Most machine learning projects fail not because the modelling is hard, but because the organisation wasn't ready to begin. This simulation turns a 100-point ML readiness scoring framework into a 3D scene: five pillars — data, infrastructure, team, budget and culture — each worth up to 20 points, rise around a central gauge tower that totals the score and points a needle at the verdict.
Surveys of enterprise AI initiatives consistently find that a majority never reach production — and the most common root cause cited is not model accuracy, but poor data readiness and lack of organisational buy-in, exactly the "soft" pillars this audit tries to make visible before a project starts.
Five glowing pillars — data, infrastructure, team, budget and culture — rise around a central gauge tower, turning a 100-point ML readiness scoring framework into a 3D dashboard that decides whether a business should greenlight its first machine learning project.
Each of the five pillars is worth up to 20 points; their sum drives both the central tower's height (colour-graded red → amber → green) and a semicircular needle gauge, while the weakest pillar is flagged separately as the likely bottleneck.
Drag each slider to score a real or hypothetical business from 0 (absent) to 20 (excellent) on that dimension. Watch the total, verdict badge and gauge needle respond instantly, or click "Random company" to explore a random scorecard.
Most enterprise AI initiatives that stall do so for organisational reasons — messy data, no infrastructure, no executive sponsor — not because the underlying model was inaccurate.