Advanced Certification in AI & Digital Transformation Project Management
Lead AI-Driven Projects with Strategic Precision This 6-month advanced program is designed to bridge the knowledge gap between traditional project management and the dynamic world of AI and digital transformation. Tailored for aspiring project leaders, the course offers real-world project sandbox simulations and training in tools used by modern tech leaders.
Cohort Info
- Program Duration: 6 Months (110 Days | 24 Weeks)
- Next Cohort Launch:
- Application Deadline:
Key Highlights
- Combines traditional Project Management techniques with AI-driven tools
- Strategic focus on digital transformation in enterprise contexts
- Project Sandbox to simulate real-world AI project execution
- Certification support aligned with PMI and AICTE guidelines
- Ideal for future project managers, team leads, and transformation officers
About Program
Course Curriculum
Modules designed to meet current industry standards.
01
Foundations of Project Management
02
Agile & Hybrid Project Management Frameworks
03
Digital Transformation Essentials
04
AI for Project Managers
05
Strategic Roadmapping & Business Case Development
06
AI-Enabled PM Tools & Platforms
07
AI Solution Design & Delivery Lifecycle
08
Team Management & Leadership in AI Projects
09
Risk Management & Ethical Governance in AI Projects
10
AI Sandbox Project Execution
11
Capstone Project
12
Career Readiness & Global Certification Preparation
What You’ll Learn
Essential Skills & Tools for Leading Projects in the Digital Age







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Real People. Real Results
Real stories of career growth, skill mastery, and success after MSM Grad programs.
Shreya N.
A final-year computer science student
I didn’t want just theory; I wanted something portfolio-driven. I was able to create a small microservice, dockerize it, and deploy it to Kubernetes using a Jenkins pipeline thanks to the containers + orchestration block and the CI/CD module. As my capstone, I posted documentation of the Terraform templates and monitoring (Prometheus + alerts) on GitHub. Even though I’m just starting out, interviewers now ask me how I designed things rather than just what I learned by heart. I felt at ease switching between the AWS and Azure consoles thanks to the multi-cloud labs.
Meera K.
Manufacturing Java Developer
I was concerned that the learning curve would be high because I come from the electronics industry. I didn’t get lost because of the order: foundations → networking/storage → IaC → security. I was able to practice explaining VPC design, RBAC, and the rationale behind our Terraform module selection thanks to weekend lab access. I can design a simple scalable setup and ship it through a CI/CD pipeline without assistance, but I don’t consider myself a “cloud architect” just yet. I was searching for that clarity.
Mohammad I.
A recent ECE graduate moving into cloud security
The security jargon made me anxious. It was manageable because of the pace: foundations → cloud-native controls → incident response. I created a small project that added basic DAST checks to a pipeline and used Terraform with secrets management. I can describe what I did and why, but it’s not enterprise-grade yet. Surprisingly helpful for interviews was the stakeholder communication coaching (what to report, when).
Zoya K.
CEO and co-founder (SaaS)
I needed a repeatable story for my team, investors, and clients—not theory. Our roadmap became a straightforward arc: problem → choice → risk → evidence, thanks to the business storytelling block. We now have a one-page template and designated spokespersons after the crisis drill revealed weaknesses in our incident communications. When my messaging was unclear, the faculty reacted negatively, which was precisely the feedback I was lacking.
Naveen T.
HR Business Partner (Manufacturing)
I came in for ‘leadership polish’ and left with practical tools: stakeholder influence grids, meeting charters, and a change narrative that survives scrutiny. The case discussions with IIM faculty were rigorous without being academic for its own sake. On a policy rollout, early manager briefings and clearer talking points improved attendance and reduced back-and-forth. It’s steady progress, not overnight transformation, and that feels sustainable.
Priya S.
IT Services Project Manager
I needed to transition into AI projects after seven years in traditional project delivery. The AI Sandbox and capstone, scoping an NLP pilot, developing a business case, and stress-testing risks were the true value, but the 6-month structure worked for my schedule. I’m a better project manager for AI projects now. Stakeholder updates are now much more transparent when Jira and Power BI are used especially for model lifecycle reporting.
Rahul M
Senior Project Manager
Coming from a PMO, concepts like model lifecycle and data governance felt ambiguous. I was finally able to work with data teams thanks to the “AI for Project Managers” and “Solution Design & Delivery” modules. The 60-hour live sessions and hybrid format were doable. An added benefit was the PMI-aligned preparation. I led an internal automation proof-of-concept right after finishing,with cleaner milestones and more realistic risk controls.
Sofia L.
Lead for Product Operations
A vague “let’s do AI” request was transformed into a phased plan with costs, outcomes, and constraints through the use of the roadmap and business-case sessions. By defining acceptance criteria, assessing impact, and estimating data requirements, the Sandbox kept things realistic. Although the tools weren’t new, I needed to make the change by using Jira/MS Project especially for AI delivery. Content that is practical and instantly helpful.
Arjun K.
Manufacturing SME Team Lead
I was concerned that it would be overly technical. It wasn’t. My ability to intelligently challenge vendors was aided by the ethics, risk, and compliance module, and the Oracle/Power BI labs produced dashboards that our leadership actually reads. I came away with a repeatable AI project charter template that was more structured and less hype. Credibility is increased by the certificate, but my communication and planning skills are what really win.
Meera K.
Manufacturing QA & Automation Lead
The pivotal moment was the RNN/LSTM/GRU module. I developed an LSTM to predict test-environment failures under the mentor’s guidance, and I discovered when a more straightforward GRU made more sense. Instead of focusing on leaderboard scores, the program stresses sound baselines, transparent metrics, and meticulous validation. Training times were realistic thanks to weekly labs on cloud GPUs. Instead of using hype, I now brief stakeholders on model assumptions and trade-offs.
Sara T.
Early Career ML Engineer
The balance between perceptrons and backprop from first principles, optimization (SGD/Adam), and modern architectures was what I valued most. I had to take responsibility for preprocessing through deployment, not just model code, because the capstone project required me to work end-to-end on an actual dataset. Although I’m still learning—GANs will require more practice—I now have a methodical approach to experimenting, documenting, and sharing my work through GitHub. HR screening was aided by the NEP/NCrF-aligned credential; however, shipping a functional model provided the true assurance.
Real People. Real Results
Real stories of career growth, skill mastery, and success after MSM Grad programs.
Shreya N.
A final-year computer science student
I didn’t want just theory; I wanted something portfolio-driven. I was able to create a small microservice, dockerize it, and deploy it to Kubernetes using a Jenkins pipeline thanks to the containers + orchestration block and the CI/CD module. As my capstone, I posted documentation of the Terraform templates and monitoring (Prometheus + alerts) on GitHub. Even though I’m just starting out, interviewers now ask me how I designed things rather than just what I learned by heart. I felt at ease switching between the AWS and Azure consoles thanks to the multi-cloud labs.
Meera K.
Manufacturing Java Developer
I was concerned that the learning curve would be high because I come from the electronics industry. I didn’t get lost because of the order: foundations → networking/storage → IaC → security. I was able to practice explaining VPC design, RBAC, and the rationale behind our Terraform module selection thanks to weekend lab access. I can design a simple scalable setup and ship it through a CI/CD pipeline without assistance, but I don’t consider myself a “cloud architect” just yet. I was searching for that clarity.
Mohammad I.
A recent ECE graduate moving into cloud security
The security jargon made me anxious. It was manageable because of the pace: foundations → cloud-native controls → incident response. I created a small project that added basic DAST checks to a pipeline and used Terraform with secrets management. I can describe what I did and why, but it’s not enterprise-grade yet. Surprisingly helpful for interviews was the stakeholder communication coaching (what to report, when).
Zoya K.
CEO and co-founder (SaaS)
I needed a repeatable story for my team, investors, and clients—not theory. Our roadmap became a straightforward arc: problem → choice → risk → evidence, thanks to the business storytelling block. We now have a one-page template and designated spokespersons after the crisis drill revealed weaknesses in our incident communications. When my messaging was unclear, the faculty reacted negatively, which was precisely the feedback I was lacking.
Naveen T.
HR Business Partner (Manufacturing)
I came in for ‘leadership polish’ and left with practical tools: stakeholder influence grids, meeting charters, and a change narrative that survives scrutiny. The case discussions with IIM faculty were rigorous without being academic for its own sake. On a policy rollout, early manager briefings and clearer talking points improved attendance and reduced back-and-forth. It’s steady progress, not overnight transformation, and that feels sustainable.
Priya S.
IT Services Project Manager
I needed to transition into AI projects after seven years in traditional project delivery. The AI Sandbox and capstone, scoping an NLP pilot, developing a business case, and stress-testing risks were the true value, but the 6-month structure worked for my schedule. I’m a better project manager for AI projects now. Stakeholder updates are now much more transparent when Jira and Power BI are used especially for model lifecycle reporting.
Rahul M
Senior Project Manager
Coming from a PMO, concepts like model lifecycle and data governance felt ambiguous. I was finally able to work with data teams thanks to the “AI for Project Managers” and “Solution Design & Delivery” modules. The 60-hour live sessions and hybrid format were doable. An added benefit was the PMI-aligned preparation. I led an internal automation proof-of-concept right after finishing,with cleaner milestones and more realistic risk controls.
Sofia L.
Lead for Product Operations
A vague “let’s do AI” request was transformed into a phased plan with costs, outcomes, and constraints through the use of the roadmap and business-case sessions. By defining acceptance criteria, assessing impact, and estimating data requirements, the Sandbox kept things realistic. Although the tools weren’t new, I needed to make the change by using Jira/MS Project especially for AI delivery. Content that is practical and instantly helpful.
Arjun K.
Manufacturing SME Team Lead
I was concerned that it would be overly technical. It wasn’t. My ability to intelligently challenge vendors was aided by the ethics, risk, and compliance module, and the Oracle/Power BI labs produced dashboards that our leadership actually reads. I came away with a repeatable AI project charter template that was more structured and less hype. Credibility is increased by the certificate, but my communication and planning skills are what really win.
Meera K.
Manufacturing QA & Automation Lead
The pivotal moment was the RNN/LSTM/GRU module. I developed an LSTM to predict test-environment failures under the mentor’s guidance, and I discovered when a more straightforward GRU made more sense. Instead of focusing on leaderboard scores, the program stresses sound baselines, transparent metrics, and meticulous validation. Training times were realistic thanks to weekly labs on cloud GPUs. Instead of using hype, I now brief stakeholders on model assumptions and trade-offs.
Sara T.
Early Career ML Engineer
The balance between perceptrons and backprop from first principles, optimization (SGD/Adam), and modern architectures was what I valued most. I had to take responsibility for preprocessing through deployment, not just model code, because the capstone project required me to work end-to-end on an actual dataset. Although I’m still learning—GANs will require more practice—I now have a methodical approach to experimenting, documenting, and sharing my work through GitHub. HR screening was aided by the NEP/NCrF-aligned credential; however, shipping a functional model provided the true assurance.
Designed for Ambitious Professionals
- AI Project Manager
- Digital Transformation Lead
- Innovation Program Manager
- Technical Program Manager (TPM)
- Strategic Consultant for AI Projects
30- 60% Average Hike
Post Course Completion
Mid-Level: ₹10–15 LPA
Senior PMs: ₹18–28 LPA
Designed for Ambitious Professionals
- AI Project Manager
- Digital Transformation Lead
- Innovation Program Manager
- Technical Program Manager (TPM)
- Strategic Consultant for AI Projects
30- 60% Average Hike
Post Course Completion