AI for Executives: Creating and Capturing Business Value Course

Introduction:

AI for executives creating and capturing business value is the leadership discipline of finding where artificial intelligence can raise revenue, lower cost or reduce risk, and then making sure those gains reach the profit and loss account. This 1-week course is for executives, senior managers and the strategy, finance, digital and product professionals who build AI business cases, and each delegate leaves with an AI value roadmap and use-case portfolio for their own organisation. It is taught at conceptual, decision-making level through multi-sector case studies, using value driver trees, which link an initiative to the financial measures it moves.

Course Objectives:

• Distinguish value creation from value capture and explain why many AI pilots improve a process without changing reported financial results
• Apply value driver trees and a value, feasibility and risk matrix to size AI value pools and rank candidate use cases
• Compare net present value with payback period when appraising AI investment proposals and their underlying assumptions
• Select value capture levers such as pricing, cost-to-serve and capacity release, and interpret P&L attribution evidence for each initiative
• Justify build, buy or partner sourcing, an AI operating model and stage-gate criteria for moving initiatives from pilot to scale
• Define an AI value roadmap and portfolio with owners, KPIs, risk controls and adoption measures for the delegate's organisation

Target Audience:

• Executives and senior managers accountable for business results, deciding which AI investments to fund, scale or stop
• Business unit and functional heads who own revenue or cost targets, judging which AI use cases will move their numbers
• Strategy, digital and transformation leads who shape the enterprise AI agenda, choosing how to sequence the initiative portfolio
• Finance leaders and business partners who appraise investments, deciding whether an AI business case and its benefits are credible
• Product and commercial leaders who design offers and pricing, judging where AI changes customer value and willingness to pay
• Senior professionals who build AI business cases for leadership, deciding which assumptions, baselines and value measures to defend

Course Outline:

Day 1: Foundations: AI Value Pools, Value Creation vs Value Capture and Readiness

• Value Creation vs Value Capture: Why AI Gains Often Miss the P&L
• AI Capability Families: Predictive, Generative and Agentic AI in Business Terms
• AI Value Pools: Mapping Revenue, Cost, Risk and Capital Levers by Function
• Adoption vs Impact Gap: Why Many AI Pilots Never Reach Production Scale
• AI Value Readiness Assessment: Rating Data, Talent, Process and Executive Sponsorship

Day 2: Frameworks for Finding, Sizing and Prioritising AI Use Cases

• Value Driver Trees: Linking AI Use Cases to Financial and Operational KPIs
• Use-Case Prioritisation Matrix: Scoring Value, Feasibility and Risk Side by Side
• Efficiency Plays vs Growth Plays: Cost Reduction Compared With Customer Experience Gains
• AI-Enabled Business Models: Data Products, Platforms and Outcome-Based Pricing Options
• Workflow Redesign Mapping: Rebuilding End-to-End Processes Instead of Automating Single Tasks

Day 3: Building the AI Business Case and Capturing Value in the P&L

• AI Business Case Structure: Benefits, Total Cost of Ownership and Key Assumptions
• Net Present Value vs Payback Period: Two Lenses for Appraising AI Investments
• Benefits Realisation Plan: Baselines, Benefit Owners, Targets and Value Tracking Cadence
• Value Capture Levers: Pricing, Cost-to-Serve, Capacity Release and Working Capital Effects
• P&L Attribution Methods: Control Groups and A/B Tests for Proving AI Impact

Day 4: Scaling AI: Sourcing, Operating Models, Adoption and Responsible AI Risk

• Build vs Buy vs Partner: Sourcing Choices for AI Models, Platforms and Talent
• AI Operating Model Options: Centre of Excellence, Hub-and-Spoke and Embedded Data Teams
• Pilot-to-Scale Stage Gates: Criteria for Scaling, Pivoting or Stopping AI Initiatives
• Deployment vs Adoption: Incentives, Reskilling and Frontline Usage Targets That Release Value
• Responsible AI Risk Register: Bias, Model Errors, Data Leakage and Value Erosion

Day 5: Case Study: Building the AI Value Roadmap and Use-Case Portfolio

• Case Value Pool Scan: Applying Driver Trees to a Multi-Sector Case Organisation
• Case Portfolio Selection: Scoring and Ranking Candidate AI Use Cases for Funding
• Case Business Case Defence: Challenging NPV, Assumptions and Chosen Value Capture Levers
• Portfolio Rebalancing vs Single Bets: Allocating Budget Across Quick Wins and Big Bets
• AI Value Roadmap and Portfolio: Completing Sequenced Initiatives, Owners, KPIs and Gates

Skills You Will Gain:

• AI Value Pool Sizing
• Value Driver Tree Analysis
• AI Use-Case Prioritisation
• AI Investment Appraisal
• Benefits Realisation Tracking
• P&L Impact Attribution
• Pilot-to-Scale Portfolio Management
• Responsible AI Risk Assessment

Why Attend This Course:

• From funding AI pilots on enthusiasm to ranking every candidate use case by value, feasibility and risk
• From business cases that list benefits without owners to benefits realisation plans with baselines, targets and tracking
• From reporting model accuracy and usage to proving financial impact through pricing, cost-to-serve and P&L attribution
• From scattered experiments to an AI value roadmap and use-case portfolio with owners, KPIs and stage gates

Conclusion:

Getting business value from artificial intelligence means treating AI as an investment portfolio whose returns must be designed, measured and banked, not as a series of technology trials. The course draws three distinctions: value creation against value capture, adoption against measured impact, and efficiency plays against growth plays. It suits executives, senior managers and the finance, strategy and product professionals who must decide where AI money goes and prove what it returned, rather than specialists seeking model-building skills or boards focused mainly on oversight duties.

Frequently Asked Questions (FAQ):

What should delegates prepare before an AI for executives course on creating and capturing business value?

No coding or data science background is needed. Delegates benefit from knowing their organisation's strategy, main cost and revenue drivers and any current AI initiatives, and from bringing outline figures or an existing business case to use in the case work.

How does an AI for executives business value course differ from an AI governance course for boards?

It centres on finding, funding and proving AI value: value pools, use-case portfolios, business cases, ROI and P&L attribution. A board governance course focuses on oversight duties, risk appetite, assurance and standards, with value treated as one item the board monitors.

Why do many AI initiatives create value without capturing business value for executives to report?

Because gains such as saved hours or better forecasts stay invisible unless someone redeploys capacity, changes pricing, lowers cost-to-serve or removes spend. Value is captured only when an owner, a baseline and a financial line item are tied to each initiative.

What does a delegate take back from an AI for executives course on creating and capturing business value?

Each delegate takes back an AI value roadmap and use-case portfolio for their organisation: ranked use cases, outline business cases, chosen value capture levers, owners, KPIs, stage gates and the main risk controls, already challenged in the case work.