The Durability Asymmetry: Why Operational Roles Have a Turnover Problem That Automation Solves

Last year, a 100-person construction firm spent $240,000 hiring a Director of Project Operations.

Six months later, she quit for a better offer.

They’d just finished training her.

The Hidden Cost of Operational Turnover

Let’s walk through what actually happened:

Month 1-4: The Search

• Posted job (800 applications, 95% unqualified)
• Screened 60 candidates
• Interviewed 15
• Made offers to 3 (2 declined)
• Cost: $40k (recruiter fees + team time)

Month 5-7: Onboarding

• Systems training
• Process documentation
• Shadowing and knowledge transfer
• First month: 20% productive
• Second month: 40% productive
• Third month: 60% productive
• Cost: $60k salary + $30k lost productivity

Month 8-13: Productive Period

• Finally operating independently
• Building out systems
• Optimizing workflows
• This is what they hired her for
• Cost: $120k salary

Month 14: The Resignation

• Competitor offered $20k more
• She gave 2 weeks notice
• Knowledge walks out the door
• Back to Month 1

Total cost: $250k for 6 months of actual productive work

Then the cycle repeats.

The Operational Role Turnover Problem

Here’s what most firms don’t realize:

Operational roles (project ops, portfolio ops, client service ops) have the highest turnover rates in most organizations.

Industry data:

• Average tenure in ops roles: 1.8 years
• Turnover rate: 35-40%
• Cost per turnover: 150-200% of salary

Why ops roles have high turnover:

1. Skills are highly transferable (easy to get better offers)
2. Work can feel repetitive (less engaging long-term)
3. Career paths are unclear (where do you go from “ops”?)
4. Growing firms poach ops talent aggressively

So you’re building your operational foundation on a workforce that, statistically, won’t be there in 2 years.

The Automation Alternative

That same construction firm came to us after their ops hire quit.

We implemented automation for $35k that handled:

• Project tracking and status updates
• Schedule coordination
• Budget monitoring
• Performance reporting
• Resource allocation tracking

The Timeline:

Week 1-2: Build

• Mapped existing workflows
• Built automation systems
• Tested with real data

Week 3: Deploy

• Went live
• Monitored for issues
• Made adjustments

Week 4: Running

• Full productivity, day 1
• Zero ramp-up time
• No training period

Months 2-24: Still Running

• Never called in sick
• Never got a better offer
• Never needed a performance review
• Never forgot how to do the work
• Zero turnover cost

The Durability Asymmetry

This is the part that changed how I think about operational roles:

Humans and automation have asymmetric durability in operations work.

Human durability curve in ops roles:

• Months 1-3: Learning (low output)
• Months 4-12: Growing (medium output)
• Months 13-24: Peak (high output)
• Month 18-30: Searching (declining engagement)
• Month 24-36: Likely gone (turnover)

Automation durability curve:

• Day 1: Full output
• Month 6: Full output + optimizations
• Month 24: Full output + accumulated knowledge
• Month 60: Full output + years of optimization
• Never: turnover

One operations director told us:

“I’ve hired this project operations role 4 times in 5 years.

Each time:

• $40k recruiting cost
• $30k training cost
• $50k lost productivity during transition
• $15k knowledge loss

Total turnover cost: $135k × 4 = $540k

The automation cost $35k and has been running for 3 years with zero degradation.

I was spending $540k on the turnover problem and calling it ‘hiring costs.'”

Where This Actually Matters

Not every role should be automated.

Let me be crystal clear about this:

Automate roles where:

• The work is repetitive and rules-based
• Consistency is more valuable than creativity
• Institutional knowledge is critical
• Turnover is high and painful

Don’t automate roles where:

• Creativity and judgment are the core value
• Relationship-building is essential
• Work requires human empathy
• Context and nuance drive decisions

The test:

If losing the person means losing months of accumulated knowledge and context… that’s a signal.

Either: keep the person and invest heavily in retention

Or: automate the role because you’re solving for knowledge stability, not human creativity

The Math That Changes The Decision

Let’s compare 5-year total cost of ownership:

Human Ops Hire (assuming 2 turnovers in 5 years):

• Year 1: $100k salary + $40k recruiting + $30k training = $170k
• Year 2: $100k salary = $100k
• Year 3: $100k salary + $40k recruiting + $30k training = $170k
• Year 4: $100k salary = $100k
• Year 5: $100k salary + $40k recruiting + $30k training = $170k
• Total: $710k

Automation (one-time implementation):

• Year 1: $35k implementation = $35k
• Year 2: $8k maintenance = $8k
• Year 3: $8k maintenance = $8k
• Year 4: $8k maintenance = $8k
• Year 5: $8k maintenance = $8k
• Total: $67k

The 5-year durability asymmetry: $643k difference

What This Means for Your Ops Strategy

Next time you’re about to hire for an operational role, ask:

“Are we hiring this person for their creative judgment… or for their ability to execute consistent processes?”

If it’s creative judgment: hire the human, invest in retention

If it’s consistent execution: that’s a durability problem disguised as a hiring problem

And durability problems have a different solution.