Kendall Guide - A System of Work for AI Adoption

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Kendall Framework Purpose & Definition

AI adoption is no longer an option but a necessity. The Kendall Framework solves the “Where to Start?” challenge by enabling teams to align on AI opportunities, prioritise their most impactful problems, and create a clear roadmap for AI deployment.

The Kendall Framework is a structured, data-driven framework designed to help organisations identify and prioritise AI opportunities effectively. 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.

Seven Principles (and One Habit) of AI Leadership

A practical framework for building high-performance AI systems, through clarity, context, collaboration, and a culture that never stops evolving.

1. Context is King

AI only becomes truly useful when it understands you. Structured context turns generic intelligence into tailored performance. Defined context transforms broad capability into purposeful outcomes that serve your organisation’s specific needs.

Application: Define purpose, scope, and constraints so solutions align with organisational goals. Pull context into AI systems at the rate of actual demand, avoiding waste from excessive accumulation and ensuring relevance.

2. Language is the Raw Material of AI

In AI, language isn’t just how you communicate—it’s the material you build with. The sharper it is, the stronger your results. Precise language is the raw material of AI and management alike. Clarity of expression creates clarity of outcomes.

Application: Use precise, unambiguous language when defining problems, context, and requirements. Invest in articulating constraints, boundaries, and success criteria clearly.

3. Problems Fuel AI

AI isn’t magic; it’s momentum. Give it a real problem, and it turns complexity into breakthroughs. AI begins by addressing real, observable problems rather than chasing technology for its own sake.

Application: Start with the problem, not the solution. Focus on the most pressing challenges, not technology trends. Let demand signal where AI creates value.

4. “Who” Anchors AI

AI gets sharper, faster, and more useful when you start with who it’s speaking for. AI must serve clearly identified stakeholders with specific needs and contexts. Accountability must be anchored in those who benefit from and guide the AI’s work.

Application: Identify stakeholders explicitly. Define their needs, perspectives, and success measures. Ensure AI systems are accountable to those they serve.

5. AI Needs to Know Your Rules to Play Your Game

AI can’t follow your rules until you teach it the playbook. Your policies, values, and boundaries turn it from a wildcard into a trusted teammate. Values and constraints ensure dependable and ethical AI use.

Application: Make organisational policies, values, and constraints explicit. Build them into the context AI uses. Establish boundaries that ensure trust and compliance.

6. Assemble AI Like a Truck

AI thrives not on scattered insights but on well-structured inputs. The path from prototype to performance is built block by block. Structured inputs form the basis for sustainable outcomes and reliable systems.

Application: Collect, structure, and version context systematically. Build context repositories that are coherent, traceable, and maintainable. Limit work in progress to maintain focus and relevance.

7. AI is a Team Sport

The smartest AI comes from shared intelligence. When teams align, structure their knowledge, and build context together, everyone wins. Collective ownership and knowledge flows enable sustained value delivery.

Application: Foster collaboration across disciplines. Build shared understanding and collective ownership. Enable cross-functional teams to contribute to context and problem definition.

The One Habit: Continuous Improvement is Non-Negotiable

AI excellence isn’t a one-time win—it’s a habit, built through constant learning, iteration, and a culture that never stops evolving. The inspect‑and‑adapt habit ensures learning, flow, and resilience.

Application: Establish regular discovery cadences. Hold Opportunity & Context Sourcing Events quarterly or half-yearly. Inspect outcomes against intent. Treat surprises as signals for adaptation. Maintain rhythm around review cycles to ensure continuous learning and adaptive alignment.

How It Works

The Kendall Framework adapts the best of Lean manufacturing, Design Thinking, and Agile philosophy for today’s AI challenges. It applies a problem‑first approach through a team-based framework that integrates management clarity, systems thinking, and iterative improvement to ensure adoption creates measurable value.

Team-Based Approach

The framework thrives on collaboration and shared intelligence:

• Problem‑First Focus: Workshops concentrate on defining and prioritising problems, ensuring that AI investments align with real needs, reducing waste, and maximising returns.
• AI Roles and Problem Identification: Participants define their roles and articulate problems from their unique perspectives. This data-driven approach ensures all voices are heard and key issues are accurately captured.
• Team Collaborative Scoring: Participants vote on which problems should be solved first, resulting in clear, shared understanding of priorities.
• Human-in-the-Loop AI Training: Data generated through workshops is used to train AI models, create high-fidelity datasets, and improve AI solutions in real-time.

Core Mechanisms

• Context as Foundation: Define purpose, scope, and constraints so solutions align with organisational goals.
• Pull‑Based Context Flow: Context is pulled into AI systems at the rate of actual demand, avoiding waste from excessive accumulation and ensuring relevance.
• Structured Collaboration: Build shared understanding and collective ownership across disciplines.
• Empirical Governance: Guide adoption through inspection and adaptation, not prediction.
• Discovery Cadence: Hold regular Opportunity & Context Sourcing Events (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. These are accountabilities, not job titles or roles. They represent areas of responsibility that enable the framework to function effectively.

AI Leadership Accountability Structure

The framework establishes a clear accountability hierarchy for AI adoption:

• Context Curator – Provides strategic oversight across multiple problem domains
  • Problem Owner – Accountable for specific problem areas and their context needs
    • Context Owner – Manages context for specific AI instances

This structure scales to support multiple problem domains. For example, a Context Curator may oversee several Problem Owners, each responsible for distinct problem domains. Each Problem Owner coordinates multiple Context Owners who manage context for individual AI implementations within that problem domain.

Kendall Coach

The Kendall Coach is accountable for helping the organisation understand and apply the framework. They mentor Context Curators, Problem Owners, Context Owners, and the Opportunity Owner, ensure consistency, and promote discipline in defining and refining opportunities and context. At scale, where multiple Opportunity Backlogs exist, the Kendall Coach may work with multiple Opportunity Owners, each accountable for their specific backlog. 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

Context Curator

The Context Curator holds strategic accountability for context coherence across multiple problem domains. They ensure that context developed for different AI implementations remains aligned with organisational strategy and maintains consistency. Rather than managing operational details, they provide oversight that enables Problem Owners to work effectively within a coherent context landscape. They are also accountable for the disciplined assembly, traceability, and publishing of context for AI, ensuring that context is collected, structured, and versioned so it remains accurate, auditable, and valuable throughout its lifecycle. They apply lean principles by enabling pull‑based context flow, delivering context as demand emerges rather than accumulating speculatively.

They enable:

• Strategic alignment of context across problem domains
• Oversight of Problem Owners and their context strategies
• Identification of cross-domain patterns and reusable context
• Resolution of conflicts or overlaps between problem domains
• Connection between strategic intent and context development
• Escalation of systemic context challenges to leadership
• Assembling and curating context into coherent and usable blocks on demand
• Maintaining traceability of context sources, changes, and dependencies
• Publishing updated context on a clear cadence for inspection and alignment
• Supporting teams in refining and validating context contributions
• Ensuring context flows at the rate of actual demand, limiting work in progress to maintain focus and relevance
• Escalating unresolved inconsistencies or context gaps
• Coaching teams to use context as a shared reference point for decisions and focus

Problem Owner

The Problem Owner is accountable for a specific problem domain and the context strategy required to address it effectively with AI. They coordinate Context Owners working on related AI implementations within their domain, ensuring context development remains focused on solving the identified problem. They maintain clarity on problem definition, stakeholder needs, and outcome measures.

They make happen:

• Clear articulation of the problem and its boundaries
• Coordination of Context Owners within their problem domain
• Alignment of context work with problem-solving goals
• Visibility into progress and impediments across their domain
• Prioritisation of context needs based on problem urgency
• Translation of problem insights into context requirements
• Feedback to Context Curator on strategic patterns and needs

Opportunity Owner

The Opportunity Owner drives disciplined use of the Opportunity Backlog as a tool for strategic clarity and adaptive alignment. A single Opportunity Owner is accountable for each Opportunity Backlog, ensuring clear ownership and ordering of priorities. The Opportunity Owner supports expressing intent through opportunities, uncovers systemic barriers, and enables outcome‑oriented decisions. They maintain rhythm around review cycles, help interpret learning, and connect strategic direction to measurable initiatives.

The Opportunity Owner makes happen:

• Cross‑team and executive alignment of strategic themes
• Predictable rhythm for clarity, accountability, and feedback
• Enabling Context Curators, Problem Owners, and Context Owners 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 Owner(s)

The Context Owner(s) are accountable for lean management of context for specific AI instances within a problem domain. A Context Owner is needed for each instance of AI usage. Working under the coordination of a Problem Owner, they facilitate capture, refinement, and maintenance of context, ensuring it remains relevant and valuable. Owners promote transparency, enable engagement, escalate blockers, and coach teams to apply context principles without excess ceremony. They embody pull‑based thinking by ensuring context is drawn from sources only when needed, maintaining flow and avoiding waste.

They create impact by:

• Enabling inspection of context, learning, and alignment on cadence
• Supporting teams in writing and refining context blocks as demand signals emerge
• Helping teams use context to guide decisions and focus
• Limiting context work in progress to what is immediately valuable, preventing speculative accumulation
• 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 framework, closing the gap between strategy, evidence, and adaptation. It emphasises clarity, discovery, and evidence‑informed prioritisation. By surfacing demand signals, this event enables pull‑based context flow and ensures context development remains aligned with actual needs.

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
• Identification of demand signals that trigger context creation or refinement
• Feedback loops into the Opportunity Backlog, Context Repository, and Roadmap

The Kendall Coach and Opportunity Owner 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.

Context 360 Workshop

The Context 360 workshop is the structured discovery session inside the Kendall Framework. Its purpose is to capture the operational reality of the organisation so AI can perform reliably in that environment. It brings together the people who understand how work actually flows and guides them through a fast, disciplined process for surfacing the context that any AI solution needs.

Across the session, participants identify the real problems worth solving, map the roles involved, and expose the rules, policies, workflows, and constraints that shape daily operations. By the end, the team has a clear and shared understanding of how the work works, what outcomes matter, and what information AI must be grounded in to be safe, accurate, and useful.

The output of the workshop is a structured “Context Bill of Materials.” This becomes the foundation for AI Operations, ensuring that any future AI solution is built on clarity instead of assumptions.

Outcomes include:

• A prioritised list of meaningful problems worth addressing with AI
• A shared operational picture across roles and functions
• Clear articulation of policies, rules, boundaries, and exceptions
• A well-structured context package ready for AI Operations
• Confidence that any AI solution will be embedded in real organisational reality

Context 360 ensures that organisations do not jump to building agents prematurely. It anchors AI work in the environment it must operate within, which is the heart of reliable and repeatable AI outcomes.

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 Owner 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 Curator maintains the repository to prevent duplication, reduce noise, and preserve clarity. Following lean principles, the repository grows organically in response to demand rather than speculative accumulation. Context is pulled into the repository as needed, ensuring flow efficiency and maintaining focus on what delivers value now.

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 Owner owns the Roadmap, ensuring it reflects priorities and remains evidence‑based and adaptive.

Summary

The Kendall Framework is a structured, data-driven framework for AI adoption. It is principled, concise, and evidence‑based. By applying it, organisations:

• Align on purpose, context, and objectives
• Establish clear accountability through Context Curators, Problem Owners, and Context Owners
• Prioritise opportunities through the Opportunity Backlog
• Maintain context integrity through Context Curators, Problem Owners, and Context Owners
• Pull context at the rate of demand, ensuring lean flow and preventing waste
• Create and adapt a Roadmap led by the Opportunity Owner
• 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.