NirjiX

First-party intellectual assets

NirjiX Frameworks and Decision Guides

The named structures behind our advisory work on enterprise AI adoption and India Global Capability Centers. Each one exists because a specific decision kept being made badly without it.

We call something a framework, guide or scorecard — not a benchmark. NirjiX does not publish benchmark datasets, and describing judgement as benchmark data would misrepresent it.

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Direct answer

What are the NirjiX frameworks?

They are the named decision structures NirjiX applies in client work and publishes openly: an AI adoption decision guide, a build-versus-rent framework, an AI value case framework, an India GCC decision guide, a comparison of captive, BOT and managed routes, a location decision scorecard, and an AI-native GCC design guide. Each states what decision it resolves, what inputs it needs, and what it produces — and each links to the working assessment or model that applies it to your own numbers.

AI decision assets

Enterprise AI

Applied when an organization is deciding what AI to adopt, what to own and how to justify the spend.

Decision guide

Enterprise AI Adoption Decision Guide

Our written position on how an enterprise should decide what to adopt, in what order, and what has to be true before a roadmap is funded. It treats readiness as a production capability question rather than a maturity score.

Used for: Setting the sequence of an AI programme before budget is committed, and giving a board a defensible reason for that sequence.

What it needs

  • — Current state of data, platform and delivery capability
  • — Candidate use cases with their owners
  • — Risk appetite and regulatory exposure

What it produces: A funded first wave, an explicit list of what is deliberately deferred, and the conditions that would change the order.

Read the readiness guide →Run the AI assessment →
Framework

NirjiX Build vs Rent Framework

A layered way of separating the AI stack into model, data, orchestration and application, then deciding ownership layer by layer instead of taking a single build-or-buy position for the whole programme.

Used for: Resolving the argument between platform teams who want to own everything and finance teams who want to rent everything.

What it needs

  • — Where genuine proprietary advantage sits
  • — Switching cost and vendor concentration by layer
  • — Available engineering capacity over a realistic horizon

What it produces: An ownership position per layer, with the switching triggers written down.

Read the framework →Build an AI plan →
Framework

AI Value Case Framework

A cost and benefit structure for AI investment that includes the lines most cases omit — evaluation, monitoring, change management and the run cost of inference — and forces a baselined benefit rather than an assumed uplift.

Used for: Producing an AI business case that survives a CFO review.

What it needs

  • — Baseline performance of the process being changed
  • — Full cost lines including run and assurance
  • — Adoption assumptions and their sensitivity

What it produces: A case with a stated confidence range and the two or three assumptions that decide the outcome.

Read the value case guide →Build an AI plan →

GCC decision assets

Global Capability Centers

Applied when an organization is deciding whether to build a capability center, how to enter, and where.

Decision guide

India GCC Decision Guide

Our position on when a capability center is the right answer, what belongs inside it, what should stay with a partner or at headquarters, and how to build a case finance will accept.

Used for: Deciding whether to proceed at all, before choosing a model or a city.

What it needs

  • — Work inventory and its dependency on local context
  • — Cost-to-serve and quality baseline today
  • — Governance capacity at headquarters

What it produces: A go, defer or partner recommendation with the scope and the value logic attached.

Read the decision guide →Run the GCC assessment →
Decision guide

Captive vs BOT vs Managed GCC Decision Guide

A comparison of the four practical entry routes on control, speed to first output, cost profile over time, and what exit actually costs under each — including the transfer terms that decide whether BOT works.

Used for: Choosing an entry route once the decision to build has been taken.

What it needs

  • — Required control over IP and people decisions
  • — Tolerance for time to first output
  • — Appetite for fixed versus variable cost

What it produces: A recommended route with the transfer or exit terms that must be negotiated up front.

Compare the models →Build the business case →
Scorecard

GCC Location Decision Scorecard

A structured way to rank candidate Indian cities on talent depth for the specific roles being hired, wage trajectory, competitive density, infrastructure and incentive reality — weighted to the scope rather than to a generic index.

Used for: Choosing a city defensibly, and knowing what would make the second choice the right one.

What it needs

  • — Role mix and seniority profile
  • — Scale trajectory over three to five years
  • — Attrition tolerance and wage inflation assumptions

What it produces: A ranked shortlist with the sensitivity that would reorder it.

Read the location guide →Run the GCC assessment →
Design guide

AI-Native GCC Design Guide

How to design a capability center around AI-enabled delivery from the start — role architecture, evaluation and assurance capability, and the governance link back to headquarters — rather than staffing a traditional center and adding AI later.

Used for: Designing a new center, or repositioning an existing one whose headcount logic is being eroded by automation.

What it needs

  • — Work that AI genuinely changes versus work it does not
  • — Assurance and evaluation requirements
  • — Headquarters governance model

What it produces: A role architecture and governance design that does not rely on headcount growth for value.

Read the AI-native GCC view →Run the GCC assessment →

Apply them

Where the frameworks are applied

Each framework is written out in full on the decision page that uses it.

Related

Explore related NirjiX capabilities

Apply a framework to your own position

The assessments run the same structures against your data and return a written view, not a score alone.

Start the AI assessmentStart the GCC assessment

No obligation. You keep the output either way.