Where AI belongs in a workflow (and where it doesn’t)

AI is excellent at interpreting messy inputs and weak at being your system of record. That’s the whole game. Use models where judgment is required—classifying an email, extracting fields from a document, summarizing a long thread, matching duplicate entities, flagging anomalies, forecasting a likely outcome, or recommending the next best action. Keep the do-this-then-that steps in deterministic automation. Keep ownership and liability with humans where money or compliance is on the line. If you remember nothing else: AI interprets; automation executes; humans own risk.

In a healthy workflow, AI sits at specific decision points. An email arrives, the model determines topic and urgency, and the orchestration layer routes it to the correct queue with the right priority. A PDF lands, the model pulls the invoice number, dates, vendor, line totals, and tax, and the flow validates those fields against a purchase order before anything is posted downstream. A case has a week of notes; the model compresses it into a clean status update, the system publishes it to the record and the team channel, and the next task is scheduled without a meeting. A customer shows behavioral signals; the model scores churn risk and the workflow creates a task, notifies the owner, and starts a standard recovery play. Knowledge workers ask a “how do I…” question; the model answers from your own SOPs with source links, and the workflow opens a ticket if confidence is low so documentation improves instead of guesses multiplying.

Just as important is where AI does not belong. Do not treat a model like a database. The truth about customers, claims, invoices, jobs, and money lives in your CRM, ERP, PMS, or warehouse—not in an LLM’s memory. Do not let a model write transactional updates directly without deterministic validation. Do not offload approvals with liability; regulated or irreversible decisions require a human’s name, timestamp, and context. Do not encode policy as vibes inside a prompt; rules should be code or configuration the business can read and audit. And never hide responsibility behind a vague “agent.” Every flow and every step needs a named owner.

A simple placement blueprint keeps you out of trouble. Triggers start the work. Optional AI steps interpret inputs or scores scenarios. Explicit rules decide the path. Deterministic actions create or update records, file documents, send messages, and set due dates. Feedback closes the loop with notifications and status changes. Everything is logged, and time limits escalate when something stalls. That structure lets you add or remove AI without rewriting the entire system because the judgment layer is modular and the execution layer is boring on purpose.

You don’t need a moonshot to see value. Start with patterns that pay quickly and hold up under real use. Turn inbound email into routed work by classifying topic and priority, extracting the few fields you actually need, and writing a properly formed ticket with the right owner and SLA timers. Convert document intake from a rework trap into a pass–fail gate: extract the fields, validate totals and dates, compare to the purchase order, write to accounts payable only when the rules are satisfied, and request missing information automatically when they’re not. Replace status meetings with automated briefings by summarizing case notes into one crisp update, posting it to the record and team channel, and scheduling the next step so momentum is built into the system. Score risk and opportunity with a transparent model, trigger a targeted follow-up when the threshold is crossed, and measure outcomes so you know the score is worth trusting. Put your knowledge base on tap by answering common questions from your existing SOPs with citations, and create a ticket when the model isn’t confident so gaps get fixed.

Guardrails are non-negotiable. Every AI output should carry a confidence score with a defined fallback. Prompts and responses should follow a structured schema, and the flow should reject anything that doesn’t validate. Writes must be idempotent—use external IDs and upserts so a repeated event doesn’t create duplicates. Approvals that move money or create legal exposure require a human-in-the-loop checkpoint in a proper UI, with a durable audit trail. Prompts and examples should be versioned and reviewed, not tweaked ad hoc in production. Accuracy for extraction and classification should be tracked with real metrics, and the only “AI KPI” that matters long term is business impact: time saved, errors reduced, cycle time improved, revenue protected.

A thirty-day rollout proves this without fanfare. In week one, choose two candidates with high volume, messy inputs, and low risk—email triage and document intake are usually ideal—and specify the single field or decision each model must produce. In week two, ship thin slices: trigger, one AI step, deterministic validation, deterministic action, logging, and a manual review queue for low confidence cases. In week three, tighten prompts, add examples, raise thresholds, write results back into source systems, and publish a small dashboard that shows accuracy and cycle time so trust builds with evidence. In week four, layer a second AI step—often summarization or recommendation—while keeping risk-bearing decisions with humans. Document the SOP and the owners so the workflow survives contact with reality.

The most common failure modes are predictable and avoidable. Trying to “AI” an entire process produces a demo, not a system; one model step per flow is enough. Black-box outcomes erode trust; log inputs and outputs, show confidence and reasoning, and attach sources. Letting a model write back without validation guarantees bad data; validate against a schema and block on low confidence. Flows with no owner simply fail in silence; give the dead-letter queue a name, an SLA, and an escalation.

Bottom line: AI isn’t your workflow—it’s the judgment layer inside your workflow. Put it where interpretation matters, keep rules explicit, make actions deterministic, and log every decision so you can defend it later. If you want this placed correctly from day one, start with a short Workflow Audit. We’ll map the decision points, add the guardrails, and ship thin slices that generate real results without turning your operations into a science experiment.