Spreadex Group
AI PM Operating System: scaled decision velocity and delivery throughput
I designed and rolled out an AI-powered PM operating system to reduce low-leverage manual work and increase execution speed. The system combined DRICE-based prioritisation, PRD automation, persona generation, and competitive analysis pipelines.
The result was a repeatable decision and delivery framework used across fintech and sports product streams. PM capacity recovered from routine work was redirected into strategy, stakeholder alignment, and faster iteration on growth-critical initiatives.
Outcomes
Capacity gain: saved approximately 20% of PM time
Workflow coverage: automated 4 high-frequency PM workflows (prioritisation, PRDs, personas, competitive research)
Cross-vertical leverage: adopted across fintech and sports delivery streams
Decision velocity: faster, more consistent prioritisation through DRICE standardisation
Execution quality: improved consistency of problem framing and requirement articulation
From Ad-Hoc PM Work to a Repeatable System
Converted fragmented PM activities into an end-to-end AI operating model with standard inputs, outputs, and quality thresholds.
AI-Augmented Prioritisation and Documentation
Integrated DRICE prioritisation, PRD scaffolding, and research synthesis into one practical execution rhythm.
Reflection
What I want to take from this project to new projects
Systemise before scaling → codified PM inputs/outputs make AI genuinely useful, not noisy.
Human-in-the-loop checkpoints → clear review gates preserved quality while accelerating drafts.
Outcome-linked automation → tying automations to delivery KPIs made adoption straightforward.
What I want to improve on
Stronger governance templates → more explicit guardrails would reduce variation between teams.
Richer prompt libraries → broader scenario coverage would improve first-pass output quality.
Tighter analytics feedback loops → direct performance feedback into prompt iteration could improve ROI further.
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