Kaara CoE

The operating system for AI across your enterprise.

An AI Centre of Excellence isn’t a team of data scientists. It’s the organisational infrastructure for enterprise-wide AI adoption. Kaara CoE builds this operating system, then transfers ownership to your team.

Assessment & Design4–6 weeks
Build & Launch8–12 weeks
Embed & Transfer8–12 weeks
Exit GoalFull Independence
The Problem We Solve

Project-by-project AI doesn’t scale. It fragments.

01. Siloed AI Initiatives

Multiple departments running AI experiments independently. No shared tools, no shared learnings, no shared governance. Every project starts from zero.

02. No Organisational Infrastructure

No operating model for AI. No use case prioritisation. No standardised delivery playbook. Smart people doing one-off projects without leverage.

03. Unproven at Scale

Isolated AI successes that can’t replicate. No repeatable pipeline for turning business problems into production AI. Scaling means starting over each time.

CoE Architecture: The Six Pillars

Most CoE offerings are consulting-led. Kaara’s CoE is delivery-led: we build it AND execute through it.

1. Strategy & Governance

AI vision and roadmap, use case prioritisation framework, AI ethics and responsible AI policy, risk management framework, steering committee structure.

Why it matters: Without governance, AI becomes a liability. Without strategy, it stays experimental.

2. Operating Model

Roles and decision rights, centralised vs. federated design, intake and triage process, Working Backwards delivery methodology, vendor and partner management.

Why it matters: The operating model determines whether AI scales or stalls.

3. Technology Standards

Model registry and lifecycle management, MLOps / LLMOps pipeline standards, data governance and quality standards, approved tool and platform stack, accelerator deployment standards.

Why it matters: Standardisation prevents fragmentation and enables compounding.

4. Talent & Culture

AI literacy for business leaders, technical upskilling for engineers, career pathing for AI roles, hiring roadmap for critical gaps, community of practice.

Why it matters: CoEs fail when the organisation isn’t culturally ready.

5. Delivery Engine

Use case pipeline and backlog, reusable delivery playbook, Production Requirements checklist, compounding metrics tracking, post-deployment monitoring.

Why it matters: This turns the CoE from a governance body into a production machine.

6. Measurement

CoE KPIs and dashboards, monthly performance tracking, quarterly executive briefing, annual maturity assessment, business value attribution.

Why it matters: If you can’t measure it, you can’t scale it, or protect its budget.

How It Works

Three phases. Full ownership transfer.
Independence at the end.

Phase 01. 4–6 WeeksAssessment & Design
Phase 02. 8–12 WeeksBuild & Launch
Phase 03. 8–12 WeeksEmbed & Transfer
  • .AI maturity assessment (organisational, not just technical)
  • .Use case inventory and prioritisation
  • .Target operating model design
  • .Governance framework architecture
  • .Talent gap analysis and roadmap
  • .Executive stakeholder alignment
  • .Operating model implementation
  • .AI governance platform setup
  • .Standardised delivery playbook
  • .AI ethics and responsible AI framework
  • .First 2–3 use cases through CoE pipeline
  • .AI literacy program for business leaders
  • .Team coaching and shadowing
  • .3–5 additional use cases (internal-led, Kaara-guided)
  • .Performance measurement framework
  • .Knowledge transfer documentation
  • .Progressive handover plan
  • .CoE maturity assessment (exit criteria)
CoE KPIs

Year-over-year targets. Measured, not estimated.

15–25Use cases in productionYear 1: 5–10
4–8 weeksAvg. time to productionYear 1: 8–12 weeks
50–70%Asset reuse rateYear 1: 20–30%
>95%Compliance scoreYear 1: >85%
80%+ trainedAI literacy (business leaders)Year 1: 50% trained
Low (internal-led)Kaara dependency ratioYear 1: High (Kaara-led)
Customer Benefits

Why enterprises choose Kaara CoE.

Delivery-Led, Not Consulting-Led

We build the CoE AND execute use cases through it during setup. You see the engine working, not just the blueprint.

Explicit Transfer of Ownership

Success is measured by Kaara’s dependency ratio decreasing. Phase 3 is designed to make your internal team fully independent.

Enterprise-Wide Compounding

When every department uses the same toolkit, governance, and delivery playbook, the enterprise compounds its AI investment, not fragments it.

Standalone Phase 1

Assessment & Design is available as a standalone deliverable. Get the blueprint and roadmap, then decide how to execute, with us or without us.

Multi-Year Partnership

CoE is the stickiest engagement in the portfolio. It naturally generates Kaara Build and Kaara Ops engagements as the pipeline produces production AI.

Proven Credibility

Kaara has built and rescued production AI through Build and Rescue. That gives CoE credibility that pure consultancies fundamentally lack.

Common Questions

FAQ

Everything you need to know about Kaara CoE.

They design CoEs. We design AND operate them. Our team executes use cases through the CoE pipeline during setup, proving the model works before we step back. That’s the difference between a strategy deck and a working engine. Plus, every use case benefits from Kaara.Code’s Enterprise Memory Layer.

Phase 1 (Assessment & Design) is a standalone deliverable at a fraction of the full cost. It gives you the blueprint and the prioritised use case roadmap. You can execute it with us, another partner, or internally. But at least you’ll have the architecture.

Hiring an AI team is necessary, and we help you do it. But a team without an operating model, governance framework, and delivery playbook is just a group of smart people doing one-off projects. The CoE is the infrastructure that makes the team effective at scale.

The explicit goal is the opposite. Phase 3 is designed to transfer ownership. We measure success by the Kaara dependency ratio decreasing. By end of Phase 3, your internal team should be running the CoE independently. We’re available for advisory, but you don’t need us.

The steering committee structure we establish in Phase 1 addresses this directly. We define decision rights, resource allocation, and use case prioritisation with input from all departments. The framework prevents any single department from monopolising AI resources.