One operating model for product, IT, data and AI
McKinsey, BCG and Gartner now describe the same target architecture from three angles. We turned it into a working system: four layers, three containers, three lenses, one source of truth.
Three angles, the same operating model
Three independent publications, a few months apart.
Business model, operating model, governance, talent and culture, technology and data. Read as one system, not five compartments.
10% on the algorithm, 20% on data and technology, 70% on people and processes. The work sits outside the model.
Project & Portfolio Management was retired as a category. Strategic Portfolio Management replaced it, with a different first question.
Four layers
The architecture reads top down. A bet hosts opportunities. Each opportunity carries initiatives. Each initiative resolves through experiments.
- 01Bet
A business result with a number, a date, and the signal that will show it was wrong. What we believe strongly enough to commit capacity to.
- 02Opportunity
A customer need, a problem or a business lever that contributes to the bet. From Teresa Torres’ Opportunity Solution Tree.
- 03Initiative
The solution explored to address one or more opportunities. What product and engineering teams carry day to day.
- 04Experiment
A concrete test of one hypothesis. It can succeed, fail, or stay inconclusive. The one level where invalidation is explicitly accepted.
Three containers
Bets live at product level. Not at portfolio level, too broad. Not at initiative level, too narrow. One simple rule settles most disagreements.
The whole organisation.
A coherent grouping by business unit, market or mission.
A team, a scope. This is where bets live.
One source of truth, three readings
Same data, no re-entry, no reconciliation. Each role asks its own question and the system answers it directly.
« What are we learning this week? »
- The opportunity tree, live
- Experiments by hypothesis and by result
- What gets invalidated each week, not just the status
The watchtower
Four layers read in one place. Every technical signal climbs back up to the bet it serves, so the committee can read it.
- 01Lifecycle
Where each use case sits across the six phases. How many are stuck, and for how long.
- 02Compliance
Which use cases are high-risk under the AI Act. Conformity assessment and human oversight, documented.
- 03Technical quality
Input data quality, model drift, performance decay, operational alerts.
- 04Value
What each use case actually captures, against the bet it serves, run cost included.
A portfolio operating system
One source of truth on bets, opportunities, initiatives and experiments, read through the PM, portfolio and data lenses, with a watchtower that ties value back to bets. That is what Fygurs is.