Sam Bourton
Geneva, Switzerland

I partner with founders, leaders, and investors to design & build with AI

15 years building one of the world's leading AI practices. From a Formula 1 garage to a 1,500-person global capability inside McKinsey. Now I work hands-on with the people shaping what comes next.

Co-founder, QuantumBlack Former McKinsey AI Partner AI Founder Retreats
// location: geneva_ch
{
  "entity": "sam_bourton",
  "role": "ai_advisor | builder | investor",
  "function": "partner(founders, leaders, investors) => build_with_ai()",
  "experience_years": 15,
  "credentials": [
    "quantumblack.co_founder",
    "mckinsey.ai_partner.former",
    "ai_founder_retreats.host"
  ],
  "scale_achieved": "5 → 1500 engineers"
}
Get in touch init_contact()
01 For Founders

I work with AI-native founders as a co-founder, advisor, and investor. Not from the sidelines, but in the work. I've built and scaled a company from five to a global acquisition. I bring that operator perspective to every founder relationship.

Co-create

Joint ventures and elastic access to distinctive AI product engineers through Progression Labs, our applied product engineering studio. We ship custom AI agents that scale. Not pocs or decks.

Co-invest

Active angel investor with 25+ investments across climate tech, health, fintech, deep tech, and enterprise AI. I join early rounds, introduce to other angels and VCs, and stay useful: product strategy, hiring, enterprise introductions, follow-on capital.

Connect

Targeted introductions to design partners, distinctive AI design & product engineering talent, and aligned capital. At the moments where leverage actually matters. Curated through years of building AI founder, tech, and angel networks across Europe and the US.

# founders.yaml
mode: operator  # not sidelines
track_record: "5 → acquisition"

services:
  co_create:
    provider: "progression_labs"
    type: "joint_venture | distinctive_engineers"
    output: ["agents", "production_systems"]
    not: "decks"

  co_invest:
    portfolio_count: 25+
    sectors: ["climate", "health", "fintech", "deep_tech", "enterprise_ai"]
    stage: "early"
    value_add: ["product_strategy", "hiring", "intros", "follow_on"]

  connect:
    network: ["design_partners", "ai_engineers", "capital"]
    regions: ["EU", "US"]
    timing: "high_leverage_moments"
02 For Enterprise

I don't advise from the outside. I embed inside organisations as a fractional product and engineering leader. Sitting in the leadership team, running workshops, shaping architecture, helping teams adopt AI and product-led innovation.

Product Operating Model

Shifting organisations from project-based IT delivery to empowered AI-native product teams. Introducing quarterly OKR cycles, domain-led team topologies, and modern product engineering practices.

AI Strategy That Ships

Reorienting AI investment away from "pilot theatre" toward measurable outcomes. Selecting architectures, standing up engineering teams, and building the systems that let organisations keep improving after I leave.

Capability Building

Embedding the culture, processes, and technical skills required for an organisation to innovate and scale AI product engineering on its own. Done this for 200+ person digital organisations across education, health, and international development.

# enterprise.yaml
engagement_model: "embedded"  # not external
role: "fractional_cpo | fractional_cto | fractional_cdo""

services:
  product_operating_model:
    transform: "project_delivery → product_teams"
    implements:
      - "quarterly_okr_cycles"
      - "domain_led_topologies"
      - "modern_eng_practices"

  ai_strategy:
    anti_pattern: "pilot_theatre"
    target: "measurable_outcomes"
    deliverables:
      - "architecture_selection"
      - "team_standup"
      - "self_improving_systems"

  capability_building:
    scope: ["culture", "process", "technical_skills"]
    org_size: "200+ engineers"
    domains: ["education", "health", "intl_dev"]
03 For Investors

I help VC/PE venture partners make better decisions about AI: before, during, and after they invest.

Investment Conviction

Hands-on technical due diligence that separates genuine AI IP from wrapper-thin hype. Reviewed hundreds of AI companies and know what real engineering looks like versus demo-ware.

Portfolio Acceleration

Operating partner and board-level support to help portfolio companies make the right product, hiring, and architecture decisions early. When it's cheap to get right and expensive to get wrong.

Enterprise Context

Because I work inside large organisations as a fractional leader, I have current, first-hand insight into what enterprise buyers actually need, how they buy, and what makes them renew.

# investors.yaml
client_type: ["vc", "pe", "venture_partners"]
lifecycle: "pre | during | post investment"

services:
  investment_conviction:
    type: "technical_due_diligence"
    filter: "genuine_ai_ip vs wrapper_hype"
    companies_reviewed: "hundreds"
    detects: "real_engineering vs demo_ware"

  portfolio_acceleration:
    level: ["operating_partner", "board"]
    decisions: ["product", "hiring", "architecture"]
    timing: "early"  # cheap_to_fix

  enterprise_context:
    source: "embedded_fractional_leader"
    insight_type: "current | first_hand"
    covers:
      - "what_enterprise_buyers_need"
      - "how_they_buy"
      - "what_drives_renewal"
Human Machine