AI for C-level executives and business owners: strategy, ROI, and a portfolio of use cases
An advanced workshop for CEOs, COOs, CFOs, and business owners that takes you from selecting the right AI initiatives to a 10-page implementation strategy with an ROI model, governance principles, a vendor checklist, and a “pilot → scale” plan.
This course is not a review of trendy tools or a general introduction to AI. It was designed for people responsible for business results, capital allocation, and the pace of change in the company. The starting point is an observation from recent market reports: boards are increasing AI spending, but many organizations still struggle to translate investment into measurable productivity and lasting returns, while the advantage appears more often when a company redesigns end-to-end processes instead of implementing isolated experiments. In BCG research from 2025 and 2026, leaders report high AI usage intensity and growing pressure for measurable business impact, while Gartner emphasizes that productivity growth expectations often need to be adjusted, and McKinsey points out that the greatest value is concentrated in selected functions and specific applications, not in dispersed initiatives. That is why the course takes the perspective of an investment decision portfolio: how to select 15 use-case candidates, how to compare value against risk, how to calculate ROI and TCO, when to buy ready-made solutions and when to build an internal advantage, how to assess vendor quality, how to structure responsibilities, and how to plan the first 90 days so you do not end up with a showcase pilot. Participants work on management artifacts, not technical demos: a use-case portfolio, an ROI model, a vendor checklist, a KPI dashboard, and a final 10-page AI strategy for their own company.
What you will learn
- You will build a portfolio of AI initiatives organized by business value, risk, and feasibility.
- You will distinguish image-building activities from use cases that truly affect revenue, margin, costs, productivity, or decision quality.
- You will calculate preliminary ROI and TCO for AI initiatives, including implementation, organizational change, oversight, and maintenance costs.
- You will make a more informed build vs buy decision for key use cases.
- You will prepare governance principles, accountability roles, and a risk escalation path.
- You will conduct vendor due diligence using a purchasing checklist and evaluation criteria.
- You will design a “pilot → scale” plan with KPIs, milestones, and continuation criteria.
- You will create a 10-page AI strategy for the company along with a 90-day roadmap.
Prerequisites
Experience in managing a company, business unit, or budget; knowledge of the basics of management finance; readiness to work in workshops using examples from your own organization. No technical knowledge is required.
Course syllabus
- Why the board is now responsible for the direction of AI: market pressure, ROI expectations, and the role of the CEO
- Three strategic AI ambitions: efficiency, decision quality, or a new growth model
- Management mini-case: two companies invest the same amount in AI, but only one builds an advantage
- Decision workshop: how to formulate an AI investment thesis for your own organization
- Quiz: recognizing weak and strong strategic justification for AI investment
- Where to find use cases with real value: processes, decisions, margin, risk, and customer experience
- Use-case card for the board: business problem, owner, value hypothesis, cost, and metrics
- Prioritization matrix: business value versus feasibility versus risk
- Comparison of full artifacts: a weak and a strong use-case card on the same example
- How to build a balanced portfolio: quick wins, core initiatives, and transformational bets
- Quiz: which initiatives go into the portfolio now, and which need to be deferred
- How to calculate AI ROI without self-deception: time savings, quality, revenue, and the cost of change
- Board dashboard: 10 metrics that show progress, adoption, and real business impact
- Risk checklist: where AI projects derail because of data, accountability, compliance, and processes
- AI oversight model: who approves, who reviews, who escalates, and when to stop deployment
- Critique of a pilot: how to distinguish a capability demo from an initiative ready to scale
- Quiz: recognize false signals of success and choose the right managerial response
- Which operating model to choose: central, federated, or hybrid
- The AI initiative launch process: from idea submission to scale decision
- 90-Day Plan for the Board: Decision Sequence, Sponsors, Milestones, and Review Rules
- Decision package for C-level: how to present the portfolio, risks, and expected return in one narrative
- Final workshop: preparing your own action map and success criteria for the company
FAQ
For CEOs, board members, business owners, heads of business functions, and people responsible for results, capital allocation, and the pace of change. This is a program for decision-makers who want to treat AI as a tool for growth, productivity, and operational advantage, not just a technology experiment.
It does not focus on trendy tools or general definitions. The emphasis is on management decisions: where AI can really improve business economics, how to build a portfolio of use cases, how to calculate value, and how to avoid a situation in which the company funds many initiatives but sees no lasting return.
Because the market has entered a phase of value selection. In Deloitte’s 2025 research, 91% of organizations reported increased AI spending, but the advantage is built primarily by those who combine investment with decision governance, KPIs, and execution at the level of entire processes, not individual implementations. McKinsey, in turn, indicates that the greatest impact on EBIT comes from workflow redesign, meaning redesigning how the company operates, rather than simply adding technology to existing patterns.
The course helps organize investment decisions around AI: from selecting areas with the highest potential, through assessing costs and risks, to determining the sequence of implementations. This makes it easier to distinguish high-value initiatives from those that mainly improve local productivity but do not change the company’s result across the entire process.
Yes — especially when an organization has already run its first pilots but lacks a common prioritization model, success metrics, and a coherent scaling strategy. This is a common moment when the board needs to move from scattered experiments to a portfolio of use cases linked to financial and operational goals.
Among other things: how to assess use cases through the lens of value and feasibility, how to understand the difference between point automation and end-to-end transformation, how to talk about ROI under uncertainty, how to set up governance, and how to make decisions that increase the chance of measurable business impact.
No. The course is designed for business leaders, not technical specialists. It explains AI from the perspective of strategy, investment, organization, and results — so that participants can make better decisions without getting into complex architecture or programming details.
Because that is where real advantage most often materializes. Current analyses by consulting firms show that the greatest return appears when a company redesigns full workflows, roles, decisions, and metrics, instead of limiting AI to individual tools or isolated improvements. In other words: it is not about “adding AI,” but about translating it into a new way the company operates.
- 4 hours
- Advanced
- Access immediately after purchase