AI is Not a Feature, it's a Labor Class
by Epochal Team
You're not buying software. You're hiring workers you can't fire.
You signed up for Glean to search internal knowledge. Or maybe Perplexity Pro for client research. Or Notion AI to draft meeting summaries.
Your team loves it. Saves 4-6 hours per consultant per week.
That's not a "feature." That's a workforce member.
And you might be managing it wrong.
The mistake that kills AI projects
2024 numbers: 42% of enterprise AI projects abandoned. Up from 17% in 2023.
MIT and Contrary Research traced the pattern. The failures all made the same mistake:
They treated AI like a feature instead of labor.
Feature thinking:
- Deploy the tool
- Train people on buttons
- Hope it works
- Wonder why adoption dies after month 2
Labor thinking:
- Define the role and responsibilities
- Set performance expectations
- Track output quality
- Build oversight and escalation paths
- Measure ROI like you would for a hire
The projects that survived past pilot? They managed AI agents like employees.
The three questions that reveal if you're set up to fail
Pull out your AI agent rollout plan. Could be Glean for knowledge search, Perplexity for research, Jasper for content, Notion AI for documentation—doesn't matter which. Answer honestly:
1. Can you list the specific workflows this agent owns?
Not "helps with research" or "assists with proposals." That's vague.
Try this instead:
- "Glean searches internal case studies and returns top 5 relevant projects when consultant asks about past client work"
- "Perplexity generates market analysis from 20+ sources within 3 hours, consultant reviews for accuracy before client meeting"
- "Notion AI drafts meeting summary from transcript within 30 minutes, account manager edits and shares with client"
If you can't write that sentence for your agent, you don't have a deployment plan. You have hope.
2. Do you have KPIs for the agent itself?
You track consultant utilization, client satisfaction, project margins.
Do you track:
- Agent output accuracy rate?
- Time saved per task?
- Escalation frequency?
- Client deliverables that used agent outputs?
If you're not measuring it, you're not managing it.
3. What happens when it screws up?
Your consultant submits a deliverable. Client finds an error. Turns out the AI hallucinated a source.
Who catches it? When? How?
If your answer is "the consultant should check," congratulations—you just hired labor without oversight.
What managing AI as labor actually looks like
Stop thinking deployment. Start thinking org chart.
Define roles:
- What does this agent do?
- What decisions can it make alone?
- What requires human approval?
- Where does it fit in your workflow?
Set performance standards:
- Accuracy thresholds
- Speed requirements
- Output format specs
- Escalation triggers
Build oversight:
- Who reviews agent outputs?
- How often?
- What's the validation process?
- Where do audit logs live?
Track the business impact:
- Hours saved per consultant
- Error rate vs. manual work
- Client feedback on deliverables
- Cost per task (agent vs. human)
This is workforce management. You already know how to do this. You just haven't applied it to AI yet.
The hard truth about your current setup
You rolled out Glean (or Perplexity, or Jasper, or Notion AI) three months ago. 20 users in pilot. 73% adoption rate. Partners are happy.
Here's what you probably don't have:
- Written workflows defining agent scope
- Regular output audits
- Error tracking and root cause analysis
- Feedback loops when agents produce bad outputs
- Version control (you're on whatever the vendor pushes)
- Contractual protections when quality degrades
You have a tool rollout. Not a workforce integration.
That's why 42% fail.
What to do this month
Week 1: Audit your current state
List every AI agent your team uses. For each one:
- What workflows does it actually handle?
- Who's responsible for validating outputs?
- What happens when it makes a mistake?
- What metrics track its performance?
If you can't answer these, you're flying blind.
Week 2: Define roles and responsibilities
Pick your highest-impact agent. Write its job description:
- Owned workflows (specific)
- Decision authority (what it can/can't do alone)
- Performance standards (accuracy, speed, format)
- Escalation paths (when it stops and asks)
Share this with your team. Make sure everyone managing the agent knows its scope.
Week 3: Build oversight
Set up:
- Output validation requirements (who checks what, when)
- Error logging (spreadsheet is fine, start simple)
- Monthly quality reviews (sit with your team, review agent outputs, spot patterns)
Week 4: Measure business impact
Track for one month:
- Time saved per consultant using the agent
- Error rate (mistakes caught before client sees them)
- Client feedback on deliverables using agent outputs
- Cost: agent fees vs. equivalent human hours
Now you have data. Now you can decide: scale, adjust, or kill it.
Why Y Combinator and VCs care about this shift
Y Combinator's Summer 2025 batch pushed "full-stack" companies—startups that don't just build AI tools, they run businesses with AI as the core workforce.
VCs are buying entire operations (law firms, clinics, logistics companies) and rebuilding them with AI labor.
Why? Because the value isn't in the software. It's in proving AI can actually run a business when you manage it like labor, not like a feature.
You're not a VC. But you're doing the same experiment at your firm.
The ones who figure out workforce management for AI? They'll run 20-30% leaner than competitors.
The ones who treat it like a cool tool? They'll be in that 42% by Q2 2026.
If you're stuck
If you're three months into your AI rollout and realizing you don't have workflows, oversight, or metrics—you're not alone.
Most operators are in the same spot. The difference is whether you fix it now or wait until a partner asks "why are we paying for this?"
We help firms build the workforce management layer for AI agents: role definition, oversight frameworks, KPI tracking, contract protection.
If you need help turning your pilot into an actual operation, let's talk.