Executive summary
A live Haris project that combines structured career assessment with deterministic runway calculations and guarded AI explanation.
The product is live. No user-growth, revenue or career-outcome figure is published.
Business context and problem
Business context
Decision support in Global creates a coordination problem across customer demand, operating ownership and commercial economics. The case is framed around the specific model described here, without extending its evidence beyond the source material.
Business problem
Major career decisions are often made without a clear view of debt, dependents, cash runway and practical trade-offs.
Commercial opportunity
Value pool
The product is designed around trusted decision support. No revenue, placement or employment-outcome model is claimed.
Decision test
The model should progress only when the activity measure, cost to serve and accountable result can be read together. Historical evidence remains limited to the operating result stated on this page.
Journey and solution design
The product combines Career DNA with the user's own financial inputs. AI explains scenarios but does not alter the underlying calculations or make the decision.
The sequence makes the handoffs and decision points visible.
Commercial model and unit economics
Commercial model
The product is designed around trusted decision support. No revenue, placement or employment-outcome model is claimed.
Unit-economics question
Revenue or volume is not enough. The business case must deduct partner share, service, support, losses, technology and the fixed operating capacity required to keep the promise.
The exhibit shows the decision logic, not achieved financial values.
Technology, data and AI
Consented personal inputs, a calculation engine, scenario rules and an LLM narrative layer with strict access and deletion controls.
Operational facts stay in source systems. AI prepares or ranks work inside defined permissions.
Operating model and governance
The user owns the decision. The product shows assumptions, uncertainty and trade-offs instead of recommending an employer or role.
AI can prepare evidence and recommendations. Named people retain consequential authority.
Implementation roadmap and KPI framework
Confirm the customer, commercial and control assumptions with one accountable sponsor.
01Test career dna and financial facts with a limited cohort and explicit stop conditions.
02Compare service, economics and control evidence before adding markets, fixed cost or broader authority.
03Each stage has a commercial, service and control gate.
The scorecard pairs activity with economics and operating quality.
Risks and dependencies
| Risk | Management response |
|---|---|
| Privacy | Financial inputs need strict access and deletion controls. |
| Explanation | An LLM must not alter deterministic calculations. |
| Dependence | The product supports a decision; it does not make one. |
Business Impact
The product is live. No user-growth, revenue or career-outcome figure is published.
Executive reading
The case is useful because it connects a real operating problem to the economics, technology, ownership and evidence required for a scale decision. It does not rely on an unverified outcome to make the model credible.