The document discusses the integration of AI in enterprises, examining the gap between expectations and reality. It poses ten critical questions regarding the effectiveness and governance of AI initiatives, focusing on measurable outcomes, data democratization, and organizational capability. Additionally, it highlights the importance of an enterprise AI platform and showcases case studies on AI-driven personalization and autonomous enterprises.

1. AI in the Enterprise:
Hype vs. Reality
Proprietary and confidential
September 12, 2019

2. AI in the Enterprise:
Hype vs. Reality
Proprietary and confidential
September 12, 2019

3. How
#2) Do you have the right
instrumentation integrated to monitor
the impact from AI projects?
What
#1) Are the AI projects focused on
delivering measurable business
outcomes?
Hype or Reality?
AI for the Sake of AI

4. How
#4) Are there long-term career paths
for AI/ML Data Scientists & Engineers?
What
#3) Is there a core AI capability under a
CDO/CTO? Or is it a ‘bolt-on’ as part of the
CIO org?
Hype or Reality?
Nurturing AI Talent

5. How
#6) Is the legacy BI/EDW environment
the main data platform for AI projects?
What
#5) Is there a Data Governance team
with an CXO commitment to truly
enable Data Democratization?
Hype or Reality?
Data as a Core Asset

6. How
#8) Does the business accept the idea
of Probabilistic Recommendations
(accompanied by Confidence Intervals)?
What
#7) Does the organization have an
appetite for Experimentation across
the Enterprise – not just cosmetic
website changes?
Hype or Reality?
The Legacy of Determinism

7. How
#10) Have AI projects been
integrated to Transaction systems
(e.g. ERP, RPA) in the last 12 months?
What
#9) Do you have an enterprise AI
platform infrastructure?
Hype or Reality?
Crossing the AI Scale Chasm

8. #1) Are the AI projects focused on delivering measurable business outcomes?
#2) Do you have the right instrumentation integrated to monitor the impact from AI
projects?
AI for the Sake of AI
#3) Is there a core AI capability under a CDO/CTO? Or is it a bolt-on as part of the CIO
org?
#4) Are there long-term career paths for AI/ML Data Scientists & Engineers?
Nurturing AI Talent
#5) Is there a Data Governance team with an CXO commitment to truly enable Data
Democratization?
#6) Is the legacy BI/EDW environment the main data platform for AI projects?
Data as a Core Asset
#7) Does the organization have an appetite for Experimentation across the Enterprise:
not just cosmetic website changes?
#8) Does the business accept the idea of Probabilistic Recommendations ?
The Legacy of
Determinism
#9) Does you have an enterprise AI platform infrastructure?
#10)Have AI projects been integrated to Transaction systems (e.g. ERP, RPA) in the last
12 months?
Crossing the AI
Scale Chasm
Hype or Reality: The 10 Questions

9. The Manifesto

10. #1: Be Ruthless About Outcomes
● Quantify outcomes
from AI projects that
can be measured in
business KPI terms
● Integrate the
instrumentation
process and
methodology into the
AI project
Business Outcome KPI
Influence KPI: AI
Project success metric

11. ● Build a centralized AI/ML
team to work on cross-
functional problems
● Create an eco-system that
enables collaboration
among Citizen Data
Scientists
#2: Invest in Building Organizational Capability
Hat tip: ‘Team of Teams’ by Gen Stanley McChrystal
The Seals: Core AI
Competency
Decentralized AI
Teams

12. #3: Elevate Data to be a First-Class Citizen
● Invest in Data
Governance
leadership from
business outside of IT
● Build out a ‘Data as a
Service’ architecture
that extends beyond
data provisioning
Data Governance and Policy Office
Enterprise Data Architecture AI and Data Science
Foundation Data
Services

13. ● Monitor and evaluate
the ability of AI
systems to get better
over time
● Continuously
challenge AI
recommendations
with field experiments
#4: Integrate Probabilistic Systems into Operating
Processes
Naive Bayes isn’t all
that naïve!

14. #5: Invest in ‘AI Platform as a Service’
● Invest in an AI@Scale
Platform that enforces
standardized AI model
Lifecycle Management
● Move away from
monolithic systems to
‘Lego Blocks’ of modules
that can be assembled to
solve specific problems

15. The Manifesto
#1: Be Ruthless About Outcomes
#2: Invest in Building Organizational Capability
#3: Elevate Data to be a First-Class Citizen
#4: Integrate Probabilistic Systems into Operating
Processes
#5: Invest in ‘AI Platform as a Service’

16. Case Studies

17.  Algorithms that leveraged the rich (often dark) datasets to
create Personalized recommendations
 Enabled successful Customer Journeys across the
Lifecycle (NBA et al)
#1: AI Driven Personalization
Across the Customer
Lifecycle
Case Studies

18.  Algorithms that leveraged the rich (often dark) datasets to
create Personalized recommendations
 Enabled successful Customer Journeys across the Lifecycle
(NBA et al)
#1: AI Driven Personalization
Across the Customer
Lifecycle
Case Studies
 Reduced ‘Time to Expertise’ by integration of Knowledge
assets into the Customer Support processes
 ‘Cognitive Automation’ integrated into the Workflow
#2: Autonomous Enterprise
Using AI Enabled
Knowledge Assets

19.  Algorithms that leveraged the rich (often dark) datasets to
create Personalized recommendations
 Enabled successful Customer Journeys across the
Lifecycle (NBA et al)
#1: AI Driven Personalization
Across the Customer
Lifecycle
Case Studies
#2: Autonomous Enterprise
Using AI Enabled
Knowledge Assets
#3: AI-Driven Experimentation
 Reduced ‘Time to Expertise’ by integration of Knowledge
assets into the Customer Support processes
 ‘Cognitive Automation’ integrated into the Workflow
 High-velocity of experiments with dynamic targeting
 Go beyond the traditional A/B Testing to explore MAB
strategies

20. Thank You
krishna.r@incedoinc.com
www.incedoinc.com