Chris June argues that the first AI build should not be about “AI strategy.” It should be about architectural measurability: turning a messy workflow into a decision-ready process with accountable review. An AI workflow is production-ready when you can measure its output quality, monitor its behaviour over time, and assign clear human responsibility for exceptions. (airc.nist.gov)
Which tasks qualify as an AI first use caseA small
team should pick work that repeats often, has a stable definition of “good,” and can be evaluated quickly after the fact. In practice, that means choosing tasks where you can log inputs, outputs, and the human outcome (approve/revise/reject) and then calculate operational impact. One strong pattern is “AI-assisted decisions with a human checkpoint,” where the AI proposes and people accept or correct. Microsoft’s guidance on human-in-the-loop workflows emphasizes that production systems need designed human oversight rather than ad-hoc supervision. (learn.microsoft.com)
Proof looks like this: you can measure cycle time, error rates, and rework rate for a defined path (e.g., “inbound request → categorization → recommended next step → human approval”). NIST’s AI RMF core highlights the role of documentation and governance processes that make review and responsibility explicit over the system’s lifecycle. (airc.nist.gov)
Implication: if you cannot define evaluation signals today, you will not be able to control quality next month. Your first deployment should improve an operating metric you already track, not create a new KPI universe.
Why not every task deserves automationNot every task should be
automated, even when it “feels repetitive.” Automation can shift your failure mode from visible mistakes to confident-but-wrong outputs, especially where the AI must interpret unstructured inputs or where users over-trust suggestions. Research and practitioner experience both point to the same risk: when humans evaluate or rely on AI suggestions, trust dynamics and task design change outcomes. For example, Microsoft research on “human-in-the-loop” and related work underscores that AI outputs can diverge from human judgment and that evaluation standards matter. (microsoft.com)
Proof in implementation terms is straightforward: if your current workflow already has a meaningful human judgment step, your “AI first” move should keep that step auditable. Human oversight-by-design and orchestration patterns exist precisely so teams can control exceptions and verify correctness before decisions are executed. (learn.microsoft.com)
Implication: automation is not a single switch. You should expect to run AI as a support layer at first—drafting, classifying, summarizing, or routing—then expand only after you have evidence that quality is stable for your data and your edge cases.
How do you prioritize by operational payoff without overbuildingPrioritize use
cases by operational payoff, but do it with a simple evaluation lens you can run in weeks, not quarters. The lens has five elements: repeatability, measurability, business proximity, reviewability, and monitoring feasibility. Operational intelligence mapping matters here: you are not only deploying a model; you are mapping operational signals (tickets, invoices, RFPs, calls, approvals) into decision-ready insights. If the feedback loop is absent, your team will be unable to learn from mistakes.Google’s MLOps architecture guidance describes the production reality: you need evaluation and then active monitoring for degradation and staleness, especially when data distribution changes. (docs.cloud.google.com)
Proof: you can usually implement monitoring faster than you think when you instrument the workflow. For many workflows, “monitoring” starts as operational telemetry: counts of approvals vs. rejections, confidence thresholds, category drift, and turnaround time distributions. NIST AI RMF also stresses ongoing monitoring and periodic review as an intrinsic governance requirement. (airc.nist.gov)
Implication: choose a first use case where you can close the loop (log → evaluate → improve routing prompts, thresholds, or rules) without standing up a large platform. Your goal is fewer minutes per case and fewer incorrect actions, not a generalized AI backbone.
When a focused AI platform tool is enough versus custom
softwareA focused tool is enough when your workflow can be expressed as document ingestion, classification, summarization, and decision routing with human approval steps. In that case, your architecture can be lightweight: connect existing systems, apply an AI step, and keep the human checkpoint. For example, Microsoft’s agent framework workflow patterns include human-in-the-loop orchestration, and Copilot Studio describes “AI approvals” as a way to reduce repetitive decision burden while keeping stages explicit. (learn.microsoft.com)
Custom software becomes necessary when you need tight integration with internal systems, deterministic business rules, or specialized evaluation logic that a tool cannot express. Another trigger is when you must support a robust audit trail (e.g., who approved what, on which evidence, using which version of the prompt/model) and you cannot rely on vendor defaults.Proof for the trade-off: risk management guidance for AI emphasizes integrating risk management into AI activities and functions, which often requires tailoring controls to your context rather than accepting a generic one-size-fits-all workflow. (iso.org)
Implication: start with a focused tool to prove value, then move to lightweight custom software only when gaps block measurement, reviewability, or monitoring.
A practical Canadian SMB example that avoids an AI platform
buildConsider a 12-person Winnipeg logistics company with a Lean ops team and a constrained budget. Their repeat problem is inbound customer requests: “change delivery,” “hold shipment,” “exception on ETA,” and “invoice question.” Today, a coordinator reads each email, classifies it, checks order status in a legacy ERP interface, and drafts the reply. A good first AI use case is AI-assisted ticket triage and reply drafting, with human approval before sending. Repeatability is high (hundreds of emails per week), business proximity is close (customer experience and operational execution), and reviewability is natural (every sent response is logged).Implementation trade-offs are real: if the AI drafts replies but the team cannot reliably detect wrong intents or missing facts, you will see rework and churn risk. That’s why human-in-the-loop orchestration and designed oversight checkpoints matter. (learn.microsoft.com)
Operational consequence: the team measures “time to first reply,” “approval rate,” and “revision count per case.” They monitor performance like an MLOps team would—at minimum, distribution shifts in categories and drift in turnaround time. (docs.cloud.google.com)Scale path: once triage accuracy is stable, they can expand from drafting to recommending next actions, then to exception routing. Crucially, they do not need a platform rewrite on day one; they need a decision architecture that preserves accountability and evidence.
Start AI with an architecture assessment you can run this month
Chris June’s editorial stance is simple: prove operational payoff with a controlled pilot and an auditable workflow, then scale the parts that hold up under monitoring. Use the lens above to pick an AI first use case, define the evaluation signals, and map how decisions are routed, reviewed, and logged.CTA: Open Architecture Assessment — If you want, start with IntelliSync’s Open Architecture Assessment: we’ll help your small team select an AI first use case, define the measurement plan, and outline the minimum decision architecture needed to make results trustworthy.