Artificial Intelligence in Business Management

This presentation provides a detailed exploration of the transformative role of Artificial Intelligence (AI) in business management, tracing its history, evolution, and diverse applications. It begins with an introduction to AI, emphasizing its ability to automate tasks, analyze large datasets, and recognize patterns in language, images, and videos. The presentation categorizes AI into various types, from Narrow AI, which specializes in specific tasks, to the conceptual Superintelligence, which surpasses human intelligence.

The historical context highlights AI's journey from its origins in the 1950s to its modern applications, covering early successes, setbacks like the AI Winter, and the resurgence driven by machine learning and deep learning. The presentation outlines AI's impact on business management, focusing on data-driven decision-making, operational efficiency, cost reduction, and strategic planning. It details how AI enhances human resources, marketing, finance, and supply chain operations, making it an indispensable tool for modern businesses.

Real-world case studies of Netflix and Amazon India demonstrate AI's practical applications. Netflix uses AI to personalize content recommendations and predict user preferences, boosting engagement and satisfaction. Amazon India leverages AI to create personalized shopping experiences, improving customer satisfaction through regionalized search algorithms and AI-generated product videos.

The presentation also addresses the challenges AI faces, such as data privacy, bias, security, and the high costs of development. It highlights the complexities involved in making AI more accessible and understandable, ensuring that its benefits are widely available. Emerging technologies like generative AI, which creates new content, and quantum computing, which promises to solve complex problems faster, are explored as the future of AI in business.

In conclusion, this presentation offers a comprehensive view of AI's impact on business management, demonstrating how it drives innovation, enhances performance, and creates a competitive edge in the global market. Through its detailed analysis of AI's capabilities, applications, and challenges, the presentation underscores AI's significance as a strategic tool for the future of business.

1. Artificial
Intelligence in
Business
Management
Presentation by,
K B Dharun Krishna,
Summer Intern (under the guidance of Dr G Muruganantham)
Department of Management Studies, NIT Tiruchirappalli.

2. Agenda
1 2 3 4 5
Introduction
to AI
History &
Evolution of AI
Applications
of AI
Challenges of
AI
Emerging
technologies
in the field of
AI
2

3. Introduction
to AI

4. Artificial Intelligence (AI) is the field of computer
science focused on creating intelligent machines
that can think and act like humans.
It involves creating algorithms and systems that
can perform tasks that typically require human
intelligence, such as recognising speech, making
decisions, and identifying patterns
4
Introduction to AI
(Geeks for Geeks, 2024)

5. Abilities of AI
•Automate repetitive tasks.
•Analyze massive datasets for hidden
patterns.
•Understand and respond to natural
language (text, audio, video).
•Recognize objects and scenes in images
and videos.
•Learn and improve on its own (thinking).
5
(Digital Aptech, 2021)

6. Approaches to AI
•Thinking like a human: Replicate human
thought processes (memory, reasoning).
•Acting like a human: Pass the Turing Test
(be indistinguishable from a human).
•Thinking rationally: Apply logic and rules
to make optimal decisions.
•Acting rationally: Act autonomously to
achieve goals in complex situations.
6
(Eric Davis, 2016)

7. Approaches to AI (contd.)
7
(Eric Davis, 2016)

8. Categories of AI
AI is
categorized
based on two
factors:
1.Capability
2.Functionality
1. Capability
Narrow AI: masters specific
tasks (like chess or image
recognition).
General AI (future): human-
level intelligence, tackles any
problem.
Superintelligence (future):
surpasses human intelligence
in all aspects.
2. Functionality
Reactive: reacts to stimuli (think
reflex-based game AI).
Limited Memory: learns from past
experiences (like self-driving cars).
Theory of Mind (future):
Understand human thoughts (not
there yet).
Self-Aware (future): has
consciousness and self-
understanding (pure sci-fi).
8
(Sunny Betz, 2024)

9. Domains of AI
•Machine Learning: Learn from data.
•Deep Learning: using multi-layered neural networks to
analyze complex data.
•Natural Language Processing: Understand human
language (text, speech, etc).
•Computer Vision: enabling machines to interpret and
understand visual information.
•Robotics: Combine AI with machines for physical tasks.
9
(Ekin Keserer, 2024)

10. 10
Domains of AI (contd.)
(Hwa-Yen Chiu et al., 2022)(Lucas Mohimont et al., 2022)

11. 11
(Visar Berisha et al., 2021)
Working of an AI model

12. History
and
Evolution
of AI

13. The Dawn of Artificial
Intelligence (1950s-1960s)
•1950: Alan Turing publishes the paper “Computing
Machinery and Intelligence”, introducing the Turing
Test.
•1956: John McCarthy coins the term “Artificial
Intelligence” at the Dartmouth Conference.
•1950s-1960s: Early AI research focuses on symbolic
reasoning and logic-based environments.
•Challenges: Limited resources and computing
capacity slow progress.
13
(Geeks for Geeks, 2024)

14. AI’s Early Achievements
and Setbacks (1970s-1980s)
•1970s: Development of expert systems to capture
expert knowledge in various domains.
•Limitations: Inability to handle ambiguity and
complex situations limits applications.
•AI Winter (1970-1980): Period of inactivity due to
lack of funding and unmet expectations.
14
(Geeks for Geeks, 2024)

15. Machine Learning and
Data-Driven Approaches
(1990s)
•1990s: Shift towards machine learning approaches.
•Rise of Machine Learning: Algorithms learn from
data using neural networks, decision trees, etc.
•Neural Networks: Gain popularity for tasks like
speech recognition and recommendation systems.
• Data-driven growth: Increased processing power
and data availability fuel AI advancements.
15
(Geeks for Geeks, 2024)

16. The AI Boom: Deep
Learning and Neural
Networks (2000s-2020s)
•2000s-2020s: Rise of deep learning, mimicking the
brain’s structure and function.
•Deep Neural Networks: Excel in image recognition,
NLP, and gaming.
•Tech Investments: Companies like Facebook,
Google, and OpenAI drive AI research.
•Innovations: Advances in speech recognition, NLP,
and computer vision.
16
(Geeks for Geeks, 2024)

17. 17
(Dr. Paul Marsden, 2017)

18. Generative Pre-trained
Transformers: A New Era in AI
•Models: ChatGPT, Gemini (formerly Bard), and
Claude push AI boundaries in content creation and
language translation.
•GPTs: Models trained on large text datasets
transforming language processing.
•GPT-4: Produces human-like writing, translates
languages, acts as a writing assistant and much
more.
18
(Geeks for Geeks, 2024)

19. Comparison of Parameters and Contextual
Window Size for Major AI Models
Model
Number of
parameters
Contextual Window
Size (Tokens)
GPT-2 1.5 billion 128,000
GPT-3 175 billions 2048
GPT-4 Not specified 8192
GPT-4 Turbo Not specified 128,000
GPT-4o 1.76 trillion *128,000
Bard 1.56 TrillionNot specified
Gemini Ultra 1.6 trillion *1 million
Claude 2 130 billion 100,000
Claude 3 Not specified 200,000
19
* Estimated value
(Google, 2024)
•Model Capability: More parameters
enhance the model's ability to
learn and generate complex, coherent
responses.

•Contextual Understanding: Larger
contextual windows allow the model
to maintain context over longer texts.

20. 20
(Geeks for Geeks, 2024)

21. Applications
of AI

22. General Applications of AI
•Virtual Assistants: Siri, Alexa, and Google Assistant
help with tasks and information retrieval.
•Navigation: Google Maps uses AI for real-time traffic
updates and route optimization.
•Recommendation Systems: Netflix and Spotify
suggest content based on user preferences.
•Smart Home Devices: Nest Thermostat and Ring
Doorbell automate home settings and security.
22
(Kawsher11, Slideshare, 2019)

23. General Applications of AI
(contd.)
•Social Media: Facebook and Instagram use AI for
content curation and targeted advertising.
•E-commerce: Amazon’s AI recommends products and
optimizes delivery logistics.
•Banking: Fraud detection systems and chatbots
improve customer service and security.
•Virtual Meetings: Noise Cancellation (Krisp), Virtual
Backgrounds, etc.
23
(Kawsher11, Slideshare, 2019)

24. 24
(Sumit Singh, 2023)

25. Applications of AI in
Business Management
•Data-Driven Decision-Making: AI analyzes large volumes of
data to provide actionable insights, helping managers stay
ahead of market trends and customer demands.
•Operational Efficiency: Automation of repetitive tasks like
data entry and scheduling improves efficiency and reduces
human error.
•Cost Reduction: Predictive analytics and optimization
identify cost-saving opportunities across inventory, supply
chains, and staffing.
25

26. Applications of AI in Business
Management (contd.)
•Strategic Planning: AI models predict market trends and
customer behaviour, aiding in product development and
market expansion decisions.
•Human Resources: Streamlining the recruitment process,
analyzing employee data for performance management,
and predicting workforce needs.
•Marketing and Sales: Personalizing customer interactions,
optimizing pricing strategies, and forecasting sales trends.
26

27. •Finance: Detecting fraudulent transactions, managing
financial risks, and automating financial reporting.
•Supply Chain and Operations: Optimizing logistics,
predicting maintenance needs, and managing inventory
levels.
27
Applications of AI in Business
Management (contd.)

28. Case Study (Netflix): Enhancing
Viewer Experience with AI
•Content Personalization: Netflix’s AI algorithms
analyze viewing habits to tailor content
recommendations, significantly increasing user
engagement.
•Predictive Analytics: By predicting user preferences,
Netflix ensures higher satisfaction and retention
rates.
28
(SA, Medium, 2023)
(Quartz, 2018)

29. Case Study (Netflix): AI-Driven
Content Strategy
•Original Content Creation: AI helps Netflix identify
trends and preferences, guiding the production of hit
original series and movies.
•Marketing Optimization: AI analyzes viewer data to
optimize marketing strategies, maximizing reach and
impact.
29
(Ryan Owen, 2022)
(Quartz, 2018)

30. Case Study (Amazon India):
Personalized Shopping
Experience
•Tailoring Experiences: Amazon leverages ML to
personalize the user journey. ML models analyze user
session details, predicting customer proficiency levels.
•Intuitive Interface: Newcomers receive onboarding
tutorials and language options, while seasoned
shoppers get personalized recommendations and
advanced features.
30
(Yasaswini Sampathkumar, 2024)

31. Case Study (Amazon India):
Personalized Shopping
Experience
•AI-Generated Product Videos: Amazon creates visually
compelling product videos for sellers using AI. These
videos engage customers and boost product discovery.
•Regionalized Search: Amazon incorporates regional
terms into search algorithms, bridging cultural gaps. For
example, a “saree” search surfaces relevant varieties
based on location
31
(Yasaswini Sampathkumar, 2024)

32. Challenges
of AI

33. Challenges of AI
•Data Privacy: Protecting user data while training AI.
•Bias: Avoiding unfair AI decisions due to biased data.
•Security: Guarding AI against cyber threats.
•Cost: Managing high expenses of AI development and
implementation.
•Complexity: Simplifying AI for better understanding
and usage.
33
(Cash Flow Inventory, 2023)

34. Emerging
Technologies
in the field of
AI

35. Emerging Technologies in
the field of AI
•Generative AI: Algorithms that create
new content (text, videos, images, etc.)
based on extensive training data. (GANs,
etc)
•Cybersecurity: AI enhances security by
detecting anomalies and safeguarding
sensitive data from unauthorized access.
35
(Will Douglas Heaven, 2024)

36. Emerging Technologies in
the field of AI
•Sustainable Technology: Optimizing
resource usage, improving energy
efficiency (1-bit LLMs, etc).
•Quantum Computing: Early-stage
technology with potential for solving
complex problems faster than classical
computers.
36
(Will Douglas Heaven, 2024)

37. Recommendations
Books
•"Artificial Intelligence: A Modern
Approach" by Stuart Russell and
Peter Norvig
•“Artificial Intelligence For Dummies”
by Luca Massaron, John Mueller.
Courses
37
•“AI for everyone” by DeepLearning.AI (6
hours) [Andrew Ng]
•“CS231n: Deep Learning for Computer
Vision” by Stanford.
•“CS224N: Natural Language Processing
with Deep Learning” by Stanford.

38. Recommendations
TED Talks
•How AI can bring on a second Industrial
Revolution?
•What happens when our computers get
smarter than we are?
•Can we build AI without losing control over
it?
•Don’t fear intelligent machines; work with
them
Videos
38
•“Artificial Intelligence is the New Electricity”
by Andrew Ng
•“The inside story of ChatGPT's astonishing
potential” by Greg Brockman
•“AI and the future of humanity” by Yuval
Noah Harari

39. Artificial intelligence is
not a substitute for
human intelligence; it is a
tool to amplify human
creativity and ingenuity.
Fei-Fei Li
AI Researcher & Professor, Stanford University
39

40. Thank you
K B Dharun Krishna
Mail: [email protected]
Website: https://kbdharun.dev
Any questions?