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Beginner (non-technical) 4–6 weeks 5 Stages

AI Strategy for Business Leaders

You don't need to code to lead an AI transformation — but you do need to understand what AI can and cannot do, how to identify high-value opportunities, how to build and manage AI teams, and how to navigate the ethical and strategic implications. This roadmap gives business leaders the knowledge to make confident, informed decisions about AI investments.

Who This Is For
Executives, managers, and non-technical leaders who need to understand and leverage AI strategically
Time Commitment
4–6 weeks
Difficulty
Beginner (non-technical)
Stages
5 stages, 15 resources

Prerequisites

Business management experience
No technical background required
Interest in AI strategy and implementation

The Roadmap

1

AI Literacy: What Leaders Must Know

1–2 weeks

Build a clear mental model of what AI is, how it works at a conceptual level, and what it can realistically achieve. Understand the difference between Machine Learning, Deep Learning, and Generative AI. Learn to separate genuine capabilities from hype, and understand the key limitations that affect business decisions.

AI, ML, and Deep Learning — the executive summary
Generative AI and LLMs — what ChatGPT and its competitors actually do
What AI is good at vs what it struggles with — realistic capabilities
The AI value chain — data, compute, algorithms, applications
Key terminology for executive conversations — inference, training, fine-tuning, RAG
Current state of AI — what's real today vs what's 3-5 years away
Free Course AI for Everyone (Andrew Ng, Coursera) Reference Harvard Business Review: AI Articles Reference Viqus AI Glossary
Artificial Intelligence (AI) Machine Learning (ML) Deep Learning (DL) Generative AI Large Language Model (LLM) Inference Fine-tuning Retrieval-Augmented Generation (RAG)
2

Identifying AI Opportunities in Your Business

1–2 weeks

Learn a systematic framework for identifying where AI can create the most value in your organization. Understand which problems are good fits for AI (and which aren't), how to prioritize AI initiatives by impact and feasibility, and how to build business cases that quantify the ROI of AI investments.

The AI opportunity assessment framework — impact × feasibility matrix
Process automation vs augmentation vs innovation — three types of AI value
Data readiness assessment — do you have the data AI needs?
Build vs buy vs partner — when to develop in-house vs use vendors
AI business case development — quantifying ROI, TCO, and time-to-value
Industry-specific AI applications — what's working in your sector
Free Report McKinsey: State of AI Reference Viqus AI Use Cases by Industry Book Prediction Machines (Agrawal, Gans, Goldfarb)
Artificial Intelligence (AI) Data Science Machine Learning (ML)
3

Building & Managing AI Teams

1 week

Understand the AI talent landscape — what roles you need, where to find talent, how to evaluate AI professionals, and how to structure AI teams for success. Learn the difference between centralized and embedded AI teams, and how to create a culture where AI initiatives thrive rather than stall.

AI team roles — Data Scientists, ML Engineers, AI Engineers, Data Engineers
Organizational models — centralized AI team vs embedded vs hub-and-spoke
Hiring and evaluating AI talent — what to look for beyond credentials
Managing AI projects — agile approaches, realistic timelines, and common failure modes
Vendor evaluation — how to assess AI product claims and vendor capabilities
Building data culture — the organizational change that AI requires
Free Guide AI Transformation Playbook (Andrew Ng) Book Competing in the Age of AI (Iansiti & Lakhani) Report LinkedIn AI Talent Reports
Data Science Machine Learning (ML) Artificial Intelligence (AI)
4

AI Ethics, Risk & Governance

1 week

AI introduces new risks — bias in decision-making, privacy violations, regulatory compliance, reputational damage, and security vulnerabilities. Learn the governance frameworks, risk assessment approaches, and responsible AI principles that protect your organization while enabling innovation.

AI bias and fairness — how it happens, how to detect and mitigate it
AI regulation landscape — EU AI Act, CCPA, sector-specific rules
Data privacy and AI — GDPR implications, consent, and data governance
AI risk management — model risk, liability, insurance, and audit
Responsible AI frameworks — principles, processes, and accountability structures
Deepfakes and AI misuse — threats to brand, security, and trust
Free Framework NIST AI Risk Management Framework Reference EU AI Act Summary Reference Responsible AI Institute
AI Ethics Algorithmic Bias AI Regulation AI Safety AI Alignment Problem Data Privacy Deepfake Responsible AI Explainable AI (XAI) Fairness
5

AI Strategy & Transformation

1 week

Synthesize everything into a coherent AI strategy for your organization. Learn to create an AI roadmap, secure executive sponsorship and board buy-in, manage organizational change, measure AI success, and position your company competitively in an AI-driven market.

Creating an AI strategy — vision, priorities, and phased roadmap
Securing executive and board support — speaking the language of ROI
Change management for AI adoption — overcoming resistance and building champions
Measuring AI success — KPIs, OKRs, and value tracking frameworks
Competitive positioning — AI as a moat vs AI as table stakes
Future-proofing — preparing for the next wave of AI capabilities
Case Studies Harvard Business School: AI Strategy Cases Report Gartner: AI Strategy Report BCG: AI at Scale
Artificial Intelligence (AI) Generative AI AI Regulation

Tools & Technologies

ChatGPT / Claude
Power BI / Tableau
Notion AI / Copilot
Perplexity AI

Career Outcomes

AI-informed executive decision-making
Ability to evaluate AI proposals and vendors
Leadership of AI transformation initiatives
Board-level AI governance and strategy

Frequently Asked Questions

Do business leaders need to learn to code?

No. Business leaders need AI literacy — understanding what AI can do, its limitations, and how to evaluate AI proposals — not coding skills. Your role is to identify opportunities, make investment decisions, build teams, and drive organizational adoption. Leave the coding to your technical team.

How do I convince my board to invest in AI?

Start with a specific, high-impact use case that has quantifiable ROI — not a vague 'AI transformation' initiative. Show the business case: what it costs, what it saves, and the timeline to value. Reference competitors using AI. Start small with a pilot project, demonstrate results, then scale. Boards respond to numbers and demonstrated impact, not AI hype.

What's the biggest mistake companies make with AI?

Starting with technology instead of business problems. Companies buy AI tools then look for problems to solve, instead of identifying their highest-value business challenges and then evaluating whether AI is the right solution. The second biggest mistake is underinvesting in data quality and organizational change management.

How long does AI transformation take?

A single AI pilot project can show results in 3-6 months. Becoming an AI-driven organization is a multi-year journey. Most companies follow a pattern: pilot (3-6 months) → prove value (6-12 months) → scale successful projects (12-24 months) → embed AI into culture and processes (ongoing). The key is starting now and learning by doing.