Kendall Guide - A System of Work for AI Adoption

© 2025 NKD Agility. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License . You are free to share and adapt this material for any purpose, even commercially, provided that attribution is given and adaptations are shared under the same license.

Kendall Framework Purpose & Definition

The Kendall Framework is a system of work for thinking about AI. Its single purpose is to answer: “Where do we start with AI?” It provides clarity and discipline in approaching AI adoption, ensuring organisations focus on problems and outcomes rather than technology trends. It does not prescribe specific tools or architectures. Instead, it directs organisations to identify, articulate, and prioritise AI opportunities through problem‑first, context‑driven, and collaborative practices. It emphasises flow of information, decisions, and value, guided by empirical inspection and adaptation.

The Kendall Framework is not intended to represent a complete system. It defines boundaries and focus, and it expects to be complemented by other practices that ensure delivery discipline, modern engineering, effective team collaboration, and observability.

Kendall Application

The Kendall Framework can be applied by organisations, teams, entrepreneurs, and governments seeking clarity on how to approach AI adoption. It is suitable for any domain where complex challenges require principled prioritisation, flow‑based thinking, and structured learning.

First Principles of the Kendall Framework

The first principles of the Kendall Framework are foundational truths:

1. Problem Before Solution – AI begins by addressing real, observable problems rather than chasing technology.
2. Context Creates Meaning – Defined context turns generic intelligence into purposeful outcomes.
3. Clarity of Language – Precise language is the raw material of AI and management alike.
4. Accountability Anchored in Stakeholders – AI must serve clearly identified stakeholders.
5. Boundaries Enable Trust – Values and constraints ensure dependable and ethical AI use.
6. Structure Builds Reliability – Structured inputs form the basis for sustainable outcomes.
7. Collaboration Sustains Value – Collective ownership and knowledge flows enable value delivery.
8. Continuous Improvement – The inspect‑and‑adapt habit that ensures learning, flow, and resilience.

How It Works

The Kendall Framework applies a problem‑first approach to AI. It integrates management clarity, systems thinking, and iterative improvement to ensure adoption creates measurable value.

• Problem‑First: Focus on the most pressing problems, not technology.
• Role of Context: Define purpose, scope, and constraints so solutions align with organisational goals.
• Collaboration: Build shared understanding and collective ownership across disciplines.
• Iteration: Govern adoption by empirical inspection and adaptation.
• Discovery Cadence: Hold regular sessions (quarterly or half‑yearly) to surface problems, adapt, and rebalance flow of priorities.

Kendall Accountabilities

The Kendall Framework defines accountabilities. Each may be fulfilled by one person or many, provided leadership and responsibility are clear.

Kendall Coach

The Kendall Coach is accountable for helping the organisation understand and apply the framework. They mentor Context Champions and Opportunity Leads, ensure consistency, and promote discipline in defining and refining opportunities and context. They do not manage day‑to‑day AI work but steward coherence and learning across teams.

They enable:

• Mentoring accountabilities in adaptive use of the framework
• Helping teams define meaningful and strategically aligned opportunities
• Reflection across discovery cycles to improve outcomes
• Challenging rigid patterns that inhibit learning
• Surfacing systemic constraints and impediments to flow
• Cultivating feedback loops for organisational learning

Opportunity Lead

The Opportunity Lead drives disciplined use of the Opportunity Backlog as a tool for strategic clarity and adaptive alignment. They support expressing intent through opportunities, uncover systemic barriers, and enable outcome‑oriented decisions. They maintain rhythm around review cycles, help interpret learning, and connect strategic direction to measurable initiatives.

They make happen:

• Cross‑team and executive alignment of strategic themes
• Predictable rhythm for clarity, accountability, and feedback
• Enabling Context Champions through guidance and coaching
• Visibility into whether opportunities align with strategy
• Identification of systemic misalignments using opportunities as signals
• Turning opportunity results into strategy‑shaping insight through deliberate feedback

Context Champions

The Context Champions are accountable for lean management of context for AI. A Context Champion is needed for each instance of AI usage. They facilitate capture, refinement, and maintenance of context, ensuring it remains relevant and valuable. Champions promote transparency, enable engagement, escalate blockers, and coach teams to apply context principles without excess ceremony.

They create impact by:

• Enabling inspection of context, learning, and alignment on cadence
• Supporting teams in writing and refining context blocks
• Helping teams use context to guide decisions and focus
• Escalating unresolved impediments or misalignments
• Supporting teams in interpreting context as shared direction

Events

Opportunity & Context Sourcing Event

This event aligns leaders and teams on the most effective problems to solve with AI. It functions as a feedback loop in the system of work, closing the gap between strategy, evidence, and adaptation. It emphasises clarity, discovery, and evidence‑informed prioritisation.

Outcomes include:

• Alignment with objectives and context
• A prioritised set of opportunities
• Clear direction for an AI roadmap
• Evidence review of outcomes against intent
• Learning orientation, treating surprises as signals for adaptation
• Feedback loops into the Opportunity Backlog, Context Repository, and Roadmap

The Kendall Coach and Opportunity Lead co‑facilitate to ensure balance and discipline. Held on a regular cadence (quarterly or half‑yearly), it inspects and adapts objectives based on evidence and rebalances flow of priorities.

Artifacts

Opportunity Backlog

The Opportunity Backlog is an ordered list of potential AI opportunities. It is refined through discovery events and maintained by the Opportunity Lead to ensure relevance and clarity. It provides visibility, supports prioritisation, and informs where to apply AI.

Context Repository

The Context Repository is a structured collection of validated contexts used by AI systems. It ensures consistency, relevance, and availability. The Context Champions maintain the repository to prevent duplication, reduce noise, and preserve clarity.

Roadmap

The Roadmap is a high‑level view of prioritised AI initiatives derived from the Opportunity Backlog. It communicates intent, sequence, and focus areas without prescribing detailed implementation. The Opportunity Lead owns the Roadmap, ensuring it reflects priorities and remains evidence‑based and adaptive.

Summary

The Kendall Framework is a system of work for AI adoption. It is principled, concise, and evidence‑based. By applying it, organisations:

• Align on purpose, context, and objectives
• Prioritise opportunities through the Opportunity Backlog
• Maintain context integrity with Context Champions
• Create and adapt a Roadmap led by the Opportunity Lead
• Inspect and adapt through Opportunity & Context Sourcing Events
• Rely on the Kendall Coach to uphold principles, expose constraints, and enable organisational learning

The result is AI adoption that is purposeful, trustworthy, adaptive, flow‑oriented, and aligned with mission and value creation.