WELCOME, EXECUTIVES AND PROFESSIONALS.

Leaders across finance, tech, risk, HR, sales and beyond, are working to capture value from generative and agentic AI.

Applying the right strategies, operating models, architecture, delivery and responsible AI best practices can be the difference between success and failure.

I’ve reviewed dozens of AI transformation playbooks.

Here are 16 must-reads:

BOSTON CONSULTING GROUP

BCG 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.

Full report here.

GOOGLE

From prototype to production

Brief: Google’s 36-slide guide provides a starting point for enterprises to take gen AI from prototype to production. Drawing on decades of experience operationalizing AI, it covers setting AI objectives, selecting the right models, evaluation, and more.

Breakdown:

• More than 60% of enterprises are now actively using gen AI in production. In the past year alone, Gemini API usage on Vertex AI has surged 36x.

• Driving value with gen AI requires defining business problems, prioritizing key use cases, and developing a comprehensive AI strategy.

• The right platform matters. Invest in an AI platform, not just models. Some use cases may require multiple models to balance performance and cost.

• You can’t improve what you don’t measure. Ensuring gen AI model reliability and accuracy is a major hurdle enterprises have to overcome.

• Responsible AI is essential. Governance should be embedded from the start to ensure secure deployment and use across enterprises.

Why it’s important: Gen AI has massive potential, but realizing it takes careful execution. Google’s guide helps companies clarify objectives, select models, measure performance, and maintain AI in production, ensuring AI adoption is strategic, scalable, and impactful.

Full report here.

GALILEO

Mastering AI agents

Brief: Galileo, a company that specializes in AI evaluation, released a 93-page guide on mastering AI agents. It covers agent capabilities, real-world use cases, and frameworks, with a strong focus on performance evaluation.

Breakdown:

• Chapter 1 introduces AI agents, their ideal uses, and scenarios where they can be excessive. It includes real-world cases from Salesforce and Oracle Health.

• Chapter 2 details frameworks: LangGraph, Autogen, and CrewAI, providing selection criteria and case studies of companies using each.

• Chapter 3 explores how to evaluate an AI agent through a step-by-step example using a finance research agent.

• Chapter 4 covers measuring agent performance across systems, task completion, quality control, and tool interaction, with five detailed use cases.

• Chapter 5 addresses why many AI agents fail and provides practical solutions for successful AI deployment.

Why it’s important: As AI agents become more prevalent, ensuring they work correctly and safely is key. This is where evaluation comes in. Galileo’s previous guide focused on "Mastering RAG," building enterprise-grade systems. Now, they’ve taken it further with agents using LLMs to complete broader, more complex tasks.

Full report here.

DELOITTE

Framework for driving gen AI adoption

Brief: Deloitte India's 42-page guide outlines strategies for leaders to drive gen AI adoption, with a focus on Global Capability Centres (GCCs), though the fundamental insights apply broadly across business areas.

Breakdown:

• The report opens with a quote from NVIDIA CEO Jensen Huang, who states that Generative AI will be bigger than the PC, mobile, and internet.

• The readiness framework (pages 8-12) scores parameters across two dimensions.

• These include ecosystem enablers, such as alignment with organizational goals and leadership buy-in, and capabilities like infrastructure, data, talent, and governance needed to deliver generative AI solutions.

• The prioritization approach (pages 13-21) identifies opportunities across process taxonomies at level 3, assesses feasibility (data, tech, etc.), and prioritizes investments (high-impact, low-hanging fruit) based on benefits and effort.

• The implementation approach spans three key phases: building a proof of concept, solution deployment, and value capture (see page 25 for an overview). It also outlines key metrics and strategies for scaling gen AI solutions successfully.

• The report includes production case studies and touches on AI agents and multi-agent systems (pages 32-34)

Why it’s important: Considering it's publicly available, this guide provides a relatively comprehensive framework for enterprises to start assessing gen AI readiness, prioritizing opportunities, and more, along with real-world examples.

Full report here.

DELOITTE

Elements for scaling gen AI

Brief: With gen AI use cases starting to proliferate across enterprises, Deloitte's new 22-slide report dives deeper into 13 key elements for scaling gen AI, expanding on a framework from its Q3 2024 adoption survey.

Breakdown:

• Deloitte defines scale as a system’s ability to handle increasing workloads while reducing unit costs. For gen AI, scaling also means transitioning from experimentation to implementation aligned with business goals.

• Strategy: Define a clear vision with executive buy-in, prioritize high-impact, low-barrier use cases, and build an adaptive ecosystem of established and emerging providers.

• Process: Standardize governance, mitigate risks, ensure data security, and adopt agile delivery models.

• Talent: Align stakeholders with the gen AI vision, drive adoption with clear roles, and balance hiring with upskilling.

• Data & Technology: Build flexible IT infrastructure, leverage agile methods, and optimize data for cost efficiency and accuracy.

• Nine key indicators signal success, including faster time-to-market, higher value realization, lower unit costs for new capabilities, and improved reusability.

Why it’s important: Leading practices, processes, and technologies continue to evolve. While change is inevitable, pursuing scaling elements today positions enterprises to unlock value with gen AI.

Full report here.

GARTNER

10 best practices for scaling gen AI

Brief: Gartner outlined its 10 best practices for scaling gen AI in enterprises, offering actionable strategies for effective implementation.

Breakdown:

• By 2025, Gartner estimates that over 30% of gen AI projects may fail post-POC due to poor data quality, inadequate risk controls, costs, or unclear value.

• Gartner recommends: Establish a continuous process to prioritize high-value use cases. Create a framework for build vs. buy gen AI solutions.

• Pilot use cases and design a composable architecture. Prioritize responsible AI, invest in data literacy and instill robust Data Engineering Practices

• Foster seamless collaboration between humans and tech. Implement FinOps to manage total ownership costs and adopt a product-centric, agile approach.

• The near-term future of Generative AI includes smaller models, open and domain-specific models, regulatory impacts, multimodal models, and autonomous agents.

• Gartner also released its gen AI Planning Workbook that supports your AI strategy across four key pillars: vision, value realization, risk, and adoption plans.

Why it’s important: As enterprises scale generative AI, Gartner's recommended best practices offer guidance to address deployment challenges. These insights can help as a starting point to prioritize use cases, ensure responsible AI, and adapt to evolving technology and regulations.

Full report here.

MICROSOFT

Red teaming 100+ gen AI products

Brief: Microsoft’s 21-page white paper details its AI red team ontology, eight key lessons from "red teaming 100 generative AI products", and five case studies from its experience since 2021.

Breakdown:

• AI red teaming probes systems for safety and security by “breaking” them to identify weaknesses and rebuild them back with stronger defenses.

• Microsoft’s ontology models attack components: actors (adversarial or benign), TTPs (Tactics, Techniques, and Procedures), system weaknesses, and downstream impacts.

• Gen AI integration introduces novel attack vectors, but AI red teams must consider both new and existing cyberattack vulnerabilities.

• Mitigations don’t remove risk entirely. AI red teaming adapts to evolving threats, raising the cost of successful system attacks.

• While automation helps orchestrate attacks, AI red teaming also relies on human expertise, cultural awareness, and emotional intelligence.

• Microsoft’s five case studies highlight vulnerabilities across traditional security, responsible AI, and psychosocial harms using their ontology.

Why it’s important: These lessons highlight the importance of both technology and human expertise in securing AI systems. The paper also references Microsoft’s PyRIT (Python Risk Identification Tool), an open-source framework to identify vulnerabilities in AI systems, valuable for enterprises organizing red teaming exercises.

Full report here.

BOOZ ALLEN

Countering enterprise AI security threats

Brief: Booz Allen’s 16-page paper explores the key risks, threats, and countermeasures necessary to ensure enterprise resilience with AI. It highlights how AI security differs from traditional cybersecurity and introduces strategies for protection.

Breakdown:

• AI security is critical due to factors like the "black box" nature of AI, hidden risks in third-party and open-source models, and vulnerabilities amplified by AI's distributed usage.

• The paper identifies key attack types across the AI lifecycle, including Data Poisoning (manipulation of training data to compromise models), and Malware (embedding malicious code in model files).

• Further threats include Model Evasion (perturbing inputs to control outputs), Data Leakage (theft of sensitive training data, IP, or model behavior) and LLM Misuse (overriding large language models to bypass safety and alignment).

• The paper introduces a five-step strategy to AI security spanning Planning (risk modelling), Measurement (red teaming etc.), Security Engineering (model scanning etc.), Operations (monitoring etc.) and Control (governance).

• It also provides a representative MoSCoW method to identify security requirements across a range of gen AI model deployments from third-party to “homegrown” models.

Why it’s important: AI security is non-negotiable. With the increasing exposure to AI, enterprises must adopt proactive security strategies to mitigate risks, protect sensitive data, and ensure the integrity of their AI systems.

Full report here.

Abode - The AI Inflection Point / Responsible AI

ADDITIONAL MUST-READS

• Agentic AI (19 reports)

• Enterprise AI case studies (20 reports)

• Enterprise AI market (10 reports)

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