
Advance from ai strategist to ai leader by applying a practical framework and roadmap to decide, invest in, and lead ai initiatives, measure ROI, and ensure adoption.
Maps the six-course ai curriculum—from creating agents without coding to ai leadership and production deployment—showing how each course complements the others and how to become a proficient ai engineer.
Define key ai terms such as large language models, gen ai, and transformers, explain training versus inference, and map the frontier labs to cloud providers and business tools.
Explore frontier and open source language models from OpenAI, Anthropic, and Google. Compare chat and reasoning models and evaluate on-premise versus API deployments for business use.
Evaluate AI providers across horizontal and vertical offerings, and implement internal or external AI solutions, such as a bank customer support bot, focused on automation, augmentation, and differentiation.
Explore the gen ai strategy framework to quantify automation, augmentation, and differentiation benefits, map technical, operational, and strategic risks, and plan data, talent, and change management for leaders.
Explore how executives adopt and implement generative AI, from internal use to customer-facing solutions, leveraging prompting, fine-tuning open-source models, and Rag for context.
Apply rag and fine-tuning to inject domain expertise into ai solutions, using vector stores for context retrieval and encoding LMs to map data to meaningful vectors.
Compare workflows and agents, detailing how LLMs control tasks, use tools, and coordinate multiple models in autonomous systems. Explore patterns like prompt chaining, routing, and evaluator optimizer, plus guardrails.
Lead cross-functional AI decision making by evaluating model choices, encoders, Rag and agents, and optimization options through a science-led, iterative experiment process that weighs costs, risks, and business metrics.
Apply a collaborative AI decision framework to guide stakeholder discussions and assess data, costs, risks, and metrics. Promote a science-led, experimental culture for responsible AI leadership.
Identify and ideate AI projects across predictive, generative, and agentic categories, then evaluate ideas by pain points, data readiness, and measurable ROI for practical, high-impact adoption.
Build AI teams with clear organizational structure and critical skills—from data scientists, ML engineers, data engineers, and data analysts to governance, recruitment planning, and product leadership.
Learn a practical AI roadmap with five phases - planning, research, build, deployment, and measure - plus a zero phase for business case, emphasizing uncertainty management, data quality, testing, and adoption.
Lead adoption of major AI projects by addressing job displacement fears, building clear rationale, and fostering engagement through upskilling, governance, and transparent metrics.
Apply a comprehensive roadmap framework to deliver AI initiatives, linking phase-based deliverables, cross-functional decision making, and ROI tracking from upfront planning to production.
This workshop equips business leaders to champion AI initiatives, then deliver and deploy AI solutions, driving change across the organization with measurable commercial impact.
This is quite different from a traditional “course”!
It’s actually not a course at all: it’s a briefing. A comprehensive, action-oriented briefing on Gen AI, designed by leaders, for leaders.
We will cover:
AI expertise from a commercial perspective
Real-world use cases; with success stories and failures
Actionable toolkits to apply to your business
From Stealth Startup to Global Enterprise
We will not cover:
Technical detail; but sufficient to drive decision-making - so we do cover RAG, fine-tuning and Agents from a business point-of-view
Using different AI tools yourself; this briefing is about transforming your organization to use AI tools!
If you are an executive, an entrepreneur, a leader, or if you are on a path to become one, then this briefing will position you for commercial success with Generative AI.
What you will learn
AI strategy, AI decision-making and AI leadership
This workshop builds expertise through 3 modules:
Module 1: Become an AI Strategist
Your path to AI Leadership, driving change with measurable commercial impact
The big picture: AI, Gen AI, LLMs, Agents, Reasoning
The Models: Frontier, Open-Source, Cost / Performance / Security implications
Commercial details: GPT wrappers vs Proprietary Models, expert prompting, hallucinations, alignment risks.
Be an AI Strategist: a framework for your business to drive strategy and investment decisions
Module 2: Become an AI Decision maker
The AI scaling laws and why they matter for your business
5 core techniques for scaling AI performance with cost/benefit - from RAG to Reasoning
Agents and Agentic AI - risks, benefits, opportunities
AI Architecture from a commercial perspective - cross functional decision-making
Be an AI Decision maker: a framework for your business to drive commercial decision-making
Module 3: Become an AI leader
Gen AI as a competitive differentiator for your business - ideation and traps
Skills, roles, org chart, governance and common talent challenges
The case for R&D: committing to a Roadmap with uncertain outcomes
Position yourself for change: opportunities & risks of Agents and workforce impact
Be an AI Leader: a framework for leading AI initiatives with measurable commercial impact
Join now!