Methodology choice is both strategic and tactical. And how does project leadership fit in?
In physics, the Standard Model organizes the so‑called ‘particle zoo’ of fundamental particles.
Let’s borrow this metaphor here.
This map through the PM zoo is oversimplified. It doesn’t cover every animal, and hybrids certainly have their place. Still, within its limits, it provides a set of guidelines, that are all too often overlooked.
Let’s consider the following examples from both a strategic and a tactical point of view:
❍ “We’re building a regulated medical device; compliance risk dominates.”
❍ “Our product is exploratory and market‑driven; adaptability is essential.”
❍ “We are becoming an AI-first organization.”
❍ “We are becoming an AI‑augmented engineering organization; human judgment and machine intelligence must co‑evolve.”
❍ “We will scale delivery across 12 teams, alignment becomes a strategic constraint.”
❍ “Our aim is to accelerate innovation across the organization.”
Sometimes the choice of a project framework is straightforward, yet we still see decisions driven by familiarity or old habits.
❍ Waterfall dressed up as Agile is still Waterfall. Rituals replace value.
❍ Software development lifecycles are frequently repurposed for data-centric AI/ML work.
Now, let’s walk through the key questions and the most likely outcomes.
Are requirements fixed, validated, and unlikely to change?
↳ Waterfall
Predictability > adaptability.
Compliance and sequencing dominate.
↴
Can the work be delivered incrementally with frequent feedback?
↳ Agile
Adaptability > predictability.
Customer feedback drives value.
↴
Is the team small (5–9), stable, and cross‑functional with clear product ownership?
↳ Scrum
Team‑level agility with tight iteration loops.
↴
Are multiple Agile teams working on a coordinated product or portfolio requiring alignment and governance?
↳ SAFe
Enterprise‑scale agility with governance and portfolio alignment.
↴
Is the project AI/ML‑driven, requires probabilistic solution dependent on data quality, experimentation, and iterative modeling?
↳ CPMAI
AI lifecycle differs from software.
Data readiness and modeling cycles drive success.
↴
Does problem require both probabilistic and deterministic solutions?
↳ Hybrid
When no single methodology fits, combine AI‑specific cycles (CPMAI) with software delivery agility (Scrum/Agile).
Choosing the framework is strategic. Executing it is tactical, and where many stumble.
Now, the real questions begin:
❍ How do we lead across technology, strategy, people, and culture?
❍ How do we align behaviors with the system, not just the process?
❍ How do we prevent methodology from becoming ritual instead of value?
This is where project leadership becomes the differentiator.
– Greg