Advice to “start small” is common, but practical guidance on what “small” means and how to pick the right starting point is rare.
For in-house legal teams automating workflows with AI, the first workflow selection is crucial. Choosing one with high variability, high risk, or limited data can lead to early failure and delay progress. A good choice acts as a proof of concept to build momentum.
Before choosing your first workflow, it’s helpful to have a framework for weighing your options.
Four Factors That Determine Workflow Readiness
1) Volume
AI workflow automation delivers the clearest return when the same type of activity recurs. If a workflow handles ten tasks or actions a year, the effort of designing, testing, and governing an AI-assisted process is unlikely to be justified. If it handles ten requests a week, the equation changes considerably.
Start by identifying the five to ten most frequent activity types your team handles. These are the candidates worth assessing further.
2) Repeatability
Volume alone is not enough. The work also needs to follow a recognisable pattern. That does not mean every task or activity is identical, but there should be a consistent set of inputs, a clear decision path, and a defined output.
Contract review, NDA approvals, invoice checks, matter intake, legal research, routine requests and administrative tasks all tend to meet this test. One-off regulatory matters, complex disputes, or anything requiring significant bespoke judgement at the outset generally do not.
3) Risk Level
Higher-risk workflows are not off-limits but do require mature governance before AI is introduced. For a first workflow, the pragmatic approach is to choose one where errors are recoverable: a misclassification that a lawyer can quickly correct, rather than a misrouted instruction with external consequences.
Low to medium risk workflows allow the team to learn how AI behaves in your specific environment, calibrate review checkpoints, and build internal confidence before applying the model to higher-stakes work.
4) Data Quality
AI performs well when the underlying data is clean and consistently structured. If your selected process relies on free-text email with no standard fields, AI can still help, but the workflow will need more upfront design to handle the variability.
Before selecting a workflow to automate with the assistance of AI, assess the current data for that process. Are matter types consistently categorised, and are requests captured in a system or arriving across multiple channels? The answers determine preparation effort for reliable automation.
A Simple Scoring Approach
Evaluate and rank each identified workflow by rating them on four factors, using a scale from one to three.
- Volume: low (1), moderate (2), high (3)
- Repeatability: low (1), moderate (2), high (3)
- Risk level: high (1), moderate (2), low (3)
- Data quality: poor (1), adequate (2), strong (3)
Workflows with a score of 10 or higher are strong candidates for a first build. Those scoring six or below need foundational work before AI is introduced. Those in between are worth progressing with clear design constraints.
What a Good First Workflow Looks Like in Practice
Legal intake and triage are common starting point for in-house teams for good reason: high volume, repeatable structure, recoverable errors, and clear outputs. AI can interpret the incoming request, suggest matter types, extract details, and update intake records. Automation applies routing, service levels, and approval paths, with a lawyer confirming actions before they occur.
That sequence is simple enough to build, test, and refine in a short timeframe with the right tools.
Before You Build
Before committing to a workflow, define three things in writing: what a successful output looks like, where human review is required, and how you will measure whether the workflow is performing. Without those definitions, it becomes difficult to assess whether the workflow is working well or simply working.
Lawcadia’s workflow automation and matter management tools are designed to support exactly this kind of structured, governed approach, giving in-house legal teams the foundation to introduce AI where it adds the most value and maintain control throughout.