How to Identify & Prioritize Use Cases for Automation

Yesterday, we explored how to identify and prioritize use cases for AI, which is fundamentally based on a bottom-up approach for auditing and documenting use cases by observing process execution and system usage. The same method applies if your goal is to recognize efficiencies through automation.

As a quick recap, these are the steps:

1. Create a cross-functional team

2. Engage a lead stakeholder from each department

3. Observe how a team executes a business process

4. Capture a list of ideas

5. Groom and prioritize your use cases

Let's talk about what to look for as you engage stakeholders (steps 2 and 3), as it will influence what you capture in your list ideas (step 4).

Start by engaging teams that are producing material and talking directly with your prospects and customers. These folks often benefit most because they reclaim time that can be spent better serving their core function, which could look like:

• Marketing creating tighter segmentation and producing more content

• Sales (or fundraisers, if you're a nonprofit) spending more time developing a relationship or managing more prospects within their pipeline

• Support improving their ability to respond quickly and resolve issues faster

• Delivery (for services based organizations) providing value faster and being able to handle more projects or implementations concurrently

You should still engage teams that are externally facing, although typically the value associated with automation is lower for these teams.

The reason for that is simple, as downstream teams becoming more efficient means you're freeing up capacity that upstream teams need to fill, which means you're not recognizing any tangible value unless you:

• Plan to reduce headcount

• Skip hiring that was needed due to capacity issues

• Have awareness of imminent growth

Let's switch our focus to generic problems and real examples that can be solved with existing automation platforms:

• Manual Data Entry: copying and pasting data from a consistently formatted email into your CRM; preparing quotes and contracts; typing data from scanned images or PDFs; drafting a response to a sales inquiry or support request based on your product or services

• Researching & Enriching Data: searching for information about a prospective account and their people; validating addresses, emails, and phone numbers; looking for relevant news about an existing customer that can influence the relationship; classifying and categorizing data based on definitions like department or seniority; distilling a support request and finding the relevant knowledgebase articles that apply

• Recurring Tasks: generating weekly reports based on data from various systems; preparing and sending customer invoices; taking regular backups of data and metadata from your systems

• Alerting: pinging sales when a prospect has actionable marketing engagement with your digital properties; sending renewal and past due reminders; flagging escalations, exceptions, or unusual usage patterns; nudging people that need to approve or perform work that is missing (e.g. expense reports)

As a final point, if you're intending to leverage AI (as discussed in yesterday's newsletter), you'll likely need to automate how that fits into business processes and interacts with other systems.

So, you might as well pair these initiatives together as you embark on your bottom-up approach to observing stakeholders.

TLDR: Look for manual data entry, research tasks, recurring work, and alerting needs. Start with externally facing teams for highest impact.