Artificial Intelligence & Business Application.pptx

Dept. of MBA, Sanjivani COE, Kopargaon
402 :Current Trends in Management
Unit No 2
Artificial Intelligence & business
Applications
Presented By:
Dr. S.P. Ghodake
(Asst. Prof. Department of MBA)
1
Sanjivani College of Engineering,
Kopargaon
www.sanjivanimba.org.in

Content
• Artificial Intelligence- Introduction, use of Artificial Intelligence in
Management
• Machine Learning. Knowledge Management: Concept
• KM Strategies – Architecture and Tools – KM Practices.
• Components and Type of Knowledge , Knowledge Building Models
• KM Cycle & KM architecture, KM tools, KM approaches
• VUCA: Introduction to Volatility, Uncertainty, Complexity, Ambiguity (VUCA)
–Significance – Challenges in Business - digitalization, globalization, and social
inclusion,
• Quantum Computing, Big Data, 5G Ecosystem and its Impact,
• Virtual reality (VR) and augmented reality (AR).

Artificial Intelligence
AI, or artificial intelligence, refers to the simulation of human intelligence in machines
that are programmed to perform tasks that typically require human intelligence, such as
visual perception, speech recognition, decision-making, and natural language
processing.
AI is a broad field that encompasses a range of subfields, including machine learning,
natural language processing, computer vision, robotics, and expert systems. AI has
the potential to transform numerous industries and improve many aspects of human
life, such as healthcare, transportation, finance, and education. However, there are also
concerns about the ethical and social implications of AI, including job displacement,
privacy concerns, and the potential misuse of AI systems.

Source : https://csaven.com/2022/08/12/what-is-artificial-intelligence/

Introduction
• Artificial Intelligence (AI) refers to the development of computer systems that can perform
tasks that typically require human intelligence, such as learning, problem-solving, decision-
making, and language understanding. It is a rapidly growing field that has the potential to
revolutionize numerous industries and transform the way we live and work. AI systems can
process large amounts of data, identify patterns and insights, and make predictions and
recommendations based on that data. They can also be trained to recognize and respond to
natural language, images, and speech, enabling them to interact with humans in more natural
and intuitive ways. However, AI also raises ethical and social concerns, such as job
displacement, privacy, bias, and the potential misuse of AI systems. As AI continues to
advance, it is important to consider both its potential benefits and its potential risks, and to
develop responsible and ethical approaches to its development and deployment.

Use of Artificial Intelligence in Management
• AI can be used in management in a variety of ways to improve efficiency,
effectiveness, and decision-making. One example is in the field of supply
chain management, where AI can be used to optimize logistics and reduce
costs. By analyzing data on factors such as shipping times, inventory
levels, and demand forecasts, AI systems can help managers make better
decisions about when and where to source materials and products, how to
allocate inventory, and how to route shipments.

Case Study of Persado

Machine Learning
• Machine learning is a subfield of artificial intelligence that involves
training computer systems to learn from data, without being explicitly
programmed to do so.
• In other words, machine learning systems use algorithms to analyze large
amounts of data, identify patterns and relationships, and make predictions
or decisions based on that data

https://www.javatpoint.com/applications-of-machine-learning

Knowledge Management
• Knowledge Management is the process of capturing,
organizing, sharing, and utilizing knowledge and information
within an organization, with the goal of improving
performance and achieving strategic objectives.
• Knowledge management involves identifying and capturing
both explicit knowledge (such as documents, reports, and
databases) and tacit knowledge (such as expertise, experience,
and best practices) within an organization. This knowledge is
then organized and stored in a way that is accessible and easily
searchable by employees across the organization.

• The goal of knowledge management is to improve decision-
making, innovation, and collaboration within the organization,
by ensuring that employees have access to the knowledge and
information they need to perform their jobs effectively.
• This can lead to improved productivity, reduced costs, and a
more competitive position in the marketplace.
• CASE STUDY FOR KNOWLEDGE MANAGEMENT

KM Strategies / Practices
• Knowledge mapping: Identify the knowledge sources, types, and locations
within the organization, and create a map that visualizes the knowledge
flows.
• Knowledge capture: Capture tacit and explicit knowledge, and convert it
into usable formats. This can be done through interviews, documentation,
or observation.
• Knowledge sharing: Encourage knowledge sharing within the
organization through collaboration tools, communities of practice, or
knowledge repositories.
• Knowledge transfer: Facilitate the transfer of knowledge from one
employee to another, or from one department to another, to ensure
continuity of knowledge within the organization.

• Knowledge retention: Preserve critical knowledge within the organization,
even when employees leave or retire, by creating knowledge retention
programs or repositories.
• Knowledge creation: Foster a culture of innovation and learning within
the organization by encouraging employees to create new knowledge and
share it with others.
• Knowledge measurement: Measure the effectiveness of KM strategies
and their impact on the organization's performance, and continuously refine
and improve the strategies.
• Effective KM strategies can help organizations leverage their knowledge
and intellectual capital, and gain a competitive advantage in their
industries.

Knowledge management (KM) architecture
• Knowledge management (KM) architecture refers to
the design of a system that supports the creation,
capture, organization, retrieval, and dissemination of
knowledge and information within an organization. It
provides a framework for managing the flow of
knowledge across the organization and facilitates the
implementation of KM strategies.

Elements of a KM architecture
• Knowledge repository: A centralized database or platform that stores all
knowledge and information within the organization. It can be in the form of a
document management system, a knowledge base, or a content management
system.
• Taxonomy: A classification system that organizes the knowledge within the
repository into categories and subcategories. This makes it easier to search and
retrieve information.
• Metadata: Information that describes the knowledge and information within
the repository, such as author, date created, and keywords. This helps in
categorization and search ability.
• Search engine: A tool that enables users to search the repository for relevant
knowledge and information based on keywords, categories, or other
parameters.

• Collaboration tools: Tools that facilitate knowledge sharing and collaboration
among employees, such as wikis, discussion forums, and social media platforms.
• Knowledge transfer mechanisms: Processes or tools that support the transfer of
knowledge from one person to another, such as mentoring, coaching, or job
shadowing.
• Analytics: Tools that measure the effectiveness of KM strategies and provide
insights into knowledge usage, sharing, and creation within the organization.
• Governance: Policies, procedures, and guidelines that govern the creation, use, and
management of knowledge and information within the organization.
An effective KM architecture aligns with the organization's goals, culture, and business
processes, and enables employees to access and use knowledge and information to
improve performance and innovation.

Components of Knowledge
• Knowledge refers to the information and understanding that an individual
or organization possesses and can use to solve problems, make decisions,
and create new ideas.
• There are three main components of knowledge:
1. Data: Raw facts, figures, and statistics that have no meaning or context on
their own. Data needs to be processed and organized to create information.
2. Information: Processed data that has meaning and context. Information can be
used to answer questions, solve problems, and make decisions.
3. Context: The circumstances, background, or environment in which the data or
information is used. Context provides the framework for understanding and
interpreting information and knowledge.

Knowledge can also be classified into different types:
• Explicit knowledge: Knowledge that is codified and can be easily shared and transferred. It
can be in the form of documents, procedures, or guidelines.
• Tacit knowledge: Knowledge that is personal and difficult to articulate or transfer. It is often
based on experience, intuition, and insights.
• Procedural knowledge: Knowledge that is related to how to do things, such as procedures,
methods, and techniques.
• Declarative knowledge: Knowledge that is related to facts, concepts, and principles.
• Experiential knowledge: Knowledge that is gained through personal experience and practice.
• Social knowledge: Knowledge that is shared and created through social interactions, such as
collaboration and communities of practice.
Understanding the components and types of knowledge can help organizations effectively manage
and leverage their knowledge assets to improve performance and innovation.

Case Study
Can AI Enhance customer Experience?
• Ms. Sushree Das

Knowledge Building Models
Knowledge building models refer to theoretical
frameworks or approaches that guide organizations in
building and managing knowledge. These models aim to
facilitate the creation, sharing, and utilization of
knowledge within the organization.
examples of knowledge building models in the
context of knowledge management:

Nonaka's SECI Model:
• This model, developed by Japanese organizational theorist Ikujiro Nonaka,
suggests that knowledge creation is a cyclical process that involves
socialization, externalization, combination, and internalization (SECI).
– Socialization involves the sharing of tacit knowledge through social
interaction.
– Externalization involves converting tacit knowledge into explicit
knowledge.
– Combination involves combining explicit knowledge into new forms.
– Internalization involves converting explicit knowledge into tacit
knowledge.

Companies uses the SECI Model to
manage knowledge and foster innovation:
• Nonaka's SECI Model is a widely recognized model of knowledge creation and management and has been
applied by many companies and organizations across various industries.
• Toyota: Toyota is known for its innovative production system, which is built on the foundation of
knowledge management. Toyota has used the SECI Model to create a culture of continuous improvement
and innovation.
• IBM: IBM has used the SECI Model to create a knowledge management system that allows employees to
share knowledge and collaborate on projects. The system has helped IBM to create new products and
services and improve its competitiveness in the marketplace.
• Siemens: Siemens has used the SECI Model to manage knowledge across its global operations. The
company has implemented a knowledge management system that allows employees to share knowledge
and collaborate on projects, which has led to improved efficiency and innovation.
• Procter & Gamble: Procter & Gamble has used the SECI Model to create a knowledge management
system that allows employees to share knowledge and collaborate on product development. The system has
helped the company to create new products and improve its competitiveness in the marketplace.
• Google: Google has used the SECI Model to create a culture of innovation and knowledge sharing. The
company has implemented a knowledge management system that allows employees to share knowledge
and collaborate on projects, which has helped the company to create new products and services.

Knowledge Management Maturity Model:
• The Knowledge Management Maturity Model (KM3M) is a framework
that is used to assess an organization's maturity in terms of its knowledge
management capabilities. The model provides a roadmap for organizations
to improve their knowledge management practices by identifying areas for
improvement and setting goals for progress.
• The model includes five stages
1. Initial
2. Repeatable
3. Defined
4. Managed
5. Optimizing.

The Knowledge Management Maturity Model (KM3M)
• Initial Stage: In this stage, the organization has no formal knowledge management practices in place.
Knowledge is managed on an ad-hoc basis, and there is no systematic approach to capturing, sharing, or
using knowledge.
• Repeatable Stage: In this stage, the organization recognizes the value of knowledge management and
begins to implement some basic practices, such as document management and knowledge sharing.
However, these practices are not yet fully integrated into the organization's culture or business processes.
• Defined Stage: In this stage, the organization has a well-defined knowledge management strategy and a
clear understanding of how knowledge management contributes to the organization's goals. Knowledge
management practices are integrated into the organization's culture and business processes, and there is a
dedicated team responsible for managing knowledge.
• Managed Stage: In this stage, the organization has a mature knowledge management system in place that
is continuously monitored and improved. Knowledge management practices are fully integrated into the
organization's culture and business processes, and there is a strong focus on knowledge sharing and
collaboration across departments and teams.
• Optimizing Stage: In this stage, the organization is constantly looking for ways to improve its knowledge
management practices and leverage knowledge for innovation and competitive advantage. The organization
is able to adapt quickly to changing circumstances and is able to use knowledge management to drive
continuous improvement and innovation.

Companies used the KM3M model:
• Accenture: Accenture is a global professional services firm that provides consulting, technology, and
outsourcing services. The company has used the KM3M model to assess its knowledge management
capabilities and identify areas for improvement. The model has helped Accenture to develop a roadmap for
improving its knowledge management practices and leveraging knowledge for competitive advantage.
• Hewlett-Packard: Hewlett-Packard (HP) is a multinational technology company that provides hardware,
software, and services. HP has used the KM3M model to assess and improve its knowledge management
practices. The company has developed a comprehensive knowledge management system that enables
employees to share knowledge and collaborate on projects.
• Emirates Airlines: Emirates Airlines is a global airline based in Dubai, United Arab Emirates. The
company has used the KM3M model to assess and improve its knowledge management practices. Emirates
has developed a comprehensive knowledge management system that enables employees to share
knowledge and collaborate on projects, which has improved customer service and operational efficiency.
• Microsoft: Microsoft is a multinational technology company that provides software, hardware, and
services. The company has used the KM3M model to assess and improve its knowledge management
practices. Microsoft has developed a comprehensive knowledge management system that enables
employees to share knowledge and collaborate on projects, which has improved innovation and
productivity across the organization.

KM Cycle
Knowledge
Creation:
Knowledge
Capture:
Knowledge
Sharing:
Knowledge
Application:
Knowledge
Evaluation:
• The Knowledge Management Cycle is a continuous process that involves
creating, capturing, sharing, and using knowledge to achieve organizational goals.
• The cycle consists of the following stages:

KM Cycle
• Knowledge Creation:
• This stage involves identifying new knowledge and creating new knowledge through research, analysis, and
experimentation.
• Knowledge Capture:
• This stage involves capturing and storing knowledge in a format that can be easily accessed and shared.
This may involve documenting knowledge in databases, wikis, or other knowledge management systems.
• Knowledge Sharing:
• This stage involves sharing knowledge with others in the organization through various channels, such as
meetings, training sessions, or online collaboration tools.
• Knowledge Application:
• This stage involves applying knowledge to solve problems, innovate, and improve organizational
performance. This may involve using knowledge to make informed decisions, develop new products or
services, or improve processes.
• Knowledge Evaluation:
• This stage involves evaluating the effectiveness of knowledge management practices and making
adjustments as necessary to improve performance.

KM approaches
• Codification approach:
• Learning organization approach:
• Personalization approach:
• Combination approach:
• Collaborative approach:

• Codification approach:
• This approach involves documenting knowledge in the form of
policies, procedures, manuals, and other written materials that can be
easily shared and accessed by employees. This approach is
particularly useful in organizations where knowledge is largely
explicit and can be easily articulated and captured.
• Personalization approach:
• This approach focuses on sharing knowledge through social
interactions and personal relationships, such as through mentoring,
coaching, and knowledge sharing networks. This approach is
particularly useful in organizations where knowledge is largely tacit
and difficult to articulate and capture.

• Combination approach:
• This approach combines elements of both codification and
personalization approaches. It involves documenting knowledge in
the form of written materials as well as encouraging social
interactions and personal relationships to facilitate knowledge sharing
and collaboration.
• Collaborative approach:
• This approach emphasizes collaboration and teamwork to create new
knowledge and innovation. It involves creating teams or communities
of practice to work together to solve problems, share ideas and
knowledge, and create new products and services.

• Learning organization approach:
• This approach emphasizes creating a culture of continuous learning
and improvement. It involves encouraging employees to learn from
their experiences, share their knowledge and ideas, and continuously
improve their skills and knowledge.

VUCA
• Introduction
• Significance –
• Challenges in Business - digitalization,
globalization, and social inclusion,

VUCA
• Volatility: Rapid changes in the business environment can make it difficult for
businesses to anticipate and respond to market shifts. For example, sudden changes
in demand or supply chain disruptions can have a significant impact on a company's
bottom line.
• Uncertainty: Uncertainty can arise from a variety of factors, including political
instability, regulatory changes, and shifts in consumer behavior. Businesses that
operate in uncertain environments must be flexible and adaptable to survive.
• Complexity: The modern business environment is highly complex, with multiple
stakeholders and interdependent systems. Companies must be able to manage this
complexity effectively, or risk becoming overwhelmed by it.
• Ambiguity: Ambiguity refers to situations where there is no clear answer or
outcome. In a VUCA world, ambiguity is a common occurrence, and companies
must be able to navigate it with skill and agility.

Introduction to Volatility, Uncertainty, Complexity, Ambiguity (VUCA)
• The word "VUCA," which means "volatile, uncertain, complex, and ambiguous,"
was first used in the military to characterise the conditions that soldiers encounter
on the battlefield.
• This idea has recently been used in the business world to characterise the uncertain
and quickly changing environment in which organisations must function.
• Businesses now face unprecedented levels of complexity and uncertainty as a result
of variables like quick technical advancement, altering geopolitical environments,
and changing customer expectations in today's globalised and linked world.
• In order to succeed in this VUCA environment, leaders and organisations are
realising the necessity of acquiring new skills and techniques.
• This entails placing more of an emphasis on creativity, agility, and teamwork as
well as taking a proactive approach to risk management and scenario planning.

Significance
• Understanding VUCA aids organisations in anticipating and becoming ready for
potential changes and disruptions in the business environment. Organisations can
be better prepared to react and deal with unforeseen events and difficulties by
understanding the complex and unpredictable nature of the business world.
• Promotes agility and adaptability in organisations:
• Organisations in a VUCA environment must be able to react rapidly and
successfully to changing circumstances. Agility, flexibility, a desire to try new
things, and innovation are all necessary for this.
• Encourages scenario planning and risk management:
• In a VUCA environment, these two processes are more crucial than ever.
Organisations must be able to recognise potential risks, create mitigation methods,
and plan for a variety of possible outcomes in order to be ready for unforeseen
circumstances.

• Stresses the value of cooperation and teamwork:
• In a VUCA environment, collaboration and cooperation are essential because they
allow organisations to draw on a variety of viewpoints and areas of expertise to
tackle complicated problems and come up with creative solutions.
• Supports a culture of ongoing learning and improvement:
• To be competitive in a VUCA environment, organisations must be dedicated to
ongoing learning and improvement. This necessitates a readiness to experiment
with new ideas, embrace change, and continuously learn from past mistakes.

Quantum Computing
• Quantum computing is a branch of computing that manipulates data using quantum
mechanical phenomena like superposition and entanglement.
• Quantum computing, which uses quantum bits or qubits, has exponentially greater
computational capability than classical computing, which uses bits that can only be
in two states (0 or 1) at simultaneously.
• Quantum computing has the potential to impact many areas of knowledge
management, such as data analysis, information retrieval, and optimization.
• The potential of quantum computing to cary out specific types of computations far
more quickly than conventional computers is one of its main advantages.
• For instance, vast databases can be searched rapidly by quantum computers to
detect correlations or patterns that would take too long for classical computers to
find. This may have important effects on knowledge management, enabling
businesses to draw conclusions and make wiser decisions from massive amounts of
data.

Big Data
• The term "big data" describes extraordinarily massive and complicated data
sets that are too complex to be processed efficiently using conventional
data processing tools and methods.
• In addition to its volume, "big" refers to the variety, speed, and reliability of
the data.
• Big data is expanding as a result of the widespread adoption of technology
in contemporary life, which has led to the production of enormous volumes
of data from sources including social media, sensors, transactions, and
other digital records. Big data presents substantial obstacles for data
administration and analysis, but it also has the ability to provide insightful
information and chances for innovation.

5G Ecosystem
• The development, deployment, and use of 5G wireless networks are
being supported by a complicated network of players and
technologies known as the 5G ecosystem.
• The fifth generation of wireless technology, or 5G, promises to
outperform earlier generations in terms of speed, latency, and
capacity.
• The 5G ecosystem includes a wide range of stakeholders, including
network operators, equipment vendors, device manufacturers,
software developers, and regulatory bodies. Each of these
stakeholders has a role to play in the development and deployment
of 5G technology.

• Network operators are responsible for building and managing 5G networks,
while equipment vendors provide the infrastructure and hardware necessary
to support these networks. Device manufacturers produce 5G-enabled
smartphones, tablets, and other devices that can connect to 5G networks.
Software developers create applications and services that run on 5G
networks, and regulatory bodies set the standards and regulations that
govern the use of 5G technology.
• The 5G ecosystem is also closely tied to other emerging technologies, such
as the Internet of Things (IoT), artificial intelligence (AI), and cloud
computing. These technologies are expected to drive innovation and create
new opportunities for businesses and consumers alike.

Big Data, 5G Ecosystem and its Impact
• By enabling quicker, more effective data processing and analysis, the 5G ecosystem and Big
Data have the potential to completely change how businesses function. The 5G ecosystem and
Big Data can have the following effects on businesses:
1. Faster and more reliable connectivity: 5G networks offer faster and more reliable connectivity than previous
generations of wireless technology. This means that businesses can access and process larger amounts of data in real-
time, enabling more informed decision-making and faster response times.
2. Increased efficiency and productivity: By leveraging the power of Big Data and 5G networks, businesses can optimize
their operations and processes. This can lead to increased efficiency and productivity, reduced costs, and improved
customer experiences.
3. Improved customer insights: Big Data and 5G networks can help businesses collect and analyze large amounts of
customer data in real-time. This can lead to deeper insights into customer behaviors, preferences, and needs, allowing
businesses to tailor their products and services to better meet customer demands.
4. Enhanced security: As businesses collect and store more data, they face an increased risk of cyber-attacks and data
breaches. However, the combination of 5G and Big Data can enable more robust and sophisticated security measures,
such as real-time threat detection and response.
5. New revenue streams: By leveraging the power of Big Data and 5G networks, businesses can develop new products
and services that take advantage of real-time data processing and analysis. This can lead to new revenue streams and
business models.

Virtual reality (VR) and Augmented reality (AR)
• Virtual reality (VR) is a computer-generated simulation of a three-dimensional environment
that can be interacted with in a realistic way using a headset or other specialized equipment.
The user is completely immersed in the virtual world, with the ability to move and interact
with objects in the environment. VR can be used for a wide range of applications, from
gaming and entertainment to education and training.
• Augmented reality (AR) is a technology that superimposes digital content, such as images,
sounds, or text, onto the user's real-world environment. AR can be experienced through a
smartphone or tablet, or through specialized AR glasses or headsets. Unlike VR, AR does not
completely replace the real world but enhances it with digital content. AR can be used for
applications such as gaming, advertising, and retail.
• Both VR and AR have the potential to transform the way we interact with digital content and
the world around us. They can be used to create immersive and engaging experiences that
were previously impossible, such as virtual travel, training simulations, and product
visualization. They also have the potential to revolutionize industries such as healthcare,
architecture, and education by providing new and innovative ways to train, learn, and
visualize complex information.

Virtual reality (VR) examples
• Gaming: VR is perhaps best known for its applications in gaming, where it can provide a fully immersive
and interactive experience. VR gaming applications include first-person shooters, racing games, and sports
simulations.
• Education: VR can be used to create immersive learning experiences, such as virtual field trips or
historical reenactments. This can be particularly useful for teaching difficult or abstract concepts, or for
providing hands-on training in a safe and controlled environment.
• Healthcare: VR is increasingly being used in healthcare to provide training and education for medical
professionals, as well as for patient rehabilitation and pain management. VR can also be used for treating
phobias and other mental health conditions.
• Architecture and real estate: VR can be used to create immersive virtual tours of properties, allowing
potential buyers to experience the space in a realistic way before making a purchase. It can also be used in
architectural design and planning, allowing architects and engineers to visualize and test designs in a virtual
environment.
• Entertainment: VR is being used to create immersive entertainment experiences, such as virtual concerts,
art exhibits, and theme park rides. These experiences can provide a new level of engagement and
interactivity for audiences.
• Military and defense: VR is being used by the military and defense industries to provide training
simulations for soldiers and to test equipment and weapons systems in a virtual environment.

Augmented reality (AR) examples
• Gaming: AR gaming applications have become increasingly popular in recent years, with
games like Pokemon Go and Ingress using AR to create immersive and interactive
experiences that blend the real world with digital content.
• Retail and advertising: AR is being used by retailers and advertisers to create interactive
product displays, allowing customers to try on virtual clothes or see how furniture would look
in their homes before making a purchase.
• Education: AR is being used to create interactive learning experiences, such as virtual field
trips or science simulations. AR can also be used to provide real-time translation of text or
speech, making it useful for language learning.
• Healthcare: AR is being used in healthcare to provide doctors and surgeons with real-time
patient data and imaging, as well as to train medical professionals on complex procedures.
• Navigation and mapping: AR is being used in navigation and mapping applications to
provide users with real-time information and directions, such as highlighting points of interest
or overlaying directional arrows on the user's view.
• Industrial and manufacturing: AR is being used in industrial and manufacturing settings to
provide workers with real-time data and instructions, allowing them to complete tasks more
efficiently and safely.

Thank You….

Artificial Intelligence & Business Application.pptx

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