Most automation programs start with the most painful workflow. Spark data shows why moderate friction repeated across hundreds of workflows can be worth more, and why team-level views matter.
The Short Answer
Automation priorities should account for total potential time savings, not just how difficult a workflow feels. In one Larridin customer, a team with 474 tracked workflows and just 2 high-friction ones had 6.7 days of potential savings, more than a team with 6 high-friction workflows. In another example, two teams with the same Friction Index had very different opportunities: 35 hours versus none.
Key Wins
What’s a Spark Insight?
A Spark Insight is a data-backed finding Larridin surfaces automatically from how your teams actually use AI, flagging the patterns worth a leadership conversation.
Who This Matters To
COO · RevOps · Finance
| If you’re asking… | The answer | Go deeper |
|---|---|---|
| How should I prioritize workflow automation? | Compare potential time savings across workflows and teams, accounting for how often work repeats and how much friction it creates. | Why volume matters |
| Is the most painful workflow the best one to automate? | Not always. In one org, a team with fewer high-friction workflows but more volume had more potential savings. | The data |
| Does a higher Friction Index mean more automation opportunity? | Not necessarily. In one org, a team with friction 18 had 120 minutes of savings vs. 59 minutes for a team with friction 25. | The data |
| Why look at team-level automation data? | Because org-level averages hide big differences. Two teams with the same friction score had 35 hours vs. zero hours of potential savings. | The data |
Automation backlogs usually fill up with whatever hurts most. That’s understandable: painful workflows are the ones people complain about. But pain and payoff aren’t the same thing. Volume is often what turns small savings into big ones.
All savings figures below are estimates, not realized savings.
| What you’re struggling with | What you actually need | How Larridin helps | Proof from Spark data |
|---|---|---|---|
| “We should automate our worst workflow first.” | Savings estimates that include volume | Estimates potential savings by workflow and team | 474 workflows, 2 high-friction; 6.7 days |
| “Every team says their process is the worst.” | An objective way to compare opportunities | Compares potential savings across teams | Same Friction Index (19): 35 vs. 0 hours |
| “Higher friction should mean bigger wins.” | Savings, not just friction scores | Pairs friction with potential time savings | Friction Index 18: 120 min; 25: 59 min |
The question you’re asked: “Where should automation start?”
What to do: Rank candidates by total potential savings, not by complaints. Look for moderate friction at high volume.
For more on comparing automation opportunities by team, see Project Teams Table.
The question you’re asked: “Which of our processes are worth automating?”
What to do: Check volume first. A small fix on a workflow that runs hundreds of times can beat a big fix on one that runs rarely.
For more on identifying workflows worth improving, see AI Workflow Mapping 101.
The question you’re asked: “How do I evaluate automation proposals?”
What to do: Ask for estimated recoverable time, implementation costs, and operational risks. Potential savings alone don't establish ROI.
For more on translating AI metrics into investment decisions, see AI Monitoring for CFOs.
Because total savings depend on how often a workflow runs, not just how hard it is.
A painful workflow that runs a few times a month may offer little recoverable time. Moderate friction in frequently repeated work can add up. In one Larridin customer, a team with 474 tracked workflows and just 2 high-friction ones had 6.7 days of potential savings, more than a team with 6 high-friction workflows.
Myth: The most painful workflows are the best automation targets.
Reality: In one org, a team with fewer painful workflows but more volume had more potential savings.
The 474 figure counts tracked workflows, not repetitions of a single workflow. Check run frequency and the savings-estimation method before treating workflow count as evidence of high repetition.
Because friction measures difficulty, not how much time could be recovered.
In one org, a QA team with a friction score of 18 had 120 minutes of potential savings, while a QC team with a higher score of 25 had 59 minutes. Friction helps identify difficult work, but estimated recoverable time is a better starting point for prioritization.
Spark callout: Two teams in the same org, a BDR team and an Info Sec team, both had a friction score of 19. The BDR team had 35 hours of potential savings. Info Sec had none.
Because averages hide where the opportunity actually is.
Org-wide friction and savings figures can obscure differences between teams. Team-level views reveal where potential savings are concentrated, including cases where a lower-friction team offers more recoverable time than a higher-friction one.
A workflow friction score measures how much difficulty people encounter when completing a workflow. Larridin calls this the Friction Index, which accounts for context switching, repetition, error recovery, and backtracking.
Estimate how much time automation could save each time a workflow runs, then multiply by how often it runs. For example, saving 10 minutes across 60 monthly runs equals 10 hours of potential savings per month.
They’re still worth fixing when they cause errors or risk. Just don’t assume pain alone means the biggest time savings.
See friction and potential savings by team and workflow, and bring them to your next operations planning session.