Main takeaways: - How to identify the use cases to build an AI/ML product? - What are the challenges that you would face and how to over come them? - How to establish stake holder buy-in and design the go-to market strategy?

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8. Purnima Bihari
TONIGHT’S SPEAKER
@ProductEvents

9. HOW TO BUILD AN AI/ML PRODUCT AND SELL IT
PURNIMA BIHARI
CHIEF PRODUCT OFFICER, SALESCHOICE INC.

10. KEY TAKEAWAYS
• What to build?
• Problem Mapping
• How to build it?
• How to select an algorithm
• How to measure accuracy and efficiency
• How to sell it?
• Go to market strategy
• Sales strategy
• How to drive adoption and measure success?
• Power interest grid
• Make it stick
• Customer success strategy

11. Product Strategy
Objective
Innovation and
Acquisition
Research based
development
Platform development
Revenue Generation
User and Business Need
focus
PROBLEM MAPPING

12. Parts of an AI
Implementation
Processes
Optimized
Augmented
Automate
Reinvent
Integration With Existing Systems and Processes
Models
Selecting right models that achieve Desired Level
of Performance and outcomes
Data Owned, Collected, Third-Party
Governance
Monitor Performance, Build Trust, Drive
Adoption

13. STEPS FOR A SUCCESSFUL AI/ ML IMPLEMENTATION
1. Identify business use cases and business needs
2. Identify Implementation role hierarchy
3. Identify data sources and data sets
4. Determine the right architecture to stream data into your platform
5. Build the Model for your Machine
6. Build your product
7. Change Management and Training
8. Deploy and Measure

14. STEP 1: IDENTIFY USE CASES
1. Big Data Analysis
• If the user has to sift through a lot of data to complete a task for search behaviour analysis, pattern search, etc.
2. Complex cognition abilities
• Autonomous driving cars, Self-sorting gallery app
3. Predictions and Forecasting
• Sales and Inventory forecasting, Predictive churn
4. Anomaly Detection
• Fraud Detection
5. Recommendation Engines
6. Human Interactions
• Siri, Alexa, Google Assistant
7. Augmenting and Creating experiences
• Snapchat filters

15. STEP 2: IMPLEMENTATION TEAM
Role Competency
Project Sponsor Main Stakeholder of the project to drive leadership intiatives
Data Strategists Ability to translate business needs into data science projects
Data Analysts/Business
Analysts
Deriving insights from large datasets using applied statistics and predictive modelling
Data enablers/Data
science engineers
Capable of automating data extraction from DB/data lakes; expert in feature
engineering, preparing and manipulating data for deriving insights using advanced
analytics and ML
Machine Learning
Experts/Engineers/Devel
opers
a ML expert, able to extract data from DB/Data lake, clean it, and run ML techniques
to derive insights; transform insights into product and advise product development

16. STEP3: DATA SOURCE IDENTIFICATION

17. STEP4: ARCHITECTURE DESIGN

18. STEP5: MODEL CREATION, TESTING AND TUNING

19. TYPES OF ML ALGORITHMS

20. STEP 6: BUILD YOUR PRODUCT

21. STEP 7: CHANGE MANAGEMENT AND TRAINING

22. STEP 8: DEPLOY AND MEASURE
Stage 1: Ad Hoc
- Poor Data Quality
- Basic Search
- Minimal governance in
Place
Stage 2: Nascent
- Quality Monitoring
- Defined Ownership
- Governance in defined
Silos
Stage 3: Evolving
- Product Attributes
Normalized
- Reusable
- Centralized
management and control
Stage 4:
Harmonized
- Product Master
management
- Competencies support
processes
Stage 5:
Integrated
- Integrated Practices
Operationalized

23. PM BEST PRACTICES IN AI/ML
• Product Management Key Drivers
1. Primary Drivers – Core competencies and High EQ
2. Identify and articulate the business value of AI in your product
3. Cost Savings and Improved customer experience
• Product Development process Innovations
• Joint discovery and shared accountability between domain experts and data science
teams
• Apply business judgement on ML approach based on performance vs data volume
• Understand accepted accuracy of AI/ML algorithms- Higher Precision vs. higher Recall or
less false positive vs. less false negative — which one you would prefer depends on the
types of business problems you are solving.
• Flexible scrum process- AI/ML product development is time consuming. Meeting your
product milestones vs superior product.

24. 1. Data Bias- Data quality vs Data volume
2. Trade-off between precision and recall - Figuring out if
your use-case requires more emphasis on precision or
recall changes the tuning of the models the engineers
would choose.
3. Cold Start Problem- There are scenarios when the
algorithms don’t have any data on the user or an item and have
cold start issues leading to sub-optimal experiences.
a. User Based- This is when the user is using your
product for the first time, and the models have no
signal on the user. Eg: Using Netflix for the first time.
Ways to solve it – prompt the user, curate other
patterns, etc.
b. Item Based- Sparse metadata. Ways to solve it-
Human annotation, Algorithmically.
4. Feedback Loop to the algorithms- Letting users intervene.
5. Explore vs Exploit – Study behavior or influence behavior.
ALGORITHMS HAVE LIMITATIONS

25. 1. No Data- Companies want AI, but have no supporting data. Without data there is
no ML.
2. Small Data- Applying ML to a small data set has it’s overheads. Affected by
outliers and over complicated models. Statistical techniques might be better suited
here.
3. Sparse Data- Lot of data, actual usable data is very low. Computations with
sparse data sets are less efficient as most of the dataset is empty.
4. High Dimensional Data- Data with a lot of attributes. Cost of computation and
storage high. Convert it into small dimensional data by using feature selection.
5. Data cleaning- Garbage In, Garbage Out. removed all outliers, have you
normalized all fields, are there bad fields in your data and corrupted fields
6. Computational Costs
DATA HAS LIMITATIONS

26. 1. Clearly articulate the AI-driven competitive advantage the product will be
bringing to the enterprise-
2. Non-feature selling points- Cost, growth, performance, brand/status, risk
reduction, accessibility, convenience/ usability, customer delight.
3. Create a customer segmentation
GOTO MARKET STRATEGY FOR AN AI/ML PRODUCT
Context Challenge Capabilities Consequences
How AI enthusiastic is
this particular vertical
vs others.
Specific challenges to
solve. Viewpoints from
various related parties-
product users to
budget approvers.
Index to measure a
customer’s AI potential
Potential Implications.
Short term vs long term
trajectory.

27. Subjects
- Involve
Players
- Collaborate
Crowd
- Inform
Context Setters
- Consult
Interest
Power
STAKEHOLDER ENGAGEMENT MODEL
POWER-INTEREST GRID

28. 1. EDUCATE CUSTOMERS ON ALTERNATIVE
TECHNICAL APPROACHES
2. TOUT YOUR HARD SCIENCE
BACKGROUND IN YOUR BRAND STORY
3. DITCH BUZZ WORDS & USE COMMONLY
USED TERMS
4. PERSONIFY YOUR PLATFORM & MAKE AI
INTERACTIVE
5. STOP TELLING TALL TALES ABOUT AI
STAND OUT FROM THE NOISY HYPE MARKET

29. QUESTIONS

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