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Making AI impact visible, measurable, and decision-led

Making AI impact visible,
measurable, and decision-led

AI adoption often fails not because tools lack capability, but because impact is poorly defined, weakly measured, or disconnected from decision-making.

Lydian Stone’s AI capacity and alpha impact modelling is designed to make the effect of AI explicit, defensible, and relevant to senior leaders. The model translates workflow-level efficiency gains into capacity, decision speed, and performance outcomes – expressed in terms investment and strategy teams recognise.

Why modelling matters

The modelling ensures AI adoption is:
Grounded in real work
Anchored to senior capacity and decisions
Measured in ways that support confidence and accountability
two hands touching each other in front of a pink background
two hands touching each other in front of a pink background

Interpreting alpha

Depending on context, this may appear as:

Improved deal selection and IC velocity
Stronger portfolio and reporting insight
Faster strategic and capital-allocation decisions
Reduced execution drag across complex initiatives

The modelling framework remains consistent, even as the expression of impact varies by organisation.

Robot arm playing chess against a human opponent.
Robot arm playing chess against a human opponent.

From forecast to proof

The model is intentionally diagnostic rather than promotional.

Initial scenarios illustrate scale and direction. These are then refined and validated through implementation, using live data and real workflows to confirm where impact is realised.

This ensures AI adoption strengthens judgement and confidence, rather than relying on optimistic projections.
person pointing white paper on wall
person pointing white paper on wall