Chelsea AI Ventures
Business AI Training Strategist Pathway 6 Weeks (Online, approx. 4-5 hours/week) or 2 Day Intensive (In-Person, London)

AI Strategy for Business Leaders: From Idea to Implementation

A practical, project-based course for managers and product leaders on how to build, manage, and launch successful AI initiatives

Target Audience

The Product/Business Unit Leader

Core Value

Master the lifecycle of an AI project and successfully launch AI-powered products and services

Key Differentiator

Project-based curriculum focused on the tactical 'how' of building and managing AI initiatives

Learning Objectives

  • Deconstruct and manage the stages of the AI project lifecycle, from business understanding to deployment
  • Develop a comprehensive business case for an AI project, including ROI projections and success metrics
  • Define the roles and responsibilities within an AI team and work effectively with technical colleagues
  • Create a basic data strategy and framework for evaluating third-party AI vendors
  • Develop a go-to-market plan for an AI-powered product or feature

Prerequisites

Professional experience in a management, strategy, or product role. Familiarity with basic business concepts.

Course Structure

Week 1: The Reliability Collapse & The Tenant Trap

Why 'Renting Intelligence' fails. Understanding the math of compound failure (0.9^6 = 0.53) and the Containment Doctrine.

Activities:

  • Audit your organizational and data readiness
  • Categorize current projects as Tenant (Rented) vs. Sovereign (Owned)
  • Calculate the reliability drop-off for a multi-step agent workflow

Week 2: The Kill Floor: Complexity Gates

Using the 3 Gates to filter bad ideas: The Math Check, The Scream Test, and The Verification Tax. Defining Synthetic vs. Sovereign Judgment.

Activities:

  • Run 5 use cases through the Scream Test (Silent vs. Loud Failure)
  • Map the 'Judgment' boundary: Where does the AI stop and the Human start?

Week 3: Assembling and Managing AI Teams

Deep dive into AI team roles: Data Scientist, ML Engineer, Data Engineer, AI Product Manager. Communication and expectation management.

Activities:

  • Design your ideal AI team structure
  • Practice technical-business translation exercises

Week 4: Sovereign Intelligence & Data Readiness

Moving from PDFs to Vector Databases. The 'Investment Placemat' and the 'Witness Check'. How to grade your data readiness (1-5).

Activities:

  • Fill out the Investment Placemat for a pilot project
  • Conduct a 'Witness Check' simulation to expose data reality

Week 5: Go-to-Market for AI Products

Unique challenges in positioning and launching AI-powered products. Building user trust and managing expectations.

Activities:

  • Develop messaging framework for AI features
  • Create launch risk mitigation plan

Week 6: Case Study Clinic - Learning from the Real World

Detailed analysis of KirokuForms product launch decisions. Workshop using a structured framework to evaluate hypothetical AI vendors.

Activities:

  • Present your complete AI project proposal
  • Peer review and feedback session

Topics Covered

AI project lifecycle management
Business case development for AI
ROI calculation and KPI definition
AI team structure and roles
Effective communication with technical teams
Data strategy fundamentals
Vendor evaluation frameworks
AI product positioning and messaging
Launch planning and risk management
Change management for AI adoption
Stakeholder management
Real-world case study analysis

Capstone Project

Develop a full-fledged AI project proposal including detailed business case, project plan, team structure, data strategy, and risk assessment matrix.

Why This Course Matters

You’ve sat through the AI strategy presentations. You understand the potential. Now your CEO turns to you and says, “Make it happen.” This is the moment where vision meets reality. Most AI initiatives fail not because of the technology, but because they are managed as temporary projects rather than permanent organizational capabilities.

This course moves beyond traditional transformation management. We bridge the gap between strategic intent and the cognitive infrastructure required to sustain it. It’s designed for leaders who need to build a resilient foundation for AI, rather than just launching a pilot.

What Makes This Course Different

While executive courses focus on the “why” and technical courses dive into the “how to code,” this program addresses the critical middle ground: how to actually get AI projects done within real organizations, with real constraints, and real people.

We don’t just teach theory—we use actual implementation examples. You’ll analyze the strategic decisions behind KirokuForms’ development and apply a structured evaluation framework to assess AI vendors. These real-world cases provide the pattern recognition you need to succeed with your own initiatives.

Course Philosophy

We believe successful AI implementation is 20% technology and 80% project management, communication, and change leadership. This course reflects that reality. You’ll spend less time on algorithms and more time on the practical skills that determine whether AI projects succeed or fail.

Every module is designed to be immediately applicable. By the end of each week, you’ll have created real deliverables you can use in your current role.

Who Should Take This Course

This course is ideal if you:

  • Have been tasked with implementing an AI initiative
  • Need to build a business case for an AI project
  • Manage or will manage technical teams working on AI
  • Want to launch AI-powered products or features
  • Must navigate the complexity of AI vendor selection
  • Need to deliver real results, not just promising pilots

If you’re responsible for making AI work in practice, not just in PowerPoint, this course provides the roadmap you need.

Ready to transform your team?

Contact us to discuss custom training solutions or group enrollment options.

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