AI Implementation in Business: Strategy to Results

About this course

This course covers the implementation of artificial intelligence.

Many organizations do not struggle with AI because the technology is ineffective. They falter due to a lack of connection to business problems, undefined ownership of results, unprepared data, insufficient user adoption, and a failure to measure outcomes.

This course instructs you on how to champion AI adoption within an organization as a transformation rather than just a technology implementation. You will learn a practical approach from initial strategy to tangible results: evaluate readiness, select appropriate use cases, develop a credible business case for your finance team, plan and oversee the initiative, redesign workflows, engage stakeholders, pilot and scale projects, and demonstrate the advantages.

The focus is on business aspects rather than technical details. No coding is involved. AI will be explained at the level necessary for leaders to ask pertinent questions, make informed decisions, and determine when to consult specialists.

What distinguishes this course

• An ongoing case study. You will track Horizon Hospitality Group, a fictional hotel operator with 18 properties, through each phase, starting from a board member requesting an "AI strategy" to seeing results after two years. The outcomes are realistic and varied: some initiatives succeed, adoption is inconsistent in areas, and at least one initial business assumption proves incorrect.
• Decision-focused lessons. Most sessions place you in the leader's position with scenarios labeled "Your call." You will make a decision first and then review the corresponding answer and rationale.
• A comprehensive toolkit. 26 downloadable templates in Excel, Word, and PowerPoint, including both blank versions and fully filled examples, ranging from readiness assessments and ROI calculators to risk registers, pilot scorecards, and KPI dashboards.
• Financial transparency. You will learn how to differentiate between cash savings and capacity improvements, account for all expenses, test assumptions through sensitivity analysis, and examine non-AI alternatives for comparison.

Topics covered

1. Fundamentals of AI-enabled digital transformation: understanding significant changes, reasons for transformation failures, and establishing a vision and objectives.
2. Evaluating an organization's AI readiness across seven dimensions while identifying the most critical gaps.
3. Identifying, testing, and ranking AI use cases based on value and feasibility.
4. Developing an AI strategy, business case, cost structure, ROI, payback periods, and securing executive backing.
5. Converting the strategy into a structured program with a timeline, governance, dependencies, and reporting mechanisms.
6. Foundations of data, architecture, integration, and making build, buy, or partner decisions.
7. Redesigning organizational processes with appropriate automation levels and human oversight.
8. Establishing AI governance and risk management frameworks that include risk assessments, responsible AI practices, the NIST AI Risk Management Framework, awareness of regulations like the EU AI Act, and incident response strategies.
9. Managing organizational change: securing sponsorship, effective communication, promoting AI literacy, addressing workforce concerns, and ensuring sustained adoption.
10. Piloting, testing, rolling out, and expanding AI initiatives throughout the organization.
11. Assessing performance and managing the realization of benefits.
12. A unified case study that integrates all components into a cohesive transformation plan.

Every module concludes with practical exercises that you can perform for Horizon or apply to your own organization, along with a final assessment to evaluate your decision-making skills in realistic scenarios.

By the completion of this course, you will be equipped to create, present, and advocate for a comprehensive AI transformation plan, steering it through pilots, scaling efforts, and ensuring benefit realization.