Benchmarks

Talent & Operating Model Benchmarks

Talent shifts, hiring blockers, the AI operating model, workflow complexity, and agentic implementation status.

Georgian + NewtonX AI, Applied Benchmarks, Wave 3 · Technical Leadership n=252 unless noted · March–April 2026

Benchmark 5.1

AI talent shifts

Survey question

How is your organization adjusting its talent mix in response to AI adoption? Select all that apply.

Audience
Technical Decision Makers · n=252 (GTM Decision Makers n=249 for comparison)
In depth
Ch 8 — The Human Side of the AI Stack
Talent-mix adjustments in response to AI — Tech vs GTM

52%

of technical leaders are upskilling existing staff — the most common talent response, with entry-level reduction at 29% for Tech against only 16% for GTM

Technical n=252 · GTM n=249 · Wave 3

Data for Talent-mix adjustments in response to AI — Tech vs GTM
CategoryTechGTM
Upskilling existing staff on AI52%43%
Hiring more AI-skilled staff48%36%
Reducing entry-level positions29%16%
Raising the entry-level AI skill bar23%22%
Holding entry-level hiring flat20%27%
No meaningful changes9%14%
0%20%40%60%Upskilling existing staff on AIUpskilling existing staff on AI — Tech: 52%52%Upskilling existing staff on AI — GTM: 43%43%Hiring more AI-skilled staffHiring more AI-skilled staff — Tech: 48%48%Hiring more AI-skilled staff — GTM: 36%36%Reducing entry-level positionsReducing entry-level positions — Tech: 29%29%Reducing entry-level positions — GTM: 16%16%Raising the entry-level AI skill barRaising the entry-level AI skill bar — Tech: 23%23%Raising the entry-level AI skill bar — GTM: 22%22%Holding entry-level hiring flatHolding entry-level hiring flat — Tech: 20%20%Holding entry-level hiring flat — GTM: 27%27%No meaningful changesNo meaningful changes — Tech: 9%9%No meaningful changes — GTM: 14%14%
TechGTM

Technical n=252 · GTM n=249 · Wave 3

Benchmark 5.2

AI talent blockers

Survey question

What are the top two blockers your organization faces in acquiring or upskilling technical talent for AI/ML? Rank up to 2, where #1 is the biggest blocker.

Audience
Technical Decision Makers · n=252
In depth
Ch 8 — The Human Side of the AI Stack
Top blockers to acquiring or upskilling AI/ML talent

46%

cite budget constraints as a top-2 blocker — the #1 barrier to AI talent acquisition, ahead of pipeline scarcity (41%) and upskilling capacity (38%)

Technical Leadership · n=252 · Wave 3

Data for Top blockers to acquiring or upskilling AI/ML talent
Category% ranked #1% in top 2
Budget constraints33%46%
Limited candidate pipeline27%41%
Lack of time / resources for upskilling15%38%
Competition from Big Tech / startups12%26%
Internal processes (slow hiring, approvals)4%21%
Employer brand not strong enough9%20%
0%20%40%60%Budget constraintsBudget constraints — % ranked #1: 33%33%Budget constraints — % in top 2: 46%46%Limited candidate pipelineLimited candidate pipeline — % ranked #1: 27%27%Limited candidate pipeline — % in top 2: 41%41%Lack of time / resources for upskillingLack of time / resources for upskilling — % ranked #1: 15%15%Lack of time / resources for upskilling — % in top 2: 38%38%Competition from Big Tech / startupsCompetition from Big Tech / startups — % ranked #1: 12%12%Competition from Big Tech / startups — % in top 2: 26%26%Internal processes (slow hiring, approvals)Internal processes (slow hiring, approvals) — % ranked #1: 4%4%Internal processes (slow hiring, approvals) — % in top 2: 21%21%Employer brand not strong enoughEmployer brand not strong enough — % ranked #1: 9%9%Employer brand not strong enough — % in top 2: 20%20%
% ranked #1% in top 2

Technical Leadership · n=252 · Wave 3

Benchmark 5.3

AI operating model

Survey question

How is AI ownership and delivery primarily structured in your organization today?

Audience
Technical Decision Makers · n=252
In depth
Ch 7 — The Governance Gap Ch 8 — The Human Side of the AI Stack
How AI ownership and delivery is structured, Wave 3

33%

have no formal AI organization — at odds with the 93% who rank AI a top-5 strategic priority, suggesting AI strategy and AI governance are advancing on very different timelines

Technical Leadership · n=252 · Wave 3 · single-select · GTM leaders answering the same question are charted in Benchmark 3.13

Data for How AI ownership and delivery is structured, Wave 3
Category% of organizations
No formal AI organization / ad hoc33%
Hybrid (centralized CoE + federated pods)31%
Federated AI pods within business units20%
Centralized AI Center of Excellence16%
Unsure / don't know2%
0%10%20%30%40%No formal AI organization / ad hocNo formal AI organization / ad hoc — % of organizations: 33%33%Hybrid (centralized CoE + federated pods)Hybrid (centralized CoE + federated pods) — % of organizations: 31%31%Federated AI pods within business unitsFederated AI pods within business units — % of organizations: 20%20%Centralized AI Center of ExcellenceCentralized AI Center of Excellence — % of organizations: 16%16%Unsure / don't knowUnsure / don't know — % of organizations: 2%2%

Technical Leadership · n=252 · Wave 3 · single-select · GTM leaders answering the same question are charted in Benchmark 3.13

Benchmark 5.4

AI workflow complexity

Survey question

How would you describe the typical complexity of the tasks or workflows AI is applied to internally in your organization?

Audience
Technical Decision Makers · n=252
In depth
Ch 3 — What Makes a Runner Ch 8 — The Human Side of the AI Stack
Typical complexity of internal AI workflows, Wave 3

22%

operate mostly or very complex AI workflows — the majority (57%) are in the mixed band, indicating the market has not yet broadly crossed into end-to-end agentic operations

Technical Leadership · n=252 · Wave 3

Data for Typical complexity of internal AI workflows, Wave 3
Very complexMostly complexMix of simple and complexMostly simpleVery simple
Share of technical leaders6%17%57%19%2%
Share of technical leaders17%57%19%0%100%
Very complexMostly complexMix of simple and complexMostly simpleVery simple

Technical Leadership · n=252 · Wave 3

Benchmark 5.5

Agentic AI implementation status

Survey question

To what extent is your organization implementing or planning to implement agentic AI in the next 6 months?

Audience
Technical Decision Makers · n=252
In depth
Ch 2 — The Agentic Divide Quantified Ch 3 — What Makes a Runner
Agentic AI implementation status, Wave 3

60%

have implemented or are currently implementing agentic AI — only 2% have no plans, confirming agentic deployment has crossed the majority threshold across B2B software

Technical Leadership · n=252 · Wave 3

Data for Agentic AI implementation status, Wave 3
Implemented & expandingCurrently implementingIndividual users onlyPlanning in 6 monthsConsideringNo plans
Share of technical leaders32%29%20%11%6%2%
Share of technical leaders32%29%20%11%0%100%
Implemented & expandingCurrently implementingIndividual users onlyPlanning in 6 monthsConsideringNo plans

Technical Leadership · n=252 · Wave 3