CHAMP Success Story: Bridging Predictive and Agile Worlds with Hybrid-Agile Mastery

By Prasad Ramamurthy Kadambi, CHAMP, PMP 

Introduction

I’ve been a certified PMP and know traditional management well. 

Next, I had the drive to master both structure and agility, delivering complex projects that demand the discipline of predictive planning as shown in PMP and the responsiveness of Agile in CHAMP.

This inspired me to pursue CHAMP certification.

CHAMP Certification Study

When I set out to become a CHAMP, I knew that success would come not from cramming, but from building a genuine understanding of how predictive and adaptive worlds come together.

Initially, I struggled a lot to install MS Project 2019/21/24 Online Desktop Client. Satya helped me to get the software installed on my PC.

I knew it won’t be easy to proceed with the CHAMP certification with my work-schedule as a program manager in a multi-national, where my workload is high. I followed the following action-plan:

• I spent 1.5 hours daily and could spend 8 hours on weekends.
• I took only one set of questions to solve for my practice.
• I have referred only Satya’s Video Lessons for my certification. I’ve had no other references or sources. 

CHAMP Certification Review

True to its claim, CHAMP is the world's only practical, hands-on Hybrid-Agile certification equipping professionals to master Scrum, Kanban, and ScrumBan integration. In the real-world, 80% of organizations actually work in that way. It’s not purely predictive or agile. 

This certification covers all the three and hence unique. 

• Hybrid-Scrum Management 
• Hybrid-Kanban Management 
• Hybrid-ScrumBan Management

As you proceed with the certification course, you will cover many areas such as: 

• Practical hands-on learning for building tracking hybrid projects, 
• Baselining of Hybrid-Agile projects, 
• Earned Value Reporting (EVM), 
• Burndown/burnup charts, 
• Other reporting such as histograms, pie charts, and Cumulative Flow Diagrams (CFDs).

CHAMP Exam Experience

I took two months to prepare for the exam. My strategy to approach the exam was simple – just go by the learnings given in the lessons.

In the exam, there are fifty questions in total. Twenty questions were very tricky and I took more time to answer them.

The questions in the CHAMP certification are exceptionally well-crafted. It’s a true reflection of the rigor and depth this credential demands. They are mostly scenario-driven and decision-oriented, consistently challenging you to think like a practitioner and ask, "What should I do NEXT?"

After completing the course, I took the CHAMP certification exam and I cleared the exam in my first attempt. My score is below. 

Suggestions for Aspiring CHAMPs 

Dos 

Do build strong fundamentals first

Master predictive (waterfall), Scrum, and Kanban individually before attempting to understand how they blend. A shaky foundation makes hybrid concepts confusing.

Do get hands-on with MS Project Agile

Don't rely on theory alone. Practice configuring Sprints, navigating Task Board, Backlog Board, and Current Sprint views, and interpreting burndown charts, CFDs, and EVM metrics. The exam rewards practical familiarity.

Do think in scenarios

Train yourself to ask, "What should I do NEXT?" for Hybrid-Scrum, Hybrid-Kanban, and Hybrid-ScrumBan situations. The exam is decision-oriented, not definition-oriented.

Do understand the "why"

Study Stacey's Complexity Model to grasp when adaptive beats predictive. Context-driven judgment is more valuable than rote memory.

Do practice with tricky questions

Focus on the subtle traps – variance reports, filter behaviours, view differences, and Sprint management commands. These separate a pass from a failure.

Do study consistently

Maintain a steady daily rhythm over last-minute cramming. Reinforcement builds retention and confidence.

Don’ts

Don't memorize blindly

Rote-learning definitions won't help when the exam presents a real-world scenario requiring judgment. Understand concepts, don't just recall them.

Don't ignore the tool

Skipping hands-on MS Project Agile practice is a common pitfall. Many questions are tied directly to tool views, fields, and behaviours.

Don't treat Scrum and Kanban as interchangeable

Understand their distinct rules, boards, and metrics. Confusing the two (especially in ScrumBan) leads to errors.

Don't overlook the "hybrid" glue

The certification is about integration—don't study frameworks in silos without understanding how predictive and adaptive elements coexist in one plan.

Conclusion

CHAMP certification reflects not just theoretical knowledge, but the practical confidence to lead complex projects that demand both the discipline of predictive planning and the responsiveness of Agile.

Brief Profile:

Name: Prasad Ramamurthy Kadambi, CHAMP, PMP

Current role: Project/Program Manager, HP Inc.

Experience: Two decades of driving results at HP Inc. from Senior Quality Analyst and Asset Management to leading complex operations with a PMP-backed, outcome focused mindset.

You may also like:

• CHAMP Success Story: One of the Best Hybrid-Agile Certification Courses I Ever Attended
• CHAMP Success Story: A Niche Certification – Never Seen Anything Like This Before
• CHAMP and MSP 2016 Live Lessons – Excellent Courses for Mastering MS Project in both Traditional and Agile Environments

Management Yogi's CHAMP Certification:

• Certified Hybrid-Agile Master Professional (CHAMP)

Why CHAMP  – Top 10 Reasons:

• Top Ten Reasons To Go For CHAMP Certification

Artificial Intelligence, Simulations, Practical Scaled Agile (CIPSA) and the Future

Ich bin dein mensh! It’s not a special prompt used in a Large Language Model (LLM) under the Artificial Intelligence (AI) umbrella, but a German movie in the 2020s meaning I’m Your Man. 

The movie’s lead character is a highly-educated woman in her forties with no family, or husband, but only “career”. Though initially very hesitant, due to strong persuasion by her supervisor, she had to bring an AI-robot home to assist her in her geological work. Ironically, the robot went on to become her perfect lover! 

The robot looks exactly like a young man (human lookalike), very charming, understands her perfectly and never gets angry – unlike us, the humans. 

Over time, the AI learns from her likes and dislikes, pleasures and pains, highs and lows as well as cooks for her, brings flowers, dances with her, gives caregiving like medicines, and even makes her sleep. One-day the woman falls in love with the AI, and in fact, demands it from the AI-robot. 

When I watched the movie, I kept on thinking:

Can this happen? Is it even possible?

In this article we will explore simulations which are already there and sometimes they go beyond reality as with AI. We also explore AI’s impact on our lives as well as management considering various aspects. I’ll include many examples, including the ones from the Certified In Practical Scaled Agile (CIPSA) framework. See here to know more about CIPSA.

First let’s take an example of a map to understand simulations and AI.

Map to GPS to Simulation 

A paper map simulates space. It presents a static simulation of geography. It's a symbolic abstraction of terrain, borders and mountains. A map informs about the terrain, but is not the terrain itself. 

A GPS goes further. It provides dynamic – not static – simulation by guiding us in real time. It tells where to turn left or right, when to stop, or have an alternate route based on traffic condition. A GPS shapes how we perceive and travel in space. 

Now, AI systems such as driverless cars simulate the cognitive process behind space exploration. It not only tells us how to get somewhere, but where to go, or why it matters. It can factor in our curiosity, context, and our personalized goals.

So, in short: 

A map simulates geography. A GPS simulates navigation. But AI simulates decision-making. 

It’s shown in the below figure.  

In a driver-less car, the decision-making is based on the rider’s habits and likings. In other words, there is a human in the loop (HITL) and it may change the route of the car based on the rider in the car. The simulation in AI shifts from external space to internal cognition. 

Now, as a management professional and leader, you would be wondering:

• How does this fit into traditional or agile management?
• Can it be applied in various fields, e.g., Scaled Agile?
• How can it help us?

Let's take an example of tasks and Scrum board using the CIPSA certification. 

Task to CIPSA Board to Simulation

A task simulates work. Whether it’s a CIPSA Sprint Backlog or individual Team Sprint Backlogs, a task or list of tasks will always be there. It's also applicable in traditional project management.

Now, consider yourself as the Principal Scrum Master (PSM), one of the key roles in CIPSA. You would be adding tasks into a schedule using MS Project (Agile) or other software tools. But the task (or activity) is not the work itself. When resources execute that task or a list of tasks, then only the actual work gets done. 

A physical or digital board, on the other hand, simulates coordination and/or collaboration. 

For example, when the CIPSA team puts tasks as cards on a CIPSA Scrum or Kanban Board (see here), it simulates coordination and collaboration among the individual Scrum teams. This integrated board has various workflow states across individual Scrum or Kanban teams. 

An AI system goes much further in simulation. 

It can simulate a part of your cognitive process and can be a companion. It not only can assist the PSM, the Chief Product Owner (CPO), or the CIPSA team in breaking a story into tasks, but also in anticipating bottlenecks, quickly informing resource overallocations, and how to resolve them. 

So, in short: 

A task simulates work. A CIPSA board simulates collaboration. An AI system simulates cognition. 

You may call it a PSM-Copilot as it simulates certain cognitive aspects of a PSM. It’s shown in the below figure.

From Manualization to Autonomous

The work that we do in our personal or professional lives can be simple, complicated, or complex. Almost all of us take the help of machines or tools in our daily lives. The usage of machine/tools can be low, medium or high. Overall, it can take four forms:

• Manualization: Low on complexity, and low machine use. For example, caregiving.
• Augmentation: High on complexity and still low on machine use. For example, brainstorming with an AI tool.  
• Automation: Low on complexity, but high on machine use. For example, report generation which is completely automated. 
• Autonomous: High on complexity and high on machine use. For example, driverless cars. In fully autonomous cars, humans are not at all needed. 

This is shown in the below figure. 

As shown in the above figure, we have four aspects, but with nuances. 

Manualization usually is low in complexity and machine use. However, in certain areas of manualization, we require a high degree of emotional understanding. For example, the manual work needed for cutting a tree and caregiving are not the same. Caregiving is low-tech, but high-touch.

When the complexity goes up, but we still need human touch to get the work done, we have augmentation. Here the machines enhance our ability to do the work. We have many such examples:

• Brainstorming,
• Planning (some aspects),
• Estimation (story points as can be used in CIPSA Scaled Agile),
• Resource levelling – also used in CIPSA Scaled Agile.

Automation can occur when the work done is typically of low complexity but with rules applied. It has high machine usage. For our case, we can apply automation in:

• Reporting,
• Processes and adherence to processes,
• Triggering alerts and suggested actions to stakeholders,
• Real-time dashboards, among others.

The final one is that of autonomous, where the decision is completely machine driven and the work is of high complexity. Taking some examples, we can have:

• Driverless (autonomous) cars,
• Pilotless planes,
• Drones for monitoring,
• Autonomous drug discovery etc.

As autonomous AI systems start to get into our lives, a question arises–what it means to be a human.

Meaning of Being a Human

We humans are no way perfect – in fact, far from it. We get disappointed, get angry, and have frustrations. At the same time, we fall in love, have kindness, and show genuine empathy. 

We humans grieve and cry when someone near and dear to us passes away. Because we truly feel so. And that’s what makes us human – not just natural intelligence, which animals also have to a certain extent. For example, elephants and dolphins display a few aspects of near-human-level intelligence!   

Remember our opening story? 

The AI-robot does all the above, or at least pretends. It pretends to empathize, pretends to care, and does the acting of forgiving or being in love. The robot can mimic our human emotions, too.  

The AI-robot is not only learning from us, with us, and by us, i.e., the humans, but trying to be us. It is the perfect lover as the opening story goes, or the perfect human! 

But then it’s not human. It doesn’t possess many human-like in-born qualities or human-like emotions. 

AI doesn’t feel on its own. It’s programmed to feel. AI doesn’t show real emotions. It’s programmed to show. For us humans though, it comes naturally from birth. From birth, we humans are conscious – not programmed. 

However, in some countries AI robots using human clothes and shorts are seen playing with children, and children are seen as enjoying such companions. It may bring in a completely new generation in the future where robots may be perceived as humans or at least no less than humans. This indeed leads to completely simulated lives and living. 

A Conclusion?

For this article, and perhaps for the first time, I’ve no conclusion as I don’t know where or how it ends! 

Current Gen-AI tools are useful in areas as I explained with our CIPSA example, e.g., building tasks, estimating story points, or resolving over-allocations. 

But the direction for AI seems to be in another way as informed with the previous example of AI robots pushed onto children. The kids are being mentally programmed to think AI as humans and live in a simulation.

In such cases, the boundary between reality and simulation is no longer blurred or invisible. It has ceased to exist.  

So, where does it lead us?

• Will AI replace us humans?
• Will AI do all the jobs done by portfolio, program, or project managers?
• How many of us sit with Agentic AIs in CIPSA Daily Scrums which mimics us and then replaces us?
• How will a PSM or CPO coordinate among the Agentic AIs? How many humans in the loop (HITL) will be there? 
• When is that expected to happen?

As said earlier, these are uncertain, but the current path taken by some organizations is leading to that direction. 

For now, I’m certain about a few things. For example, AI can’t procreate on its own, though there are quite a few attempts to manipulate it.  

So, is our future about complete AI with a few humans? Also, what about the previous questions to managers and leaders? 

Your thoughts and comments are welcome.

--

This article is dedicated to the memory of my father, the late Harendra Nath Dash, who passed away seven years ago on June 11, 2019. The world moves forward by people who give, not by people who take. He gave a lot and changed many lives, but took very little back. 

This article is free to read, learn, and share. It’s a tribute to him and his teachings.

References

[1] Certified In Practical Scaled Agile (CIPSA), by ManagementYogi.com

[2] Agile and Artificial Intelligence (AI) – Three Cs of a User Story and Three Cs of a Prompt, by Satya Narayan Dash, CIPSA, CHAMP.

[3] The Future of Project Management: PMBOK 8th Edition with Artificial Intelligence, by Satya Narayan Dash, CIPSA, CHAMP.

The Anatomy of a Benefits Register in Program Management

Just as deliverables are to projects and strategic objectives are to portfolios, so too are benefits to programs! I keep on saying: 

Project delivers. Program coordinates. Portfolio decides.

In other words, a project produces deliverables, a program coordinates for benefits and a portfolio decides on components – determining which to start, stop, suspend, or resume to meet strategic business objectives of an organization.

Benefits may be delivered individually by specific program components or realized collectively through integrated work orchestrated by the program manager. It is the program manager’s responsibility to ensure these benefits are realized in a timely manner. 

Now, irrespective of your desired certification – whether Portfolio Management Professional (PfMP), Program Management Professional (PgMP), or Project Management Professional (PMP) – it pays to understand the concept of benefits and benefits management.

In particular, it’s crucial for program management when you pursue the Program Management Professional (PgMP) certification. 

In fact, there is a dedicated performance domain (PD) called Benefits Management. This directly maps to various phases of the Program Life Cycle Management PD. In addition, there is a distinct principle (PR): Benefits Realization.

 

Benefit and Benefits Register

Benefit is the gains and assets realized by the organization and other stakeholders as the result of outcomes delivered by the program. 

Simply put, the essence of benefits is to capture gains. 

Next, the benefits register collects and lists the planned benefits for the program. It’s the repository where in which benefit profiles are recorded for each benefit. 

One can’t identify all possible benefits at the outset for a program as there can be unplanned, unexpected and/or emergent benefits. However, Benefits Register being the central repository is a powerful tool in Program Management.

The Anatomy of a Benefits Register

Did you notice that I informed about benefit profile, which is recorded in the Benefit Register? A benefit profile is a description of the benefit (to be delivered by a program), its intended beneficiaries, and criteria for its realization. 

When you complete a profile for each benefit, it helps in analysis and planning. 

A benefit profile description includes:

• What part - what the benefit is?
• Who part - who is the benefit is for?
• When part - when the benefit is intended?
• Why part - Why we need the benefit?
• Categorization - the Benefit's categorization
• Criteria for Measurement – Metrics/measure and to determine benefit realization.

Each benefit profile will become a part of the Benefits Register. This in turn supports the Benefits Management Plan (BMP).

Benefit Profile

In the below figure, I’ve outlined a benefit profile which will give an understanding of the benefit’s attributes and measures. The reference for it taken from the Project Management Institute (PMI). 

As noted earlier, this benefit profile will be recorded in the Benefits Register and will used throughout the program life cycle, though it’s first created during Benefits Identification stage.

Sample Benefits Register

Now, you’d thinking how does a Benefit Register look like? I’ve already provided a sample in a previous article of Benefit Management in Project (see here). This is replicated below.

I’m going to expand a bit more on it to have the below Program Benefits Register.

As I expand on Benefits Register with the categorization and attributes, I’ve the following representation. In the real-world program management, one can maintain a spreadsheet, e.g., XLSX file. 

The previous table is continued below. I’ve kept the first column of Benefit ID below for continuity.  

Did you notice the entry of key stakeholders above? 

This is because benefits are fundamentally about gains realized by the beneficiaries (or stakeholders). 

Elements of Benefits Register

Next, let’s go through the elements of the above Benefit Register. Do note that the Standard for Program Management (SPgM) only informs about the Program Benefits Register, not the Benefit Profile!

• Benefit ID – The benefit identifier, which uniquely identified the benefit.
• Benefit Label (Name) – The name of the benefit.
• Benefit Description – The illustration of the benefit.
• Benefit Categorization – The category to which a benefit belongs to. One a benefit can belong to multiple categories!
• Benefit Owner – The person or group who will own the benefit.
• Expected Timing – This can be divided into two, i.e. Start Date and End Date. This informs when the benefit is expected to be delivered.
• Mapping – Mapping of planned/expected benefits to the program component(s). 
• Risks – The risks assessment of the benefits and probability of achieving the benefit.
• Dependencies – Dependencies for the benefit.
• Assumptions – Assumptions associated with the benefit.
• Metrics – The metrics needed to measure the benefits.
• Target Value – The target value of the benefits – preferably quantifiable.

I’ve not added all the fields in the spreadsheet shown above, e.g., Risk Probability. Each program benefit should be assigned a risk probability. Indeed, several factors can drive the probability. For example, the number of components needed to realize the benefit can be one of the factors. 

Taking another example, one of the attributes can be the status or progress indicator for each benefit. This also can be added to the above spreadsheet. 

The depth and breadth of attributes will determine the intensity with which you – the Program Manager – will track the benefits coming from the program components. 

In Summary

In my earlier linked article, I’ve informed about Benefits Management Plan being used in the PMP certification. 

A number of PMP certified professionals pursue PgMP certification. When you go for the Program Management Professional (PgMP) certification, the Benefits Register is one of the key artifacts to know along with:

• Program Benefits Management Plan,
• Program Benefits Management phases (multiple ones),
• Program Benefits Map,
• Program Principle – Benefits Realization (a distinct principle), and
• Program Performance Domain – Benefits Management (a dedicated domain). 

That's quite a few! Isn't it?

However, when you follow and prepare with ManagementYogi’s courses and/or books, you’ll learn them not only in-depth, but also in a practical manner. You're also going to remember them. 

References

[1] Benefits Realization Management for Projects, by Satya Narayan Dash, CIPSA, CHAMP.

[2] Portfolio Management - Benefits Dependency Map, by Satya Narayan Dash, CIPSA, CHAMP.

[3] The Standard for Program Management, by Project Management Institute (PMI).