DLG AI Sprint
Rapidly test a tightly scoped AI opportunity with explicit success criteria, evaluation and decision gates.
What the engagement can cover
Problem and user definition
Defined in relation to the organisation, selected workflow and evidence available.
Data/sample preparation
Defined in relation to the organisation, selected workflow and evidence available.
Controlled proof
Defined in relation to the organisation, selected workflow and evidence available.
Quality/evaluation criteria
Defined in relation to the organisation, selected workflow and evidence available.
Human workflow design
Defined in relation to the organisation, selected workflow and evidence available.
Scale / stop / adapt recommendation
Defined in relation to the organisation, selected workflow and evidence available.
Typical outputs
How decisions are made
Agree objective and constraints.
Gather the minimum evidence required.
Develop the proposed approach.
Test against quality, value and risk.
Stop, adapt, deploy or scale.
What DataLgorithmics does not assume
Scope, duration, technology choices, commercial terms and expected benefits are not fixed in advance. They depend on the organisation, selected use case, data, integration requirements, risk, delivery model and evidence gathered during the engagement.
Connect learning with delivery
Engagements can be combined with Academy pathways so executives, business teams, engineers and governance functions build the capability needed to operate the resulting systems.
Explore Academy pathwaysDiscuss DLG AI Sprint
Share the organisation, current challenge, intended outcome and timeframe.
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