DLG AI Governance Programme
Build a practical governance system for AI inventory, risk, approvals, vendors, evidence and monitoring.
What the engagement can cover
AI inventory design
Defined in relation to the organisation, selected workflow and evidence available.
Policy architecture
Defined in relation to the organisation, selected workflow and evidence available.
Risk and approval workflow
Defined in relation to the organisation, selected workflow and evidence available.
Human oversight model
Defined in relation to the organisation, selected workflow and evidence available.
Vendor/model diligence
Defined in relation to the organisation, selected workflow and evidence available.
Monitoring, evidence and review
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 Governance Programme
Share the organisation, current challenge, intended outcome and timeframe.
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