AI Strategy for Business Leaders: Build, Buy or Wait

AI Strategy for Business Leaders is a practical course on making sound decisions about artificial intelligence in your organization. You will learn how to identify the AI use cases worth pursuing, choose between building, buying, partnering, orchestrating or waiting, evaluate AI vendors and contracts, calculate total cost of ownership, design AI pilots that reach production, drive user adoption, and measure return on investment in a way your board will trust.

AI adoption is treated throughout as a business investment decision. Each framework is applied to a realistic business case, so you can follow every decision from first idea to board approval.

AI fundamentals for business leaders

• Predictive AI, generative AI and AI agents: what each does, where each fits, and the risks each carries

• The four ways to improve AI output, in order of cost: prompting, retrieval-augmented generation (RAG), fine-tuning and training a model from scratch

• Where AI performs well in business processes, and where it consistently falls short

Finding high-value AI use cases

• How to review every business function for AI opportunities

• How to score and prioritize AI use cases with a structured rubric

• Which processes should not be automated, and how to screen them out early

Build, buy, partner, orchestrate or wait

• A decision framework for choosing the right AI sourcing option

• Why orchestration, assembling foundation models, retrieval, tools and workflows, is now the dominant pattern

• Frontier models versus open-weight models, and hosted APIs versus on-premises deployment

• How data residency requirements in client contracts shape your technology choices

• Total cost of ownership (TCO): licensing and usage, integration, data preparation, change management, monitoring and evaluation

AI procurement and vendor management

• The due diligence questions to ask every AI vendor

• The AI contract terms that carry real risk, including IP indemnification and its carve-outs, restrictions on training with your data, and notice periods for model changes

• How to assess data readiness before you commit investment

• The AI team you need: product owners, data engineers, domain experts and change leads

Scaling AI across the organization

• Why AI pilots stall, and how baselines, success criteria, production owners and integration budgets fix it

• Change management for AI adoption, and how to respond to the rational reasons people resist new tools

• Risk-tiered AI governance that speeds up approval for low-risk use cases and focuses review where it matters

• How to measure AI ROI credibly, with a baseline and a clear separation of hard savings and soft benefits

The board and your first 90 days

• How to present an AI business case to the board around purpose, risk, full cost, success measures and accountability

• How to build a 90-day AI implementation plan with owners, dates and stop conditions

• How to pressure-test your AI strategy against real scenarios: vendor savings claims, tools bought without approval, and pilots that succeed while the business case does not

Practical tools you can use at work

Apply each framework with downloadable templates, including an AI use case scoring rubric, an automation exclusion list, a build, buy and orchestrate decision matrix, a hosting and data residency decision sheet, a total cost of ownership template, AI vendor procurement questions, an AI contract terms checklist, a data readiness assessment, pilot design criteria, an AI adoption tracker, AI risk tiers, an ROI worksheet, a board one-pager and a 90-day AI plan.