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3. AI-First Business Model

1. What is this track?

The AI-First Business Model is the most radical track: the organisation builds an entirely new business model in which AI forms the core of value creation. The question is no longer "How do we improve our existing process with AI?", but: "Which new product, service or market position only becomes possible thanks to AI?"

This track is intended for organisations or business units that deliberately choose innovation as a growth strategy, or that see their current business model coming under structural pressure.

2. Characteristics of this Track

  • Scope: New business unit, product or service — separate from the existing core activity.
  • Time horizon: 12–36 months to commercial product.
  • Risk profile: High — much uncertainty, experimentation is the norm.
  • Typical driver: Market displacement, technological breakthrough, or strategic choice for growth through innovation.

3. Core Activities

Step 1 — Opportunity Identification

Analyse which new value AI makes possible that was previously unavailable or unaffordable:

  1. Scale without linear costs: Which services could not previously be offered because they were too labour-intensive?
  2. Personalisation at scale: Which customer needs are now too expensive to serve individually?
  3. Speed as product: Which decisions or outputs can now be delivered in real-time?
  4. Data as asset: Does the organisation have unique data that, combined with AI, produces a distinctive product?

Step 2 — Business Model Design

Use a structured framework to design the new model:

Building block Question
Value proposition Which problem do we solve, for whom, and why is AI essential?
Customer segment For which customer are we creating value? Is this a new segment?
Channels How do we reach and serve this customer?
Revenue model How do we generate revenue? (Subscription, transaction, licence, data?)
Key resources Which data, models and competencies are critical?
Cost structure What are the dominant costs? (Training, compute, data acquisition?)

Step 3 — Validation before Scale

Validate the model in small iterations before significant investment:

  1. Problem validation: Confirm that the customer experiences the problem as painful.
  2. Solution validation: Test the value proposition with an early version (Validation Pilot).
  3. Business validation: Confirm willingness to pay and scalable revenue model.
  4. Technical validation: Prove that the AI core achieves the required quality (see Evidence Standards).

4. Governance for New Business Models

AI-first products bring new compliance questions:

  • Product liability: Who is liable if the AI product causes harm?
  • Intellectual property: Who owns the model outputs?
  • Data sovereignty: May customer data be used to improve the model?
  • Transparency obligation: Does the customer need to know that AI made a decision?

Involve the Guardian and legal advisors early in the design phase.

5. Success Factors

  • Separate from the core: Keep the innovation unit organisationally and budgetary separate from the existing operation to avoid corporate immunity.
  • Customer first: Go to the customer early — avoid internal echo chambers.
  • Build iteratively: Launching small versions quickly and learning is superior to building internally for a long time.
  • Leadership as champion: A visible sponsor at board level is essential.