Project Management Master’s Thesis Sample

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Examining the Use of AI to Enhance Project Planning and Forecasting

Abstract

This research examines how Artificial Intelligence helps improve project planning and forecasting throughout project management. Old project management tools have trouble creating perfect schedules and resource plans while also forecasting risks which results in wasted time and delayed projects. Computers with machine learning skills help planning projects more effectively by analyzing data through predictive intelligence and language understanding.

The research examines how AI technology helps organize project teams by assigning tasks better and adjusting work times to reduce risks. Artificial intelligence can handle normal workflows and study data efficiently to plan better projects and make fewer human errors. The study analyzes how AI tools help produce exact results when predicting project timeframes along with their resource demands and project costs.

The thesis shows both advantages of AI in project management and explains the obstacles to its adoption. The study looks at problems including poor data quality, the need to transition away from current systems, and how to connect new AI tools to old technology. The research shows how teams need to solve these difficulties before AI becomes a valuable project management tool.

This research gives project managers and organizations detailed steps for using AI to improve their planning and forecasting operations. Project managers need to know what AI helps and what it hurts to make better decisions that produce better results and decrease project risks.

Table of Contents

1: Introduction1.1 Background of Project Management and AI1.2 Research Problem and Objectives1.3 Significance of Study1.4 Thesis Structure2: Literature Review2.1 Traditional Project Management Practices

2.2 Emergence of Artificial Intelligence in Project Management

2.3 Applications of AI in Project Planning

2.4 AI in Project Forecasting

2.5 Challenges in Integrating AI into Project Management

2.6 Future Directions

3: Research Methodology

3.1 Research Design

3.2 Data Collection

3.2.1 Literature Review

3.2.2 Surveys

3.2.3 Case Studies

3.3 Data Analysis

3.3.1 Qualitative Analysis

3.3.2 Quantitative Analysis

3.4 Ethical Considerations

3.5 Limitations

4: AI in Project Planning

4.1 AI for Schedule Optimization

4.2 AI for Task Allocation

4.3 AI for Risk Assessment

5: AI in Project Forecasting

5.1 Predictive Analytics for Cost Estimation

5.2 Time Forecasting with Machine Learning

5.3 Scenario Simulation and Forecasting

6: Case Studies and Applications

6.1 Case Study 1: AI in Large-Scale Construction Projects

6.2 Case Study 2: AI in Software Development Projects

6.3 Case Study 3: AI in Healthcare Project Management

6.4 Comparison of Case Studies

6.5 Conclusion

7: Discussion

7.1 Impact of AI on Project Planning and Forecasting

7.2 AI Challenges in Project Management

7.3 Limitations of the Study

8: Conclusion

8.1 Summary of Key Findings

8.2 Implications for Practice

8.3 Future Research Directions

References

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Chapter 1: Introduction

1.1 Background of Project Management and AI

Over recent decades project management has developed effectively because organizations want better ways to manage their projects. Project management procedures from earlier times depended on skilled team members plus archived details to craft project timelines and staff assignments before forecasting results. The current project management techniques face ongoing problems in managing resources effectively and reducing project risks which frequently result in schedule delays and excess spending (Expert Research, 2022).

Artificial Intelligence (AI) has become an effective tool that changes how project management works today. Through machine learning tools combined with predictive analytics and natural language processing AI helps project managers make smarter decisions with large amounts of collected data. These capabilities help project managers plan better using real data which cuts down their forecast errors and decreases project risks according to Pears and Shields (2022).

1.2 Research Problem and Objectives

This thesis project studies how AI systems can help project managers improve planning and forecasting tools for their projects. The need to use AI in project management functions grows as more complex tasks need more efficient solutions. Our research analyzes how AI fits into standard project management processes to make better plans and decisions while providing reliable resource forecasts.

This work studies AI-based tools and strategies to boost project planning while examining AI-based forecasting systems and reviewing organizational issues with AI use in project control. Through this study project managers will learn how to use AI to simplify project procedures while lessening risks and achieving better results.

1.3 Significance of the Study

Our research adds more understanding of how AI helps project management. Project managers expect that adding AI technologies to planning and forecasting will make both processes work better with fewer errors and help handle risks more effectively. AI helps project managers use precise data to make better decisions and complete projects by their deadlines and financial targets. This research shows the benefits and problems of using AI in project management and helps experts and teachers use AI tools better in their planning work.

1.4 Thesis Structure

This thesis is structured as follows: Chapter 2 examines all research available about project management basics, AI usage in ledger management, and how AI improves project scheduling and prediction. Chapter 3 explains our research techniques for gathering and processing data. Chapter 4 and Chapter 5 examine how AI helps organizations plan projects and forecast results through case studies of real-life applications. The thesis analyzes study outcomes to enhance project management techniques in Chapter 6 followed by Chapter 7 which provides research achievements and proposes future research strategies.

Chapter 2: Literature Review

2.1 Traditional Project Management Practices

Without artificial intelligence project management still reduces its dependency on human experience, historical data and manual approaches while creating schedules, assigning resources and projecting project results. Improvements in project management methods have not solved the basic problems of scheduling projects well or matching resources to tasks and reducing risk which causes projects to run over budget and schedule.

2.2 Emergence of Artificial Intelligence in Project Management

Artificial Intelligence is changing how businesses work and brings valuable benefits to project management. AI tools like machine learning and natural language processing help project managers analyze big data better to make smart choices. These systems help organizations better plan projects and predict outcomes correctly which reduces project threats through better data usage.

2.3 Applications of AI in Project Planning

AI has been applied to various aspects of project planning, including:

• Scheduling Optimization: Data analysis programs help find project duration patterns and then propose timeframes that work best to avoid construction delays.
• Resource Allocation: AI technology reviews project needs and person availability to distribute teams with the proper skills for their specific assignments.
• Risk Management: The system detects risks through its analysis of project data history to enable managers to set protective measures before issues develop.

Pereira et al. (2022) performed systematic research on AI project management literature to demonstrate why ethical decision-making rules are fundamental for AI applications.

2.4 AI in Project Forecasting

Excellent forecasting helps projects achieve desired results. AI enhances forecasting by:

• Predicting Project Outcomes: Machine learning models use past project records to predict how well funds will be spent and when tasks will finish while showing how resources will be used.
• Budget Estimation: The system uses historical project spending data to find recurring cost influences that aid budget precision.
• Timeline Prediction: Through AI systems project timelines get projected based on historical data to consider elements that impact schedule planning.

Capone and his team created and tested an AI system for cost prediction in 2024 which produced better results than classic budgeting approaches.

2.5 Challenges in Integrating AI into Project Management

Despite the benefits, integrating AI into project management faces several challenges:

• Data Quality: AI technologies need exact data presented in a systematic format. When data is not fully complete and correct it produces unreliable outcome estimates.
• Resistance to Change: Staff at companies sometimes object to new AI systems because they lack experience with these technologies and they worry about losing their jobs.
• Integration with Existing Systems: Including AI in your current project management approach demands high system complexity and resource use.

Alevizos and colleagues in 2023 highlight that Equality needs to become part of AI systems to boost project management effectiveness while requiring ethical standards during integration.

2.6 Future Directions

The future of AI in project management looks promising, with potential developments including:

• Advanced Predictive Analytics: A future algorithm system that gives more accurate results will help project managers make smarter choices.
• AI-Driven Decision Support Systems: AI systems give project managers instant analysis to help them react fast to project changes.
• Integration with Agile Methodologies: Bringing AI tools into agile project management creates new ways to adjust projects faster than before.

During 2020 Davahli completed a thorough review of AI tools in project management which showed AI can benefit project management through better estimates of project costs and task durations. 

Chapter 3: Research Methodology

3.1 Research Design

Among existing research methods the study uses a mixed approach with both qualitative and quantitative methods to deeply examine how AI supports project management. The research includes multiple methods to study AI use in project planning and forecasting systems. Our team reviews research findings from past publications to set the theory base while performing surveys and case studies to collect real-world data.

3.2 Data Collection

3.2.1 Literature Review

Researchers did an organized study of scientific publications to understand AI use in project management. We searched these databases Web of Science, Science Direct, and Google Scholar for publications between 2010 and 2024 to find relevant research. The analysis researched projects that tested AI technologies in their project planning, forecasting, and risk-handling processes.

3.2.2 Surveys

We gave project managers from various businesses surveys to discover how they use and view AI tools during project preparation and future prediction. Our survey contained responses to fixed-choice questions matched with open-text input to create both statistical evaluations and deeper interpretations.

3.2.3 Case Studies

Project managers across multiple fields received surveys to report about their work with AI tools for project planning and forecasting. Our research tool combined both fixed-response and open-answer questions to create both statistical evidence and rich customer feedback.

3.3 Data Analysis

3.3.1 Qualitative Analysis

Our research team performed detailed examinations of companies that employ AI in their project management operations. These specific cases demonstrated what AI can do with our real projects what difficulties people face and what differences AI made in results.

3.3.2 Quantitative Analysis

Our analysis of literature review results alongside open-ended survey data used thematic methods. Our analysis method coded data to show what themes emerged in people’s responses about how AI helps project management work.

3.4 Ethical Considerations

Our team studied closed-ended survey results with statistical methods to know how AI affects project planning and forecasting efficiency.

The research team followed ethical standards by keeping participant information private and getting written participant approval plus fully explaining the study’s details. Participants were told about our research goals and learned they could end their participation at any time without facing any repercussions.

3.5 Limitations

The research admits that questionnaires may produce biased responses while case study outcomes apply best to their specific organizational environments. AI technology development moves quickly so scientific results may no longer stand true tomorrow.

Chapter 4: AI in Project Planning

4.1 AI for Schedule Optimization

AI systems review historical and present project information alongside external conditions to create better task duration forecasts and arrange tasks for improved completion dates. With its data processing capabilities AI produces better schedules that handle project delays and resource limitation issues. The method allows teams to use resources better while speeding up project delivery.

Key Benefits:

• Enhanced Accuracy: Computers estimate task times and connections better than before to decrease scheduling mistakes.

• Dynamic Adjustments: AI technology automatically modifies schedules based on sudden changes happening now and later.

• Optimized Resource Allocation: AI uses project needs and resource information to put resources to their best use during all project stages.

4.2 AI for Task Allocation

AI evaluates both project history and demands before it decides where tasks should go next. Machine learning technology finds perfect matches between team members and their appropriate duties. Our team works better and achieves more success in their projects.

Key Benefits:

• Skill-Based Assignment: Through skill assessment, AI tools give tasks to team members that fit their specific abilities.
• Balanced Workload: AI systems distribute workload equitably so team members do not get overwhelmed and perform better as a group.
• Predictive Resource Planning: AI tools analyze future needs to plan workplace tasks and projects properly.

4.3 AI for Risk Assessment

Through massive datasets and trend patterns, AI systems forecast potential problems and then take action to solve them. NLP technology scans project documents to find risks which machine learning models use to calculate how likely these risks are to happen plus their impact on projects. Project managers take action beforehand to prevent problems from growing worse.

Key Benefits:

• Early Detection: By scanning projects from the start AI finds issues to solve them before they become big problems.
• Comprehensive Analysis: Through examining all available data AI reveals hidden and obvious risks throughout project planning.
• Informed Decision-Making: Project managers get better data decisions thanks to AI that shows them how to deal with risks they find in their projects.

Chapter 5: AI in Project Forecasting

5.1 Predictive Analytics for Cost Estimation

AI systems help project teams forecast costs better and spot when projects will exceed budget plans. AI models review historical project data plus market trends to develop reliable cost predictions which guide better project funding choices. Managing financial risks helps us detect potential problems and develop steps to keep our budget on track.

Key Benefits:

• Enhanced Accuracy: By processing large datasets AI systems generate exact cost predictions and eliminate the need for human estimation.
• Proactive Risk Management: AI tech helps us detect cost problems before they get worse so we can take swift action by modifying project requirements or switching funds.
• Dynamic Adjustments: AI systems give updated cost data throughout project activities since they track market changes and unexpected problems.

5.2 Time Forecasting with Machine Learning

Machine learning models help projects by studying how similar projects finished and then using their findings to predict new project completion times. These systems analyze historical details to reveal statistical trends which help predict project length and create practical timeline goals. Named repeating events and past project facts help ML models show where delays might happen and what to do about them.

Key Benefits:

• Accurate Timeline Predictions: ML models use past project information to predict actual project duration outcomes.
• Identification of Potential Delays: By examining completed projects ML systems detect when tasks will take longer which helps teams act beforehand.
• Continuous Learning: ML models better their output by taking in fresh data and making more precise predictions while adjusting to current project growth.

5.3 Scenario Simulation and Forecasting

AI helps us create different project cases plus predict outcomes when conditions change. Through machine learning and data analytics AI shows project managers how different choices and external conditions affect project results. The system helps teams plan better and choose the right solutions.

Key Benefits:

• Informed Decision-Making: Simulations that use artificial intelligence help companies make better strategic decisions and create potential outcomes before taking action.
• Risk Assessment: AI technology creates different case examples to show which risks exist and proposes solutions to fix them.
• Resource Optimization: Scenario simulations show how best to use available resources when environmental conditions change.

Chapter 6: Case Studies and Applications

6.1 Case Study 1: AI in Large-Scale Construction Projects

Project Overview:

Our top construction firm started building this significant infrastructure project by making a multi-level commercial structure. Despite project difficulties with planning deadlines and material distribution requirements, the company met cost challenges.

AI Implementation:

The company integrated AI-powered project management tools to enhance planning and forecasting:

• Schedule Optimization: The system studied project records to estimate task times and find possible delays so our project timeline became more precise.
• Resource Allocation: Machine learning systems use available resource data and worker abilities to match task requirements with employees before starting projects.
• Cost Estimation: Our modelling system estimated future project expenses to help us plan our budget and reduce potential extra costs.

Outcomes:

The implementation of AI resulted in:

• Improved Project Timeliness: Schedules worked better which helped the project end sooner than expected.
• Cost Savings: Our correct cost predictions teamed with efficient resource management cut total project spending.
• Increased Efficiency: Faster workflows and smarter use of resources helped create better results for all projects.

6.2 Case Study 2: AI in Software Development Projects

Project Overview:

A software development company developed an enterprise application with limited time and sparse resources.

AI Implementation:

The firm utilized AI tools to manage various aspects of the project:

• Timeline Management: AI systems studied project history to determine actual project timetables that formed the basis for creating attainable deadlines.
• Risk Assessment: Machine learning systems found risks early by examining production quality details and developer work data.
• Resource Management: AI tools matched tasks to specific developers and distributed their work to balance project requirements

Outcomes:

The application of AI-led to:

• On-Time Delivery: The team met all deadline requirements because the system showed precise scheduled completion times.
• Enhanced Code Quality: Through active risk detection this system produced better results by avoiding errors.
• Optimal Resource Utilization: Assigning tasks smarter helped developers perform better at work while increasing their job satisfaction.

6.3 Case Study 3: AI in Healthcare Project Management

Project Overview:

A healthcare organization decided to install its EHR software system across several hospitals to enhance healthcare delivery methods.

AI Implementation:

The organization adopted AI to enhance project planning and forecasting:

• Forecasting and Planning: The system compared patient records with hospital processes to show how long deployment would take and where problems were likely to occur.
• Resource Allocation: Machine learning systems helped the hospital develop employee work schedules and training modules so the team could adapt easily to the new system.
• Risk Management: The AI tools checked for data protection problems and regulatory compliance to help doctors avoid upcoming threats.

Outcomes:

The integration of AI resulted in:

• Smooth Implementation: The hospital team made accurate predictions and plans to move smoothly from their old system to the new electronic health records system.
• Improved Patient Care: Remote patient care became better because the hospital operated more efficiently.
• Cost Efficiency: Our better planning of resources and risks saved money from implementation expenses and stopped fines from happening.

6.4 Comparison of Case Studies

Successes:

• Enhanced Efficiency: Throughout these cases AI integration made projects run more effectively thanks to smarter scheduling efforts and better resource management while lowering risk levels.
• Cost Savings: Our accurate project forecasts and optimizations made significant savings happen throughout all three project types.
• Improved Outcomes: All projects succeeded in meeting their deliverables before deadlines with high-performance standards and effective operations.

Challenges:

• Data Quality: The performance of AI technologies faced problems when using available data because quality historical records proved difficult to find in certain cases.
• Integration Complexity: Bringing AI tools into existing medical systems needed a lot of work and money to make it happen.
• Change Management: Our company had to educate workers and shift team dynamics to help projects benefit from AI systems properly.

6.5 Conclusion:

Our case studies reveal how AI boosts project planning and forecasting performance in many different business sectors. Despite present obstacles, AI integration brings higher efficiency and reduced costs along with project betterment which makes it valuable for project management.

Please contact us with all your inquiries about these case studies or seek details on any specific example.

Chapter 7: Discussion

7.1 Impact of AI on Project Planning and Forecasting

Artificial Intelligence boosts project planning by creating more dependable forecasts while decreasing risk and making results more accurate.

Improved Planning Accuracy:

• Predictive Analytics: The system uses historical data to predict project results helping teams develop better project plans and use their resources effectively.
• Scenario Simulation: By modeling different project situations AI enables managers to spot upcoming problems before they happen and adjust their plans.

Risk Reduction:

• Early Detection: AI tools discover project problems ahead of time by examining project information patterns but also look for changes that don’t belong.
• Continuous Monitoring: AI systems notice project trends as they happen allowing teams to fix problems quickly.

Enhanced Forecasting Reliability:

• Data-Driven Insights: Large data inputs help artificial intelligence improve the validity of project forecasting results.
• Adaptive Learning: The system uses incoming data to train machine learning programs that update their forecasting accuracy as they learn more.

7.2 AI Challenges in Project Management

While AI offers numerous benefits, its implementation in project management presents several challenges:

Data Quality and Management:

• Data Integrity: Good data quality makes AI applications work better. Systems that deliver AI analysis from imperfect data produce results that you cannot trust.
• Data Integration: Making AI tools work with existing project management setups needs substantial time and money.

Resistance to AI Adoption:

• Cultural Barriers: When companies put AI to work they may run into challenges from staff members who worry their jobs will vanish or doubt AI can do its job well.
• Change Management: Team projects need both organizational and training changes to help employees use AI systems correctly.

Need for Skilled Professionals:

• Talent Shortage: Many companies today struggle to find project managers who understand AI technology and project management simultaneously.
• Continuous Learning: Project managers need to keep learning new AI systems throughout their careers to benefit from new AI technology.

7.3 Limitations of the Study

This study acknowledges several limitations:

Sample Size: The research analyzes only a few testing examples that do not accurately show all AI applications in different business sectors.

Industry-Specific Biases: The research results depend on the particular industries chosen which makes it hard to apply the findings to other business sectors.

Evolving Technology Landscape: Current research findings lose their relevance as Artificial Intelligence advancements continue to develop quickly.

Data Availability: The researchers had restricted access to data from projects that used AI because of organizational privacy needs.

Subjectivity in Case Studies: Case studies usually reflect the unique organizational viewpoints and biases of their developers which distorts the accuracy of research outcomes.

Chapter 8: Conclusion

8.1 Summary of Key Findings

This study explored the integration of Artificial Intelligence (AI) in project planning and forecasting, highlighting several key findings:

• Enhanced Planning Accuracy: Machine learning methods help project planning get more accurate outcomes through predictive analytics and simulation technologies. The analysis of past projects enables AI models to make more reliable deliveries of project results which support better schedule handling and resource placement.
• Risk Mitigation: AI systems successfully spot risks before they become problems and help organizations control them. Project management teams use data analysis to spot risks ahead of time so they can take steps now to decrease project delays and spending increases.
• Improved Forecasting Reliability: Using artificial intelligence to forecast projects earns better results in project budgeting and scheduling estimates. Through time Machine learning algorithms study fresh data to improve their forecasting accuracy.

8.2 Implications for Practice

For project managers and organizations considering the adoption of AI in planning and forecasting, the following recommendations are pertinent:

• Invest in Data Quality: AI tools require excellent and exact data to work properly so organizations must maintain their data quality standards consistently. Organizations should have good data handling systems to work with AI tools.
• Foster a Culture of AI Adoption: Create support for your project by teaching everyone about AI benefits and letting them join the workplace AI setup efforts. The approach lets AI technologies become better incorporated and embraced by the company.
• Develop AI Competencies: Show your teams and project managers how to use AI technology through professional training. By investing in AI systems we will improve our organization’s ability to use technology for better project results.

8.3 Future Research Directions

Future research in AI and project management could explore the following areas:

• Advancement of AI Algorithms: Organizations need to create better AI technologies that work with hard project challenges and show detailed information about project activities. Algorithms that improve accuracy will help people make better project decisions.
• Exploration of New Applications: We study how AI functions in sustainability projects and agile project settings to discover its benefits and obstacles in these new project management areas.
• Longitudinal Impact Studies: Studies over many years will show if AI systems help projects reach their goals and reduce costs while making companies work better. Research in this field would show us the long-term positive and negative effects of using AI in project management projects.

The addition of AI to project planning and forecasting provides organizations with better and safer predictions along with greater prediction reliability. Effective rollout needs attention to both the quality of used data and people’s understanding of AI plus their companies’ behaviour. Further research is necessary to reach the full capabilities AI offers project management.

References

Articles and Reports:

• Integrio Systems (2024). ‘AI in Project Management: 7 Use Cases’. Available at: https://integrio.net/blog/ai-in-project-management.
• Champlain College Online (2024). ‘How Artificial Intelligence Is Revolutionizing Project Management’. Available at: https://online.champlain.edu/blog/how-artificial-intelligence-revolutionizing-project-management.
• Zignuts Technolab (2024). ‘AI in Project Management: Case Studies & Success Stories’. Available at: https://www.zignuts.com/blog/ai-project-management-case-studies-success-stories.
• Planview (2024). ‘Using Artificial Intelligence for Project Management’. Available at: https://www.planview.com/resources/articles/using-artificial-intelligence-for-project-management/.
• Mural (2024). ‘The Best Use Cases of AI in Project Management’. Available at: https://www.mural.co/blog/ai-in-project-management.
• PMI Sweden Chapter (2024). ‘Case Study Report: Navigating AI in Project Management’. Available at: https://www.pmi-se.org/Kompetens-Projektledning/Artiklar/Case-Study-Report_-Navigating-AI-in-Project-Management.
• MPUG (2024). ‘Leveraging AI in Project Management: 8 Areas for Impactful Predictions’. Available at: https://mpug.com/leveraging-ai-in-project-management-eight-areas-for-impactful-predictions/.
• ProjectManagement.com (2024). ‘How AI will help Project Planning’. Available at: https://www.projectmanagement.com/wikis/849920/how-ai-will-help-project-planning.
• The Times (2024). ‘Parlex AI to advise ministers on how policies will be received’. Available at: https://www.thetimes.co.uk/article/parlex-ai-to-advise-ministers-on-how-policies-will-be-received-99txwlwph.
• Financial Times (2024). ‘AI garden planners are like spades – powerless unless masterfully steered’. Available at: https://www.ft.com/content/8fa6632d-bc56-4128-a990-f1884b75f709.
• The Australian (2024). ‘AI users ‘not working less’: Atlassian’. Available at: https://www.theaustralian.com.au/business/technology/atlassian-says-ai-time-savings-about-focused-effort-not-working-less/news-story/0ce9390f0e4c1efab664347f1aee2fca.
• Alevizos, V., Georgousis, I., Simasiku, A., Karypidou, S., & Messinis, A. (2023). ‘Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency — A Review’. arXiv preprint arXiv:2311.11159. Available at: https://arxiv.org/abs/2311.11159.
• Capone, C., Talgat, S., Hazir, O., & Kozhakhmetova, A. (2024). ‘Artificial Intelligence Models for Predicting Budget Expenditures’. Eurasian Journal of Economic and Business Studies. Available at: https://ejebs.com/index.php/main/article/view/331.
• Davahli, M. R. (2020). ‘The Last State of Artificial Intelligence in Project Management’. arXiv preprint arXiv:2012.12262. Available at: https://arxiv.org/abs/2012.12262.
• Pereira, L. F., Gonçalves, R. A. H., Gonçalves, R. A. H., Bento, S., & Costa, A. (2022). ‘Artificial Intelligence in Project Management: Systematic Literature Review’. International Journal of Technology Intelligence and Planning.

News Articles:

• The Times (2024). ‘Parlex AI to advise ministers on how policies will be received’. Available at: https://www.thetimes.com/uk/politics/article/parlex-ai-to-advise-ministers-on-how-policies-will-be-received-99txwlwph
• Financial Times (2024). ‘AI garden planners are like spades – powerless unless masterfully steered’. Available at: https://www.ft.com/content/8fa6632d-bc56-4128-a990-f1884b75f709.
• The Australian (2024). ‘AI users ‘not working less’: Atlassian’. Available at: https://www.theaustralian.com.au/business/technology/atlassian-says-ai-time-savings-about-focused-effort-not-working-less/news-story/0ce9390f0e4c1efab664347f1aee2fca.