⚡ Quick Summary
Published by AlixPartners, this playbook is written for chief executives who want to realise value from their AI investments. It opens from a tension the document states directly: CEOs say AI is their most important opportunity, yet more than 80% of all AI projects fail, and leaders fall into common traps such as lack of alignment with business strategy, failure to build strong foundations, poor use case selection, cultural impediments to change and a gap to operationalisation.
The guidance is organised across three areas. Strategy covers strategic alignment, stakeholder engagement and change management, and use case selection. Execution covers model building, model deployment, continuous learning and improvement, and performance and ROI tracking. The foundational pillars are split into technical (data, technology, operations) and organisational (AI skills, organisation structure, risk and compliance). Each element pairs a critical question with a short list of key elements.
Three client stories illustrate outcomes, including 47% higher revenue from contacted customers at a retailer, 45% higher click-through rates, and a 3-point increase in on-time and in-full rates for some clients. The playbook closes with a 1–3 AI readiness self-assessment and the instruction to "Think big, start with purpose, learn fast, and scale with confidence."
🧩 What’s Covered
The playbook moves from rationale, through strategy and execution, to foundations, examples and self-assessment.
- Why do you need a playbook?: The case for a disciplined approach, with figures of $27 billion invested in AI startups and $150 billion by corporations in AI applications and projects, five common traps, and the differing concerns of the CEO, investors and board, customers and employees.
- Strategy: A blueprint for AI-driven transformation built on three elements — strategic alignment (linking AI strategy to corporate strategy, financial goals, value proposition and customer needs, with competitive intelligence and an AI investment strategy), stakeholder engagement and change management (focus on people, identify the right stakeholders, training and empowerment), and use case selection (strategy alignment, feasibility analysis, near-term results, value, investment and ROI assessment).
- Execution: Four elements for turning vision into results — model building (data processing, model development, testing and refinement), model deployment (integration planning, deployment management, user training and support), continuous learning and improvement (continual refinement, trend analysis and response, performance benchmarks), and performance and ROI tracking (ROI tracking, performance monitoring).
- Foundational pillars, technical: Data (accessibility, governance, infrastructure), technology (strategic partnerships, AI platform, tool and model repository) and operations (MLOps, monitoring and maintenance, continuous improvement).
- Foundational pillars, organisational: AI skills (talent acquisition, continuous learning culture, collaborative integration), organisation structure (leadership and vision, cross-functional alignment) and risk and compliance (governance framework, regulatory compliance, risk management, privacy and security, dashboards).
- Client stories: Three engagements — hyper-personalisation at a large-scale retailer, predicting fulfilment failure to improve on-time and in-full rates, and AI-driven pricing models delivering 24% higher revenue over five years for one client.
- Self-assessment: An illustrative AI maturity exercise scoring eleven questions across strategy, execution and foundations on a 1–3 scale, from "Not yet started" to "Currently being implemented and/or established and ongoing".
- Closing and About us: The advice to start from the business rationale, contact details for four AlixPartners leaders, and a description of the firm.
💡 Why it matters?
For executives, board members and programme leaders deciding where to direct AI investment, the playbook provides a structure for linking AI initiatives to business objectives rather than to technology fashion, and a common vocabulary across strategy, execution and foundations. Its critical questions and key elements can be reused as an agenda for leadership review, and the illustrative maturity assessment offers a starting point for judging readiness in data, technology, operations, people, risk and compliance. The risk, compliance and governance pillar is where the playbook connects AI value creation to regulatory compliance, privacy and security obligations, though it names no specific regime or standard.
❓ What’s Missing
The playbook stays at a management level: no legal instrument, standard or regulatory regime is named, so the compliance pillar gives no mapping to specific obligations, and the governance framework, dashboards and metrics are described in outline only. The claim that more than 80% of AI projects fail is sourced to an "AlixPartners estimate" without method. There is no implementation detail on platform selection, data architecture, model evaluation or privacy engineering, and no coverage of generative AI-specific harms or incident response. The self-assessment is illustrative rather than a scored instrument, and the client stories describe AlixPartners engagements without naming the organisations.
👥 Best For
Chief executives, board and audit committee members, and transformation or AI programme leads who need a structure for reviewing whether AI initiatives are aligned to business strategy, adequately founded and measurable. It also suits governance and risk teams seeking critical questions to raise with business sponsors before funding or scaling AI use cases.
📄 Source Details
The document is Practical AI for CEOs: When it really matters. A playbook for realizing value from your AI initiatives, published by AlixPartners, LLP and carrying the imprint "AlixPartners 2024" (page 17). It runs to 17 pages in English; no version number, series or reference number is printed. It cites the 2024 AlixPartners Disruption Index and an AlixPartners estimate for its headline figures but gives no URL for itself. The input was the text extraction of all 17 pages, including the cover, contents, sections, client stories, self-assessment, contacts and legal disclaimer.
