COURSERA(1)

1.

Impact of Emerging Technologies
on IT Project Management
Olennikov Yaroslav

2.

Сontent
1) Introduction: Impact of AI, ML, IoT, Blockchain, and Big Data on project
management.
2) Key Benefits: Automation, inclusivity, better communication.
3) SmartPM Almaty: AI platform for resource optimization in city projects.
4) Sovereign AI Models: Independence, security, and economic benefits.
5) OpenAI vs. Yandex AI: Use cases in text, voice, and advertising.
6) AI Project Examples: Success (efficiency, accuracy) vs. failure (errors, delays).
7) Conclusions: Testing and optimization as keys to success

3.

Analyze the Impact of Emerging Technologies on Project Management
The integration of new and emerging
technologies such as Artificial
Intelligence (AI), Machine Learning
(ML), the Internet of Things (IoT),
Blockchain, , Big Data, and Cloud
Computing has revolutionized various
industries, including project
management. The primary objective of
this project is to systematically analyze
how these technologies influence and
transform traditional project
management practices.

4.

Automativasion
IT solutions allow you to
automate routine tasks such as
planning, monitoring and
control of task execution, which
significantly speeds up these
processes and reduces the
likelihood of errors.

5.

Inclusivity
New IT technologies enhance
accessibility for people with disabilities
and foster diversity by providing equal
opportunities for all. They offer quality
education and training through online
platforms, support flexible working
conditions, and enable social
integration. Technologies facilitate
feedback and participation in decisionmaking processes, creating a more
inclusive society.

6.

Improved Communication
Modern project management tools
include features for real-time
communication and collaboration,
allowing teams to stay connected, even if
they are in different parts of the world.

7.

My theme
SmartPM Almaty is an AI-based platform designed for project managers, entrepreneurs, and
government agencies in Almaty. It facilitates efficient city-scale project management by improving
coordination, deadline control, and resource management.
AI algorithms analyze ongoing city projects and suggest optimal allocation of human, material, and
financial resources. The system helps prevent duplication of efforts across projects from different
organizations.

8.

Research and Analysis
Countries are developing sovereign AI models driven by several crucial factors:
independence, security, economic benefits, and regulatory control. By developing
indigenous AI, nations aim to reduce reliance on foreign technologies, safeguarding
their national interests and maintaining control over data. Security is a paramount
concern, with sovereign AI ensuring data protection and mitigating the risks of
breaches and cyber threats. Economically, sovereign AI stimulates growth by fostering
innovation, creating jobs, and enhancing global competitiveness. Furthermore, these
models allow nations to set their own regulations, ensuring ethical and responsible AI
usage. This regulatory control is vital for addressing bias, transparency, and
accountability concerns in AI systems. Ultimately, the pursuit of sovereign AI models
reflects a desire for greater autonomy, enhanced national security, economic
prosperity, and ethical governance of technology. These efforts position countries as
active and influential players in the global technological landscape.

9.

Develop GPT
GPT-1
GPT-2
GPT-3
GPT-4
Duplicate answers from
internet. Maximum 8
words
Start change words and
long of sentences got
better
Own generate and detail
answer
Abstraction and
confidence
117 000 000 parameters
1 500 000 000
175 000 000 000
??????

10.

Potential SMART goals
Specific:
Develop an AI-powered customer support system to reduce response times.
Integrate AI into our sales forecasting process to improve accuracy.
Measurable:
Achieve a 30% reduction in average customer response time within six months.
Increase sales forecast accuracy by 20% in the first year of AI implementation.
Achievable:
Utilize existing AI technologies and tools to build the customer support system.
Leverage historical sales data to train the AI model for forecasting.
Relevant:
Enhancing customer support aligns with our goal of improving customer satisfaction.
Accurate sales forecasting supports our strategic objective of increasing revenue.
Time-bound:
Implement the AI customer support system within the next six months.
Complete the integration of AI into the sales forecasting process may take one year.

11.

Open AI
GPT models, developed by OpenAI, provide a wide array of functionalities beneficial
for commercial use. These include text generation, which can be used for creating
content, chatbots, and customer support systems. They also offer translation
services, allowing for seamless communication across different languages. Question
answering capabilities enable these models to serve as virtual assistants, providing
quick and accurate responses to queries. Additionally, GPT models assist in code
completion, making them valuable tools for developers by enhancing coding
efficiency and reducing errors.
Overall, these functionalities make GPT models versatile tools for various industries,
driving innovation and efficiency. For commercial use, the cost for GPT-4 is $0.02 per
1K input tokens and $0.08 per 1K output tokens.

12.

Yandex AI
Yandex AI is a suite of AI technologies developed by Yandex, including text
generation models like YandexGPT and YaLM. YaLM 100B, Yandex's largest
language model, is used in multiple projects such as Yandex Search, the voice
assistant Alice, and generating advertisements. Alice, Yandex's voice assistant, uses
AI to perform tasks and respond to user queries. Yandex Translate offers AI-powered
translation services supporting multiple languages. Yandex Browser integrates AI
features to enhance the user experience with intelligent web browsing and quick
answers. These technologies are applied across various services to improve search
results, generate content, and provide smart responses. For commercial use,
Yandex offers different tariff plans depending on the specific AI service and usage
needs.

13.

Work Breakdown Structure (WBS) for OpenAI and Yandex AI
OpenAIYandex AI
OpenAIYandex AI
Timeline
1. Define Project Objectives
1. Define Project Objectives
Jan 1 - Jan 15
2. Gather User Requirements
2. Analyze Market and Competitor Tech
Jan 16 - Jan 30
3. Develop OpenAI Models
3. Develop Yandex AI Models
Feb 1 - Feb 28
4. Perform Functional Testing
4. Conduct User Acceptance Testing
Mar 1 - Mar 15
5. Set Up Production Environment
5. Deploy AI Models
Mar 16 - Mar 31
6. Monitor and Optimize Performance
6. Monitor and Optimize Performance
Apr 1 - Apr 15
7. Provide User Support
7. Provide User Support
Apr 16 - Apr 30
8. Conduct Post-Implementation Review
8. Conduct Post-Implementation Review
Start at 1 may

14.

Estimated Budget to Serve 4.5 Million Users
In today's rapidly evolving technological landscape, leveraging advanced AI systems is crucial for businesses aiming
to deliver efficient services. Implementing a project that integrates OpenAI or Yandex AI technologies to serve 4.5
million users requires meticulous planning and significant financial investment. The estimated budget for such a
project can be outlined across several phases.
Planning and Initialization, including defining objectives, identifying stakeholders, developing a charter, and
assembling a team, is estimated at $300,000. Research and Development, covering requirement analysis, feasibility
studies, and research on AI models, would cost around $600,000. Development and Testing, which includes model
development, integration, and comprehensive testing, is budgeted at $1,000,000.
Deployment and Implementation, involving setting up the production environment and optimizing performance, is
estimated at $800,000. Training and Support, which covers creating training materials and user support, is budgeted
at $400,000. Finally, Project Closure, including documentation and reviews, would cost about $200,000.
In total, the estimated budget for this comprehensive AI project is approximately $3,300,000, ensuring effective
implementation and support for 4.5 million users

15.

Risks of Using OpenAI and Yandex AI Models
OpenAi
High Costs: Utilizing OpenAI
can be expensive, especially
for small and medium-sized
businesses.
Limited Availability: Some
features of OpenAI might be
unavailable in certain regions
or to specific users.
Yandex Ai
Dependence on Foreign
Technologies: Relying on
foreign AI technology can lead
to dependency and risk of
data leakage.
Privacy Issues: Yandex AI
may collect and analyze a
large amount of user data,
potentially leading to privacy
violations.
Kazakhstan Law
Dependence on Foreign
Technologies (OpenAI): This
could pose national security
risks as data might be
accessible to foreign states or
companies.
Privacy Issues (Yandex AI):
Privacy breaches could lead
to the leakage of personal
data of Kazakhstan's citizens,
also posing a security threat.

16.

Execution and Monitoring
Execution involves implementing the project plan, integrating AI models, ensuring they work
seamlessly with existing systems, and deploying these models to production. Key tasks include:
1. Integrating AI Models: Ensuring the models function smoothly within the existing infrastructure.
2. System Compatibility: Verifying the AI models align with the current systems.
3. Deployment: Rolling out the integrated models to a live environment.
Monitoring ensures the AI models operate as expected and meet performance standards. Key
aspects include:
1.
2.
3.
4.
Performance Metrics: Tracking accuracy, response time, and efficiency.
Error Handling: Identifying and resolving issues quickly.
User Feedback: Collecting feedback to improve the models.
Compliance: Ensuring adherence to local regulations, such as Kazakhstan's data protection
laws.

17.

Essentials of Project Closure in AI Integration
Closing an AI integration project steps:
1.
2.
3.
4.
5.
6.
Finalize Documentation:
○ Prepare and complete all project documents, including technical specifications, user guides, and reports.
Evaluate Project Outcomes:
○ Conduct an analysis of the project’s goals, comparing planned and actual outcomes, and identify key successes
and issues.
Obtain Stakeholder Approval:
○ Ensure formal approval from all key stakeholders, confirming that all requirements and expectations have been
met.
Post-Implementation Review:
○ Conduct a detailed review of the project, documenting lessons learned and recommendations for future projects.
Archive Project Information:
○ Systematize and securely store all project data and documents to ensure future access and regulatory compliance.
Celebrate Achievements:
○ Recognize and celebrate the team’s successes to boost morale and acknowledge the contributions of each
member.

18.

Report on Successful AI Technology Implementation for Task Management
Project: Integration of AI for Automated Customer Request Processing
Completion Date: —--------------
Team Members: AI Development Team, Customer Support Department
Project Goal: The goal of this project was to implement AI technology to automate the process of customer request handling, aiming to reduce response time and increase customer satisfaction.
Project Overview: The project involved developing and integrating an AI model for automatically classifying and processing customer requests. Both OpenAI and Yandex AI technologies were utilized for this purpose. The project was completed on time and within budget.
Key Results:
1.
2.
3.
4.
Reduced Response Time: Average response time to customer requests decreased by 40%, from 24 hours to 14 hours.
Increased Accuracy: The accuracy of request classification using AI reached 95%, significantly improving processing efficiency.
Improved Customer Satisfaction: Customer satisfaction levels increased by 30%, as evidenced by survey data.
Reduced Staff Workload: Automation of request processing reduced the workload of support staff by 25%.
Project Phases:
1.
2.
3.
4.
5.
Planning and Preparation:
○
Defining project objectives and tasks
○
Forming the project team
○
Developing the project plan
Research and Development:
○
Analyzing requirements
○
Developing the AI model
○
Testing and validating the model
Integration and Testing:
○
Integrating the AI model into existing systems
○
Conducting functional and load testing
Deployment and Launch:
○
Training staff
○
Launching the system into production
Monitoring and Optimization:
○
Continuous performance monitoring of the system
○
Making necessary adjustments and optimizations
Conclusions and Recommendations: The implementation of AI technology for automating customer request processing was successful and provided significant benefits to the company. It is recommended to continue utilizing and expanding AI technologies for other business processes, taking into
account the experience and data obtained.
Signatures: Project Manager: [Name Surname]
Date: —---------------

19.

Report on Unsuccessful AI Technology Implementation for Task Management
Project: Integration of AI for Automated Customer Request Processing
Completion Date: —--------Team Members: AI Development Team, Customer Support Department
Project Goal: The goal of this project was to implement AI technology to automate the process of customer request handling, aiming to reduce response time and increase customer satisfaction.
Project Overview: The project involved developing and integrating an AI model for automatically classifying and processing customer requests. Both OpenAI and Yandex AI technologies were utilized. Unfortunately, the project
faced several challenges and did not meet the expected outcomes.
Key Challenges and Issues:
1.
2.
3.
4.
Inaccurate Data Classification: The AI models struggled with accurately classifying customer requests, achieving only 65% accuracy, which led to numerous misclassifications.
High Response Time: Instead of reducing response time, the integration of AI increased the average response time to 30 hours, due to frequent system errors and slow processing speeds.
Low Customer Satisfaction: Customer satisfaction levels decreased by 20%, as indicated by negative feedback and increased complaints about the system's inefficiency.
Technical Issues: Frequent technical glitches and downtime disrupted the workflow, causing significant delays and frustration among both customers and staff.
Project Phases:
1.
2.
3.
4.
5.
Planning and Preparation:
○
Defined project objectives
○
Assembled project team
○
Developed project plan
Research and Development:
○
Analyzed requirements
○
Developed AI model
○
Faced challenges in training and validation of the model
Integration and Testing:
○
Integrated AI model into existing systems
○
Conducted testing, which revealed significant issues in functionality and performance
Deployment and Launch:
○
Launched system into production
○
Experienced numerous technical issues and user complaints
Monitoring and Optimization:
○
Constantly monitored the system
○
Made continuous adjustments, but issues persisted
Conclusions and Recommendations: The implementation of AI technology for automating customer request processing encountered several critical challenges and did not achieve the desired outcomes. It is recommended to
conduct a thorough analysis of the issues faced, refine the AI models, and consider additional training data and testing before any further deployment. The experience from this project highlights the importance of robust testing and
validation processes.
Signatures: Project Manager: [Name Surname]
Date: —---------

20.

RoleTasksDates
RoleTasksDates
RoleTasksDates
Project Manager
Plan project phases
Jan 1 - Jan 15, 2024
Monitor progress and ensure milestones are met and
Coordinate team efforts
Throughout the project
Conduct regular meetings
Weekly (every Monday)
Manage risks and develop mitigation strategies
Throughout the project
Implement features based on requirements
Jan 16 - Mar 15, 2024
Perform unit testing and debugging
Jan 16 - Mar 30, 2024
Document code for maintainability
Ongoing, finalized by Mar 31, 2024
Conduct functional and user acceptance testing
Apr 1 - Apr 30, 2024
Manage bug reports and track issues
Apr 1 - Apr 30, 2024
Provide feedback on testing results
Apr 1 - Apr 30, 2024
Set up CI/CD pipelines for automated builds and
deployments
Apr 1 - Apr 30, 2024
Maintain a stable development and testing
Throughout the project
Developer
QA Engineer
DevOps (if required)