Ai-Strategy on AI Knowledge Base

AI Capabilities and Limitations

Mon, 01 Jan 0001 00:00:00 +0000

A concise introduction to what AI can and cannot do — the companion course to the AI Fluency Framework. Explains the machine properties (how AI actually works) that the 4D Framework competencies respond to. Essential for product managers and business leaders who need to understand AI capabilities at a conceptual level before making product decisions. Pairs with the AI Landscape for Product Leaders learning path.

AI Fluency: Framework & Foundations

Mon, 01 Jan 0001 00:00:00 +0000

Anthropic’s foundational AI fluency course teaching the 4D Framework — Delegation, Description, Discernment, and Diligence — for effective AI collaboration. Co-created with university professors (UCC, Ringling College), this goes beyond prompt engineering to teach a systematic approach for working with AI effectively, efficiently, ethically, and safely. The 4D Framework provides a mental model that transfers across any AI tool, not just Claude. Start here before the audience-specific fluency courses.

AI for Everyone

Mon, 01 Jan 0001 00:00:00 +0000

Andrew Ng’s definitive non-technical introduction to AI — the single best starting point for anyone who wants to understand what AI can and cannot do without writing a line of code. Covers AI terminology, what machine learning is, what data it needs, and how to spot opportunities for AI in your organization. Over 4 million enrollments make this the most popular AI course ever created. Take this before any other course if you’re new to AI.

Emerging Architectures for LLM Applications

Mon, 01 Jan 0001 00:00:00 +0000

Reference architecture for the LLM application stack, covering data pipelines, embedding models, vector databases, orchestration frameworks, and operational tooling.

The Economic Potential of Generative AI: The Next Productivity Frontier

Mon, 01 Jan 0001 00:00:00 +0000

Comprehensive analysis of generative AI’s economic impact across industries, quantifying $2.6-4.4 trillion in annual value potential and identifying which business functions will see the greatest transformation.

The EU's AI Act and How Companies Can Achieve Compliance

Mon, 01 Jan 0001 00:00:00 +0000

Practical guide to the EU AI Act — the world’s first comprehensive AI law — explaining risk categories, compliance requirements, and what companies need to do to prepare.

The Rise of the AI Engineer

Mon, 01 Jan 0001 00:00:00 +0000

Analysis of AI Engineer as a distinct emerging role from ML Engineer, arguing that productizing foundation models via APIs requires engineering skills, not research backgrounds.

What Is an AI Agent?

Mon, 01 Jan 0001 00:00:00 +0000

McKinsey’s explainer on AI agents — what they are, how they differ from chatbots, and how they will impact business operations and the workforce.

What Product Managers Need to Know About LLMs

Mon, 01 Jan 0001 00:00:00 +0000

A comprehensive non-technical guide to LLM capabilities, limitations, and practical applications for product managers building AI-powered features.

Who Owns the Generative AI Platform?

Mon, 01 Jan 0001 00:00:00 +0000

Analysis of value distribution across the generative AI stack — applications, models, and infrastructure — with data on margins, cost structures, and where economic value accrues.

16 Changes to the Way Enterprises Are Building and Buying Generative AI

Mon, 01 Jan 0001 00:00:00 +0000

Data-driven analysis of how enterprise leaders are increasing AI budgets and shifting toward multi-model, open-source strategies while moving from experimentation to production.

AI Fluency for Educators

Mon, 01 Jan 0001 00:00:00 +0000

Applies the 4D AI Fluency Framework specifically to teaching and institutional strategy. Designed for faculty, instructional designers, and educational leaders who need to integrate AI into curricula responsibly. Covers practical scenarios: classroom policies, assignment design, academic integrity, and helping students develop AI skills. Take this after the Framework & Foundations course for education-specific applications.

AWS Certified AI Practitioner

Mon, 01 Jan 0001 00:00:00 +0000

AWS’s foundational AI certification — validates understanding of AI/ML concepts, generative AI, and responsible AI within the AWS ecosystem. The training materials are free; only the exam costs $150. This is the most accessible cloud AI certification available and carries weight in enterprises that use AWS. Good for PMs and technical leaders who want a formal credential demonstrating AI literacy.

Designing a Responsible AI Program? Start with this Checklist

Mon, 01 Jan 0001 00:00:00 +0000

Eight critical questions organizations should answer before implementing enterprise-wide responsible AI programs to avoid rushing deployment and wasting resources.

How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025

Mon, 01 Jan 0001 00:00:00 +0000

Updated enterprise AI survey showing 81% of enterprises now use 3+ model families, with data on procurement patterns, multi-model optimization, and Anthropic’s growing enterprise penetration.

Introduction to AI

Mon, 01 Jan 0001 00:00:00 +0000

Created by the University of Helsinki, this is the most accessible and comprehensive non-technical AI course available. With over 2 million signups from 170+ countries and a 4.8/5 rating, it covers AI concepts, machine learning, neural networks, and societal implications through interactive exercises. The 30-hour estimate is generous — most complete it in 15-20 hours. Unlike Andrew Ng’s course which is video-based, this is primarily text-based with embedded exercises, making it excellent for self-directed learners who prefer reading to watching.

Building LLM Applications for Production

Mon, 01 Jan 0001 00:00:00 +0000

Comprehensive guide to production LLM challenges covering prompt engineering, evaluation, cost analysis, latency, fine-tuning vs prompting tradeoffs, and testing strategies.

Generative AI Glossary for Business Leaders

Mon, 01 Jan 0001 00:00:00 +0000

Plain-language glossary of essential generative AI terms designed for everyone in a company regardless of technical background.

Generative AI's Act Two

Mon, 01 Jan 0001 00:00:00 +0000

Analysis of generative AI’s transition from technology-driven novelty to customer-focused value creation, with updated market maps and vendor landscape organized by use case.

AI Fluency for Nonprofits

Mon, 01 Jan 0001 00:00:00 +0000

Applies AI fluency concepts specifically to nonprofit contexts — fundraising, program delivery, volunteer management, and impact measurement. Addresses the unique constraints nonprofits face: limited budgets, data sensitivity, and mission alignment. Take this after the Framework & Foundations course for sector-specific applications.

Generative AI Foundations

Mon, 01 Jan 0001 00:00:00 +0000

AWS’s free introduction to generative AI — covers foundation models, training approaches, and how generative AI fits into the AWS ecosystem. Provides the AWS perspective on AI infrastructure, which complements the Anthropic and Google viewpoints in this knowledge base. Useful for teams evaluating Bedrock as their AI deployment platform.

Introduction to Responsible AI

Mon, 01 Jan 0001 00:00:00 +0000

Google’s concise introduction to responsible AI practices — covers fairness, interpretability, privacy, and security in AI systems. At just 30 minutes, this is the fastest way to understand the principles that should guide any AI deployment. Pairs well with the AI Safety learning paths in this knowledge base for a more comprehensive view of safety across providers.

Building AI

Mon, 01 Jan 0001 00:00:00 +0000

The practical follow-up to Introduction to AI — bridges conceptual understanding to hands-on AI development. Covers machine learning algorithms, neural networks, and AI project planning with optional Python exercises. Designed for people who completed the intro course and want to go deeper without committing to a full computer science curriculum. The Python exercises are optional, making it accessible to non-developers who want to understand the mechanics.

Azure AI Fundamentals (AI-900)

Mon, 01 Jan 0001 00:00:00 +0000

Microsoft’s structured learning path for the AI-900 certification — covers AI workloads, machine learning principles, computer vision, NLP, and generative AI on Azure. The most enterprise-oriented AI fundamentals course available, designed for professionals who need to understand AI in the context of cloud services and business applications. Each module has knowledge checks and the path directly prepares you for the AI-900 certification exam.

AI Landscape for Product Leaders

Mon, 01 Jan 0001 00:00:00 +0000

A non-technical introduction to the AI provider landscape for product managers, executives, and business leaders. This path builds your understanding of what AI models can do, how they’re priced, and how to think about provider selection — without requiring any coding knowledge.

By the end of this path, you’ll be able to: evaluate AI provider options, understand cost structures for budgeting, and frame build-vs-buy decisions for your organization. You’ll have the vocabulary and mental models to discuss AI capabilities with your engineering team and present AI strategy to leadership.

AI Use Cases & Business Applications

Mon, 01 Jan 0001 00:00:00 +0000

A practical guide to identifying, evaluating, and prioritizing AI use cases for your product or organization. See concrete examples of AI in production — from customer support to document processing — and learn frameworks for assessing ROI and choosing the right approach.

By the end of this path, you’ll be able to: identify high-value AI use cases in your organization, evaluate whether a use case needs simple AI or complex agents, build an ROI-backed business case for AI investment, and have informed conversations with engineering about implementation feasibility.

Understanding AI Agents & Protocols

Mon, 01 Jan 0001 00:00:00 +0000

A non-technical guide to AI agents and the emerging protocol standards for product leaders and executives. Learn what agents are, why they matter for your product, and how the industry is standardizing agent communication through protocols like MCP, A2A, and AG-UI.

By the end of this path, you’ll understand the difference between chatbots and agents, how agents connect to external tools and data, and what enterprise agent deployment looks like. You’ll be equipped to evaluate whether your product needs agent capabilities and what infrastructure choices your team should be making.

AI Safety & Risk for Decision-Makers

Mon, 01 Jan 0001 00:00:00 +0000

A concise path for executives, product leaders, and board members who need to understand AI risks and safety without technical depth. Covers data privacy, hallucination risk, safety engineering principles, regulatory compliance (EU AI Act), and responsible AI governance.

By the end of this 2-hour path, you’ll understand the key risks of deploying AI in production, what questions to ask your engineering team about safety measures, and how to establish an AI governance framework for your organization.

AI Architecture for Technical Leaders

Mon, 01 Jan 0001 00:00:00 +0000

A technically rigorous guide to AI system architecture for product managers and executives who can evaluate architecture diagrams. This path walks through the major architectural decisions in modern AI systems — from the LLM application stack to agent frameworks, communication protocols, and multi-model strategies.

By the end of this path, you’ll be able to: evaluate your engineering team’s architecture proposals with informed questions, understand the protocol landscape (MCP, AG-UI) for platform integration decisions, and assess whether your system architecture is ready for multi-model, multi-agent production workloads.

AI Vendor & Platform Evaluation

Mon, 01 Jan 0001 00:00:00 +0000

A systematic framework for evaluating AI providers, platforms, and build-vs-buy decisions. This path equips technical leaders with the analytical tools to compare vendors across capability, cost, deployment, and strategic dimensions — going beyond feature matrices to assess lock-in risk, deployment flexibility, and long-term platform strategy.

By the end of this path, you’ll be able to: build comparative cost models across providers, structure vendor evaluation around the accuracy-latency-cost tradeoff triangle, assess deployment topology requirements, and frame build-vs-buy recommendations with supporting market data.

AI Cost Modeling & Unit Economics

Mon, 01 Jan 0001 00:00:00 +0000

A practical guide to AI cost modeling and unit economics for technical leaders who need to build defensible financial projections. This path bridges the gap between per-token API pricing and board-level business cases, covering macro AI economics, provider pricing structures, token consumption drivers, optimization levers, and ROI frameworks.

By the end of this path, you’ll be able to: build per-query cost models for AI features, project AI infrastructure costs at scale, identify the highest-impact cost optimization levers (which are architecture decisions, not tuning exercises), and construct ROI analyses that connect engineering costs to business value.

AI Risk & Safety for Technical Leaders

Mon, 01 Jan 0001 00:00:00 +0000

A bridge between engineering safety practices and board-level risk governance for technical leaders who need to evaluate their team’s safety architecture and communicate residual risks to stakeholders. This path covers hallucination mitigation, data protection, enterprise guardrails, regulatory compliance, and production readiness assessment.

By the end of this path, you’ll be able to: evaluate whether your engineering team has implemented defense-in-depth safety, assess compliance requirements under the EU AI Act, conduct technical due diligence on AI projects before production deployment, and translate engineering risk into board-level governance language.