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course | Advanced AI Project Management and Implementation

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DIGTR-3168 | Advanced AI Project Management and Implementation

Course Sector : Digital Transformation and Innovation

Duration
Date from
Date to Course Venue Course fees Book a course
5 Days06/09/202610/09/2026Online$2,150 Book now
5 Days23/11/202627/11/2026Dubai$4,250 Book now
5 Days29/03/202702/04/2027Athens$4,950 Book now
5 Days18/04/202722/04/2027Riyadh$4,250 Book now

Course Introduction

Artificial Intelligence (AI) initiatives are complex, requiring careful planning, cross-functional coordination, and rigorous management to deliver tangible business value. This program equips participants with advanced skills to manage AI projects end-to-end—from ideation and use case definition to deployment and evaluation. Participants will learn how to define AI project objectives, manage resources, and align technical delivery with business outcomes.

 

The course emphasizes practical strategies for working with data scientists, engineers, and stakeholders to ensure projects meet business goals within realistic timelines. Participants will gain tools, frameworks, and best practices to mitigate risks, monitor progress, and optimize AI project success.


Course objective

  • Define AI use cases that align with strategic business objectives.
  • Develop detailed AI project plans, including timelines, milestones, and resources.
  • Coordinate effectively with data, engineering, and development teams.
  • Monitor AI project progress and implement risk mitigation strategies.
  • Ensure AI initiatives deliver measurable business outcomes and value.

Course audience

  • AI Project Managers / AI Program Managers

  •  Digital Transformation & AI Transformation Managers
  •  Product Managers (AI / Data Products)

Course Outline | DAY 01

AI Strategy and Use Case Development

 

  • Identifying opportunities for AI within the organization
  • Aligning AI initiatives with business strategy and goals
  • Defining high-impact AI use cases
  • Prioritizing AI projects based on feasibility and value
  • Establishing success metrics and KPIs

Course Outline | Day 02

AI Project Planning and Governance

 

  • Developing AI project roadmaps and timelines
  • Resource planning: teams, budgets, and infrastructure
  • Defining roles and responsibilities for AI initiatives
  • Risk assessment and mitigation strategies
  • Project governance and reporting frameworks

Course Outline | Day 03

Collaboration with Data and Development Teams

 

  • Understanding data requirements for AI projects
  • Coordinating with data engineers, data scientists, and software developers
  • Managing dependencies and integration challenges
  • Agile and iterative approaches for AI project delivery
  • Communication strategies for cross-functional teams

Course Outline | Day 04

AI Model Deployment and Change Management

 

  • Planning for deployment in production environments
  • Monitoring AI model performance and quality
  • Addressing model drift and performance issues
  • Change management and adoption strategies
  • Feedback loops for continuous improvement

Course Outline | Day 05

Evaluating Outcomes and Scaling AI Initiatives

 

  • Measuring project success against business KPIs
  • ROI analysis for AI initiatives
  • Lessons learned and knowledge sharing
  • Scaling AI solutions across the enterprise
  • Case studies of successful AI project delivery
Course Certificates
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BOOST’s Professional Attendance Certificate “BPAC”

BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.

Request a Quote
Yes. AI-powered project management tools help automate scheduling, resource allocation, risk prediction, task prioritization, and progress tracking to improve project outcomes.
Yes. The CPMAI (Cognitive Project Management for AI) certification is valuable for professionals managing AI projects, as it provides practical frameworks for planning, delivering, and governing AI initiatives.
Many AI projects fail because of poor data quality, unclear business objectives, lack of stakeholder support, insufficient expertise, unrealistic expectations, and weak change management.
Advanced AI projects include predictive analytics platforms, recommendation systems, generative AI applications, computer vision solutions, fraud detection systems, intelligent chatbots, and autonomous decision-making systems.
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