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Roman Estimation Technique
Welcome to Lean, Agile, LeSS and Systems Thinking newsletter
Agile Section
Roman Estimation
There are so many estimation techniques as shown below..

Recently came across this new one called “Roman Estimation”. Thought would interesting to share with the readers.
Steps for Roman Estimation
Step 1: Ask the Right Question “Can this item be completed within a single sprint or within a predefined timebox (e.g. one week)?”
Step 2: Thumbs Up / Sideways / Down Voting Each team member votes: 👍 Yes – it fits the timebox 👎 No – it's too big 👉 Sideways – unsure or need discussion
Step 3: Discuss the Disagreements If there is no unanimous thumbs up, the team discusses why people think it's too big or are unsure.
Step 4: Refine the Item (if needed) If the item is too large, break it down or clarify requirements until the team can confidently vote 👍.
Step 5: Mark the Outcome Use simple notation:
1 if it fits the timebox
2 if it doesn’t and needs refinement
Step 6: Move On Quickly Keep it fast—focus on sizing, not precise estimation.
Check this article out for more details
Original Draft of Agile Manifesto
We are all familiar with the final version of the Agile Manifesto

However, have you seen the draft version before it was published ? Looks like they had divisions like the one below. I like this version below which is easier to grasp. What about you ? Which version do you like and why ? Send me a message :-)

AI Section
Future AI Roles
Came across this article where the author envisages AI roles of the future. A few examples below, but read the above article for the
🔹 Chief AI Officer: Leads AI strategy and oversight.
🔹 Head of AI: Directs AI initiatives and teams.
🔹 AI Architect: Designs scalable AI system architecture.
🔹 AI Product Manager: Manages the AI product development lifecycle.
🔹 AI Risk & Governance Specialist: Ensures AI compliance and risk management.
🔹 AI Ethicist: Addresses ethical implications of AI.
🔹 Model Manager: Oversees the AI model lifecycle.
🔹 Model Validator: Verifies AI model accuracy and reliability.
🔹 AI Developer: Builds and implements Gen AI & Agentic systems.
🔹 Model/ML Engineer: Develops and deploys machine learning models.
🔹 AI Application Developer: Integrates AI into business applications.
🔹 AI App Dev Platform Architect: Designs AI development platform infrastructure.
🔹 AI Cybersecurity Analyst: Mitigates AI-specific cybersecurity risks.

LeSS Section
Planning Large Scale Product Development
Here is the section from the “Practices of Scaling Lean and Agile Development” Book. This paragraph is packed with wealth of knowlege
Planning large-scale agile development is simpler than traditional approaches—or at least should be. We notice that this simplicity is disconcerting for some, because the traditional paradigm of management is that big complex work needs big complex planning and control by project managers.
But there is a different way: the emergence of order from self-organizing Scrum feature teams. Top-down planning and control is not particularly effective in systems with variability and discovery—because the plans assume something relatively static or deterministic—and the approach grows even less effective as these non-linear systems grow larger.
Complex systems on the boundary of chaos and order (chaordic systems [Hock99])—and that includes big development groups—cannot be truly planned or controlled from above. No false dichotomy regarding “no planning” versus “top-down planning” …
In Scrum, the group does start by creating a Release Backlog for a future goal, identifying and estimating the product features. But after that starting point, agile planning emphasizes continual learning and adapting.
How to do that within a large chaordic system?
By
(1) encouraging self-organization and bottom-up emergence of order,
(2) increasing transparency and feedback, and
(3) making it easy to frequently inspect and adapt.
This is precisely what agile approaches such as Scrum offer. Consequently—in contrast to those that assumed agile development was for small groups—agile planning is especially use ful for the large scale.
Systems Thinking Section
20 Different types of Thinking
We have heard of Systems Thinking, Design Thinking, Critical Thinking… but did you know that there are 20 most popular ones.
Cambridge university press has the detailed list of these 20 thinking approaches with detailed analysis. If you have the habit of reading, this is a treasure.

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