AI Automation ebook · 5-page PDF
Deploying Generative AI: Blueprints for Success
Proven frameworks and strategies for successfully deploying generative AI in enterprise environments.
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What’s inside
- The four deployment patterns
- Ground it in owned data
- Evaluation before users
- Guardrails that survive contact with users
- Cost control from day one
- Rollout and operations
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The four deployment patterns
Most enterprise generative AI deployments fit one of four patterns: assistant over documents (retrieval-augmented answer), extraction and structuring (documents into fields), drafting (outbound text with human review), and orchestration (multi-step workflows that call tools). Choose the pattern before the platform; each implies a different architecture, evaluation method and risk profile.
- Retrieval assistant: strong for policy, product and procedure questions; needs clean sources and citations.
- Extraction: strong for invoices, POs and intake forms; needs a validation layer against the system of record.
- Drafting: strong for replies and proposals; needs review until edit rates prove quality.
- Orchestration: strongest value but highest blast radius; needs step-level audit and rollback.
Ground it in owned data
A generic model cannot answer questions about your contracts, prices or procedures. Retrieval over your own corpus fixes that, but only if the corpus is clean and permissioned. Build the ingestion pipeline with source ownership, refresh cadence and access control from the start, and require citations so a reviewer can check the answer.
Evaluation before users
Create a gold set of 100 to 300 real questions or documents with agreed answers. Measure answer correctness, citation correctness (did it cite the right source?), abstention quality (did it say 'I don't know' when it should?) and harmful-content rate. Re-run on every change and keep a regression log.
Guardrails that survive contact with users
Guardrails are layered: input filtering, retrieval scoping, prompt constraints, output validation, and a human decision point for anything that leaves the organisation or changes a record. Log the prompt version, retrieved sources, output and the reviewer action for every consequential interaction.
Assume prompt injection and untrusted documents. Never let retrieved text carry instructions that change the workflow or the permissions of the caller.
Cost control from day one
Track cost per interaction, per document and per workflow, not the monthly bill. Cap context, cache stable prompts, route simple work to smaller models and use deterministic code where it is cheaper and more reliable. Set per-workflow budgets and alert on variance.
Rollout and operations
Start with a single workflow and a bounded population. Put the eval harness in CI so a prompt change cannot ship without a score. Give each workflow an owner, a runbook, a rollback and a published autonomy level. Review the portfolio monthly for value, cost and incidents.
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