Your engineers adopted AI in a weekend. The rest of the company did not.
Technology companies assume they are AI native because engineering is. Human+ measures every function, so the gap between engineering and go-to-market stops being invisible.
For companies that sell AI capability and need their own adoption story to hold up.
Inside The AI Native Company Audit
- Why engineering adoption is the worst proxy for company adoption
- A function-by-function readiness scorecard
- How to evidence your own AI capability to customers and investors
- A 90 day plan for your lowest scoring function
Free ebook
The AI Native Company Audit
Personalized to your organization and emailed to you in minutes.
The pattern we see
In software companies, engineering typically scores a full tier above sales, support and finance on the same behavioral scale.
Where AI adoption succeeds or stalls in technology
Engineering productivity beyond code completion
The gains that last come from review, testing and documentation behavior, not from acceptance rates in the editor.
Go-to-market content and outbound
Sales and marketing teams can multiply output, and quickly hit a judgment problem about accuracy that scoring surfaces early.
Customer support deflection and quality
Support is often the clearest measurable win in the company, and the function most exposed if judgment is weak.
Finance, legal and operations
The quiet functions where adoption is lowest and the hours available are largest.
Five behaviors, measured directly
Human+ scores every employee on five behavioral dimensions in about fifteen minutes. Here is what each one means for your teams.
AI Fluency
Does every function, not just engineering, know how to get real value from the tools?
Judgment
Can a support or sales rep tell a confident wrong answer from a correct one before a customer sees it?
Adaptability
How quickly do non-engineering teams change a working process?
Collaboration
Does engineering's practice reach the rest of the company?
Ethical Reasoning
Are people clear on customer data boundaries and what may be shared with a model?
What changes when you can see it
Measuring every function at once turns an assumption about being AI native into an evidenced position you can share with your board and your buyers.
1 tier
typical gap between engineering and go-to-market
Per function
scoring so investment goes where the gap is
15 min
per employee, once per cycle
Reading for technology leaders
See your own technology baseline.
Start with a free audit, or walk through the platform with our team.