Embracing AI in Project Management: Final Insights & Future Vision
- 1. Session 13: Final Session — Embracing AI AI Applications in Project Management Workshop OnAcademy Kaveh Momeni May 2025
- 2. Agenda
- 3. Session 13: Final Session — Embracing AI Empowering Project Managers for an AI-Driven Future ● Introduction ● Course Recap ● AI Trends & Future of Project Management ● AI vs. Human Capabilities in PM ● Optimizing Human-AI Collaboration ● Cultivating AI-Ready Mindsets ● Essential Skills for Future-Proof PMs ● Implementation Roadmap & Best Practices ● Ethical & Practical Considerations ● Q&A
- 4. Introduction
- 5. ● 15+ Years of Project Management Over 100 delivered projects across software, construction, manufacturing, healthcare, and marketing. ● Certified PMP® (#1943115) & Global Credentials Includes IPMA, Google, and recognized bodies! ● Comprehensive AI/ML Certifications 12 specialized + 3 comprehensive from DeepLearning.AI 15 specialized + 2 comprehensive from IBM +25 specialized from Amazon, Nvidia, Google Cloud, Hugging Face & … ● Automation & AI Leadership at Chaharsotoon Advanced Jira-integrated AI systems, significantly streamlining workflows and enhancing multi-discipline collaboration. ● AI-Driven Project Optimization Implements real-time forecasting, risk analysis, and resource allocation for measurable performance gains. ● Focus on Digital Transformation Integrates AI solutions with legacy systems to streamline operations and accelerate ROI. Kaveh Momeni COB & AI Lead at Chaharsotoon @kvmmn
- 6. Journey So Far …
- 7. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 7 Course Recap ● Knowledge Management ● Risk Management & Predictive Analytics ● ChatGPT & Prompt Engineering ● Workflow Automation ● Training & Development ● Scope, Time & Cost Management ● Quality & Resource Management ● Procurement Management ● Stakeholder & Communication Management ● Change & Issue Management ● Decision Support Systems ● Continuous Learning & Improvement ● AI Agents & the Future of Work
- 9. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 1 — Rapid Adoption of AI in Project Management ●AI adoption in project management has surged, with 80% of companies moderately or extremely familiar with AI usage in PM functions or software. ●The driving force? Demonstrable improvements in efficiency, risk mitigation, and decision-making quality. 2 — Market Growth ●The AI in PM market is now projected to exceed $6.5 billion by 2028, with some analysts predicting an even faster ramp-up to $10 billion by 2030, fueled by generative AI and AI agent adoption. (Remember our previous stats? $5.7B by 2028, CAGR 17.3%) 3 — Task Automation ●It's now estimated that AI could automate up to 50% of routine project management tasks by 2028 (e.g., progress tracking, report generation, meeting scheduling), freeing up PMs for strategic work. (Our previous stats?! 42% by 2027). 9 AI Trends Impacting PM
- 10. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 4 — AI Compute Growth ●The doubling of AI-relevant compute power roughly every 6-10 months means increasingly sophisticated AI tools will become accessible, democratizing capabilities previously available only to large enterprises. 5 — Rise of AI Agents and Autonomous Project Assistants ●Intelligent AI agents are emerging that can independently manage sub-tasks, monitor risks, communicate updates, and even draft project documentation, acting as tireless assistants to the project team. Example: An AI agent could monitor a complex project schedule, flag potential delays based on resource availability and past performance data, and suggest alternative resource allocations – all with minimal human intervention. 10 AI Trends Impacting PM
- 11. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | Operational Efficiency Gains ●Studies show AI tools can boost PM productivity by 20-30% by automating tasks like scheduling, resource allocation, and progress reporting. Enhanced Risk Management ●AI can analyze vast datasets of historical project data, identify subtle risk patterns, and even predict 'black swan' events with greater accuracy than traditional methods. Think early warnings for supply chain disruptions or subcontractor defaults based on sentiment analysis of news and financial reports. Smarter Decision-Making ●Leverage AI for 'what-if' scenario planning, optimizing project plans under various constraints, and providing data-backed recommendations for critical decisions. Explainable AI (XAI) is becoming crucial here, ensuring PMs understand the 'why' behind AI suggestions. 11 Key Drivers for AI Integration in PM
- 12. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | Cost Optimization ●AI can optimize resource usage, predict budget overruns with greater accuracy, and identify cost- saving opportunities in procurement and execution. Team Augmentation, Not Replacement ●AI handles the repetitive, data-intensive tasks, allowing human team members to focus on creativity, critical thinking, complex stakeholder management, and leadership – areas where human intelligence excels. Increased Competitive Advantage ●25% increase in EBITDA (McKinsey). ●Companies leveraging AI in PM report faster project delivery times, improved quality, and higher client satisfaction, leading to a distinct competitive edge and market leadership. Strategic Alignment ●AI tools can ensure individual projects remain tightly aligned with overarching business objectives by continuously monitoring KPIs and providing insights on portfolio performance. 12 Key Drivers for AI Integration in PM
- 13. AI in Action
- 14. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | Healthcare AI optimizing clinical trial timelines, predicting patient load for resource allocation, and personalizing project plans for medical device development. Logistics AI-powered demand forecasting, dynamic route optimization saving millions in fuel, and automated warehouse management reducing errors and improving speed. Construction AI for predictive maintenance of equipment, site safety monitoring via computer vision (detecting PPE non-compliance), and optimizing construction sequencing to reduce delays and costs. 14 Real-World AI in Action
- 15. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | Software Development AI-assisted code generation, automated testing, bug prediction, and intelligent sprint planning based on team velocity and complexity. Marketing AI for campaign performance prediction, personalized content generation for stakeholder communications, and optimizing marketing spend across project phases. Energy Sector AI for predicting energy demand, optimizing grid management projects, and managing renewable energy project installations efficiently. 15 Real-World AI in Action
- 16. Human + AI
- 17. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | Strengths of AI ●Automation of routine tasks (e.g., report drafting, meeting summaries, initial risk registers). ●Advanced predictive risk and cost analysis using complex algorithms and vast datasets. ●Unparalleled efficiency, consistency, and speed in data processing and pattern recognition. ●Generating creative first drafts for project plans, communication, or even brainstorming solutions. 17 Generative AI vs. Human Project Managers
- 18. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | Strengths of Humans ●Strategic judgment, nuanced ethical oversight, and complex decision-making in ambiguous situations. ●Deep complex problem-solving, creativity, innovation, and sophisticated interpersonal skills (empathy, negotiation, leadership). ●Contextual understanding, adaptability to unforeseen circumstances, and 'gut feeling' based on experience. ●Building trust and rapport with stakeholders, motivating teams, and managing human-centric conflicts. 18 Generative AI vs. Human Project Managers
- 19. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 19 Human-AI Collaboration – The Optimal Model ● AI as initial draft and routine manager Let AI handle the first pass on documents, data analysis, scheduling options, and monitoring routine metrics. ● Human oversight for strategic refinement and ethical alignment PMs then apply critical thinking, contextual knowledge, and ethical considerations to refine AI outputs, make strategic choices, and ensure alignment with human values and organizational goals. ● Importance of Prompt Engineering This is a critical new skill. Clearly defining tasks, providing context, and structuring queries for AI (especially Generative AI) is key to getting valuable outputs. Think of it as 'briefing' your AI assistant effectively. ● Collaboration Frameworks (fixed vs. growth mindset) Organizations need frameworks where AI tools are seamlessly integrated into PM workflows, and teams are encouraged (growth mindset) to experiment, learn, and adapt with these new tools. ● Iterative Feedback Loops Establish processes for PMs to provide feedback on AI performance, allowing for continuous improvement of AI models and their application within the specific organizational context.
- 20. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 20 Organizational Mindsets & AI Integration ● Fixed vs. Growth Mindset in Project Teams ○ Fixed Mindset: Views AI as a threat, resists new tools, fears obsolescence. Leads to slow adoption and missed opportunities. ○ Growth Mindset: Views AI as an opportunity, embraces learning, experiments with new tools, focuses on augmentation. Leads to innovation and competitive advantage. ● Impact of organizational culture on AI adoption success A culture that encourages experimentation, tolerates initial mistakes (as learning opportunities), promotes cross-functional collaboration, and champions AI literacy from the top down will see significantly higher success rates in AI integration.
- 21. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 21 Organizational Mindsets & AI Integration ● Strategies to cultivate growth-oriented cultures: ○ Leadership buy-in and active advocacy for AI. ○ Invest in continuous training and AI literacy programs for all PMs. ○ Create 'AI sandboxes' or pilot programs for safe experimentation. ○ Celebrate early wins and share success stories of AI-human collaboration. ○ Incentivize AI adoption and skill development. ○ Foster psychological safety, so team members feel comfortable asking questions, trying new AI tools, and even failing forward.
- 22. Way Ahead …
- 23. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 23 Future-Proofing Your PM Career 1 — AI literacy & prompt engineering ●Beyond just understanding AI concepts, it's about knowing how to effectively interact with, guide, and leverage AI tools – especially mastering prompt engineering for generative AI. 2 — Data-driven decision making ●Developing skills in data interpretation, understanding AI-generated analytics, and using these insights to make informed, strategic decisions. This includes basic understanding of data science principles. 3 — Enhanced communication & transparency ●Clearly communicating AI-driven insights to diverse stakeholders, explaining how AI is used in projects, and fostering trust in AI-assisted processes. 4 — Ethical and responsible AI governance ●Understanding the ethical implications of AI in PM (bias, privacy, accountability), and championing responsible AI practices within projects and organizations.
- 24. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 24 Future-Proofing Your PM Career 5 — Adaptability and Continuous Learning ●The AI landscape is evolving rapidly. A commitment to lifelong learning and adaptability will be paramount. 6 — Strategic Thinking & Complex Problem Solving ●As AI handles routine tasks, PMs will need to elevate their focus to more strategic challenges and complex, multi-faceted problems that require human ingenuity. 7 — Change Management ●Clearly communicating AI-driven insights to diverse stakeholders, explaining how AI is used in projects, and fostering trust in AI-assisted processes. 8 — Ethical and responsible AI governance ●Guiding teams and organizations through the transition of integrating AI into their workflows and culture.
- 25. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 25 Strategic Implementation Roadmap 1. Pilot AI integration (small-scale projects) 2. Iterative scaling with continuous feedback 3. Ethical AI implementation and transparency standards 4. Ongoing education and skill refinement 5. Develop an AI Center of Excellence (CoE) or Champion Network
- 26. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 26 Challenges & Ethical Considerations ● Data quality and bias risks AI models are only as good as the data they are trained on. Biased or poor-quality data can lead to skewed insights, unfair outcomes, and flawed project decisions. Emphasize the need for robust data governance. ● Transparency and explainability The 'black box' nature of some AI can be a barrier. Project managers and stakeholders need to understand, at an appropriate level, how AI arrives at its conclusions, especially for critical decisions. Demand XAI features from vendors. ● Regulatory compliance and ethical AI use Navigating evolving regulations like the EU AI Act. Ensuring AI use aligns with legal requirements, industry standards, and ethical principles (e.g., fairness, accountability, privacy).
- 27. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 27 Challenges & Ethical Considerations ● Human-centric values in AI-driven PM Ensuring AI serves human goals and values, augments human capabilities rather than devaluing them, and that ultimate accountability remains with human project managers. ● Integration Complexity Integrating new AI tools with existing legacy PM systems and enterprise platforms can be technically challenging and costly. ● Job Displacement Fears & Resistance to Change Address concerns about AI replacing jobs by focusing on augmentation and the new skills required. Manage change proactively. ● Security Risks AI systems and the data they process can be targets for new types of cyber threats. Robust security measures are essential.
- 28. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 28 Navigating the AI Ethical Landscape ● Governance frameworks (GDPR, EU AI Act) Understand the principles of major regulations and how they apply to project data and AI systems. The EU AI Act, for example, categorizes AI systems by risk level, with significant implications for PM tools classified as high-risk. ● Case studies of AI ethical challenges (e.g., Amazon, Apple Card) ● Importance of proactive monitoring and governance Establish internal AI ethics boards or review processes. Regularly audit AI tools for bias and performance. Ensure clear lines of accountability for AI systems used in projects. ● Building "Responsible AI by Design" Incorporate ethical considerations from the very beginning of AI tool selection, development, or integration in project management, not as an afterthought.
- 29. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 29 Vision for AI-Enabled Project Management ● AI as a strategic partner, not just a tool or replacement Envision AI as an intelligent collaborator that provides foresight, uncovers hidden opportunities, and empowers PMs to make more strategic, impactful decisions. ● Enhanced project outcomes through intelligent tools Faster delivery, lower costs, reduced risks, improved quality, and significantly higher stakeholder satisfaction. Projects that consistently meet or exceed their objectives. ● Sustainable competitive advantage through continuous innovation Organizations that effectively embrace AI in PM will continuously learn, adapt, and innovate, creating a lasting edge in their respective industries. AI will fuel a new wave of project management excellence. ● The Rise of the "AI-Augmented PM" A new breed of project manager who skillfully blends human expertise in leadership, strategy, and stakeholder engagement with the analytical power and efficiency of AI. This PM is more strategic, more effective, and more valuable than ever.
- 30. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 30 ● Engage in AI literacy and certification Actively seek out courses, workshops (like this one 😅!), and certifications in AI and its application in PM. ● Foster a collaborative, growth-oriented team culture Champion AI adoption within your teams and organization. Encourage experimentation, knowledge sharing, and a mindset of continuous learning. ● Develop comprehensive governance and ethical standards Advocate for or participate in creating clear ethical guidelines and governance frameworks for AI use in your projects and organization. ● Connect and collaborate (LinkedIn, AI communities, forums) Join professional groups, attend webinars, and engage in discussions to stay updated on the latest AI trends, tools, and best practices in project management. Next Steps?
- 31. AIPM Workshop | S2. Risk Management & Predictive Analytics | OnAcademy | March 2025 | 31 1. Identify One PM Task to Experiment with AI This Quarter Pick a routine task (e.g., meeting minutes, risk brainstorming, status report drafting) and explore how an AI tool could assist. 2. Become an AI Champion in Your Organization Share what you've learned. Volunteer for pilot projects. Help demystify AI for your colleagues. 3. Start building your "Prompt Library" Begin curating effective prompts for common PM tasks that you can use with generative AI tools. New Actionable Steps?
- 32. - End of final session - Thank You! Questions?