The Executive AI Playbook

Artificial intelligence is no longer experimental. It is reshaping customer trust, operating models, and competitive advantage faster than most leadership teams are prepared for.

This white paper provides a practical, executive-level framework for governing AI, aligning it to business outcomes, and moving from isolated pilots to durable impact.

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Version 1.1.1

What's new in this edition

The first edition landed in a world where AI was, for most enterprises, a recommendation engine - a model that suggested and a human that decided. Eighteen months on, that assumption no longer holds for the most consequential deployments. v1.1.1 addresses three shifts directly.

  1. 1.
    A new taxonomy for the 2026 AI stack

    Retires the Narrow vs. General AI dichotomy. Three layers - specialized models, frontier general-purpose models, and agentic systems - replace it. Sourcing moves from a Public vs. Proprietary table to three paths: frontier API, retrieval-augmented hybrid, and fully custom or fine-tuned.

  2. 2.
    Governance for AI that acts

    A new section on agentic governance. Action risk versus model risk. The three modes of human-in-the-loop. A scope-of-action charter, kill-switch criteria, and a mapping from every existing playbook artifact to its agentic extension.

  3. 3.
    AI unit economics

    The question executives most consistently ask first - what does this cost, and how does the math change as costs fall - was the question the first edition said the least about. New material on per-transaction inference cost, build-vs-buy at frontier prices, pricing under ~10x annual cost decline, and capacity commits vs. on-demand.

Why This Paper Exists

AI failure is rarely technical. It is almost always a leadership and execution problem.

Ambition Without Ownership

AI is frequently declared a strategic priority, yet executive ownership, decision rights, and success metrics remain unclear.

Without explicit accountability at the C-suite level, initiatives fragment into pilots that never compound into enterprise value.

Leadership Risk Has Changed

AI introduces ethical, regulatory, reputational, and customer-trust risks that traditional governance models were not designed to manage.

Systems that learn and adapt require new forms of oversight and decision-making.

Delay Is No Longer Neutral

Inaction carries cost. Early movers benefit from compounding advantages in data, learning velocity, and execution maturity.

Waiting for clarity often results in falling behind faster than anticipated.

What You'll Learn

An execution framework for leaders navigating irreversible change.

The Three Forcing Functions

How AI fundamentally alters leadership decision cycles, competitive dynamics, and accountability models.

Pitfalls: Shiny Objects & Black Boxes

Why organizations chase tools without strategy and deploy systems they cannot explain, govern, or trust.

The Execution Framework

A structured model connecting AI initiatives to business outcomes and executive ownership.

Governance & Guardrails

Practical guidance for managing risk, ethics, compliance, and decision rights at scale.

The 2026 AI Stack & Sourcing Paths

A three-layer taxonomy - specialized, frontier, agentic - and three sourcing paths (frontier API, retrieval-augmented hybrid, fully custom) for the decisions executives actually face.

Governing Agentic Initiatives

What changes when AI acts rather than recommends: action risk, the human-in-the-loop spectrum, scope-of-action charters, and kill-switch criteria.

AI Unit Economics

Per-transaction inference cost, build-vs-buy at frontier prices, pricing AI features when marginal cost falls ~10x per year, and capacity commits vs. on-demand.

A Phased Roadmap

Overlapping weeks-and-months phases from pilots to repeatable, enterprise-grade AI capability - and when to abandon the phased model entirely.

Leadership Behaviors

The executive habits required to sustain advantage in AI-driven organizations.

The Collaboration

Authors and contributors to the Executive AI Playbook.

Bill Sanders

Alignment & Execution

Bill Sanders, with 20 years of collaborating closely with C-suite leaders, specializes in aligning AI strategies with business goals. His work has guided Fortune 500 executives through technology transformations. As an execution strategist, Bill's insights in this paper offer practical frameworks for AI leadership.

Andrew Stevens

CTO & CISO

Andrew Stevens is an executive technology leader and entrepreneur who has scaled AI and cloud businesses from inception through acquisition. Operating across Europe and the US, he combines deep expertise in cloud architecture, machine learning, and security with a track record of building and leading high-performing teams across fintech, media, gaming, and travel.

Olivia Storelli

CEO & AI Strategy

Olivia is the CEO and Co-founder of Sakura Sky, where she helps corporations turn cloud and AI investment into strategic assets. With over 20 years of experience, she combines the strategic discipline of scaling complex businesses across multiple continents with the deep technical expertise required to deliver secure, private, and well-governed technology solutions.

Jennifer Capo

Program Development & Facilitator

Jennifer Capo designs executive programs to navigate AI adoption. Her facilitation expertise ensures this paper's insights are clear and practical, drawing from her partnership role with Roebling Strauss & Sakura Sky's AI Executive Accelerator.

From Strategy to Action

A companion experience designed to turn executive intent into decisive execution.

The custom Executive AI Accelerator program translates the frameworks outlined in this white paper into focused executive action.

This program compresses months of internal debate into a structured, decision-driven engagement aligned to your organization's strategy, governance requirements, and operating reality.

Explore the Executive AI Accelerator

Key Takeaways from the Playbook

A short view of what the paper argues.

AI is no longer experimental. It is reshaping customer trust, operating models, and competitive advantage faster than most leadership teams are prepared for. AI failure is rarely technical - it is almost always a leadership and execution problem.

  1. 1. Three forcing functions reshape leadership. AI compresses decision cycles, expands executive risk into ethical and reputational domains, and breaks the assumption that initiatives can succeed without explicit C-suite ownership.
  2. 2. Two predictable failure modes. Shiny-object pursuit (chasing tools without a strategy) and black-box deployment (using systems leadership cannot explain, govern, or trust). Both compound across the portfolio.
  3. 3. An execution framework. A structured model connecting AI initiatives to business outcomes and executive ownership, with explicit decision rights, success metrics, and a phased roadmap from pilots to enterprise-grade capability.
  4. 4. Governance is a capability, not a constraint. Risk, ethics, compliance, and decision rights have to be designed in at the start - not bolted on when audit asks the question for the first time.
  5. 5. Governance shifts when AI acts. Agentic systems carry action risk on top of model risk. Scope-of-action charters, kill-switch criteria, and an explicit human-in-the-loop mode are now part of the playbook.
  6. 6. Unit economics decide what to ship. Per-transaction inference cost, build-vs-buy at frontier prices, and pricing under ~10x annual cost decline are now first-class executive questions - not back-of-envelope ones.

“Delay is no longer neutral. Inaction carries cost. Early movers benefit from compounding advantages in data, learning velocity, and execution maturity.”

- The Executive AI Playbook

Written for CEOs, board members, and senior executives. Co-authored by Bill Sanders, Andrew Stevens, Olivia Storelli, and Jennifer Capo. This page reflects v1.1.1 (June 2026).

Related Reading

Where the argument in this playbook goes next.

White Paper
The Autonomous Horizon

The successor argument: from governing AI as a portfolio of pilots to engineering an autonomous enterprise. Translates this playbook's leadership frame into a four-pillar operating model and a board-level diagnostic.

Read the paper →
Executive Program
Executive AI Accelerator

The companion two-day program that puts this playbook into practice. Compresses months of internal debate into a structured, decision-driven engagement for your leadership team.

Explore the program →

Download the White Paper

Written for CEOs, board members, and senior executives. Now v1.1.1 - with chapters on agentic governance and AI unit economics.

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v1.1.1 - June 2026