How to transform isolated data scientists into an 80+ person distributed organisation delivering hundreds of production AI models
By The AK Dispatch • A case study in organisational design for AI at scale: reducing marginal deployment cost to near-zero
The principle that once foundational platforms are built, the marginal cost of deploying additional AI products approaches zero (ε → 0)
Cost_n+1 → ε as n → ∞
Where n = number of deployed models
Continuous design → research → MVP → test → evaluate → deploy cycles
Complex systems teams build once, stream-aligned teams deploy many times
Self-service deployment eliminates manual bottlenecks
Deploy at scale to create numerous opportunities for business impact
Four distinct team types, each with a specific mandate, working in concert to enable epsilon cost scaling
To access top talent and maintain proximity to operations, teams were distributed across three continents:
Close to headquarters and major operations
Access to regional operations and tech talent
Expanding operational presence
Strategic Rationale: Proximity to operations ensured teams understood business context, whilst global distribution provided access to diverse talent pools and enabled 24/7 development cycles.
Outcome: Clear transformation roadmap
Outcome: Platform ready for scale
Outcome: Production AI at epsilon cost
Outcome: Self-sustaining AI capability
The 40% of resources dedicated to platform teams was the key enabler. Without robust data and MLOps infrastructure, stream-aligned teams would have remained stuck in the old paradigm.
Placing domain engineers directly within data science teams eliminated the 6-month "translation" phase where data scientists tried to understand problems they weren't qualified to solve.
Research teams building reusable patterns meant stream-aligned teams didn't reinvent the wheel. One transformer architecture was deployed hundreds of times across different use cases.
Technology adoption depended on user acceptance. The design team ensured solutions fit the cognitive models and workflows of both field workers and expert engineers.
The Epsilon Moment: When marginal cost approaches zero, the strategic question shifts from "Can we afford this?" to "How many use cases can we deploy this week?"
This shift from scarcity mindset to abundance mindset fundamentally changes how organisations approach AI investment.
Resist the temptation to deliver quick wins before building proper infrastructure. The platform investment is painful upfront but pays dividends exponentially.
Data scientists alone cannot define good problems. Domain engineers alone cannot build sophisticated models. Embedding them together is non-negotiable.
Complex systems teams do deep research once; stream-aligned teams deploy many times. This separation of concerns prevents every team from solving the same hard problems.
The most sophisticated model is worthless if users won't adopt it. Invest in design thinking from day one, especially when serving diverse user populations.
Projects have end dates; products have continuous improvement cycles. The design → research → MVP → test → deploy loop should never stop.
Scaling AI is not about having more data scientists writing more notebooks. It's about designing organisations where the marginal cost of deploying the next AI product approaches zero.
"When you can deploy a sophisticated ML model in hours instead of months, when engineers can self-serve rather than wait for data scientists, when patterns are reused rather than rebuilt—that's when AI transforms from a cost centre to a value multiplier."
— From building an 80+ person distributed AI organisation delivering $100M++ annual impact
Insight by The AK Dispatch
Based on real organisational transformation in the energy sector | Several €100Ms value creation across 10+ companies and sectors
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