AI-Native Jobs To Be Done
AI-native businesses do not win because they use AI. They win because they close jobs faster, safer, and with better proof.
Each job below is a loop. Close the loop or do not automate it.
1. Understand The Customer Job
When a customer struggles through a repeated workflow, they want a better before-and-after state, so progress costs less time, money, risk, or attention.
Close the loop:
- Capture the current workaround.
- Name the cost of leaving it unchanged.
- Gather evidence outside the building.
- Decide what proof would change the customer's belief.
Kill signal: the team can describe an AI feature but cannot describe the customer's current workaround.
2. Redesign The Work
When the workflow is visible, the business wants to rebuild it around the outcome, so agents, systems, and people are assigned to the right parts of the flow.
Close the loop:
- Map inputs, decisions, outputs, exceptions, and handoffs.
- Remove artifact work that only exists because old systems were weak.
- Put agents on repeatable perception, decision, action, memory, and escalation.
- Keep humans at judgment, taste, relationship, exception, approval, and liability gates.
Kill signal: the tool is selected before the workflow is redesigned.
3. Instrument Proof
When the redesigned flow runs, the business wants a gauge, so output quality, cost, speed, risk, and customer value can be compared to baseline.
Close the loop:
- Pick one primary metric per loop.
- Record baseline before intervention.
- Capture exceptions and review cost.
- Compare live output to the standard.
Kill signal: success is described as adoption, not measured improvement.
4. Govern The Work In Motion
When agents act inside live workflows, the business wants embedded controls, so speed does not outrun accountability.
Close the loop:
- Classify sensitive inputs and outputs.
- Define approval thresholds.
- Add critic checks and audit trails.
- Name the human owner for each irreversible decision.
Kill signal: governance is a policy page no workflow reads.
5. Improve The Next Run
When a cycle completes, the business wants the next run to start wiser, so learning compounds instead of staying in chat logs, meetings, or memory.
Close the loop:
- Capture source evidence, decisions, feedback, corrections, and outcomes.
- Separate REALITY, DREAM, and CONSUMED capability.
- Update the prompt, standard, workflow, or instrument.
- Retire work that fails its kill signal.
Kill signal: the same mistake appears twice with no change to the system.
6. Distribute Value
When proof exists, the business wants it to travel, so trust, demand, cash, referral, or reusable know-how increases.
Close the loop:
- Turn proof into a case, diagram, customer story, benchmark, or standard.
- Route private evidence into private memory and public know-how into
/playbook. - Route public proof into /journeys when it is safe to show.
- Track whether distribution creates replies, citations, intros, bookings, sales, or reuse.
Kill signal: the business ships good work that nobody can find, trust, or act on.
The Compound Test
At the end of each cycle, ask:
| Compound Layer | Did It Improve? |
|---|---|
| Understanding | We know better what matters. |
| Explanation | We can explain it with less drag. |
| Creation | We create more value with less waste. |
| Distribution | The proof reaches better next actors. |
If all four improve, the loop compounds. If one breaks, that is the next job.
Context
Questions
Which job is open right now?
- Is the customer job proven?
- Is the workflow redesigned?
- Is the proof instrumented?
- Is the governance inside the flow?
- Did the next run get wiser?
- Did the proof distribute into value?