The AI talent shortage is narrower than it looks
A 40 minute session on why most AI roadmaps stall on roughly six unfillable roles, and how to structure a small applied team that actually gets to production.
Why the shortage is narrow, and where it actually sits
Most companies are not short of AI talent broadly. They are short of a small number of people who have kept a machine learning system working in production.
This session covers where the applied AI talent shortage genuinely sits, why the usual responses such as raising compensation or hiring more juniors do not resolve it, and how to structure a small team so that the roles which are actually scarce are used correctly.
Aimed at engineering and product leaders with an AI roadmap that has stalled on hiring, and at anyone planning a first production AI capability who wants to avoid staffing it as a modelling exercise when it is mostly a data and evaluation exercise.
Four things this session covers directly
Where the shortage actually sits
Why it is concentrated in senior applied engineers with production experience, not across AI roles generally.
Why the usual fixes do not work
Raising compensation redistributes the same population rather than growing it, and why widening the geography is the lever that actually works.
The roles a small team really needs
Why data engineers usually outnumber machine learning engineers, and why evaluation ownership is the role most often missing.
The hiring sequence that works
Why the senior applied lead has to be hired before the supporting layer, and what goes wrong when it is not.
Your host for this session
The gap is never as many roles as people think. Most AI roadmaps are blocked on two to four people, and the mistake is building a plan around eight or ten and then wondering why none of the roles close.
Rishi works with clients on team composition for applied AI mandates, translating a capability requirement into the specific roles and seniority it actually needs rather than a generic AI hiring plan.
Built for people planning the team
- Engineering and product leaders with an AI roadmap that has stalled on senior hiring
- Anyone planning a first production AI capability and unsure how to size the team
- Talent and HR leaders supporting an AI hiring plan that keeps missing its own targets
- Leaders considering whether to widen the geography of a stalled search
Frequently asked questions
Is this specific to a particular AI use case?
No. The talent structure applies whether the mandate is a recommendation system, an agentic workflow or a predictive model. The session focuses on team composition and hiring, not on a specific technical approach.
Will there be a recording?
Yes, sent to everyone who registers whether or not they attend live.
Do you cover agentic AI teams specifically?
The core structure applies to both, and we will note where agentic systems need an additional role for evaluation and containment, which is a common gap in existing plans.
Is this useful if we already have an AI team in place?
Yes, particularly if that team is struggling to reach production. The session covers the sequencing and ratio mistakes that are common even in teams that are already hired.
Can I send questions in advance?
Yes, include them at registration and we will prioritise covering them during the session.
Related reading
Register for this session
Save your spot for October 1st. If you cannot attend live, register anyway and we will send the recording.