Consulting
AI Readiness & Strategy
SERVICE ENGINEERING GUIDE
Choose AI investments that solve a real problem and can be governed. We assess use-case value, data readiness, technical feasibility and operational risk, then define an evidence-led path from experiment to adoption.
What we deliver in practice
Use-case portfolio
Run workshops to identify tasks suitable for prediction, extraction, knowledge assistance or automation. Score value and feasibility, and retain conventional alternatives when they better serve the requirement.
Prioritised use-case portfolio and scoring rationale.
Data & organisation readiness
Assess data access, quality, labelling and ownership alongside skills and operating processes. Identify blockers that must be resolved before a proof of concept can demonstrate useful results.
Readiness assessment and enablement actions.
Architecture & sourcing choices
Compare hosted services, private deployments and build-versus-buy approaches. Make tradeoffs around integration, data controls, latency, cost and support explicit rather than selecting a model by popularity.
Options matrix and target AI architecture.
Governance & pilot charter
Define evaluation data, release gates, human responsibilities and escalation. Set a bounded pilot scope and commercial assumptions, with clear criteria to continue, redesign or stop.
Pilot charter, risk register and adoption roadmap.
The technology, explained
Evaluation set
Representative examples with reviewed expected outcomes, kept separate from development where appropriate. It measures usefulness and failure modes before release.
Human approval gate
A defined point where an authorised person reviews a proposed action before execution. It needs clear context, permissions and a record of the decision.
TCO
A lifecycle view of acquisition, licences, operation, skills, support and retirement costs. Estimates should state their assumptions and excluded items.
Reference architectures & design practices
Illustrative patterns for project discussions. Final topology, sizing, products and acceptance criteria are developed against your requirements. These are BrightGate concept diagrams, not vendor-certified designs.
AI investment decision gates+
On smaller screens, scroll the diagram sideways to see all components.
Each investment passes explicit decision gates. A technically impressive demo is not sufficient: the pilot must also satisfy data, risk, operational and value criteria. Advisory work defines the plan; implementation is scoped separately.
Design practices to apply
- Compare AI with a non-AI baseline.
- Identify an accountable business and data owner.
- Decide release thresholds before evaluating the pilot.
Systems, products & official references
Examples to evaluate during scoping. Selection, supported features, hardware and licences depend on the agreed solution; a product mention does not imply a certification or an included purchase.
AI portfolio & readiness scorecards
Decision tools to compare benefits, effort, data constraints and risk.
Model and deployment options
Evaluate technology families against the task and operating environment.
Official reference Microsoft AI technology choicesNIST AI RMF
A risk-management reference for lifecycle roles and evaluation planning.
Official reference NIST AI Risk Management FrameworkA result your team can accept
Acceptance evidence
Prioritisation
Review scoring with business and technical owners.
Pilot readiness
Confirm access to representative data and a measurable baseline.
Governance
Agree release decisions, human responsibilities and risk ownership.
View the complete capability checklist
- AI opportunity discovery and use-case prioritisation
- Data readiness, quality and ownership assessment
- Build-versus-buy and model-platform evaluation
- Feasibility, value hypotheses and pilot success measures
- Responsible AI governance and human-oversight design
- Privacy, access and deployment-option assessment
- Adoption planning, operating model and skills development
- Phased investment roadmap and implementation handover