If you don’t solve a real job, relevance is gone
Job first → Then scale → Add AI.
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The feed is full of AI: hype, slop, “built in a weekend.”
Useful (at times) noise. But it can also hide the only question that decides relevance:
Does the thing solve a real customer job, measurably?
When attention is scarce and choice compresses, people don’t buy “positioning.”
They buy progress:
“Will this get me to the outcome I need?”
“How fast?”
“What happens when it goes wrong?”
Job first → Then scale → Add AI.
If this progress isn’t real, nothing downstream saves you. You scale nothing but disappointment.
JTBD, without jargon
A job is the moment someone is trying to make progress:
“Help me decide without regret.”
“Help me fix this without wasting an evening.”
“Help me trust this enough to commit.”
Todd Irwin’s (Author of De-Positioning | Founder & CEO, Fazer) framing is aligned with this: stop obsessing over differentiation theater. Win by owning the hero pain point, not by shouting “different.”
Why AI makes this more visible
AI doesn’t create the value problem, it just exposes it way faster. When the job is unclear or weak, you see the same patterns:
- The promise inflates because the truth is thin.
- Edge cases multiply because reality doesn’t match the promise.
- More tickets, more escalations, more of “Wait, what did you promise?”
Value layer first: Job → measurable outcome → evidence.
Three real-world gut checks cutting through AI noise
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Dell: people don’t buy “AI”, they buy a better computer
Ahead of CES 2026, Dell leadership explicitly said its product messaging wasn’t “AI-first” anymore, shifting away from the “AI PC” push.
Learning: “AI” is not a job. The job is still battery, speed, reliability, ease, price-to-value.
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Humane AI Pin: big narrative, missing progress
Humane shut down the AI Pin business and sold assets to HP after disappointing reviews and lack of orders.
Learning: A concept doesn’t earn relevance. Only delivered progress does.
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Healthcare: when the job is “safe guidance,” confident wrong becomes a hazard
ECRI ranked misuse of AI chatbots as the top health technology hazard for 2026, warning about risks from incorrect or misleading outputs being used for health decisions.
Learning: In high-stakes jobs, gaps are harm, liability, and trust collapse.
The value-layer test
Answer these to be ready to talk about proof, policies, or voice:
- What job are we solving? One sentence a customer would say.
- How do we know we solved it? One metric.
- Where is the evidence? One artifact or behavior, not a claim.
- When it fails, do we resolve or resist? Cost now versus trust later.
Tool: Promise Card (Job → Metric → Evidence → Owner)
One card per core promise. Keep it ruthless.
- Job: Help me … Backlink: Your values aren’t random vibes. They’re your operating logic.
- Success Metric: We know we solved it when … Backlink: Proof is the new interface — what gets believed.
- Evidence: Where can I see it (page / product behavior / workflow)? Backlink: Policies are brand design.
- Language Guardrails: What we say / don’t say Backlink: If AI can speak for you, your voice is now operational.
- Owner: … + Review Cadence: … Backlink: Your brand is how your systems behave when no one is looking.
If this is hard to fill out, don’t fix messaging. Fix the job.
Where this sits in the series
This is the foundation layer:
→ Solve a real job (value)
→ Make it believable (proof)
→ Make it safe when it breaks (policy/support)
→ Make it coherent under pressure (voice + operating rules)
Closing check
If you can’t name the job, measure it, and point to evidence, you don’t have a positioning problem. You have a relevance problem.
