Only if used meaningfully, can AI achieve substantial business impact. Answer 10 critical questions to know if AI in your enterprise is real or hype.

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