Welcome executives and professionals. Leaders across tech, finance, sales, marketing, HR, risk, and beyond are racing to capture value from AI agents, agentic AI, and generative AI.

Strategies, operating models, architectures, delivery approaches, and responsible AI practices are evolving rapidly.

To help you stay ahead and save time, I’ve distilled the key takeaways from the top AI leadership playbooks published in the past six months.

OPENAI

AI leadership guide

Brief: OpenAI’s 15-page leadership playbook outlines five steps: Align, Activate, Amplify, Accelerate, Govern, offering guidance, customer stories, and practical actions to help leaders adopt AI and build AI-first organizations.

Breakdown:

• Employees adopt faster when leaders link AI to their skills, purpose, and the company's competitive advantage, while visibly showing support.

• Nearly half of employees lack AI training; leaders should encourage experimentation, tailored learning, and normalize upskilling.

• Scaling impact requires sharing knowledge, use cases, prompts and more across teams to turn isolated successes into reusable progress.

• Acceleration relies on reducing friction: flexible infra, clear decision authority, and lightweight approvals to move pilots into prod fast.

• Speed doesn’t mean ignoring risk. Practical governance ensures safeguards, enabling rapid action with clarity, trust, and accountability.

Why it’s important: AI is advancing faster than most leaders expected. Early adopters are already growing revenue ahead of peers, but many enterprises lag. Success depends on creating conditions for teams to adapt confidently and treating AI as a fundamental shift in how work gets done.

MCKINSEY

Agentic AI playbook for CEOs

Brief: McKinsey released a CEO playbook to address the “gen AI paradox,” outlining how AI agents can unlock scalable value and the CEO’s strategic mandate to lead transformation in the agentic era.

Breakdown:

• Nearly eight in ten companies report using gen AI, yet just as many report no significant bottom-line impact. Think of it as the “gen AI paradox.”

• Enterprise-wide copilots scaled fast but offer limited value; 90% of transformative, vertical use cases remain stuck in pilot mode.

• AI agents can break the paradox by shifting gen AI from a reactive tool to a proactive, goal-driven collaborator, automating complex processes.

• Beyond efficiency gains, agents unlock agility and revenue streams. Realizing this value demands rethinking workflows from the ground up.

• A new AI architecture paradigm, the ‘Agentic AI Mesh’, is needed to enable scale but the real challenge is human: earning trust and driving adoption.

Why it’s important: Agentic AI is not an incremental step, it is the foundation of the next-generation operating model. CEOs who act now won’t just gain a performance edge. They will redefine how their organizations think, decide, and execute. The time for exploration is ending. The time for transformation is now.

UNIVERSITY OF UTAH

The AI leadership blueprint

Brief: The AI Leadership Blueprint, a 98-page workforce transformation guide from The University of Utah, gives leaders a practical roadmap to integrate generative AI responsibly, strategically, and effectively.

Breakdown:

• Designed for leaders needing both strategic overview and actionable guidance to begin integrating generative AI responsibly.

• The Strategic Planning Pathway (Sections 1,3,6) enables consensus building on AI strategy, structure, leadership, policy, and ROI.

• The Implementation Pathway (Sections 2,4,5,6) supports deployment, from readiness checks to workforce training programs.

• The Risk and Governance Pathway (Sections 8,3,7) centers on risk management, and change management practices.

• The appendix includes nine templates, from AI policy checklists and ROI templates to risk matrices, to accelerate progress.

Why it’s important: This blueprint emphasizes frameworks for implementation, governance, ethics, and organizational readiness instead of prescribing specific technologies. With AI changing quickly, model-specific advice can become obsolete, but durable structures provide lasting value.

WRITER

CMO shares agentic AI marketing opportunities

Brief: The CMO of enterprise AI company WRITER shared how AI has changed marketing: what once took weeks now takes hours. Customers demand hyper-personalized experiences, requiring both speed and custom fit from leaders.

Breakdown:

• Gen AI is the freelancer producing content from a prompt; Agentic AI is the manager and team, turning goals “launch a campaign” into execution.

• The matrix above maps agentic AI use cases across marketing functions and funnel stages, the foundations of an implementation roadmap.

• Teams can start with high-impact, low-complexity cases like competitive research, then progress toward multi-agent campaign orchestration.

• The insights cover six agentic AI use cases in marketing, outlining challenges, agentic workflows, enterprise examples, and outcomes.

• Qualcomm saved 2,400 hours monthly with personalization at scale, while 3,000 Salesforce employees reported a 20% productivity gain.

Why it’s important: Speed without custom fit leads to generic, off-brand content, while fit without speed means losing opportunities to the competition. The winners will master orchestration, building AI agents that embody their brand intelligence and execute their strategic vision at unprecedented speed.

TENEO

CEO advisory firm unveils AI software strategies

Brief: CEO advisory firm Teneo published four playbooks on how AI is reshaping the software landscape and how incumbents can adapt their strategies and operations to stay competitive against AI-native disruptors.

Breakdown:

• Leveraging insights from 300+ software vendors, Teneo’s guidance supports disciplined decision-making across four core areas.

• The product strategy playbook emphasizes that control of proprietary data and APIs, more than feature count, is now the defining moat.

• The pricing playbook outlines pricing and packaging strategies, including why splitting AI into a separate tier or add-on can often backfire.

• The GTM playbook shows how to win in non-linear AI buying journeys, shaped by a growing number of digital and human touchpoints.

• The customer success playbook emphasizes how outcome-based models help align professional services with customer ROI.

Why it’s important: AI is central to growth for most software firms, presenting vast opportunities to create value but also real threats. Companies that stray from decisions based on customer value, competitive positioning, and margin discipline because “AI changes everything” are unlikely to succeed.

SALESFORCE

Agentic AI transformation playbook

Brief: Salesforce released a playbook for becoming an agentic enterprise, sharing successes, challenges, customer stories, and downloadable accelerators, to help organizations turn AI ambition into real change.

Breakdown:

• The playbook maps agentic AI readiness with a maturity model, leadership shifts, and strategic pillars for leveraging AI agents in the enterprise.

• Prepare your workforce for agent collaboration with Salesforce’s "four R’s": Redesigning, reskilling, redeploying, and rebalancing.

• Identify high-impact starting points using an structured use case framework focused on outcomes and measurable business value.

• Transform data into an asset through effective governance while unlocking the “dual dividend” of human potential and customer loyalty.

• Learn from companies like Indeed and WeWork and apply insights using downloadable accelerators like the AI vision statement.

Why it’s important: Salesforce’s playbook offers a roadmap for agentic transformation, from vision to execution. It helps enterprises unlock AI’s “dual dividend,” and positions them to evolve toward the future advanced agent environments, robotics and “enterprise general intelligence” (EGI).

MICROSOFT

CIO AI agent governance playbook

Brief: Microsoft released a 40-slide CIO playbook on expediting Copilot Studio and agent adoption by leveraging Power Platform foundations, a low-code suite used by 97% of Fortune 500 companies to automate knowledge work.

Breakdown:

• Agents create opportunities but also risks. CIOs should govern them like digital labor: assign trackable IDs, define roles, and monitor performance.

• Extend your Power Platform foundation to unify governance across agents, apps, and automation, ensuring consistency and reducing duplication.

• Use a Zoned Governance Model: centralized policy with progressive autonomy across personal productivity, collaboration, and enterprise.

• Culture will make or break your agent strategy. Invest in role-based training and communities to help teams learn, share, and scale.

• The playbook includes a CIO checklist, plus additional resources on agent cost control, governance, and implementation.

Why it’s important: CIOs are uniquely positioned to lead the agent shift, evolving Power Platform governance where it serves the enterprise, enabling agent autonomy, AI-driven decisions, and responsible AI, while placing strategic bets on emerging players in a rapidly evolving AI-native ecosystem.

GOOGLE

Agentic AI services playbook

Brief: Google published a 72-page playbook to help service providers, spanning advisory, implementation, and maintenance, capture the agentic AI opportunity. The guidance is largely vendor-agnostic.

Breakdown:

• Strategy firm BCG estimates the total addressable market (TAM) for agentic AI services at ~$1T globally, just among Google Cloud partners.

• This opportunity stems from long-standing pain points that have hindered enterprises from maximizing productivity and growth.

• Tailored agents now offer a path to solve many enterprises challenges (e.g. loan underwriter agents assessing risk/issuing approvals in finance).

• Google outlines strategies for evolving service provider offerings, including transaction-based and outcome-driven pricing models.

• The report includes TAM data, high-impact use cases across tech, finance, CPG, healthcare, and more, plus separate playbooks for each industry.

Why it’s important: Google Cloud has a smaller share of the enterprise market than AWS and Microsoft, but its growth is accelerating, fueled by AI and partners, which contributed 80% of its incremental revenue last year. The role of ecosystems in driving success has never been more important.

BOSTON CONSULTING GROUP

AI-first companies win the future

Brief: Boston Consulting Group (BCG) published an 18-slide executive perspective outlining how AI-first companies are rewriting the business playbook, generating tens of millions in revenue with a few dozen employees.

Breakdown:

• AI is driving down the cost of knowledge, labor, and delivery, while increasing revenue potential for companies setup for scale.

• AI-first companies are redefining competitive advantage, unlocking rapid growth with lean, highly specialized teams.

• Cursor, an AI-powered code editor, reached $100 million in annual recurring revenue within a year, with fewer than two dozen employees.

• Brand trust, direct customer relationships, proprietary IP, and unique datasets are increasingly important to AI-first success.

• AI-native firms are decentralizing technology and building lean teams; 50-70% smaller with 1.5–2x pay for specialized top talent.

Why it’s important: The rise and rapid adoption of AI is transforming how businesses operate, lowering barriers to entry, and enabling small, focused teams to scale quickly. As AI-first companies redefine cost structures and growth dynamics, those that fail to adapt risk being left behind.

CAPGEMINI

AI transformation blueprint

Brief: Capgemini and Everest Group released a 25-page blueprint for scaling AI transformation, outlining the journey from pilot to production, key challenges, and the value of successful enterprise-wide implementation.

Breakdown:

• Organizations such as ABN AMRO, BMW, and Eneco eMobility are leveraging AI to drive productivity and customer experience gains.

• Successful scaling demands strategic alignment, strong data, robust governance, and ongoing talent development. All deeply interconnected.

• AI must be aligned with the business value chain, with clear KPIs and responsible AI practices embedded throughout the delivery lifecycle.

• The blueprint features a use case identification framework pictured above, operating models, sourcing strategies, and an ABN AMRO case study.

• Agentic AI projects are set to grow 48% in 2025, according to a separate, newly published 100-slide Capgemini research report.

Why it’s important: Scaling AI isn’t just a technical challenge, it’s a full-scale enterprise transformation. This blueprint offers a structured, actionable starting point to help organizations move from isolated pilots to organization-wide impact, grounded in strategy, governance, and measurable value.

BOSTON CONSULTING GROUP

AI agents, MCP and agentic abundance

Brief: Boston Consulting Group (BCG) published a 37-slide report on AI agents, covering how they are evolving, where they have product-market fit, how reliable and effective they can be, MCP’s role in agentic workflows, and building at scale.

Breakdown:

• BCG explores how agents are moving beyond simple 'if-statements' toward more autonomous agents and multi-agent systems.

• It outlines how coding agents are the first to reach product-market fit, with organizations realizing significant value from agentic workflows.

• Bloomberg’s compliance agents rigorously check facts and identify edge-case risks, reducing time-to-decision by 30–50%.

• BCG details six key dimensions for tracking agent performance, including reasoning and planning, task autonomy and execution, and more.

• It explains how MCP help unlock agentic workflows through one unified protocol and highlights the emerging role of agent-to-agent protocols.

Why it’s important: In less than half a year since its launch by Anthropic, the Model Context Protocol (MCP) has been rapidly adopted by OpenAI, Microsoft, and others. BCG's effort to unpack MCP’s role and its significance as a meaningful step towards broad applications of agentic systems in production is a valuable read.

PALO ALTO NETWORKS

Agentic AI threats

Brief: Palo Alto Networks simulated attacks on AI agents built with CrewAI and AutoGen frameworks to explore vulnerabilities like data leaks, credential theft, and tool misuse. The cybersecurity firm then outlined defense strategies.

Breakdown:

• Enforce safeguards in agent instructions to block out-of-scope prompts. Deploy content filters to detect prompt injection attempts at runtime.

• Sanitize tool inputs, apply strict access controls and perform routine security testing, such as Dynamic Application Security Testing (DAST).

• Enforce strong sandboxing with network restrictions, syscall filtering and least-privilege container configurations.

• Use a data loss prevention (DLP) solution, audit logs and secret management services to protect sensitive information.

• Combine multiple safeguards across agents, tools, prompts and runtime environments to build resilient defenses.

Why it’s important: As AI agents see broader real-world adoption, understanding their security implications is critical. Most vulnerabilities are framework-agnostic, rooted in insecure design patterns, misconfigurations, and unsafe tool integrations, not in the frameworks themselves.

PWC STRATEGY&

AI adoption approach

Brief: PwC Strategy& shared a 20-page playbook outlining its 9-step AI adoption approach, featuring illustrative accelerators from a recent manufacturing engagement. The core elements are industry-agnostic.

Breakdown:

• Begin with an AI readiness check to identify adoption constraints, list potential use cases, and rank them by value and feasibility.

• Build a business case, group use cases into implementation waves, and enlist executive sponsorship based on a shared view of expected benefits.

• Define the operating model and architecture to enable scale, then build a roadmap to plan the release of use cases’ and supporting initiatives.

• Monitor use cases, track results, and refine the roadmap. All 9 steps are explored in greater detail throughout the playbook.

• It also features illustrative accelerators like PwC’s Use Case Compass, operating model archetypes, responsible AI framework, and more.

Why it’s important: While more profitable business models are now possible leveraging AI-enabled capabilities, many enterprises face challenges in capturing value. PwC Strategy& offers a pragmatic, structured approach to start addressing these challenges and driving AI adoption.

OPENAI

Identifying enterprise AI use cases

Brief: OpenAI published a 34-page guide to help enterprises identify use cases that deliver value, featuring practical checklists and backed by insights from 300 of its most successful implementations and 2M+ business users.

Breakdown:

• Start by targeting common workplace challenges: repetitive low-value tasks, skill bottlenecks, and processes involving ambiguity.

• Teach employees "primitives of AI use cases" to accelerate discovery: content creation, research, coding, data analysis, ideation and automation.

• For each primitive, OpenAI shares common starting use cases, along with customer case studies and practical checklists.

• Leverage a value/effort framework to prioritize high-impact, department-specific use cases that drive value and are feasible to implement.

• OpenAI highlights a high-value, low-effort example: Tinder built a GPT for prototyping, testing, and debugging, no coding required.

Why it’s important: OpenAI brings firsthand insight into what drives successful enterprise AI. Its approach is grounded in practical, proven wins. Yet achieving success requires more than just selecting the right use cases. In a separate 24-page guide, OpenAI distills seven AI adoption lessons from frontier firms.

DELOITTE

What board directors need to know

Brief: Deloitte published its AI Governance Roadmap, a 16-slide guide, to help boards understand their role in AI governance. It explores key questions and resources for overseeing AI, regardless of the organization's AI maturity.

Breakdown:

• Evaluate AI approach within corporate strategy, oversee execution, and help management adapt strategy to AI risks and opportunities as needed.

• Oversee AI risks (strategic, functional, and external) to the company’s overall strategy, integrating them into the enterprise risk program.

• Define oversight ownership at the board level (e.g. the full board, existing committee, new committee, or sub-committee). Consider an AI advisory council or adding an AI-expert board member.

• Monitor performance against AI-specific strategic, financial, and operational goals. Establish a consistent evaluation process.

• Assess if management has the skills to execute the AI strategy. Understand AI’s impact on recruitment, development, and incentives.

• Cultivate trustworthy AI with appropriate disclosures and communications. The board tracks AI usage, ensuring adherence to ethical standards.

Why it’s important: Although AI is not new, the increasingly proliferation of gen AI in enterprises brings governance topics to the forefront. The decisions leaders make today in balancing opportunity and risk will shape their enterprises and society at large for years to come.

BOOZ ALLEN

Building enterprise gen AI applications

Brief: Booz Allen shared its framework for building enterprise gen AI, built on six architecture layers, LLMOps practices for monitoring and improvement, and governance, risk, and compliance (GRC) frameworks.

Breakdown:

• The six architecture layers span Infrastructure, Platform, LLM, Data & Pipeline, Agent & Capability, and UI/Application (see diagram above).

• LLM deployment option trade-offs evaluated include on-premises self-managed, cloud self-managed, and cloud-hosted API.

• The report explores orchestrating LLMs by task complexity and cost, and data pipelines to support real-time, domain-specific use cases.

• The importance of embedding human oversight into AI workflows where appropriate is emphasized, guided by the "agentic spectrum."

• Beyond architecture, Booz Allen outlines core concepts of LLMOps, alongside governance, risk, and compliance frameworks.

Why it’s important: Generative AI is transforming enterprise knowledge, decision-making, and user interaction. Success requires more than model access; it demands a layered architecture, strong data pipelines, human oversight, and rigorous governance to align adoption with business goals.

BOSTON CONSULTING GROUP

AI playbooks for senior executives*

Brief: Boston Consulting Group (BCG) released eight playbooks to guide CEOs and senior executives (CFOs, COOs etc.) on AI transformation, including gen AI. Each ~25-slide playbook draws from 1,000+ AI programs, offering strategies, roadmaps, and case studies to drive enterprise value.

Breakdown:

• For Finance Leaders, the 23-slide Finance playbook explores AI opportunities, evolution of processes, operating models, and more.

• For Technology Leaders, the 24-slide Data & Digital Platforms playbook covers maximizing data value, evolving tech stacks for AI, and more.

• For Operations Leaders, the 20-slide Supply Chain playbook highlights gen AI applications and starting points, while the 24-slide Customer Service playbook explores economics, teams, and more.

• For Risk Leaders, the 32-slide Risk & Compliance playbook details AI-driven risk management, capability evolution, and more.

• For Sales Leaders, the 23-slide Customer Engagement playbook covers ideation, personalization, and communication, while the 23-slide B2B Sales playbook focuses on team evolution, strategies, and more.

• For People Leaders, the 22-slide HR playbook outlines future structures, tools, skills, and gen AI performance gains.

Why it’s important: These playbooks for leaders provide actionable strategies and real-world learnings to increase the likelihood of success in realizing tangible value from AI investments.

*Note: Several of the BCG executive playbooks predate the past six months but remain relevant.

AWS released a 17-page executive guide on agentic AI, covering what makes it unique, business outcomes, and steps for leaders to drive adoption.

Stanford published a 14-page playbook for building enterprise AI, covering use case selection, models, architecture, risk, and more.

MORE MUST-READ BREAKDOWNS

• AI Agent Playbooks (16 playbooks)

• AI Agent, Agentic AI Use Cases (2195)

• Agentic AI Case Studies (40 cases)

• AI Use Case Prioritization (12 frameworks)

• AI Strategy Playbooks (16 playbooks)

ENTERPRISE AI EXECUTIVE

Agentic and generative AI are evolving rapidly in the enterprise, driving a new era of AI transformation.

Twice a week, we review hundreds of the latest agentic and generative AI best practices, case studies, market dynamics and innovations to bring you what is driving material value — and why it’s important.

Example sample editions:

• McKinsey reveals CEO agentic AI playbook.

• OpenAI's best practices from 300 implementations.

• What board directors need to know.

• McKinsey: The state of open source AI.

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