AI projects often stall—not because the technology isn’t ready, but because they fail to align with strategy, prove financial viability, or persuade executives to fund them. Align AI: Finance, Strategy, and Funding equips intermediate-level project and program managers with the skills to bridge that gap. In Lesson 1, you’ll analyze corporate strategy and OKRs to confirm that AI initiatives directly support financial targets. In Lesson 2, you’ll evaluate financial viability using cost-benefit analysis, discounted cash flow, and sensitivity testing to assess whether AI features create measurable value. In Lesson 3, you’ll integrate strategy and financials into a persuasive, executive-ready business case designed to secure funding. Through case studies, readings, hands-on labs, and reflective dialogues with Coursera Coach, you’ll practice real-world tasks like backlog prioritization, financial modeling, and business case drafting. By the end, you’ll be ready to turn promising AI ideas into fundable, strategically aligned initiatives.

AI projects involve shifting data, evolving models, and strict governance needs that traditional project management often cannot address. This short course helps you plan, track, and deliver AI initiatives using practical, job-ready tools. You’ll learn to track project health, evaluate deliverables for quality, and apply agile and CRISP-DM methods for adaptive progress. By the end, you’ll be able to identify risks early and lead AI projects with confidence.

This course teaches learners how to analyze, evaluate, and systematically manage risks in AI projects. Learners explore technical, regulatory, and operational risks across the system lifecycle, from data collection to deployment and monitoring. They practice comparing mitigation strategies using structured tradeoff frameworks that weigh cost, timeline, and effectiveness. Hands-on activities include facilitating a SWIFT session to surface data-privacy risks, evaluating privacy-preserving techniques, and configuring tools like Jira to track risks automatically.

Learners also build and submit a sample risk register that scores, prioritizes, and documents risks with clear ownership and mitigation plans. By the end, learners will confidently identify and manage AI risks, apply structured frameworks to real-world projects, and create practical documentation that strengthens accountability, compliance, and decision-making in AI initiatives.

Every AI project begins with optimism and ends with negotiations. This course gives you the tools to keep both in check so the work stays meaningful and finishable. In this course, you’ll learn how to define and manage the scope of AI projects so they deliver measurable business value. Designed for intermediate project and program management professionals, this course bridges traditional project management practices with the unique challenges of AI initiatives. You’ll practice analyzing functional and non-functional requirements to determine what’s in-scope versus out-of-scope, and translate these into scope statements and Work Breakdown Structures (WBS) that align with time, budget, and compliance constraints. Through scenarios, discussions, and hands-on activities, you’ll gain the confidence to prevent scope creep, manage trade-offs, and keep AI projects on track. By the end, you’ll have practical tools to structure AI initiatives and communicate scope decisions clearly with stakeholders.

Successful AI projects start with clarity, not code. This short, hands-on course helps you turn vague business goals into structured, measurable, and feasible AI problem statements. You’ll learn to evaluate whether your data is ready for modeling, estimate labeling requirements, and identify early risks such as imbalance, poor quality, or limited resources. Using real-world scenarios, you’ll apply the SMART framework to define objectives that are specific, measurable, achievable, relevant, and time-bound. By connecting business outcomes with technical success metrics like precision and recall, you’ll gain the confidence to frame AI projects that deliver measurable impact and align teams from idea to implementation.

Optimize AI: Plan, Evaluate, and Learn – equips project and program managers with the skills to guide AI systems through change and uncertainty. In this course, you’ll learn how to analyze performance data to plan retraining, evaluate algorithm families under real-world constraints, and design continuous-learning strategies with canary deployments and rollback safeguards. Through scenario-based discussions, hands-on activities, and practical tools like MLflow dashboards, evaluation matrices, and retraining calendars, you’ll practice making informed decisions under pressure. By the end, you’ll be able to detect risks early, balance accuracy and speed, and sustain reliable AI systems that align with business goals.

AI projects succeed or fail on the strength of their data. In this course, you’ll learn to diagnose pipeline bottlenecks, enforce governance standards, and plan data workflows that keep projects on track. Through real-world scenarios, you’ll trace issues like stale feature vectors, audit data handling for compliance with regulations such as General Data Protection Regulation (GDPR), and design actionable plans for data acquisition, storage, and annotation. You’ll also practice translating technical findings into project-ready deliverables, like drafting a Jira epic for large-scale data labeling.

Engineer AI Models: Explain, Tune & Experiment prepares program and project managers to guide AI projects beyond “just working” toward being trusted, explainable, and reproducible. You’ll learn how feature engineering and hyperparameter tuning improve model performance, how explainability methods like SHAP and LIME build stakeholder confidence, and how structured experimentation ensures reliable results. Through real-world scenarios — from boosting fraud detection F1 scores, to presenting credit approval models to risk committees, to planning experiments in Jupyter — you’ll gain the skills to ask the right questions, guide technical teams, and translate complex model outputs into business impact. By the end, you’ll know how to move AI projects from black box to business-ready.

AI models create value, but they also create risks — from data drift and bias to regulatory non-compliance. In this short, practical course, you’ll learn how to make those risks visible, measurable, and governable. First, you’ll explore the main categories of model risk and practice mapping them to governance controls and KPIs. Next, you’ll learn how to evaluate model validation results against standards such as SR 11-7, the Basel Principles, and the EU AI Act, identifying compliance gaps and recommending corrective actions. Finally, you’ll draft a simple model-risk control framework with clear documentation standards, escalation paths, and review cadences. By the end, you’ll be able to demonstrate governance skills that help organizations deploy AI responsibly and maintain trust.

Align, Analyze, and Communicate: AI Projects - Learner will be able to bring stakeholders into alignment, spot and fix communication breakdowns, and design clear communication plans that keep AI projects on track. Through videos, readings, practice activities, and a final scenario-based assessment, learners would've built the skills to facilitate collaboration, improve information flow, and match communication strategies to the needs of different groups. These practical tools will help them guide their own projects with more confidence and clarity, ensuring smoother teamwork and stronger outcomes in real-world AI contexts.

Deploying an AI model is only the beginning—keeping it reliable, explainable, and impactful in production requires strong MLOps skills. In this course, learners apply best practices to orchestrate the deployment lifecycle using continuous integration, continuous delivery, and tools like GitLab and Kubernetes. They analyze real telemetry data to investigate error spikes, trace root causes, and resolve performance issues with monitoring platforms such as Kibana. Finally, learners evaluate whether deployed models deliver on technical and business goals, comparing KPIs like conversion lift against targets and recommending next steps. Through guided labs, case studies, and discussions, learners gain practical experience in deploying, diagnosing, and evaluating AI systems with confidence.

Managing AI projects requires more than ambition; it requires precision in planning and evaluation. In this course, Learners will learn how to define clear, measurable milestones with exit criteria, map dependencies to uncover critical path risks, and evaluate milestone completion reports against scope, quality, and readiness standards. Through videos, readings, and hands-on practice, they’ll gain confidence in turning vague project goals into structured milestones that drive accountability. Learners will practice using tools like PERT charts to identify blockers, analyze real-world milestone conflicts such as GPU procurement delays, and work through case studies where they must decide whether to approve or reject milestone closure. By the end, learners will be able to create milestone schedules, anticipate risks, and make evidence-based go/no-go decisions that ensure AI projects stay on track and deliver results with quality.

Artificial intelligence (AI) projects are some of the most exciting and fast-moving initiatives in today’s organizations. But while AI systems can fail because of technical problems, in practice they often fail for another reason: poor execution. Blockers aren’t tracked, responsibilities blur, teams lose alignment, or deliverables don’t meet the quality standards promised to stakeholders.

This course, AI Project Implementation: Playbooks, QA, and Readiness, is designed to help you avoid those pitfalls. It focuses on two practical skills that every project manager and program lead needs: coordinating project workstreams with implementation playbooks and validating deliverables through quality assurance (QA) and acceptance testing. Together, these skills ensure that AI projects don’t just get built—they get delivered in a way that is reliable, accountable, and ready for real-world deployment.