprise AI: Data
Do I have
equitable
training data?
Is data clean
and accurate?

14. The case for
Data
Do we have enough equitable
data?
Labeling
Data
Augmentation
Global Models

15. Example: Voice Data Transcriptions
Labeling

16. Boost the number of training images
Data Augmentation

17. We started with customer specific models

18. Mix data from
multiple tenants
‘One’ Global
Model
We are experimenting with Global Models

19. Is data clean and accurate?
The case for Data
Data Cleansing Feature SelectionDirty Data

20. Does my ML
setup support
rapid iteration?
Key Considerations for Successful Enterprise AI: Setup
Are there
templates for
common
configurations
?
HOW TO SETUP ENTERPRISE AI PRODUCT

21. Does my ML setup support rapid iteration?
The case for Setup
Simple Initial Setup1
Review Scorecard/Evaluate Model
2
Fine Tune
Recommendations
3

22. Are there templates for common configurations?
The case for Setup

23. Am I providing
model
transparency to
my users?
Key Considerations for Successful Enterprise AI: Trust
Am I proving
ROI with my
users?
Compliance
and
Governance?

24. Am I providing model transparency to my users?
The case for Trust

25. The case for
Trust
Are we proving ROI to our users?
Lift Over
Baseline
A/B Testing
Map KPI to ML
Metric

26. Lift over Baseline
Recommendation Builder: Predicted Lift Prediction Builder: Predicted vs Actuals

27. Model Performance
Model Accuracy ##
Segment A vs Segment B
Actions Taken
Map Business Metrics to ML Metric + A/B Testing
Search
ROI Dashboard
Associated Business KPIs
Yr/ Yr Churn Rate | % of Late Invoices

28. Compliance & Governance
The case for Trust

29. How do we
renegotiate the
deal between
people and
machine?
Key Considerations for Successful Enterprise AI: Change
Am I
drastically
changing an
existing
workflow?
Is the new
business of AI
same as old?

30. Completely
Manual
Fully
Automated
Human-Machine Interactions...
How do we renegotiate the deal between people and machine?
The case for Change
Circumvent fear of AI taking away
jobs
What should humans ‘continue’
doing (more intellectual tasks)
What should they ‘stop’ doing
(likely mundane and tedious)

31. Am I drastically changing an existing workflow?
The case for Change
More
Likely
Less
Likely

32. Outbound
PM
Scaling challenges because AI lives in
long tail
Need help to define the problem and
the KPIs and map them to ML metrics
Emerging Tech.
Evangelist
AI ServicesSoftware
Is the new business of AI same as old?
The case for Change

33. Value to
Customers
Stage of AI Development
Cool
Marketing
Demo
Fancy
Algorithms
Pilot
General
Adoption
Commodity
Chasm
of Data,
Setup,
Trust &
Change
The Tipping Point of Enterprise AI Product

34. Thank You!
Twitter: @mayukhb
Linkedin: linkedin.com/in/mayukhbhaowal

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