---
title: "How to choose your first AI workflow | McIntosh Systems"
description: "Choose a first AI workflow by weighing business value, available records, review effort, authority, and adoption before investing in a wider deployment."
url: "https://mcintoshsystems.com/insights/choosing-your-first-ai-workflow"
---

Implementation guide

# How to choose your first AI workflow

Choose recurring work with usable records, a clear owner, and output you can check.

By McIntosh SystemsFor business leaders

## Name the cost of the current workflow.

“Reduce time spent copying incoming PDFs into our records” names work you can inspect and a result you can measure.

Follow a record from arrival to completion. Measure hands-on effort and elapsed time. Delays may come from missing information or authority, rather than a task AI can improve.

Name the intended change: more team capacity, faster decisions, fewer corrections, or clearer status. Each needs its own measure.

## Choose a workflow you can evaluate.

Compare candidates using the records and exceptions your team actually encounters.

Questions for comparing first AI workflows

| Criterion | What to establish |
| --- | --- |
| Business value | What changes if the work takes less effort or reaches a decision sooner? |
| Frequency | Does the work recur often enough to justify implementation and provide a useful sample? |
| Source records | Can you access representative documents, including incomplete and unusual cases? |
| Review effort | Can a reviewer check the output efficiently against its source? |
| System access | Can the result reach the system where the next step happens? |
| Ownership | Who owns the workflow, its exceptions, and the decision to change it? |

A large theoretical benefit is insufficient if access is unresolved or reviewers cannot check the output.

## Keep authority explicit.

Reading a document and updating a business system require different permissions. Define authorization, result checks, and who resolves exceptions.

Keep known calculations and routing rules in conventional software. AI output should include sources, visible uncertainty, and a route for exceptions.

Match review to the consequence of an error. A draft can be corrected before use; a record update needs authorization and confirmation.

## Scope the first implementation around a complete task.

Define the start and completion point, sources, systems, permissions, reviewer, and exception path. Test through the step where the team uses the result.

Set a baseline. Compare total effort, including review and correction, and observe actual use. Keep expected benefits separate from measured results.

Can the team complete the task with less effort and acceptable quality? Use the evidence to expand, revise, or choose another technique.

## Further reading

For voluntary guidance on AI risk management, see the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework). It does not replace assigning ownership of your workflow.

[Next: measure the business value](https://mcintoshsystems.com/insights/measuring-ai-business-value)[Our AI implementation service](https://mcintoshsystems.com/services/ai-implementation)

## Questions about choosing a first workflow

**What makes a good first AI workflow?**

Recurring work, accessible records, an owner, and a practical review step. The benefit must hold after review, corrections, and operating costs.

**Should we automate an entire department first?**

Start with a defined workflow. Broader deployments add dependencies and make results harder to attribute. Expand when the first workflow demonstrates value.

**When should we use conventional automation instead?**

Use conventional software for known calculations, approval rules, and stable field transfers. Evaluate AI where varied documents or language require interpretation.

## Bring one workflow into focus.

Tell us where your team spends its time and what changes if the task becomes easier to complete.

[Discuss a workflow](https://mcintoshsystems.com/contact)

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