AI Automation Planning: Define the Task Before the Tool

タスクを正確に説明してください。

1 つの反復可能なアクティビティから始めます。入力、期待される出力、例外、および結果の責任者を記録します。タスクを明確に説明できない場合は、自動化方法を選択する前に時間をかけて理解してください。要件によっては、AI ベースのアプローチよりも単純なルールに適合する場合があります。

有用な結果がどのようなものかを定義します。

チームがレビューできる代表的な例を作成します。厄介なケース、不完全な入力、担当者にエスカレーションする必要がある状況を含めます。ワークフローを拡張する前に、結果をどのように評価するかについて合意します。評価は、印象的ではあるが無関係なデモンストレーションではなく、実際のタスクを反映する必要があります。

レビューとアクセスを明示的に保ちます。

どの出力に人間の承認が必要か、またワークフローがどの情報にアクセスできるかを決定します。障害の処理と出力を修正するためのルートについて話し合います。プロトタイプ中に便利だからという理由だけで、より広範囲のアクセスを許可することは避けてください。最終決定の所有権は表示されたままにする必要があります。

拡張する前にパイロットを確認してください。

パイロットと現在のワークフローを、同意した基準と比較します。制限事項と未解決の質問を記録します。より広範なロールアウトは、パイロットの結果、運用要件、およびそれをサポートする人々に基づいて個別に決定されます。

Scope the pilot before selecting a tool

Choose one task with a clear start and finish. Observe how a person handles it today, including the checks they perform before acting. Identify whether the proposed automation prepares information, recommends a decision or performs an action; these require different review boundaries.

Build a small, representative evaluation set with agreed expected outcomes. Test incomplete inputs, ambiguous requests and cases outside the intended scope. Record incorrect outputs and the work needed to correct them. Compare the pilot with the existing process using the same task definition, then decide whether to revise, expand or stop. Keep any production rollout separate from the initial experiment.

A one-page automation opportunity sheet

Complete this sheet with the person doing the task and the person who would operate the automation. It should expose uncertainty before a technical demonstration.

• Task boundary: trigger, required inputs, intended output and actions the automation must never take.
• Current process: responsible role, checks, common exceptions and existing escalation routes.
• Candidate method: simple rules, assisted drafting or another approach, with the reason for considering it.
• Evaluation: representative cases, unacceptable errors and the person reviewing each result.
• Operating plan: access, monitoring, fallback, correction ownership and expected running costs.
• Decision gate: evidence needed to continue, revise the scope or end the pilot.

Avoid automating an unclear decision

If staff disagree about the correct outcome, resolve the rule or policy before asking a tool to reproduce it. A model cannot settle an unowned business decision merely by generating a confident answer.

Do not assess only successful outputs. Review incorrect actions, uncertain cases and time spent checking results. Keep access restricted to the information needed for the pilot, and avoid presenting a prototype demonstration as evidence that the wider workflow is ready to operate without supervision.

Does automation always need AI?

No. A stable task with explicit conditions may be better served by rules or an ordinary software workflow. Consider AI where the task warrants evaluation, and compare it against a simpler baseline rather than assuming the more complex option is preferable.

ILLUSTRATIVE PLANNING EXAMPLE

Example: routing incoming support requests

A support team wants to route enquiries by subject. Define the available categories and give the pilot a representative set of anonymized messages. Include messages covering two topics and requests that do not fit any category. Decide which uncertain results return to a person rather than moving straight into a queue.

Record the routing decision, reviewer correction and reason for escalation. Use that evidence to decide whether to adjust the task, change the method or stop the pilot.

FURTHER READING

Official resources to explore

Explore the related official guidance below. The templates and examples are practical starting points to adapt to your own project.

• NIST AI Risk Management Framework Playbook