1. Industrializing AI

2. AI is not just for fun anymore ...
… it’s time to demonstrate real results that drive tangible business outcomes

3. Why invest in AI?

4. The AI-data flywheel effect ...
There are a few ways to bootstrap the flywheel:

5. … can create winner-take-all scenarios
But you need to be willing to invest to do AI properly!
Yesterday, technology was about enablement.
Today, AI is about transformation.
So, you need to invest heavily to:
Get the flywheel going
Rethink the corporate strategy
Transform the business to align
Implement a data strategy
Integrate the AI into your business
Manage people change
Implement AI-specific governance model and controls

6. State of the market

7. Front-runners are pursuing AI aggressively
88% of leaders increased investments in AI vs 62% of laggards. 1
90% of leaders have an AI strategy and are eager to scale 2
72% of leaders prioritize revenue-generating applications over cost-savings ones 3
Deepening their commitments to AI.
Eager to scale AI throughout their enterprise.
Prioritize revenue-generating applications over cost-savings ones.
-- AI in Business Gets Real, MIT Sloan and BCG, 2018

8. Key innovations have made industrialized AI a reality
Way more compute
Way more data
New architectures
Better simulators
Easier to program
Faster training and inference
More data to learn from
Solve more complex problems
Unlimited training; speed up time
Relatively speaking
Deepening their commitments to AI.
Eager to scale AI throughout their enterprise.
Prioritize revenue-generating applications over cost-savings ones.

9. But there are key factors affecting the pace and extent of adoption
Technical feasibility
Cost
Labour market dynamics
Economic benefits
Regulatory and social acceptance
Technical feasibility: AI can solve specific tasks, not entire business processes. It’s important to break down problems into component parts to understand feasibility.
Cost: technical, transformation, people costs all need to be factored in.
Technical - data acquisition, cleansing and integration costs can be significant
Transformation - processes need to be changed to fully realize benefits of AI
People - retraining
Labour market dynamics: with labour arbitrage, sometimes it’s still cheaper to throw people at the problem in certain regions of the world
Economic benefits:
Top-line growth opportunities make the best business cases
Soft factors are critical too - improved accuracy, quality, risk management
Regulatory and social acceptance
Trust and explainability
Controls
Cyber security

10. Industrializing AI

11. AI transformation playbook
Build your AI strategy 1
Execute the right pilot projects 2
Build your in-house AI capability 3
Build your AI strategy incrementally!:
Design a strategy aligned with the Data-AI flywheel
Identify core processes ripe for intelligent automation
Analyze, prioritize and bundle opportunities into themes
Develop a roadmap of AI assets that create a competitive advantage
Build your data asset portfolio
Execute the right pilot projects:
Identify pilot projects early to gain momentum and experience
Key criteria for initial pilot projects:
Short in duration (6-12 months)
Technically feasible using generally available AI tools
Deliver enough return demonstrate value, but limited in scope to contain risk
Build your in-house AI capability
To complement your AI solution delivery partners, as well as to provide a long-term, sustainable solution
Develop a training plan to upskill your AI workforce (not only engineers, but practioners)
Develop a recruiting strategy to acquire the right talent
Create cross-functional teams of AI practitioners
Update your operating model to account for new AI roles

12. A good AI strategy delivers compounding returns
Content Creation
Instant global distribution
Complexity
Content curation
2018: Netflix tied HBO for most wins by a network at 23 each.
Netflix got the most nominations with 112
Incremental asset that are added
Task / Occupation
Business Process
Business Model
Scope of change

13. Applied AI solutions

14. “
We are ...
World class leaders in Machine Learning and Optimization 1
Cultivate the next generation of AI- driven companies with global reach and impact 2
Training ground for your future AI workforce “
Business-savvy technologists with deep supply chain expertise 3
We solve your complex business challenges using advanced mathematical techniques.

15. We apply a proven, agile approach to solve complex business challenges using advanced mathematical techniques
AI Advisory
Blueprint
Proof of Concept
(PoC)
MVP
Scale-up
Value vs. feasability

16. “AI decodes the nuances of chicken speech”
So, even though AI may not be just for fun anymore …
“AI decodes the nuances of chicken speech”
Value vs. feasability

17. THANK YOU