Solutions
Data and operations
Make sure the data AI needs is available, owned and reliable, and that the processes using it are defined end to end.
The problem
The necessary data exists but is scattered, without clear owners or quality controls, and the processes that should use it are not defined end to end.
How we intervene
We work on data availability and reliability, the design of the processes that consume it, and the operating model that sustains both after the project ends.
How the work unfolds
- A map of data sources, owners and observed quality gaps.
- Target process design, with control points and owners.
- Agreed follow-up indicators, each with its source.
- Accompanied implementation with internal teams and vendors.
Every project runs on the AGORA Delivery Framework —Qualify, Discover, Design, Deliver and Sustain—, which defines how the work is carried out. It is distinct from the commercial journey, which describes what can be engaged. See the methodology →
Deliverables
- A map of data sources, owners and observed quality gaps.
- Target process design, with control points and assigned owners.
- Defined follow-up indicators and their source.
- An accompanied implementation plan, with roles for internal teams and vendors.
Client participation
Internal teams and vendors execute; AGORA directs the work, resolves dependencies and verifies results against what was agreed.
Where it fits in the commercial journey
- OperationalizeTurn governance into organizational execution.
The commercial journey describes what can be engaged. No organisation needs all four stages.
Next step
Let's discuss scope based on your organisation's context.
Sector references correspond to the professional trajectory of the firm's members, not to AGORA Advisory contracts.
Other solutions
AI readiness and strategy
Bring order to the AI initiatives that already exist and turn them into a prioritisation leadership can defend, before committing resources to pilots that never scale.
Governance and risk
Build the AI governance model the organisation can sustain and demonstrate: policy, inventory, risk classification, roles and evidence proportional to its context.
AI operating model
Turn governance into execution: approved rules applied to how each AI use is bought, built and approved, with every step leaving evidence.