AIGR™ Framework

A structured framework for AI Governance and Readiness.

AIGR™ is Knightsbridge Consulting’s practical framework for assessing AI adoption, identifying governance gaps and building a controlled roadmap for responsible, accountable and audit-ready AI use.

Explore AIGR™ Discuss AI Governance
Overview

AI governance should start with visibility, not bureaucracy.

Many organisations are already using AI across productivity tools, analytics platforms, customer systems, supplier products and internal automation. The challenge is that adoption often spreads before governance catches up.


AIGR™ provides a structured way to understand where AI is being used, who owns it, what risks exist, which controls are missing and what steps are needed to move towards responsible, governed AI adoption.

AIGR™ is not designed to slow AI adoption. It is designed to make AI adoption visible, controlled and scalable.
AIGR™ structure A practical framework for AI governance maturity and readiness.
Assess Understand current maturity, gaps and risk exposure.
Inventory Map AI use cases, systems, owners and suppliers.
Govern Define policy, controls, oversight and accountability.
Ready Prepare for assurance, audit and executive scrutiny.
Framework components

The four stages of AIGR™.

AIGR™ is designed to move organisations from fragmented or informal AI use towards a practical governance model with visibility, ownership, controls and readiness.

A

Assess current AI governance maturity.

The first stage reviews how AI is currently being used and governed across the organisation. It identifies gaps in ownership, policy, risk assessment, supplier oversight, data protection, security, reporting and executive visibility.

Current-state review
Governance maturity assessment
Stakeholder interviews
Existing policy review
Risk and control gap register
Executive maturity summary
I

Inventory AI use cases, systems and suppliers.

The second stage creates visibility. It maps where AI is being used, what business process it supports, who owns it, what data it touches, whether suppliers are involved and what level of risk or control is required.

AI use-case register
System and supplier mapping
Business owner mapping
Data category review
Risk classification
Control ownership view
G

Govern with practical controls and accountability.

The third stage defines the governance model. This includes policy, acceptable use rules, approval routes, human oversight, supplier due diligence, incident escalation, control ownership and reporting routines.

AI acceptable use policy
AI risk assessment template
Governance roles and responsibilities
Approval and escalation model
Supplier AI due diligence
Board and management reporting
R

Ready the organisation for assurance and scrutiny.

The final stage prepares the organisation for internal assurance, leadership review, audit questions, customer scrutiny and emerging AI governance expectations. The output is a practical roadmap and evidence pack.

Readiness roadmap
Evidence pack structure
Board briefing pack
Audit preparation support
Control improvement plan
Governance operating rhythm
Outputs

What an AIGR™ engagement can produce.

AIGR™ is designed to produce tangible, executive-ready outputs rather than abstract governance commentary.

01

AI Governance Maturity Assessment

A structured assessment of current AI governance maturity, gaps, strengths and priority areas for improvement.

02

AI Use-Case Inventory

A register of AI systems, tools, use cases, business owners, data categories, suppliers and risk classifications.

03

AI Risk and Control Gap Register

A practical view of missing controls, exposure areas and remediation actions required to improve governance.

04

AI Policy and Control Framework

Acceptable use rules, risk assessment templates, approval routes, oversight requirements and escalation processes.

05

Supplier AI Governance Checklist

A structured approach for assessing AI capabilities embedded in third-party products, platforms and services.

06

Board-Level Readiness Report

An executive-ready summary of AI exposure, governance gaps, control priorities and recommended roadmap.

Why AIGR™ matters

AI governance fails when it becomes either too theoretical or too restrictive.

Many organisations either ignore AI governance until risk becomes visible, or create heavy policies that teams do not follow. AIGR™ is designed to sit between those extremes.


It gives leadership enough visibility and control to manage risk, while allowing teams to continue exploring practical AI-enabled improvement.

RISK
Shadow AI adoption Teams use AI tools without central visibility, clear ownership or data handling controls.
RISK
Supplier AI exposure Third-party systems introduce AI capabilities without sufficient due diligence or accountability.
RISK
Policy without operation AI rules exist on paper but are not embedded into workflow, approval or reporting routines.
RISK
Weak executive visibility Senior leaders cannot clearly see where AI is being used, what risk exists or what action is required.
Engagement model

How an AIGR™ engagement is typically delivered.

The engagement can be delivered as a focused diagnostic assessment or expanded into a wider AI governance implementation roadmap.

01 Scope

Confirm the business context, teams, systems, governance concerns and intended output.

02 Discover

Review documents, interview stakeholders and identify known AI use cases and governance practices.

03 Assess

Evaluate maturity, risks, ownership, supplier exposure, policies and control gaps.

04 Design

Create the governance framework, control model, reporting structure and improvement roadmap.

05 Report

Deliver the executive summary, evidence pack, gap register and prioritised action plan.

Best fit

When AIGR™ is most useful.

AIGR™ is particularly useful for organisations that already have AI activity underway but do not yet have a clear governance model around it.

01
AI tools are being used informally

Teams are adopting AI, but leadership lacks visibility of use cases, data exposure and control requirements.

02
The board wants a clearer view of AI risk

Senior leaders need a concise assessment of current exposure, maturity and priority action areas.

03
Supplier AI capabilities are expanding

Vendors and platforms are embedding AI features that require due diligence and oversight.

04
Policies exist but are not operationalised

The organisation needs controls, templates, escalation routes and reporting routines that teams can follow.

05
AI governance needs to become repeatable

The organisation wants to move from one-off reviews to an ongoing governance rhythm.

Discuss AIGR™

Need a structured AI governance readiness assessment?

Speak to Knightsbridge Consulting about using AIGR™ to assess AI governance maturity, identify control gaps and create a practical roadmap for responsible AI adoption.

Contact Us AI Governance