Benchmarks

GTM Benchmarks

Workflow dependence, tool enablement, use-case adoption, business impact, the GTM motion shift, and the commercial org’s AI operating model and talent mix.

Georgian + NewtonX AI, Applied Benchmarks, Wave 3 · GTM Leadership n=249 unless noted · March–April 2026

Benchmark 3.1

GTM workflow AI dependence

Survey question

What share of your current GTM tech stack workflows depend on AI features?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Share of GTM workflows that depend on AI, Wave 3

87%

of GTM teams surveyed have half or fewer workflows dependent on AI — individual adoption is wide, but deeper integration into cross-functional workflows remains shallow

GTM Leadership · n=249 · Wave 3

Data for Share of GTM workflows that depend on AI, Wave 3
76–100%51–75%26–50%1–25%0% — none
Share of GTM teams2%9%34%52%1%
Share of GTM teams9%34%52%0%100%
76–100%51–75%26–50%1–25%0% — none

GTM Leadership · n=249 · Wave 3

Benchmark 3.2

GTM tool AI enablement

Survey question

Of the tools currently employed by your GTM team, which best describes their level of AI enablement?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
GTM tools rated advanced or fully AI-enabled, Wave 3

59%

rate general-purpose AI tools as advanced or fully AI-enabled — along with content creation tools at 51%, the only categories crossing the 50% threshold of the 13 measured

GTM Leadership · n=249 · Wave 3

Data for GTM tools rated advanced or fully AI-enabled, Wave 3
Category% advanced + fully AI-enabled
General-purpose AI tools (ChatGPT, Claude, Gemini)59%
Content creation tools51%
AI workflow automation platforms48%
Demand-side platform (DSP)43%
Vibe coding solutions (low/no-code)43%
Marketing automation platform41%
AI search optimization (GEO/SAO)38%
Sales messaging and enablement tools38%
Customer success tools36%
Social media tools32%
Lead scoring and enrichment tools31%
CRM platform31%
Website optimization28%
0%20%40%60%General-purpose AI tools (ChatGPT, Claude,Gemini)General-purpose AI tools (ChatGPT, Claude, Gemini) — % advanced + fully AI-enabled: 59%59%Content creation toolsContent creation tools — % advanced + fully AI-enabled: 51%51%AI workflow automation platformsAI workflow automation platforms — % advanced + fully AI-enabled: 48%48%Demand-side platform (DSP)Demand-side platform (DSP) — % advanced + fully AI-enabled: 43%43%Vibe coding solutions (low/no-code)Vibe coding solutions (low/no-code) — % advanced + fully AI-enabled: 43%43%Marketing automation platformMarketing automation platform — % advanced + fully AI-enabled: 41%41%AI search optimization (GEO/SAO)AI search optimization (GEO/SAO) — % advanced + fully AI-enabled: 38%38%Sales messaging and enablement toolsSales messaging and enablement tools — % advanced + fully AI-enabled: 38%38%Customer success toolsCustomer success tools — % advanced + fully AI-enabled: 36%36%Social media toolsSocial media tools — % advanced + fully AI-enabled: 32%32%Lead scoring and enrichment toolsLead scoring and enrichment tools — % advanced + fully AI-enabled: 31%31%CRM platformCRM platform — % advanced + fully AI-enabled: 31%31%Website optimizationWebsite optimization — % advanced + fully AI-enabled: 28%28%

GTM Leadership · n=249 · Wave 3

Benchmark 3.3

Most valued GTM AI tools

Survey question

What are the AI tools your GTM team finds most valuable? (Top 5 named)

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Most valued GTM AI tools, Wave 3

43%

name Claude as a top-5 most valued GTM AI tool — the single most mentioned tool, with three foundation-model chatbots (Claude, ChatGPT, Gemini) ranking in the top 5 alongside two legacy CRMs

GTM Leadership · n=249 · open-ended, W3 debut · mentions n=106 / 85 / 74 / 72 / 35 / 27 / 27 / 24 / 18 / 14

Data for Most valued GTM AI tools, Wave 3
Category% of GTM leaders naming top-5
Claude / Anthropic43%
Salesforce34%
HubSpot30%
ChatGPT / OpenAI29%
Gemini / Google14%
Gong11%
Clay11%
Copilot / Microsoft10%
ZoomInfo7%
Marketo6%
0%10%20%30%40%50%Claude / AnthropicClaude / Anthropic — % of GTM leaders naming top-5: 43%43%SalesforceSalesforce — % of GTM leaders naming top-5: 34%34%HubSpotHubSpot — % of GTM leaders naming top-5: 30%30%ChatGPT / OpenAIChatGPT / OpenAI — % of GTM leaders naming top-5: 29%29%Gemini / GoogleGemini / Google — % of GTM leaders naming top-5: 14%14%GongGong — % of GTM leaders naming top-5: 11%11%ClayClay — % of GTM leaders naming top-5: 11%11%Copilot / MicrosoftCopilot / Microsoft — % of GTM leaders naming top-5: 10%10%ZoomInfoZoomInfo — % of GTM leaders naming top-5: 7%7%MarketoMarketo — % of GTM leaders naming top-5: 6%6%

GTM Leadership · n=249 · open-ended, W3 debut · mentions n=106 / 85 / 74 / 72 / 35 / 27 / 27 / 24 / 18 / 14

Benchmark 3.4

GTM AI use case adoption

Survey question

What is your current level of AI adoption in each of the following Go-to-Market (GTM) areas?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
AI adoption level by GTM use case, Wave 3

74%

use AI either broadly or for specific use cases in marketing messaging and content — the most widely adopted GTM use case

GTM Leadership · n=249 · Wave 3

Data for AI adoption level by GTM use case, Wave 3
BroadlyLimited use casesPilotingNo adoption yet
Marketing messaging and content40%34%14%12%
High-value research30%34%21%15%
Sales/BDR/SDR messaging & enablement29%30%21%20%
Chatbots (website or in-app)28%19%16%38%
Revenue operations24%39%18%18%
Website or app development21%34%17%28%
Demand generation20%35%22%22%
Marketing workflow automation19%38%22%21%
Lead scoring and segmentation13%33%22%33%
Customer retention, expansion, support11%34%24%31%
Partner/channel marketing support9%22%20%49%
Marketing messagingand content40%34%14%12%High-value research30%34%21%15%Sales/BDR/SDRmessaging & enablement29%30%21%20%Chatbots (website orin-app)28%19%16%38%Revenue operations24%39%18%18%Website or appdevelopment21%34%17%28%Demand generation20%35%22%22%Marketing workflowautomation19%38%22%21%Lead scoring andsegmentation13%33%22%33%Customer retention,expansion, support11%34%24%31%Partner/channelmarketing support9%22%20%49%0%100%
BroadlyLimited use casesPilotingNo adoption yet

GTM Leadership · n=249 · Wave 3

Benchmark 3.5

GTM business impact

Survey question

How has AI adoption impacted each of the following GTM metrics?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap Ch 6 — AI Meet P&L
AI impact on GTM metrics, Wave 3

85% & 81%

report positive AI impact on marketing team efficiency (85%) and SDR/BDR efficiency (81%) — around a 30-point gap versus hard ROI metrics like CAC, NRR, LTV-to-CAC and revenue per rep

GTM Leadership · n=249 · Wave 3 · question format changed between waves, not wave-comparable

Data for AI impact on GTM metrics, Wave 3
Category% positive% negative
Marketing team efficiency85%2%
SDR / BDR efficiency81%2%
Sales cycle velocity73%3%
Conversion rates58%1%
Customer acquisition cost (CAC)54%3%
Net revenue retention48%2%
LTV-to-CAC ratio47%10%
Revenue per rep42%3%
0%20%40%60%80%100%Marketing team efficiencyMarketing team efficiency — % positive: 85%85%Marketing team efficiency — % negative: 2%2%SDR / BDR efficiencySDR / BDR efficiency — % positive: 81%81%SDR / BDR efficiency — % negative: 2%2%Sales cycle velocitySales cycle velocity — % positive: 73%73%Sales cycle velocity — % negative: 3%3%Conversion ratesConversion rates — % positive: 58%58%Conversion rates — % negative: 1%1%Customer acquisition cost (CAC)Customer acquisition cost (CAC) — % positive: 54%54%Customer acquisition cost (CAC) — % negative: 3%3%Net revenue retentionNet revenue retention — % positive: 48%48%Net revenue retention — % negative: 2%2%LTV-to-CAC ratioLTV-to-CAC ratio — % positive: 47%47%LTV-to-CAC ratio — % negative: 10%10%Revenue per repRevenue per rep — % positive: 42%42%Revenue per rep — % negative: 3%3%
% positive% negative

GTM Leadership · n=249 · Wave 3 · question format changed between waves, not wave-comparable

Benchmark 3.6

GTM motion

Survey question

What is your organization’s primary go-to-market motion?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Primary GTM motion, Wave 3

25%

identify account-based as their primary GTM motion — now the leading motion, potentially linked to the marketing and SDR/BDR efficiency gains that help an ABM motion scale

GTM Leadership · n=249 · Wave 3

Data for Primary GTM motion, Wave 3
Category% naming primary
Account-based25%
Partnership-led / channel-led20%
Product-led growth18%
Outbound17%
Inbound12%
Founder-led growth3%
Event-led growth2%
Community-led growth1%
0%10%20%30%40%Account-basedAccount-based — % naming primary: 25%25%Partnership-led / channel-ledPartnership-led / channel-led — % naming primary: 20%20%Product-led growthProduct-led growth — % naming primary: 18%18%OutboundOutbound — % naming primary: 17%17%InboundInbound — % naming primary: 12%12%Founder-led growthFounder-led growth — % naming primary: 3%3%Event-led growthEvent-led growth — % naming primary: 2%2%Community-led growthCommunity-led growth — % naming primary: 1%1%

GTM Leadership · n=249 · Wave 3

Benchmark 3.7

Agentic GTM impact

Survey question

How do you expect agentic AI to impact your GTM function in the next 12 months?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Expected agentic AI impact on the GTM function (next 12 months)

66%

Of GTM Decision Makers expect high or transformational agentic AI impact on their GTM function in the next 12 months — only 8% expect low impact or are uncertain

GTM Leadership · n=249 · Wave 3

Data for Expected agentic AI impact on the GTM function (next 12 months)
TransformationalHighModerateLowUncertain
Expected impact18%48%26%4%4%
Expected impact18%48%26%0%100%
TransformationalHighModerateLowUncertain

GTM Leadership · n=249 · Wave 3

Benchmark 3.8

GTM tool adoption

Survey question

Which of the following types of tools are currently employed by your GTM team in your organization? Select all that apply.

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Tools currently employed by GTM teams, Wave 3

81%

employ general-purpose AI tools for their team — the highest of all 13 tools measured and +9 points higher than CRM, the #2 most employed tool

GTM Leadership · n=249 · Wave 3

Data for Tools currently employed by GTM teams, Wave 3
Category% employing
General-purpose AI tools (ChatGPT, Claude, Gemini)81%
CRM platform72%
Website optimization (SEO, dynamic apps, chatbots)60%
Sales messaging and enablement tools58%
Marketing automation platform55%
Content creation tools54%
AI workflow automation platforms52%
AI search optimization (GEO/SAO)52%
Social media tools49%
Lead scoring and enrichment tools45%
Customer success tools39%
Vibe coding solutions (low/no-code)30%
Demand-side platform (DSP)12%
0%20%40%60%80%100%General-purpose AI tools (ChatGPT, Claude,Gemini)General-purpose AI tools (ChatGPT, Claude, Gemini) — % employing: 81%81%CRM platformCRM platform — % employing: 72%72%Website optimization (SEO, dynamic apps,chatbots)Website optimization (SEO, dynamic apps, chatbots) — % employing: 60%60%Sales messaging and enablement toolsSales messaging and enablement tools — % employing: 58%58%Marketing automation platformMarketing automation platform — % employing: 55%55%Content creation toolsContent creation tools — % employing: 54%54%AI workflow automation platformsAI workflow automation platforms — % employing: 52%52%AI search optimization (GEO/SAO)AI search optimization (GEO/SAO) — % employing: 52%52%Social media toolsSocial media tools — % employing: 49%49%Lead scoring and enrichment toolsLead scoring and enrichment tools — % employing: 45%45%Customer success toolsCustomer success tools — % employing: 39%39%Vibe coding solutions (low/no-code)Vibe coding solutions (low/no-code) — % employing: 30%30%Demand-side platform (DSP)Demand-side platform (DSP) — % employing: 12%12%

GTM Leadership · n=249 · Wave 3

Benchmark 3.9

Agentic autonomy for GTM leaders

Survey question

What is the highest level of autonomy your organization is comfortable allowing AI agents to have?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Highest comfortable agent autonomy level — GTM, Wave 2 vs Wave 3

33%

of GTM leaders are comfortable giving AI agents autonomy — 29 points lower than the technical leaders in this survey, and unchanged in the nine months since Wave 2

GTM Leadership · W2 n=308 → W3 n=249

Data for Highest comfortable agent autonomy level — GTM, Wave 2 vs Wave 3
CategoryWave 2Wave 3
Requires full human oversight18%16%
Recommends, requires approval48%49%
Autonomous in low-risk environments23%25%
Mostly autonomous, escalates critical8%7%
Fully autonomous1%1%
Not sure2%2%
0%20%40%60%Requires full human oversightRequires full human oversight — Wave 2: 18%18%Requires full human oversight — Wave 3: 16%16%Recommends, requires approvalRecommends, requires approval — Wave 2: 48%48%Recommends, requires approval — Wave 3: 49%49%Autonomous in low-risk environmentsAutonomous in low-risk environments — Wave 2: 23%23%Autonomous in low-risk environments — Wave 3: 25%25%Mostly autonomous, escalates criticalMostly autonomous, escalates critical — Wave 2: 8%8%Mostly autonomous, escalates critical — Wave 3: 7%7%Fully autonomousFully autonomous — Wave 2: 1%1%Fully autonomous — Wave 3: 1%1%Not sureNot sure — Wave 2: 2%2%Not sure — Wave 3: 2%2%
Wave 2Wave 3

GTM Leadership · W2 n=308 → W3 n=249

Benchmark 3.10

Agentic barriers

Survey question

Which of the following are the biggest barriers or challenges your organization faces in trusting, scaling, and productizing agentic AI? Select up to 3, ranked.

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Top-3 barriers to agentic adoption — GTM, Wave 3

57%

rank inconsistent or unreliable performance among their top-three barriers to agentic AI adoption — 21 points ahead of the next-closest concern

GTM Leadership · n=249 · Wave 3

Data for Top-3 barriers to agentic adoption — GTM, Wave 3
Category% any top-3 rank% ranked #1
Inconsistent or unreliable performance57%38%
Responsible / trustworthy AI concerns36%13%
Privacy and data security issues35%7%
Regulatory and compliance challenges30%11%
Limited control / governance over AI actions22%6%
Lack of internal expertise or AI talent21%5%
Ambiguous business case or unclear ROI17%4%
Lack of explainability / interpretability14%6%
Legacy systems or integration difficulty14%3%
Lack of standardized frameworks13%2%
High cost of scaling, training, deploying9%2%
Difficulty tracking the right success metrics7%1%
Resistance from employees or leadership6%2%
Job displacement and workforce impact4%0.4%
0%20%40%60%Inconsistent or unreliable performanceInconsistent or unreliable performance — % any top-3 rank: 57%57%Inconsistent or unreliable performance — % ranked #1: 38%38%Responsible / trustworthy AI concernsResponsible / trustworthy AI concerns — % any top-3 rank: 36%36%Responsible / trustworthy AI concerns — % ranked #1: 13%13%Privacy and data security issuesPrivacy and data security issues — % any top-3 rank: 35%35%Privacy and data security issues — % ranked #1: 7%7%Regulatory and compliance challengesRegulatory and compliance challenges — % any top-3 rank: 30%30%Regulatory and compliance challenges — % ranked #1: 11%11%Limited control / governance over AIactionsLimited control / governance over AI actions — % any top-3 rank: 22%22%Limited control / governance over AI actions — % ranked #1: 6%6%Lack of internal expertise or AI talentLack of internal expertise or AI talent — % any top-3 rank: 21%21%Lack of internal expertise or AI talent — % ranked #1: 5%5%Ambiguous business case or unclear ROIAmbiguous business case or unclear ROI — % any top-3 rank: 17%17%Ambiguous business case or unclear ROI — % ranked #1: 4%4%Lack of explainability / interpretabilityLack of explainability / interpretability — % any top-3 rank: 14%14%Lack of explainability / interpretability — % ranked #1: 6%6%Legacy systems or integration difficultyLegacy systems or integration difficulty — % any top-3 rank: 14%14%Legacy systems or integration difficulty — % ranked #1: 3%3%Lack of standardized frameworksLack of standardized frameworks — % any top-3 rank: 13%13%Lack of standardized frameworks — % ranked #1: 2%2%High cost of scaling, training, deployingHigh cost of scaling, training, deploying — % any top-3 rank: 9%9%High cost of scaling, training, deploying — % ranked #1: 2%2%Difficulty tracking the right successmetricsDifficulty tracking the right success metrics — % any top-3 rank: 7%7%Difficulty tracking the right success metrics — % ranked #1: 1%1%Resistance from employees or leadershipResistance from employees or leadership — % any top-3 rank: 6%6%Resistance from employees or leadership — % ranked #1: 2%2%Job displacement and workforce impactJob displacement and workforce impact — % any top-3 rank: 4%4%Job displacement and workforce impact — % ranked #1: 0.4%0.4%
% any top-3 rank% ranked #1

GTM Leadership · n=249 · Wave 3

Benchmark 3.11

Agentic AI expected ROI

Survey question

What is your organization’s primary expected return from investing in agentic AI? Select up to 3, where #1 is most important.

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Expected return from agentic AI, Wave 3

78%

name increased efficiency and automation of workflows among their top-three expected returns from agentic AI — 14 points clear of cost reduction and the only return cited by more than three in four teams

GTM Leadership · n=249 · Wave 3

Data for Expected return from agentic AI, Wave 3
Category% any rank% ranked #1
Increased efficiency and automation of workflows78%38%
Cost reduction and improved resource allocation64%18%
Enhanced decision-making and strategic insights47%14%
Improved customer experience and engagement42%14%
New revenue streams or business models31%12%
0%20%40%60%80%Increased efficiency and automation ofworkflowsIncreased efficiency and automation of workflows — % any rank: 78%78%Increased efficiency and automation of workflows — % ranked #1: 38%38%Cost reduction and improved resourceallocationCost reduction and improved resource allocation — % any rank: 64%64%Cost reduction and improved resource allocation — % ranked #1: 18%18%Enhanced decision-making and strategicinsightsEnhanced decision-making and strategic insights — % any rank: 47%47%Enhanced decision-making and strategic insights — % ranked #1: 14%14%Improved customer experience and engagementImproved customer experience and engagement — % any rank: 42%42%Improved customer experience and engagement — % ranked #1: 14%14%New revenue streams or business modelsNew revenue streams or business models — % any rank: 31%31%New revenue streams or business models — % ranked #1: 12%12%
% any rank% ranked #1

GTM Leadership · n=249 · Wave 3

Benchmark 3.12

Internal AI workflow complexity

Survey question

Thinking specifically about your organization’s internal use of AI, how would you describe the typical complexity of the tasks or workflows AI is applied to?

Audience
GTM Decision Makers · n=249
In depth
Ch 5 — The GTM Efficiency Gap
Complexity of internal AI workflows, Wave 3

9%

apply internal AI to mostly or very complex workflows — 63% sit in a simple-and-complex mix and 27% remain mostly simple, putting internal AI use well short of multi-step, multi-team automation

GTM Leadership · n=249 · Wave 3 · single-select, sums to 100%

Data for Complexity of internal AI workflows, Wave 3
Category%
Very simple tasks (single-step lookups, basic suggestions)4%
Mostly simple tasks (short, well-defined, one tool)23%
Mix of simple and complex workflows63%
Mostly complex workflows (multi-step, multiple tools or sources)8%
Very complex workflows (end-to-end, multi-team)1%
Unsure / don't know1%
0%20%40%60%80%Very simple tasks (single-step lookups,basic suggestions)Very simple tasks (single-step lookups, basic suggestions) — %: 4%4%Mostly simple tasks (short, well-defined,one tool)Mostly simple tasks (short, well-defined, one tool) — %: 23%23%Mix of simple and complex workflowsMix of simple and complex workflows — %: 63%63%Mostly complex workflows (multi-step,multiple tools or sources)Mostly complex workflows (multi-step, multiple tools or sources) — %: 8%8%Very complex workflows (end-to-end,multi-team)Very complex workflows (end-to-end, multi-team) — %: 1%1%Unsure / don't knowUnsure / don't know — %: 1%1%

GTM Leadership · n=249 · Wave 3 · single-select, sums to 100%

Benchmark 3.13

AI operating model (GTM)

Survey question

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

Audience
GTM Decision Makers · n=249
In depth
Ch 7 — The Governance Gap
AI operating model — GTM vs technical leadership, Wave 3

33%

of GTM leaders report no formal AI organization — ad hoc is their single most common operating model, against 27% for technical leaders, who most often report a hybrid structure (Benchmark 5.3)

GTM Leadership n=249 · technical comparison from Benchmark 5.3, Technical Leadership n=252 · Wave 3 · single-select · a further 2% of GTM leaders answered unsure; Benchmark 5.3 reports no unsure band for technical leaders

Data for AI operating model — GTM vs technical leadership, Wave 3
CategoryGTMTechnical
No formal AI organization / ad hoc33%27%
Hybrid (centralized CoE + federated pods)31%33%
Federated AI pods within business units20%14%
Centralized AI Center of Excellence (CoE)16%25%
0%10%20%30%40%No formal AI organization / ad hocNo formal AI organization / ad hoc — GTM: 33%33%No formal AI organization / ad hoc — Technical: 27%27%Hybrid (centralized CoE + federated pods)Hybrid (centralized CoE + federated pods) — GTM: 31%31%Hybrid (centralized CoE + federated pods) — Technical: 33%33%Federated AI pods within business unitsFederated AI pods within business units — GTM: 20%20%Federated AI pods within business units — Technical: 14%14%Centralized AI Center of Excellence (CoE)Centralized AI Center of Excellence (CoE) — GTM: 16%16%Centralized AI Center of Excellence (CoE) — Technical: 25%25%
GTMTechnical

GTM Leadership n=249 · technical comparison from Benchmark 5.3, Technical Leadership n=252 · Wave 3 · single-select · a further 2% of GTM leaders answered unsure; Benchmark 5.3 reports no unsure band for technical leaders

Benchmark 3.14

AI talent blockers (GTM)

Survey question

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

Audience
GTM Decision Makers · n=249
In depth
Ch 8 — The Human Side of the AI Stack
Top-two AI talent blockers — GTM vs technical leadership, Wave 3

55%

cite lack of time or resources for upskilling as a top-two AI talent blocker — the #1 barrier on the GTM side, where technical leaders instead lead with budget (Benchmark 5.2)

GTM Leadership n=249 · technical comparison from Benchmark 5.2, Technical Leadership n=252 · Wave 3

Data for Top-two AI talent blockers — GTM vs technical leadership, Wave 3
CategoryGTM top-2Technical top-2
Lack of time / resources for upskilling55%38%
Budget constraints42%46%
Internal processes (slow hiring, approvals)38%21%
Limited candidate pipeline28%41%
Employer brand not strong enough16%20%
Competition from other employers10%26%
0%20%40%60%Lack of time / resources for upskillingLack of time / resources for upskilling — GTM top-2: 55%55%Lack of time / resources for upskilling — Technical top-2: 38%38%Budget constraintsBudget constraints — GTM top-2: 42%42%Budget constraints — Technical top-2: 46%46%Internal processes (slow hiring, approvals)Internal processes (slow hiring, approvals) — GTM top-2: 38%38%Internal processes (slow hiring, approvals) — Technical top-2: 21%21%Limited candidate pipelineLimited candidate pipeline — GTM top-2: 28%28%Limited candidate pipeline — Technical top-2: 41%41%Employer brand not strong enoughEmployer brand not strong enough — GTM top-2: 16%16%Employer brand not strong enough — Technical top-2: 20%20%Competition from other employersCompetition from other employers — GTM top-2: 10%10%Competition from other employers — Technical top-2: 26%26%
GTM top-2Technical top-2

GTM Leadership n=249 · technical comparison from Benchmark 5.2, Technical Leadership n=252 · Wave 3

Benchmark 3.15

AI talent shifts (GTM)

Survey question

How is your organization adjusting its commercial / go-to-market talent mix in response to AI adoption? Select all that apply.

Audience
GTM Decision Makers · n=249
In depth
Ch 8 — The Human Side of the AI Stack
GTM talent adjustments vs technical leadership, Wave 3

16% v 29%

only 16% of GTM leaders are reducing entry-level positions against 29% of technical leaders (Benchmark 5.1) — AI is reshaping the technical org’s junior pipeline far faster than the commercial one

GTM Leadership n=249 · technical comparison from Benchmark 5.1, Technical Leadership n=252 · Wave 3 · multi-select, 1.59 selections per respondent on average

Data for GTM talent adjustments vs technical leadership, Wave 3
CategoryGTMTechnical
Upskilling existing staff on AI43%52%
Hiring more AI-skilled staff36%48%
Holding entry-level hiring flat27%20%
Increasing entry-level hiring with a higher AI skill bar22%23%
Reducing entry-level positions16%29%
No meaningful changes14%9%
0%20%40%60%Upskilling existing staff on AIUpskilling existing staff on AI — GTM: 43%43%Upskilling existing staff on AI — Technical: 52%52%Hiring more AI-skilled staffHiring more AI-skilled staff — GTM: 36%36%Hiring more AI-skilled staff — Technical: 48%48%Holding entry-level hiring flatHolding entry-level hiring flat — GTM: 27%27%Holding entry-level hiring flat — Technical: 20%20%Increasing entry-level hiring with a higherAI skill barIncreasing entry-level hiring with a higher AI skill bar — GTM: 22%22%Increasing entry-level hiring with a higher AI skill bar — Technical: 23%23%Reducing entry-level positionsReducing entry-level positions — GTM: 16%16%Reducing entry-level positions — Technical: 29%29%No meaningful changesNo meaningful changes — GTM: 14%14%No meaningful changes — Technical: 9%9%
GTMTechnical

GTM Leadership n=249 · technical comparison from Benchmark 5.1, Technical Leadership n=252 · Wave 3 · multi-select, 1.59 selections per respondent on average