INNOVATION-VERSE
GCC Value & Economics Engine
A first-class engine that runs alongside the diagnostic and feeds Shape My GCC and Transform My GCC. Ten economic layers, a value bridge from investment to enterprise value, and an explicit basis on every number.
Economic inputs
Everything you enter is tagged USER INPUT wherever it appears downstream.
Economic mandate
The economically optimal centre depends on the mandate. Lowest cost is not assumed to be best.
The stated objectives are cost-weighted, so cost economics legitimately dominate the design. Say so openly rather than dressing a delivery case as a capability case.
This profile biases the archetype ranking in Shape My GCC and Transform My GCC, so the recommended design follows the value the enterprise is actually trying to create. Current shaped archetype: Capability Center.
GCC Value Bridge
Investment → operating cost → productivity → capability → innovation / IP → revenue → enterprise value.
- Investment$13.4MCALCULATED OUTPUTOne-off setup, transition and ramp investment.
- Operating cost$20.0MCALCULATED OUTPUTAnnual run cost; $28.0M advantage against the retained-geography equivalent.
- Productivity$2.9MAI ESTIMATE57 FTE-equivalent of capacity created.
- Capability creation$1.3MAI ESTIMATECapability and talent value against the alternative sourcing route.
- Innovation / IP$0kAI ESTIMATENot claimed at this mandate.
- Revenue / business impact$400kAI ESTIMATERevenue enablement and speed effects.
- Enterprise value$32.9MCALCULATED OUTPUTTotal modelled value across all layers, cost advantage included.
Risk / resilience value and AI / automation value are shown separately so they are never absorbed into a cost-saving headline.
Ten economic layers (A–J)
Cost, productivity, capability, talent, innovation, revenue, speed, resilience, AI and enterprise value.
Cost economics
$20.0M annual run cost; $28.0M cost advantage against the retained-geography equivalent.
400 people at $38k fully loaded.
Modelled at 9% of workforce cost; replace with the enterprise's own ratio.
Modelled per seat; highly location-specific.
Service-stage supervision ratio.
Build support during entry.
Entity, site entry, infrastructure — one-off.
Recruitment, knowledge transfer and dual-running — one-off.
Derived from the two cost-per-person figures you entered.
Productivity economics
14% modelled productivity effect — about 57 FTE-equivalent of capacity created.
Derived from the automatability profile of the capabilities in scope.
Discounted heavily: weak data foundations cap realisable AI benefit.
Persistent teams reduce hand-back and re-planning loss.
Sum of the three effects above.
Capacity released, not headcount removed — keep the two distinct.
Capability economics
$849k modelled capability value — the question is build vs buy, not cheap vs expensive.
Owned capability, retained knowledge, slower to stand up.
Modelled at a 35% premium over owned run cost; replace with your actual vendor rates.
Cost of holding the same scope in the retained geography.
Derived from the capability mix in scope. Scarce capability rarely survives a vendor contract.
Indicative; validate against the cost of the alternative sourcing route, including acquisition.
Talent economics
$482k modelled talent value; attrition alone carries $1.2M of annual replacement cost.
Scarcity determines hiring velocity and premium, not just salary.
Modelled at 20% attrition and 40% of annual cost to replace.
Limited at delivery stage — career depth is the lever.
Autonomy determines whether leadership is an overhead or a lever.
Automatable scope must be met with a funded reskilling path, or productivity value converts into attrition.
Innovation economics
Innovation is not a stated objective — no innovation value is claimed.
Do not model value the mandate does not ask for.
Revenue economics
No revenue objective is stated — no revenue contribution is claimed.
Not claimed.
Direct only where the centre owns a revenue-bearing product.
Customer-facing scope creates measurable revenue exposure, in both directions.
Only credible where the centre holds decision rights over release.
Speed economics
$400k modelled value from cycle-time, development, transformation and decision velocity.
Follow-the-sun coverage only helps where hand-offs are designed out.
Every escalated decision class is a cycle-time cost.
Change capacity is capacity, and must be resourced as such.
Indicative; validate against actual lead-time measures.
Resilience economics
$300k modelled resilience value from diversification and continuity.
Diversification only counts where scope is genuinely dual-run.
The more the centre owns, the higher the continuity exposure.
Concentration grows with capability held, not only headcount.
Reported separately so it is never buried inside cost savings.
AI economics
$2.3M modelled AI and automation value; realisation is bounded by data foundations rated 1/3.
Discounted to 40% — the data does not yet support the ambition.
Rule-based and workflow automation, ahead of any AI claim.
Agentic autonomy requires governed data and named human accountability.
Modelled as a share of technology spend; excludes remediation of data foundations.
Released capacity is not a headcount reduction unless the enterprise decides to take it as one.
Enterprise value
$32.9M total modelled value, of which 15% is not labour cost difference.
Revenue and speed effects the business can recognise.
Capability and talent value the enterprise would otherwise buy.
Not claimed at this mandate.
Share of modelled value that is not labour cost difference.
A sum of separately modelled layers. It is not an ROI and should never be quoted as one.
- Every figure is indicative decision support, not a benchmark and not a forecast.
- Cost inputs are user-supplied; ratios are Innovation-Verse modelling assumptions and should be replaced with enterprise actuals or a licensed benchmark.
- No figure here is attributable to Zinnov, nasscom, Gartner or EY. Those sources inform which value dimensions are modelled, not the numbers.
- AI productivity is heavily discounted because data foundations are rated weak.
- Most modelled value is still labour arbitrage. That is a delivery economics profile, not a capability economics profile.
- The stated objectives are cost-weighted, so cost economics legitimately dominate the design. Say so openly rather than dressing a delivery case as a capability case.
Business case
Each value type reported separately. There is deliberately no single ROI figure.
Setup, transition and ramp.
Run cost at the modelled headcount.
Against the retained-geography equivalent — not a saving until the enterprise takes it.
Capacity created, not headcount removed.
Against the alternative sourcing route.
Retention, scarce-skill access and leadership leverage.
Only claimed where innovation is a stated objective.
Only claimed where revenue is a stated objective.
Cycle-time, decision and transformation velocity.
Diversification and continuity.
Included in productivity; shown separately, not counted twice.
A sum of separately modelled layers. Not an ROI.
Build and stabilise — value realisation is partial by design.
Capability effects begin to exceed arbitrage.
Steady-state value at the stated mandate.
Indicative payback on cash-realisable value: 12 months. Share of modelled value that is labour arbitrage: 85%.
Sensitivity & uncertainty
What would have to be true for the case to change.
| Driver | Swing tested | Effect | Basis |
|---|---|---|---|
| Centre cost per person | ±15% | Cost advantage moves by about $2.3M a year. | USER INPUT |
| Data foundations | 1/3 → 3/3 | AI value could rise from $266k toward $2.0M. | INNOVATION VERSE ASSUMPTION |
| Attrition | 20% ±5pp | Replacement cost moves by about $304k a year. | INNOVATION VERSE ASSUMPTION |
| Ramp speed | 45% → 65% year-one realisation | Payback shortens from about 12 months to about 8 months. | INNOVATION VERSE ASSUMPTION |
| Decision-rights transfer | Autonomy 1/3 → 3/3 | Capability, speed and innovation layers all lift; none of them are realisable while decisions escalate. | AI ESTIMATE |