Evolution of AI/ML, Growth of Applications
and Adoption in Enterprises
Arpan Kumar Kar
Information Systems | Chair Professor | DMS
Indian Institute of Technology Delhi
Emergence of Digital Transformation
Smart People
Smart Governance Smart Economy
Digital
Transformation
Smart Living Smart Mobility
Smart Environment
Huge scope for Domain /
Data generation
analytics & AI functional scope
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The Classic Use Cases that started AI adoption
Big Data Exploration 360o View of the Individual Security/Intelligence
Find, visualize, understand all big Extend existing customer views by Extension
data to improve decision making incorporating additional internal and
external information sources Lower risk, detect fraud and
monitor real time cyber security
Operations Improvement Intelligence Augmentation
Analyze a variety of machine data for Integrate big data and data warehouse
improved business results capabilities to increase operational efficiency
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How the “Industrial Ecosystem” evolved
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Analytics Evolution Landscape
Cognitive
Early 2010
Prescriptive & Predictive
Mid and Late 2000
Value
Diagnostic
Early 2000
Descriptive
1990s
Complexity
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The transformation of value
Grover, P., & Kar, A. K. (2017). Big data analytics: a review on theoretical contributions and tools used in literature. Global Journal of
Flexible Systems Management, 18(3), 203-229.
So how to make meaning out of this data?
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Operations
Management
Financial
Business Rules
Management
AI / ML Statistical
Algorithms Analysis
Operational
Research
Strategic Marketing
Management Management
Human
Resources
Management
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Analytics to Data Science to AI focus
Industrial Engineering Computer Science Statistics
Operational Research Machine learning Regression, SEM
Nature inspired
Large scale optimization Factor analysis
computing
Linear / Non linear Social media
Econometrics
Programming analytics
Allocation problems Graph theory Anova, Manova
Etc Etc etc
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Building blocks of AI
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Understanding Intelligence
What is intelligence?
• Reasoning
• Learning
• Adaptivity
A truly intelligent system adapts itself to deal with changes in problems (automatic
learning)
Machine intelligence needs training for solving processes centric problems, something
like that of humans problem solving behavior
Intelligent systems display machine-level intelligence, reasoning, often learning, not
necessarily self-adapting
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Characteristics of intelligent algorithms
Possess one or more of these:
• Capability to extract and store knowledge
• Human like reasoning process
• Learning from experience (or training)
• Dealing with imprecise expressions of facts
• Finding solutions through processes similar to natural evolution
Recent trends
• More sophisticated Interaction with the user through
• natural language processing
• speech recognition and synthesis
• image analysis and computer vision
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Understanding AI/ML landscape in a nutshell
AI Super Set
MVL
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Evolution of AI/ML algorithms
Kar, A. K. (2016). Bio inspired computing–A review of algorithms and scope of applications. Expert Systems with Applications, 59, 20-32.
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Evolution of Intelligent Systems
Kar, A. K. (2016). Bio inspired computing–A review of algorithms and scope of applications. Expert Systems with Applications, 59, 20-32.
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Q1: Zone of theory Q2: Zone of Application Research
development
Bacterial foraging, Bat, Bee colony
Amoeba, Artificial plant optimization Cuckoo search, Firefly algorithm
Bean optimization Flower pollination
Dove, Eagle , Fruit fly
Glow-worm, Grey wolf
Krill-herd , Lion , Monkey, Wolf
Q4: Zone of Commercialization
Q3: Zone of rediscovery Neural networks
Ant colony optimization
Leaping Frog, Shark Genetic algorithm
Wasp Particle swarm
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But everything went for a radical change
Cloud computing – Algos as
Growing computing power
packages and utilities
Hijacked by Select
Algos
Big data analytics Governance of AI
Data, Compute, Access, Efficiency
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Era of Digital Transformation
Popularity
AI as
of AI
services
specialists
Analytics
Black box
as
view of AI
Packages Emergence
of SMAC
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Meet the Hijackers
Decision trees Kmeans and
Neural Networks
& random KNN (distance
(DNN, CNN, RNN)
forests based algos)
Support Logistic
Naïve Bayes
vector regression
classifiers
machines (funny story!)
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Application objectives which became popular!
Pattern
Clustering Classification Regression
Associations
Sequence Anomaly
Summarization Network mining
mining detection
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Example of Domains
Suitable vs
Real vs Fake Social
Email vs Spam Unsuitable
Profiles
Suppliers
Suitable vs Real vs Fake
Financial markets
Unsuitable detection
prediction
Creditors (purchase, news)
Image / Video Product / page Portfolio
mining Recommendation optimization
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Inherent Complexities impact Adoption
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Functional Adoption in Operations Management
Grover, P., Kar, A. K., & Dwivedi, Y. K. (2020). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and
social media discussions. Annals of Operations Research, 1-37.
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Most of AI run in the second
and third layer in retailing
and CPG industries
Weber, F. D., & Schütte, R. (2019). State-of-the-art
and adoption of artificial intelligence in retailing.
Digital Policy, Regulation and Governance.
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AI Applications in Retail
Weber, F. D., & Schütte, R. (2019). State-of-the-art
and adoption of artificial intelligence in retailing.
Digital Policy, Regulation and Governance.
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Drivers for Adoption in Enterprises
(from multi-stakeholder perspective)
VARIOUS PREMISES
Efficiency
Business Owners
Innovation
DOMINANT STAKEHOLDERS
Automation
Research
Novelty
Manual intervention
Adoption of AI
Adopt to change
Emotion
Application Owners
Support
Experience
Personal growth
Experiential learning Kushwaha, A. K., & Kar, A. K. (2020, December).
Micro-foundations of Artificial Intelligence
Fear of failure Adoption in Business: Making the Shift. IFIP LNCS
Proceedings. Springer, Cham.
Fear of upgradation
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Governance issues to be deliberated…
Trade offs: PII
Ethical use of Privacy
Use vs Service
Data preservation
Quality
Transparency Explainability Outcome
of outcome of outcome reliability
Accountability
Fairness and Human in the
of Adverse
Trust Loop
Outcome
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Thank you
http://www.business-fundas.com & https://tech-talk.org
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