AI Business Model Disruption and Competitive Moats Course – Amsterdam, 22–26 Feb 2027

AI Business Model Disruption and Competitive Moats is a 1-week training course for executives, business unit heads and corporate planners who must judge how AI will reshape their industry, producing an AI Disruption Exposure Brief for the board. Generative and predictive models are compressing margins, unbundling services and rewarding firms with proprietary data. Jupiter Prime Training offers AI Business Model Disruption and Competitive Moats for leaders defending and renewing their position.

Why AI Business Model Disruption and Competitive Moats Matters Now

Three groups are rewriting the rules at once. Model providers sell capabilities that once required whole departments, new entrants use them to undercut incumbents on price, and customers quickly learn to expect instant, personalised answers. Firms that read these shifts early can reprice, rebundle and deepen their moats; firms that wait find their most profitable services turned into commodities. The course uses established strategy lenses such as industry forces, platform economics and scenario planning, and links board oversight to the NIST AI Risk Management Framework (AI RMF) and the ISO/IEC 42001 AI management system standard.

Who Gains Most from AI Business Model Disruption and Competitive Moats

• Executives accountable for the long-term competitive position of a business
• Business unit heads whose revenue lines face automated or low-cost substitutes
• Corporate planners who build scenarios and strategic options for the board
• Commercial leaders who set pricing and packaging for products and services
• Board advisers who frame questions on disruption exposure and response

An executive committee gains most when its strategy, commercial and finance leads attend together.

AI Business Model Disruption and Competitive Moats Capabilities

Participants turn disruption headlines into a reasoned view of exposed margins and durable defences.

• Assess which revenue lines face substitution, unbundling or price compression — business unit heads
• Weigh the strength of data, network, switching-cost and brand moats — the executive committee
• Build divergent disruption scenarios with signposts to monitor — corporate planners
• Test pricing shifts such as outcome-based and usage-based models — commercial leaders
• Calculate the platform or ecosystem play available to the business — partnership leads
• Communicate a disruption exposure view and response options to directors — the board

Key AI Business Model Disruption and Competitive Moats Terms

• Competitive moat — a lasting source of advantage that keeps rivals from eroding profits.
• Unbundling — splitting a bundled service into parts that AI-enabled entrants sell separately and cheaply.
• Margin compression — the narrowing of profit per unit as automation lowers the cost floor for every competitor.
• Outcome-based pricing — charging customers for a measured result rather than for hours, seats or units supplied.
• Network effect — a platform's rising worth to each user as more users join.
• Signpost — an observable market event that indicates which disruption scenario is unfolding.

AI Business Model Disruption and Competitive Moats Day by Day

Day 1: How AI Rewrites Industry Economics

• Industry Forces Lens Revisited — Spotting Where AI Shifts Bargaining Power
• Cost Floor Collapse — Anticipating Margin Compression in Knowledge Services
• Unbundling and Rebundling Patterns — Reading Moves by New Entrants
• Customer Expectation Shifts — Gauging Demand for Instant Personalised Service
• Disruption Case Library — Learning From Media, Travel and Legal Services

Day 2: Testing Competitive Moats

• Moat Strength Audit — Rating Data, Network, Switching-Cost and Brand Defences
• Proprietary Data Valuation — Judging Which Records Rivals Cannot Copy
• Network Effect Diagnostics — Measuring Whether Scale Still Protects the Business
• Distribution and Relationship Assets — Weighing Channels Models Cannot Replace
• Moat Erosion Timeline — Estimating How Quickly Defences Weaken

Day 3: Business Model and Pricing Responses

• Business Model Canvas Stress Test — Finding Fragile Revenue Streams
• Outcome-Based and Usage-Based Pricing — Choosing Models for AI-Enabled Offers
• Platform and Ecosystem Plays — Deciding Whether to Orchestrate or Join
• Cannibalisation Trade-Offs — Balancing New Offers Against Legacy Profit
• Partnership and Acquisition Options — Securing Capabilities Faster Than Building

Day 4: Scenarios and Board Decisions

• Disruption Scenario Planning — Building Four Divergent Industry Futures
• Signposts and Trigger Points — Monitoring Which Future Is Unfolding
• No-Regret Moves and Real Options — Sequencing Commitments Under Uncertainty
• NIST AI RMF and ISO/IEC 42001 Oversight — Framing Board Questions on Exposure
• Board Paper Structure — Presenting Disruption Risk Without Alarm or Complacency

Day 5: Practising the AI Disruption Exposure Brief

• Insurance Broking Case — Applying the Moat Strength Audit
• Professional Services Case — Testing Outcome-Based Pricing Under Scenarios
• Revenue Line Heat Map — Plotting Exposure Across Own Portfolio
• Response Option Shortlist — Ranking Defend, Reinvent and Partner Moves
• AI Disruption Exposure Brief — Presenting Findings to a Mock Board

Applying AI Business Model Disruption and Competitive Moats After the Course

• Run a moat strength audit with one business unit and plot its revenue lines on an exposure heat map.
• Facilitate a disruption scenario planning session for the strategy team and agree the signposts to monitor each quarter.

AI Business Model Disruption and Competitive Moats Questions (FAQ)

What is AI business model disruption and competitive moats, and why does it matter now?

It is the study of how AI changes industry economics and which defences protect a firm's profits. Low-cost model capability lets entrants unbundle services quickly, so incumbents need an early, reasoned view of exposure.

Which roles gain most from AI business model disruption and competitive moats?

Executives, business unit heads, corporate planners and commercial leaders gain most. The focus is market position, not technology.

How does AI business model disruption and competitive moats differ from an AI strategy and leadership course?

It looks outward at industry structure and rivals rather than inward at capability building and leadership. A strategy and leadership course shapes internal direction; this one tests where markets are moving and how defensible your position is.

What do you bring back from AI business model disruption and competitive moats?

You bring back an exposure view of your own revenue lines, a moat audit and a set of disruption scenarios with signposts. Response options and a board-ready narrative come with them.

AI Business Model Disruption and Competitive Moats: The Way Forward

The AI Disruption Exposure Brief first informs which revenue lines the board chooses to defend, reinvent or exit. Executives use it to set strategic priorities and corporate planners use it to track signposts. After the first planning cycle, revisit the moat erosion timeline and pricing assumptions as entrants and customer behaviour evolve.