Artificial Intelligence, Simulations, CIPSA, and the Future

Ich bein 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 CIPSA framework.

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.

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 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 AI-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 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 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 a 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.

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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.