AI Implementation Guide
Integrate AI where it adds leverage—not noise.
Use this guide to move from discovery to production with confidence. It focuses on the parts that determine success: data quality, workflow design, guardrails, and change management.
Scope an AI Discovery SprintChecklist at a glance
- •Align use cases with measurable business objectives
- •Validate data access, quality, and permissions early
- •Plan evaluation benchmarks and human review paths
- •Document the deployment pipeline and fallbacks
Discover & Prioritise
Audit workflows to surface manual effort, latency, and decision bottlenecks. Prioritise the use cases that align with business goals and have accessible data.
- •Map the end-to-end process with the people running it
- •Quantify effort, frequency, and business impact
- •Identify available data sources and access patterns
Prototype Responsibly
Build proof-of-concepts that demonstrate value quickly while keeping compliance in mind.
- •Start with curated datasets or retrieval pipelines
- •Use evaluation harnesses to compare prompts and models
- •Document expected behaviour and guardrails before rollout
Deploy & Operationalise
Ship the automation with runtime observability, safe fallback paths, and cost controls.
- •Instrument latency, cost, and quality metrics
- •Add human-in-the-loop reviews where decisions matter
- •Create runbooks for on-call and incident response
Measure & Improve
Track the impact on efficiency, accuracy, and user experience. Iterate based on feedback and new training data.
- •Survey users for qualitative sentiment
- •Compare pre- and post-launch KPIs
- •Retrain or fine-tune models with annotated examples
Tooling Reference
Models & APIs
Data & Retrieval
Orchestration
Monitoring
Governance Checklist
Explore further
AI Workflow Automation Case Study
Reducing onboarding effort by 68% with retrieval-augmented workflows and intelligent triage.
What is RAG?
Plain-language explanation of retrieval augmented generation and when to use it.
Choosing the Right LLM
A rubric for evaluating models on cost, latency, and brand alignment.
Want hands-on help?
I work with teams to design, prototype, and deploy AI workflows with the right guardrails. Let’s start with a discovery call.
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