The Stratenity Flywheel
A compounding loop of Diagnose → Design → Build → Execute that turns strategy into realized value, continuously.
Why a Flywheel?
Momentum compounds when insight leads to design, design leads to build, and build leads to outcomes that feed the next diagnosis. The Stratenity Flywheel is our operating model for perpetual improvement: short cycles, clear gates, and measurable value at every turn.
How we measure momentum
- Cycle time: weeks from Diagnose to shipped value.
- Throughput: # of investments exiting each gate.
- Adoption: % of target users engaged after Execute.
- Value realized: verified financial/operational impact.
The Four Stages
1) Diagnose — Understand the situation
We start by mapping signals across customers, operations, tech, and finance. Hypotheses become bet statements tied to metrics and constraints.
Fortune-scale healthcare payer with rising rework costs. Signal analysis across intake, claims, and provider ops quantified $120M annual leakage. Bet framed: “Automate intake validation & claims triage to cut leakage by 25% in 12 months,” with KPIs on first-pass yield and rework rate.
2) Design — Formulate the plan
Translate bets into a ranked portfolio. Define success thresholds, stage-gate criteria, and funding tranches. The output is a 90-day roadmap with owners.
Mid-market retailer debating ERP upgrade vs. supply-chain automation. Portfolio design ranked warehouse automation first, with a $8M tranche and stage gates on inventory turns, fill rate, and working capital. Produced a 90-day roadmap with owners and financial guardrails.
3) Build — Create the components
Deliver incremental value behind thin slices: data products, service APIs, UI shells. Architecture favors reuse: unified data, model services, and shared ops.
Global bank piloting AI for compliance. Built thin slices: a sanctions-check API, exception dashboard, and risk-scoring model on a shared data layer. Result: 40% reduction in manual review time and faster cross-BU rollout readiness.
4) Execute — Implement and act
Launch with adoption plans, business change, and measurable outcomes. We capture realized value and new signals, feeding the next Diagnose.
Energy utility launching AI service channels. Execution included enablement, incentive design, and value dashboards. Achieved 62% adoption in 90 days and validated $14M annual savings, with new signals routed to the next Diagnose cycle.
Applying the Flywheel in Project Management
The Stratenity Flywheel is not just for strategy, it directly enhances project management by structuring work into repeatable cycles that improve clarity, speed, and measurable outcomes.
- Diagnose: Assess project signals including scope changes, risks, resource bottlenecks, or stakeholder misalignment. Frame hypotheses as project bets tied to delivery KPIs (budget, time, quality).
- Design: Translate project bets into stage covering gated roadmaps with success thresholds. Align funding, owners, and reporting cadence (often 90-day increments).
- Build: Deliver thin slices of value in features, process improvements, or prototypes, validated against project gates. Reuse shared data, templates, and tools for efficiency.
- Execute: Deploy deliverables into production or operations with adoption playbooks. Track value realization (savings, revenue, cycle time gains) and feed signals back into the next Diagnose cycle.
This Flywheel approach enables PMOs and delivery teams to run projects like continuous improvement loops instead of one-off initiatives, ensuring sustained impact and learning.
Industries that Benefit Most
The Stratenity Flywheel compounds impact across industries that face complexity, compliance, and rapid change.
Healthcare & Life Sciences
- Claims Automation: Diagnose $120M leakage, design automation roadmap, build validation APIs, execute adoption training to cut rework 25%.
- Patient Intake Modernization: Diagnose long cycle times, design digital triage process, build intake kiosks + AI chat, execute rollout to improve throughput 30%.
- Clinical Trial Data Integration: Diagnose siloed trial data, design unified platform, build APIs for CRO partners, execute adoption to accelerate trials 20%.
Financial Services
- Compliance Copilot: Diagnose bottlenecks in AML checks, design automation stages, build sanctions API, execute rollout reducing manual review 40%.
- Digital Lending Transformation: Diagnose credit process delays, design risk scoring roadmap, build AI underwriting engine, execute adoption to cut decision time 50%.
- Fraud Detection Analytics: Diagnose fraud losses, design detection thresholds, build ML anomaly models, execute deployment reducing fraud by 18%.
Retail & Consumer Goods
- Supply Chain Modernization: Diagnose 18% lag in turnover, design automation roadmap, build AI routing, execute pilot achieving 12% improvement.
- Omnichannel Personalization: Diagnose low digital engagement, design segmentation model, build AI product recommenders, execute adoption boosting conversion 22%.
- Returns Optimization: Diagnose high returns cost, design predictive triggers, build smart returns portal, execute rollout cutting return costs 15%.
Energy & Utilities
- Customer Service AI: Diagnose high call volumes, design service channel roadmap, build AI assistant, execute adoption with 62% usage in 90 days.
- Predictive Maintenance: Diagnose asset downtime patterns, design maintenance schedule model, build IoT dashboards, execute rollout reducing outages 20%.
- Grid Modernization: Diagnose inefficiency in grid balancing, design smart-grid roadmap, build AI forecasting tools, execute pilot saving $8M annually.
Manufacturing & Industry 4.0
- Predictive Quality: Diagnose high defect rate, design QC checkpoints, build AI vision model, execute adoption reducing defects 25%.
- IoT Factory Optimization: Diagnose bottlenecks in throughput, design sensor network, build IoT analytics layer, execute pilot improving output 15%.
- Digital Twin Simulation: Diagnose long design cycles, design simulation roadmap, build digital twin prototype, execute rollout cutting cycle time 30%.
Note: The numbers and outcomes shown above are illustrative based on past project experiences. Your organization’s results should be calibrated to its specific data, processes, and constraints.
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