Methodology
1. Opportunity Identification
We map your current workflows across three lenses—process measurement, automation, and generative AI—to spot friction, quantify impact, and surface the quickest paths to value.
What We Measure
- Process bottlenecks and inefficiencies — where people wait, rework, or copy‑paste
- Automation potential — rules, triggers, and integrations that remove manual steps
- Generative AI suitability — tasks that need synthesis, drafting, or reasoning
Roadmap Definition
- Scalability — how quickly a win can be rolled out across teams and tools
- Complexity — effort, data availability, and change management
- Value — measurable impact on cycle time, quality, or cost
Outcome: a prioritized backlog balancing quick wins and foundational bets.
Measurement × Automation — reduce waste with instrumentation‑backed changes.
Automation × GenAI — offload repetitive work and accelerate content or decisions.
Measurement × GenAI — identify where models actually move KPIs.
The sweet spot — measurable, automatable, and GenAI‑suitable. These become first roadmap items.
2. AI‑Augmented Organization
Move from ideas to measurable outcomes. We prioritize initiatives by value and complexity, optimize execution paths, and make success visible through clear KPIs.
Value & Project Map
Start now
High value • Low effort
Scale soon
High value • High effort
Evaluate
Low value • Low effort
Discard
Low value • High effort
Prioritize initiatives by plotting expected value against implementation effort. Start with high‑value, low‑effort wins; schedule larger bets, and de‑scope the rest.
We chart candidates by impact and effort to decide what starts now, what scales later, and what we discard.
Process Optimization
Automated Execution
Decrease human input required in task.
Assisted Execution
Increase throughput, quality, and reduce cycle times.
KPIs
1. Improved Resource Allocation
Track efficiency, capacity gains, and cycle‑time reduction.
2. Minimal Change Management
Integrate smoothly into existing workflows and software.
3. Organizational Trust in Generative AI
Build confidence through clear governance and benefits.
3. AI‑Native Organization
The rise of 10× roles — a paradigm shift in the economics, role definition and speed of knowledge work.
AI‑native organizations treat software and models as first‑class operators. Work is decomposed into programmable workflows where humans design, decide and build trust, while machines execute the repetitive and the real‑time. The implications: variable costs shift to a fixed platform, capacity scales sub‑linearly with headcount, cycle times collapse from batch to continuous, and quality becomes consistent through instant feedback loops. Traditional organizations scale linearly with staffing and timeboxes; AI‑native reverses this with always‑on service, self‑serve generation, and human attention reserved for judgement and relationships.
AI‑native teacher‑exam academy built by Mosaiq foundersProcess (speed & scale)
Question response time
Mock exams & corrections
Economics (cost to serve)
Service price
Curriculum production
Capacity per teacher
AI handles routine; humans coach.
Performance (market & outcomes)
Learner preference (blind)
