AI strategy
Find where AI creates actual leverage.
AI strategies often begin with technology. We begin with operations.
Argbit examines where work happens, where decisions are made, where information moves and where friction exists. From there we determine where AI can create measurable value.
Productised engagement
AI Opportunity & Architecture Sprint
A focused engagement designed to identify and prioritise practical AI opportunities — and to establish the architecture required to deliver them.
Format
Fixed
Scoped engagement
Output
8artefacts
Decision-ready
Outputs
Everything produced is something an executive team can act on.
- Operational opportunity map
- Prioritised AI use cases
- Value-versus-complexity assessment
- Technical feasibility
- Risk assessment
- Target architecture
- Recommended experiments
- Delivery roadmap
How we assess
Operations first, architecture second, technology last.
A use case only earns engineering investment when the value is measurable, the feasibility is understood and the operating model can absorb it.
Where we look
- Processes with high manual handling
- Decisions made from unstructured information
- Queues, backlogs and triage points
- Knowledge trapped in documents and people
- Handoffs between systems and teams
What we test
- Economic value of the work being changed
- Technical feasibility with current models
- Data and integration readiness
- Regulatory and risk constraints
- Operational ownership after delivery
What you leave with
- A ranked opportunity portfolio
- A target architecture
- A delivery roadmap with sequencing
- An explicit list of things not to build
Exploring where AI belongs in your operation?
An Opportunity Sprint gives you a ranked portfolio, a target architecture and a delivery roadmap — including the use cases we would advise against.
Discuss an AI Opportunity SprintNo AI theatre. Just engineering.