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Transforming Project Management – The Collaboration of AI and Agile

Executive Summary

The rapid development of artificial intelligence (AI) poses new challenges and opportunities for the world. As we know, AI is the ability of machines to perform tasks that normally require human intelligence, such as reasoning, learning and decision making. AI encompasses a range of technologies, including machine learning, natural language processing, computer vision, robotics, and more. These technologies can be combined to create systems capable of learning and decision-making. AI has had a significant impact on various aspects of our society already, including healthcare, finance, transportation, entertainment, and more. It has transformed industries and changed the way we live and work. One of the distinguishing features of AI is its ability to learn and improve over time. Machine learning algorithms, for example, can analyse data and adapt their behaviour to make better decisions as they receive more information.

Agile on the other hand is a project management approach that emphasizes flexibility, collaboration, customer-centricity, and iterative progress. The Agile approach is known for its ability to promote responsiveness to change, reduce the risk of project failure, and deliver value to customers more quickly. Agile methodologies promote a project management process that encourages frequent inspection and adaptation, a leadership philosophy that encourages teamwork, self-organization and accountability, a set of engineering best practices intended to allow for rapid delivery of high-quality software, and a business approach that aligns development with customer needs and company goals. Agile is commonly used in software development, but its principles have been applied successfully in various industries, including marketing, manufacturing, and healthcare.

Although Agile has been in existence for almost two decades now and has been extremely powerful and popular, the project management community is still struggling to make the software projects successful. Numerous Industry reports indicate that while the success rate of IT projects have indeed risen over time, they still hover below 30%. A statistic that raises significant concerns and underlines the urgent need for innovation in this domain.

The integration of Artificial Intelligence (AI) with Agile could be a solution and the collaboration between the two can transform the way software development and project management is being done currently. This transformative synergy between AI and Agile methodologies would address the challenges faced by organizations in an increasingly complex and fast-paced business environment.

This article focuses about “How AI can enhance Agile Project Management in the new world of technology.” Through a compelling case study, we will chronicle our journey of integrating AI and Agile, highlighting the successes achieved and the insights gleaned.

Integrating Artificial Intelligence (AI) with Agile methodologies can indeed be a powerful combination, transforming the landscape of project management in various ways.

 

Power of AI

The power of AI is immense and continues to grow as the field advances. AI includes a wide range of technologies and applications that leverage machine learning, neural networks, and other techniques to simulate human intelligence and perform tasks that would typically require human intelligence. Here are some of the key aspects of the power of AI:

  1. Automation: AI can automate repetitive tasks, freeing up human workers to focus on more creative, complex, and strategic activities.
  2. Data Analysis and Insights: AI can process and analyse vast amounts of data quickly and accurately. It can discover patterns, trends, and insights that may be difficult or impossible for humans to discern from large datasets.
  3. Decision Support: AI can provide data-driven insights that assist decision-makers in various domains.
  4. Predictive Analysis: AI can analyse new information, learn from data and results in near real time, providing actionable recommendations and significantly reduce errors.
  5. Accessibility: AI can make technology more accessible to individuals with disabilities by providing assistive tools like speech recognition, text-to-speech, and voice-controlled devices.
  6. Natural Language Processing (NLP): NLP allows AI systems to understand, interpret and generate human language. This technology is used in chatbots, virtual assistance.

The Agile Advantage

Agile is a highly flexible and collaborative approach to project management that emphasizes iterative and incremental development. Unlike traditional project management methodologies like the ones based on Waterfall model, which have a linear and sequential approach, Agile divides the project into small increments. These increments involve minimal planning and are not directly dependent on each other. This allows for greater flexibility in making changes as the project progresses.

 

Here’s a simpler infographic that represents the Agile Project Management process. It breaks down the Agile cycle into different key stages: Plan, Design, Develop, Test, Deploy, Review and Launch, illustrated in a circular flowchart showing the iterative nature of Agile methodologies:

 

Here are some key elements which make Agile so powerful –

 

  1. Flexibility and Adaptability: Agile allows teams to adapt to changes quickly and efficiently. The iterative process accommodates changes in requirements, even late in the development process, ensuring the product is as close as possible to the user’s needs.
  2. Increased Collaboration and Ownership: Agile promotes close collaboration between developers, stakeholders, and customers. This inclusive approach encourages team ownership and empowers all members to contribute to decision-making, leading to more innovative solutions and a shared sense of accountability.
  3. Faster Time to Market: With Agile, the software is developed in incremental, manageable units, allowing the team to deliver working software more frequently. This means features can be released to market more quickly, providing a competitive advantage and faster return on investment.
  4. Continuous Improvement: Agile methodologies involve regular reflection on the processes and practices, allowing teams to identify and implement improvements continuously. This commitment to excellence can lead to higher quality products and more efficient workflows.
  5. Enhanced Quality: Agile’ s emphasis on frequent testing and reviews throughout the development cycle leads to early detection and correction of defects, which can improve the overall quality of the software.
  6. Customer Satisfaction: By involving the customer in the development process through regular demonstrations and iterations, Agile ensures that the product aligns with customer needs and expectations, leading to higher satisfaction and better user experiences.

In the IT industry, numerous agile frameworks are utilized, with Scrum being the most prevalent. Consequently, Agile is often perceived synonymously with Agile Scrum.

 

Applying AI in Agile

Utilizing the potential of AI within agile project management can be implemented at different levels.

Level 1: Foundational Agile: This is where the organization wants to focus on enhancing the effectiveness of the core.

Level 2: Scaled Agile: This is where the organization wants to focus on enhancing the effectiveness of the Scaled Agile Frameworks implementations. In this category we will focus on some areas that hold significance within scaled contexts.

Level 3: Project Management: This is where the organization wants to enhance their effectiveness at the broader level of implementations considering end to end project management. In this category we will focus on Process Groups or different stages of the Project management lifecycle.

 

In the following sections we would delve further into the above topics.

 

Level 1: Foundational Agile

Implementing AI within Agile Scrum involves integrating AI technologies and principles into the various stages of the agile scrum implementation of the organization, including planning, execution, review, and retrospective. Applying AI in Agile Scrum can significantly improve the efficiency of teams by automating repetitive tasks, providing actionable insights, facilitating decision-making, and enhancing collaboration.

Although there can be many ways in which AI tools can be applied, in our view, scrum events are the core areas where the AI can influence and enhance the effectiveness the most.

Here’s a breakdown of how AI can be implemented in Agile Scrum events:

Sprint Planning

  • Product Backlog Creation: LLM-based AI tools compiles high-level requirements, reducing workload.
  • Backlog Items Prioritization: AI streamlines prioritization of Product backlog, minimizing cognitive load.
  • User Story elaboration and refinement: AI enhances stories and acceptance criteria, aligning with project goals.
  • User Story Estimation: AI provides initial story point estimates for effective sprint planning.
  • Sprint Goal Identification: AI aids in defining sprint goals by using insights and past performance data.
  • Identify Potential Impediments: AI anticipates sprint impediments and addresses potential obstacles.
  • AI-Bot based facilitation: AI-powered bots assist in planning, reminders, discussions, and note compilation.

 

Daily Stand-up and Execution

  • Alignment with Sprint Goal: AI monitors team progress, aligning activities and providing predictive insights.
  • Action and Impediment Tracking: AI systems track and communicate action and impediment status, for timely resolution.
  • Generating Standup Summary: AI transcribes and summarizes key discussions from stand-up meeting.
  • Coding Support: AI enhances code comprehension and documentation for faster development.
  • Enhanced Code Review and testing: AI improves code review processes and test case generation.
  • AI-Bot Facilitation: AI Facilitates daily scrums, tracking updates and aiding communication.

 

Sprint Review

  • Sprint Performance Analysis: AI analysis sprint performance, highlighting goal achievements and improvement areas.
  • Review Deck Preparation: AI aids in crafting presentation materials with essential metrics and visual.
  • Feedback Analysis: AI summarizes stakeholder feedback, identifying trends and opportunities.
  • AI-Bot Facilitation: AI bots manage Sprint Review flow, organize presentations, and capture feedback.

 

Sprint Retrospective

  • Sprint Performance Analytics: AI analyses sprint outcomes, highlighting trends, and achievements for retrospective discussions.
  • Effectiveness Analysis: AI evaluates post-retrospective actions, measuring continuous improvement.
  • Retrospective Reports: AI generates detailed reports focusing on key development areas and process refinement.
  • AI-Bot Facilitation: AI streamlines retrospectives, promoting communication and team engagement.

 

Level 2:  Scaled Agile

When the complexity of the context increases and there is a need to have supporting models for the same, Scaled Agile Models can be helpful. Although there are a wide range of frameworks and models available to be used by teams, we would limit our scope to just key concepts that would be relevant for most models.

Applying AI in Scaled Agile contexts can improve team efficiency by optimizing resource allocation, enhancing decision-making, automating repetitive tasks, and facilitating cross-team collaboration. Here are some key area illustrating how AI can enhance team efficiency in Scaled Agile.

 

 

Resource Allocation Optimization

AI-powered models use historical data and project requirements to forecast resource needs, optimizing allocation across Agile teams. These systems dynamically adjust resources based on real-time progress and project demands, continuously optimizing distribution.

Cross-Team Coordination

AI tools identify task dependencies across teams, aiding proactive management. Integrated with collaboration platforms, AI enhances communication, providing real-time translations and discussion summaries, facilitating cross-team coordination for efficient planning and execution, including multi-team ceremonies like Scrum of Scrums.

Predictive Analytics for Planning

AI algorithms utilize historical sprint data to predict future velocities and team capacities, helping in accurate planning. They forecast feature completion timelines based on past performance and market dynamics, enabling realistic timelines and effective feature prioritization.

Automated Testing and Quality Assurance

AI-driven tools automate test case generation and execution, focusing on high-risk areas to enhance testing processes. In CI/CD environments, AI prioritizes tests based on historical impact and recent code changes, improving testing efficiency and quality.

Continuous Improvement through Insights

AI-generated insights identify trends and improvement areas, providing actionable feedback for Agile process enhancement. This helps teams address bottlenecks and recurrent issues, facilitating targeted process improvements and efficient delivery.

Automated Release Management

AI-enhanced release management tools automate planning, scheduling, and deployment, reducing manual effort and expediting delivery. They analyse dependencies and risks, generating release plans and coordinating deployments while monitoring quality metrics for continuous improvement and decision-making.

 

Level 3: Project Management

Reaching a much broader context where we look at end to end project management, there are many more possibilities to leverage AI. While the project management landscape is extensive, we will confine our focus to potential optimizations within the Project Management Process groups.

Project Initiation

  • Enhanced Project Evaluation and Feasibility: AI utilizes data analysis to assess project viability, simulate scenarios, identify risks, and optimize resources, ensuring alignment with legal and market requirements.
  • Effectively Identify Stakeholders and their influence: AI utilizes natural language processing (NLP) to parse organizational documents, detecting relevant individuals and analyzing roles and influence, enhancing stakeholder management.
  • Auto Prepare Project Charter: AI automates project charter creation by analyzing historical data, suggesting objectives and deliverables, identifying stakeholders, forecasting risks, and recommending resource allocation, streamlining project initiation.

 

Project Planning

  • Prepare Project Plan with assistance: AI analyzes historical data to improve project plans, including cost estimates, timelines, resource planning, and technology selections, improving accuracy and relevance.
  • Auto Plan Calendar: AI optimizes calendar scheduling by analyzing meeting patterns, suggesting optimal times, adjusting plans dynamically and sending reminders for timely task completion, enhancing productivity and time management.
  • Effective Risk Management: AI algorithms analyze large datasets to identify and prioritize risks, assisting in strategic mitigation planning, enhancing overall risk management capabilities.

 

Project Execution

  • Improve Resource Efficiency: AI matches team skills with project needs, adjusts allocations dynamically, forecasts requirements, identifies skill gaps and facilitates timely training for optimal resource utilization throughout the project lifecycle.
  • Task Automation: AI automates routine and repetitive tasks, freeing up team members for complex work, increasing productivity and accelerating project timelines.
  • Enhanced Knowledge Management: AI automates collection and organization of project data and documents, simplifying search and retrieval. Machine learning analyzes historical data, generates insights for better decision-making and predict project outcomes.

 

Project Monitoring & Control

  • Enhanced Progress Tracking against objectives: AI automates data collection and analysis for real-time monitoring of project KPIs, providing predictive insights and data-driven recommendations for proactive management, ensuring alignment with strategic goals and timely achievement of project milestones.
  • Auto Risk Monitoring and Control: AI improves risk management by automating detection, analysis, and prioritization of risks, facilitating quicker and more effective response strategies to mitigate threats.
  • Effective Change Control: AI evaluates and prioritizes change requests using historical data and predictive analytics, ensuring timely and appropriate handling of changes, streamlining change management processes.

 

Project Closure

  • Enhanced Project closure: AI automates documentation and analysis of project outcomes, comparing them with the initial objectives to assess success and identify lessons learned. It streamlines record archiving and ensures regulatory compliance.
  • Generate Best Practices and Lessons Learnt: AI analyses project data to generate Best Practices and Lessons Learnt document, contributing to the organization’s knowledge repository. This enhances future projects decision making by utilizing gained knowledge.

 

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Implementation Strategy and AI Integration

General Implementation Strategy

While organizations may opt to implement the concepts independently in different ways and still benefit, our recommendation would be to apply as per the following steps –

Step 1: Generate Awareness: This is the crucial first step to conduct sessions and trainings to generate awareness in the organization, including the leadership team.

Step 2: Focus on the foundation: Identify the tools for Agile Scrum that can enhance different aspects of the Agile Scrum implementation. The focus should be to cover all critical areas for Level 1

Step 3: Broaden the impact: Identify the tools for Scaled Agile context. Focus should be to identify and cover all critical high impact areas for Level 2. Integrate it back to Level 1.

Step 4: Complete the coverage: Identify the tools for project management critical areas for Level 3 and complete the implementation by integrating back to Level 1 and Level 2.

Step 5: Feedback Loop: There would be a continuous need for the review and revise the implementation with a feedback loop based on what is working what is not. This should be driven by organizational KPIs and the impact of the above implementations on the same.

Create your own GPT Model

Creating a GPT model aligns your organizational goals with Agile innovation, a process marked by its iterative nature and the collective expertise of Agile and AI teams. Customization is expansive, propelled by your vision and ambition.

Modern LLM platforms facilitate the creation of tailor-made GPT models, eliminating the complexity of coding, thereby democratizing the customization process. This personalized GPT model harnesses the power of public GPT models, seamlessly integrating it into the organization’s unique ecosystem.

 

Outlined below, and depicted in the accompanying diagram, are the steps that sync with the previously discussed implementation strategy:

Create and Configure the Model: Set up a custom GPT instance for your organization, complete with a unique name and a defined purpose. Configuration includes specifying the target user demographics and establishing access controls, among other preparatory details.

Train the Model: Feed the model organizational-specific data to contextualize it. The breadth of training is contingent on your preferences, balanced against considerations such as data security.

Deploy the Model: Move the model into production for end-user interaction. A phased approach often works best, initially releasing it to a focus group before extending it to the broader target audience.

 

Once active, this model serves as a multifaceted asset across various levels, it supports foundational Agile practices, enhances Scaled Agile processes, and contributes to advanced Project Management. Its applications range from providing real-time insights and conducting sophisticated analytics to delivering NLP-based recommendations, thus empowering decision-making at every level.

 

Case Study

Introduction

Amidst the constantly evolving landscape of technology and organizational management, our organisation recognized the imperative need for a sophisticated tool to thoroughly analyse our delivery performance. With this vision in mind, we embarked on an ambitious project to develop a custom Generative Pre-trained Transformer (GPT) model tailored specifically to our organizational context. This AI-powered model leverages natural language processing (NLP) to provide a comprehensive view of project progress and delivery health, aligning closely with our strategic objectives and operational needs.

 

Objectives

The initiative aimed to achieve key objectives:

  • For the Leadership Team: Provide an NLP-based overview of organizational delivery health, identifying projects needing immediate attention.
  • For Project and Program Managers: Facilitate insights into project and program progress, highlight potential risks, and suggest actionable plans based on core KPIs.
  • For the PMO: Enable the generation of consolidated reports and conduct complex analytics, enhancing decision-making and strategic planning processes.
  • For Other Stakeholders: Provide a versatile tool that aids in various analytical and reporting needs, supporting the broader organizational goals.

 

Methodology

The development of our GPT model was meticulous, rooted in a deep understanding of our organizational DNA. We provided the model with an extensive dataset, including:

  • Details of our delivery excellence model, including core delivery KPIs, their importance and definitions.
  • Details of our Organizational Agile Scrum Model and LeSS-based Scaling Model, reflecting our approach to scalable agility.
  • Organizational information and data for ongoing projects and programs, with sensitive data masked for security reasons.
  • Weekly updates on organizational KPIs, to keep the model informed of the latest project developments and outcomes.
  • Additional information provided to the model with NLP based interactions.

 

This comprehensive training enabled the GPT model to generate accurate insights reflective of our unique organizational context and dynamics.

Implementation and Results

Upon implementation, the GPT model rapidly became an indispensable tool across our organization:

  • Leadership Team utilized the model for NLP-based delivery health analysis, allowing enabling strategic interventions.
  • Project Managers gained access to nuanced project insights, with detailed guidance on risk management and performance optimization against core KPIs.
  • PMO benefited from automated report creation and complex analytics, streamlining operations significantly.

 

The model’s integration into our operational processes has led to enhanced efficiency, reflected in core delivery KPIs improvements –

  • Substantial improvement observed on Budget Performance (CPI)
  • Marginal improvement in Schedule Performance (SPI)
  • Immediate improvement in Scope Performance (RPI)
  • Significant improvement in Quality Performance (DDD)

 

 

Conclusions and Future Directions

The development and implementation of our custom GPT model have marked a milestone in our journey towards leveraging AI for organizational excellence. By providing targeted, AI-driven insights, the model has played a pivotal role in empowering our stakeholders at all levels with the information they need to make informed decisions.

 

Looking ahead, we are committed to further enhancing the model’s capabilities, exploring avenues such as predictive analytics to foresee project trajectories and organizational trends. Our journey with AI is just beginning, and we are excited about the possibilities this technology holds for the future of organizational management and delivery health monitoring.

Challenges and Future Outlook

Based on the project experience and implementation, the collaboration between AI and Agile methodologies poses both challenges and promising future outlooks. By addressing these challenges and leveraging the potential of AI technologies, organizations can enhance efficiency, decision-making, collaboration, and overall project success within Agile environments.

 

Conclusion

In this article, we have explored the transformative potential of integrating Artificial Intelligence (AI) with Agile methodologies in project management. Through a comprehensive analysis of the implementation Strategies and Use Cases, challenges, opportunities, and future outlook, it is evident that the collaboration of AI and Agile holds immense promise for driving innovation, efficiency, and success in project delivery.

The collaboration of AI and Agile empowers project managers and teams to navigate complexity, uncertainty, and change with confidence. By embracing a culture of experimentation, continuous learning, and adaptation, organizations can stay ahead of the curve, seize new opportunities, and remain resilient in the face of evolving market dynamics.

 

As we look to the future, the collaboration of AI and Agile methodologies will continue to shape the landscape of project management, unlocking new possibilities and redefining best practices. To fully realize the potential of this transformative partnership, organizations must invest in talent development, technology infrastructure, and organizational culture that fosters innovation, collaboration, and agility.

In conclusion, the fusion of AI and Agile methodologies represents a paradigm shift in project management, paving the way for unprecedented levels of efficiency, effectiveness, and excellence. As we embark on this journey of transformation, let us embrace the opportunities that lie ahead and seize the potential to revolutionize the way we work, create, and deliver value in the digital age.


 

References

The Agile Manifesto
http://agilemanifesto.org/
 
Ken Schwaber and Jeff Sutherland-The Scrum GuideTM
https://scrumguides.org/scrum-guide.html
Project Management Institute
https://www.pmi.org/pmbok-guide-standards/practice-guides/process-groups-a-practice-guide
 
 
 
 
LLM Based AI Tools
·       ChatGPT
https://chat.openai.com/
·       Gemini
https://gemini.google.com/app
·       claude
https://claude.ai/chats
·       llama2
https://www.llama2.ai/
·       GitHub Copilot
https://github.com/features/copilot
·       Jasper
https://www.jaspar.com/
AI Schedulers
·       https://zapier.com/
AI Bots
·       Geekbot
https://geekbot.com/
Scrum Assistant
·       Troopr – Online Planning Poker
https://www.troopr.ai/
·       Stepsize
https://stepsize.com/
·       Spinach
https://www.spinach.io/
·       Otter
https://www.otter.ai/
·       Fireflies
https://www.fireflies.ai/
·       Power Retro
https://www.powerretro.io/
Slide Deck Generator
·       Gamma
https://gamma.app/
Image Generator
·       Playground
https://playground.com/
·       Dall e 3
https://openart.ai/
Productivity
·       Taskade
https://www.taskade.com/
·       Notion
https://www.notion.so/
·       Asana
https://www.asana.com/

 


About the Authors:

Dinesh Sharma
https://www.linkedin.com/in/hidineshsharma

Over 27 years of experience in IT Industry with more than 16 years in Project management. Extensive Project, Program and Delivery Management and more than 11 years into Agile Based Models.

Worked with a number of organizations both product based and service based across the globe. Experience working with wide range of domains and technologies. Exceptional track record of delivering a high number of projects and programs with 100% success rate.

Authored and published a large number of articles and whitepapers on many topics in Agile and Project Management.


Bhavika Nayyar
https://www.linkedin.com/in/bhavika-nayyar-project-manager

Over 13 years of IT industry experience, specializing in Project Management & Scrum Master roles. Have successfully managed different types of project methodologies including Agile & Waterfall, demonstrating proficiency in all phases.

Have been able to deliver end to end projects with consistently exceptional results. Extensive project management experience handing end to end execution of large-scale projects.

With a strong background in stakeholder management, resource management, project requirements gathering, project budget planning.


 

Brewing Success: Managing Your eLearning Project One Cup at a Time

Coffee is a staple of my morning routine.  With little deviation, I make my coffee as soon as I sleepily saunter into the kitchen.  I don’t think much about it, the process is nearly hard-coded at this point: choice of favorite mug, mug placed, coffee prepped, brew cycle on- then I await the pop-pop-popping of hot water as the sweet aroma fills my kitchen. My brain is hyper focussed on both the sounds and smells like a Pavolivan trained dog awaiting the liquid award.  Then finally, I add cream and a touch of honey (yes honey, it’s delicious!) and after my first sip, my morning is ready to begin.

 

I get it. I’m in a rare class of those who need this cup of coffee to go beyond the simple, great, I have coffee.  I admittedly spend a lot of money on this caffeinated nectar.  It’s my one true pleasure each morning. I have a deep appreciation for connoisseurs who hone their skills and master the art of selecting beans, grinding them to perfection, and brewing a rich, flavorful caffeinated beverage. I read about coffee entrepreneurs and love to smell fresh beans when I’m in a coffee shop.  I even enjoy reading the descriptions of flavors: medium body with tasting notes of nutty, sweet chocolate, mild citrus and a bright finish….yes please, I’ll have a cup of that!

 

I realize that for the vast majority of the population, the process behind an excellent cup of coffee doesn’t really matter, it’s about the end result done right.   Yet one morning, I started thinking about both my morning routine and the overall coffee process, going from a bean in a field to a liquid in someone’s cup. And that’s when it hit me that the process of making coffee bears a striking resemblance to eLearning project management.   As it happened that morning, I had a busy day of deliverables and thought to myself, “huh, as clients receive their final deliverables, they’re likely unaware of all the careful planning, execution, and evaluation that goes on behind the scenes. They want their deliverable, done correctly and as expected”.   So I set out to write about the two processes and their similarities.

 

Selecting the Right Beans (Project Initiation/Needs Assessment)

A high-quality, aromatic coffee bean sets the foundation for a rich, silky cup of Java and a well- organized pre-project launch process is paramount to the success of an eLearning project.

For example, a coffee’s success includes bean variety, the growing region, climate (including altitude) and how the beans are harvested and processed.  Typically, there’s no need to think about anything else in the process. Sound familiar?

 

As PMs, the project initiation phase is arguably the most critical stage of the entire project.  Above all other tasks, a PM’s job is to review and/or confirm the target audience, determine what, if any constraints or risks are present, understand scope, draft a schedule, confirm resources and ensure that all source materials were shared. In other words, conduct a thorough needs assessment.   An air-tight project initiation sets the stage for a balanced and mellow project experience. Again, if done right, there’s no need for your learners to think about anything else in the process.

 

Measuring and Grinding (Project Planning)

Precision is key in both coffee and project planning. How one grinds coffee beans will play a significant role in the flavor, aroma, and strength of your final drink.  If your beans aren’t measured correctly before grinding, or if the grind size doesn’t match the intended strength, that cup of coffee will taste acidic, weak, or sour, all things I cannot tolerate.

 

In eLearning, the best way to avoid a weak project finish is early preparation to ensure that all team resources are informed of the project objectives, deliverables, schedule and risks. An eLearning project is only as successful as the individual parts.  The more project information a PM shares with the project team members, the higher the probability of success.  Kickoffs are the best way to communicate with your project teams.

 

Kickoffs ensure that your team understands the schedule, the deliverables and that everyone knows the part they’ll play in the project.  It’s a time for the team to ask questions, get to know each other if they don’t already, and to understand accountability for the project success.  Projects started without kickoffs often go sideways because they’re missing precision from the start.   Take the time to measure the project needs beforehand, so that your project begins from a position of strength.

 

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Power on- Whirring, Sizzle, Gurgle, Drip! (Official Project Kick-off)

Few things are better to coffee lovers than hearing the sound of the coffee machine preparing for your morning brew. It’s surely my favorite part, as I detailed at the start of this story.

 

The official kick-off with your learning team is akin to powering on your coffee machine.  Kick-off meetings are the time to confirm project scope, timing of milestones, agreements around project responsibilities, establishing meeting cadence, verifying source material status, and highlighting risks.  Kick-off meetings set the foundation for a strong project start.

 

The best way that PMs can level-set expectations is via note-taking.  Holding everyone on a client call responsible for their individual or collective parts is key, and live scribing is highly suggested so that the collective team agrees to action items and deadlines.  With the advent of AI-based tools that are creating quite the sizzle in our industry, note taking is easier than ever before so there’s no need to skip this important step!

 

Brewing (Project In-Flight, Monitoring & Control)

Obviously, the best part of the brewing process is your satisfying cup of coffee.  The preparation of your coffee beans invites the flavors from coffee grounds, and at that point, your coffee should brew as expected.  Still, like any process, problems might arise.  For example, the water temperature (targeted between 195°F to 205°F) could be off, or the wrong type of brewing method is used for the ground type which would heavily influence the role in flavor, or worse yet, user error- inserting the wrong sized filter or not measuring the water correctly.  Keeping a close eye on the brewing process throughout and adjusting parameters as needed is the best way to achieve and brew the perfect cup.

 

Similarly, an eLearning project that is meticulously and methodically organized (during initiation and planning), should percolate to an ideal state, assuming the PM’s involvement includes excellent communication, detailed awareness, shared notes and prompt resolutions.   Regularly assessing project progress is critical so that you can identify and rectify any issues quickly.  Incorporating feedback from stakeholders is an ideal way to glean that your near final product is meeting/has met expectations.

 

Ask open, thoughtful questions during status meetings that begin with “How”, “What”, or “Tell me” as examples.  Sending short (3 question max) surveys mid-project works too.  Dropping a quick email that simply reads, “I am interested in hearing your feedback around how the project is progressing to date” could glean rich insight of an issue that may have not been covered during a standing meeting.

Enjoying the Final Cup (Project Closure)

Finally, it’s time to serve your eLearning project like a perfectly brewed cup of coffee. Present the finished product to stakeholders and ensure they have the tools and knowledge to make the most of it. After successfully conducting LMS testing and launching, your project is complete.  Celebrate your team’s hard work and savor the sense of accomplishment.

 

And so, much like brewing a good cup of coffee, managing an eLearning project involves distinct phases, each crucial for a satisfying end result. Skipping or rushing through any phase can lead to a less-than-desirable outcome. By carefully tending to each step, eLearning project managers can ensure a successful and effective learning experience for their audience.  If you brew your eLearning projects with the same care and precision as you would your favorite cup of coffee, you’ll serve up excellence every time.

Cumulative Cost and Budget Spreadsheet Tool

As both a Project Management practitioner and a College Professor and University Instructor, I have found that there are very few simple-to-use templates for creating a time-based project schedule and budget. This package has a series of 4 Excel spreadsheets (but any standard spreadsheet program should also work) that allow a student or practitioner to build a time-based budget that shows unit activity (scope) planned period (time), and cumulative cost all on one page. It has been created to demonstrate a fictitious but realistic budget for the purchase, installation, and testing of a mid-size local area network of about 200 desktop and laptop computers and  5 servers. The project is expected to take 15 weeks from start to finish with a total budget of $402,200.

4 spreadsheets in total allow readers to copy and build their budget.

Sheet 1,2,3 shows the building blocks of creating a time phase schedule for buying and installing laptops, desktops, and servers. Unit activity and planned unit prices drive the planned equipment budget. Similarly, there are a few human resources planned with daily labour rates driving the total labour cost plan. The summary cost lines from the equipment and labour detail sections are then summed to yield the total planned cost for each week and cumulatively.

Sheet 1 shows the numeric data entry only. It was started at row 30 to leave space for a data-driven graphic table to be added later.

Download Sheet 1

 

Sheet 1 15-week budget before a graphic table with input cells yellow. This is the same as sheet one except for the user input cells for unit installations, unit prices, labour days, and labour rates have been shown on a yellow background to ease of use. The other cells are formula-driven.

Download Sheet 1 with Yellow Input Cells

Sheet 2 shows the addition of a data-driven graphic table inserted above the numeric spreadsheet. The instructions for creating the table are:

  1. Select (curser click on) rows 35 and 36 to include all cells from C35 to Q36
  2. Click on the Insert tab from the top of the spreadsheet
  3. Click on the Column Icon
  4. Select (Click on) the top left 2-D column icon

Download Sheet 2

 

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A two-column graphic showing the weekly planned cost and total cumulative will be superimposed on your spreadsheet. It will not likely be properly positioned or sized to line up with your numeric entries and weekly columns. You will need to use your cursor to grab the corners of the graphic table to stretch and position the table to line up with your numeric entries.

Sheet 3 is the same as spreadsheet 2 after the graphic column table has been stretched and positioned to fit above the numerical data it represents.

Download Sheet 3

 

Sheet 4 adds a Gantt chart showing key milestones and dates.

Download Sheet 4

Best of PMTimes: The Greatest Challenges When Managing a Project

What do you find to be the hardest part of managing a project? I bet if you asked ten different project managers that question you would get at least six or seven different answers.

 

I believe that many on the outside of project management looking in probably think it is easy. Be organized and you’ve got it made, right? I wish it was that easy but then again if that was all there was to it I guess the pay would be considerably less than it is and we’d all miss the challenge.

No, project management about much more than just being organized but you already know that. What do you find to be the most difficult aspects of the daily project management grind? For me, and from what I’ve perceived from many of my colleagues, it comes down to a fairly common list of about five things, depending on the types and sizes of projects and the clients we are dealing with, of course. There are always those variances. Let’s consider these five items.

 

The project budget.

The project budget has to be on here, likely always #1 or #2 on every project. 95% of the population has problems managing their own money! That doesn’t make them that much better at managing someone else’s!

The project budget is always a challenge. Unlike your own budget where it’s only you or a few people spending, for a project budget, you may have 87 different people, places or things charging to it. The project budget status can go from healthy to dire straits overnight as charges come through accounting and hit your project and now you must go figure out why.

Staying on top of the budget every week by updating the budget forecast with actual charges from the week before and re-forecasting it for the remainder of the project is one way to combat those budget surprises. Perhaps the only way. And, by doing this you can just about guarantee that it doesn’t go more than 10% out of control vs. the 50% overage that an unchecked budget can quickly realize. The 10% overage is a fairly reasonable/easy fix. You may never recover from the 50% overage.

 

Scope management / change control.

Scope management and change control are two of those two-word phrases that are basically like four-letter words in the world of project management. Scope management is always a challenge for the project manager and project team because some things are close calls on whether they are in or out of scope. Plus, we aren’t always thinking in terms of “scope” when we are plugging through the work or fixing issues. And change control results in those ugly change orders for which customers have to pay extra, and that’s always a fun thing for the PM to bring to the project sponsor’s attention to obtain approval.

 

Resolving team conflicts.

Some people actually thrive on conflict. Not me. I’d prefer that we all just get along and do our jobs. That’s why I like project management better than, say, managing a team of application developers who report directly to me. I’ve done that; I’ve had staff at several different organizations where I’ve worked. Resolving conflicts, personnel issues, giving performance reviews – these are a few of my least favorite things.

 

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Pleasing everyone with the status report.

You can please some of the people all of the time. You can please all of the people some of the time. But you can’t please all of the people all of the time. Is this true? With status reporting it seems to be the case. But if you want to maintain your sanity and have time to manage your other projects and job requirements, it is in your best interest to find a status report format that works for everyone. By everyone I mean all stakeholders who care to hold your status report in their hands and a few who don’t care but you want them to care.

Create a usable and informative dashboard for everyone – especially for the project sponsor’s and senior management’s viewing pleasure. For your senior management, a few of the key stakeholders, and possibly some high-level players on the customer side, this may be all they ever want to see. It can be some high-level percentages or possibly a green-yellow-red stoplight approach to reporting the timeline, tasks, and budget health. Beyond that you want the weekly detail that goes into any good status report. This status report should drive the weekly team and client meetings. You will want to report on completed tasks, what’s happening now, what’s coming up soon and all outstanding issues and change orders.

The status report can be painful and a huge weekly chore on your to-do list, but if you can figure out how to create a one-size-fits-all approach to status reporting on your project, you’ll save time and effort overall by not creating several different reports trying to please everyone on your project routing list.

 

Getting all detailed requirements documented.

This one can be a real headache. Why? Because it seems that no matter how hard you try, no matter how many eyes are on it, no matter how many experts are involved and no matter how much your project client participates and insists “that’s it”, you’ll eventually find that something was overlooked.

It’s ok because the fault usually lies with the project customer and they end up paying for the extra work and time in the form of a change order. Still, customers don’t like change orders, and it usually means some painful re-work. It would be nice always to get it right the first time. But that’s almost never the case.

 

Call for input

Project management is challenging. Period. Some parts are harder than others. Some we master. Some we never really get used to or we seem to at least always make them hard. I wish I had a magic formula or all the time in the world on every project so that we could do everything well and everything right, but that is never the case. We always need to cut corners somewhere, and that doesn’t make most of these challenges easier…only harder.

How about our readers? What are your biggest challenges or least favorite activities associated with managing projects? What have you found to be your most troubling parts of managing a project?

 

Published on: 2016/04/05

Embracing AI-driven Project Management: A Guide for PMs

AI project management software is revolutionizing the landscape of products and services. A host of tools are making their way to the market to capitalize on their potential. Given their ability to automate tasks, analyze data, aid insights, and improve the bottom line, AI project management tools will rapidly become part of standard operating procedure within companies, transforming traditional project management.

This rapid transformation has left project managers wondering how their job expectations will change, whether they will be replaced by AI, and how to adapt to the change. This article aims to cover those questions.

 

Table of Contents

AI Project Management Today……………………………………………………….. 1

Analysis………………………………………………………………………………….. 2

Rewriting……………………………………………………………………………….. 2

Summarization………………………………………………………………………… 3

Generating Reports…………………………………………………………………… 3

How to Adapt and Thrive with AI Project Management Tools………………… 4

Embrace the Changes in the Project Management Role……………………. 4

Learn Continuously About New Technologies…………………………………. 4

Think of AI Project Management Tools as “Interns”…………………………. 5

Picking the Best AI Project Management Tools…………………………………… 5

AI Project Management Tools are the Future…………………………………….. 6

 

AI Project Management Today

As industries are becoming more competitive, project managers expect more output from their employees and themselves. AI project management tools assist PMs to select and prioritize projects based on company and stakeholder needs. They help PMs ensure that company resources are allocated to the most impactful projects in the most impactful way.

AI tools help increase the per hour value of work, especially because employees are happier and more productive working with AI. In fact, a McKinsey report found that the productivity gains among junior workers are higher than they are for senior workers.

 

For project managers, AI can perform many productivity boosting tasks including:

Analysis

AI project management tools can analyze and report on work you or your teams do and offer improvements. Some AI tools can spot patterns in historical project data and generate project plans optimized for your company and stakeholder needs. They can also spot areas of improvement in employee work.

For instance, any project manager will tell you how bad requirements can have negative downstream effects like rework. Requirements errors make-up 70 to 85 percent of the cost of rework.

When you write requirements for projects and/or products, AI tools can help you rate the quality of your requirements based on the 6C’s (i.e., clarity, completeness, conciseness, consistency, correctness, and context). The results are rated on a scale of 0% to 100%.

 

Rewriting

Beyond just offering analysis, you can also write and rewrite content and documentation using AI. The rise of ChatGPT has dramatically shifted the landscape of many industries and professions, including project management. You can automate project planning, generate project status reports, summarize meetings, and even develop training materials.

You can also write, and rewrite documents based on drafts and get those results formatted in bullet form or as a paragraph. The best tools also allow you to select and edit the AI’s output to your team’s specifications.

Monitoring Progress

AI project management tools offer PMs the ability to monitor the progress of their projects or sprints. Team members and stakeholders can visually see how the project is progressing against the planned timeline. With AI project management software tools, you can communicate with your team and update project progress. This can help improve team communication and collaboration, leading to more successful project delivery.

 

Summarization

Project managers often must grapple with vast amounts of data in bite-sized pieces to make informed decisions. AI tools can create a brief abstract or reframe requirements in different terms so PMs can make decisions quickly. The ability to reframe requirements also has explanatory power so that team members from different disciplines can better understand their colleagues’ intentions.

 

Generating Reports

Current tools can generate reports from your documentation with one click. An AI project management driven method leaves tools to summarize meeting notes or project updates in seconds with pre-structured headers, tables, and more, these tools ensure that project managers have perfectly formatted content.

While these are some basic tools that project managers can use, AI project management tools can go even further. This includes converting requirements data into user stories, translating requirements from language to language for better collaboration, and even elaborate on existing requirements.

 

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How to Adapt and Thrive with AI Project Management Tools

 

There’s no doubt that AI in project management will be the new paradigm in the near future. A Gartner study said that AI will handle 80% of project management tasks by 2023. The best way to deal with this upcoming change is to adapt to it. Here’s how:

Embrace the Changes in the Project Management Role

A change in the role of project manager is inevitable in many ways. Here are a few ways you can adapt and thrive in the evolving role of PM.

  • Focus on Soft Skills: With automation handling routine tasks, the PM role will focus more on soft skills like leadership, strategy, and building high-performing teams. Organizations will need the leadership skills of PMs to keep their companies competitive by switching to AI tools.
  • Strategic Alignment: While AI will lead to gains in productivity, using this productivity boost in the right way will involve PMs focusing more on aligning projects with strategic goals for results. This includes using other strategic tools like baselining and documentation tools to direct the power of AI in the best possible way.

Learn Continuously About New Technologies

The future is a hybrid of machine speed and human judgment. To keep up with these rapid changes, PMs should stay updated with the latest technologies and tools that can help them in their work. This includes new AI technologies as well as other technologies coming down the pipeline.

A key skill for the future is data literacy. In a recent report, 31% of organizations said data-related skills like data management, analytics and big data, are the highest talent development priorities.

And with AI taking over the manual aspects of the job, the PMs role will become even more human-centric, with soft-skills like critical thinking, conflict resolution, and deal making becoming more important.

A culture of learning within companies is important as almost 30% of employees consider learning as a key factor when considering a new position.

 

Think of AI Project Management Tools as “Interns”

Virtual assistants will gradually shift PM activities towards coaching and stakeholder management. AI tools are best understood as competent interns that need some oversight while doing administrative or manual tasks.

The dominant paradigm will involve PMs dealing with more human-oriented tasks that involve soft skills, deep market research, and making critical decisions on product positioning.

 

Picking the Best AI Project Management Tools

Intelligence: Any artificial intelligence must be powerful enough to tackle the task at hand. When looking for a tool, get one that gives you high quality output when automating tasks, analyzing data, and providing context.

User-friendliness: PMs in competitive industries are often time stressed. An AI project management tool must be easy to use. The best tools give you options both for a prompt-based interface and a button-based interface.

Interaction with other tools: An AI tool in isolation is not useful to project managers. However, an AI tool that is integrated into a larger ecosystem of tools is extremely valuable. The best tools typically come with packaged software that allow PMs to document, test, and review their project management performance.

Collaboration: PMs know that success in any project is a team sport. So, an AI tool that simplifies project requirements will make it easier for developers to understand business analysts and vice-versa. A tool that can translate requirements accurately is even better since distributed teams can perform across the world.

 

AI Project Management Tools are the Future

In conclusion, AI has the potential to significantly enhance project management practices. By embracing AI tools like virtual project assistants, continuously learning about new technologies, using AI for improved decision-making, automating administrative tasks, and using AI for project selection and prioritization, project managers can adapt to this new landscape for improved outcomes and profitability. It’s an exciting time for project management as we explore these new possibilities.

 

 

Source: Futuristic Architect Businessman Industry 40 Engineer Stock Photo 1196903896 | Shutterstock [AS1]
Caption: AI-driven project management increases employee productivity across the board. [AS2]
Source: AI Improves Employee Productivity by 66% (nngroup.com) [AS3]
Caption: Summarizing helps project managers get the gist of long documents to make quick decisions. [AS4]
Caption: AI is best seen as a project management intern. [AS5]
Source: Blue Printer Paper · Free Stock Photo (pexels.com) [AS6]
Caption: Learning and career growth are particularly valued among younger employees. [AS7]
Source: Workplace learners: new workplace expectations | Statista [AS8]