AI GTM Survey 2026

Two years ago, the loudest argument in software was whether AI would replace salespeople. This year, we asked 111 founders how AI had actually impacted how they sell.

Sentiment is up. Measured productivity is up. The “AI = layoffs” narrative is mostly wrong. And the performance gap between the best and worst GTM teams has become an AI-deployment gap.

Founders are more confident about AI, not less.

Median sentiment about 2026: 7.0, up from 6.7 in 2024.

If you were expecting an “AI disillusionment” cycle, the data doesn’t show one. 63% of founders call AI very useful or transformational. Only 6% call it overhyped or net negative.

Expectations for AI's productivity impact have outrun measurement.

In 2024, when we asked founders a single question about AI’s productivity contribution, the median answer was 10% (with 37% observing zero gain).

In 2026 we split the question. The median founder perceives a 28% productivity gain — but the median measured gain is 10%, unchanged from 2024.

Measurement hasn’t moved. Perceptions have. The new story isn’t convergence — it’s an 18-point gap between what founders feel AI is delivering and what they can actually count.

The top and bottom of the market are pulling apart, and the growth gap can be attributed to AI.

Split respondents into top and bottom quartiles by 2025 ARR growth: the top quartile posts an efficiency score more than 2× the bottom (90 vs 42), spends roughly 4× more on AI tooling per month, and is twice as likely to run hybrid AI rather than pure copilot.

Every company is leveraging AI in some way, and the ones who’ve built sophisticated AI deployments are pulling away from the ones who haven’t.

Efficiency score = net new ARR divided by total burn. If your company added $10M in ARR and spent $15M to achieve that new ARR, the score is 0.67.

The “AI as layoff tool” narrative is mostly wrong.

When asked about their primary AI investment objective, our respondents shared that:

41% — Improve rep productivity without changing headcount
39% — Increase revenue / pipeline
10% — Haven’t made a significant AI investment
7% — Reduce headcount / costs

Only 7% of founders frame AI as a cost-cut play. And the companies cutting SDRs aren’t outperforming, they’re underperforming. Median ARR growth for HC-cutters is 20%; for HC-growers, 40%. The companies winning are adding SDRs and leaning on AI to make them more productive. The companies cutting are doing so because their growth has stalled.

Buyer AI doesn't slow deals, but it does change the objections.

We expected buyer-side AI — AI-generated RFPs, automated evaluations — to lengthen sales cycles. It didn’t. Cycle change medians are 0% across every level of buyer AI usage.

But it did reshape objections: when buyers use AI in 25%+ of deals, “show me the AI ROI” goes from 0% to 36% of objections. “We’re building this internally with AI” goes from 38% to 50%. But the fear that AI will replace the product actually drops from 31% to 23%. Once buyers have used AI themselves, they become less worried about it as an existential threat to vendors.

If you sell to AI-fluent buyers, your most important assets are now (1) measurable ROI proof and (2) a credible integration story.

What we'd watch in 2027

A few open questions this survey raised.

Outcome-based pricing sits at 12%. Will it double?
“Tried autonomous, reverted” is a 4% cohort today. Will it grow?
Will the top/bottom efficiency gap (90 vs 42) become structural?
Will buyer AI use cross 50%? Today only 22% of founders report encountering it often (defined as, in over 25% of their deals).

Question by question

The full distribution behind every finding.

All 25 survey questions, charted. The “Key cohort cuts” set below shows the variables we used to split respondents into the buckets behind each finding above.

Key cohort cuts

The variables behind the five findings.

How respondents split on AI deployment, AI spend, buyer-side AI exposure, headcount change, primary AI objective, and 2025 growth.

Question asked

“How would you describe your AI deployment model for sales development?”

Question asked

“What is your estimated monthly spend on AI GTM tools?”

Question asked

“How often do you encounter buyers using AI tools in the purchasing process (AI-generated RFPs, automated evaluations, AI negotiation)?”

Question asked

“How has your SDR/BDR headcount changed in the last 12 months?”

Question asked

“What was the primary objective of your AI GTM investment?”

Question asked

“How much did your ARR grow in 2025 as a percent?”

Part 1

Company basics.

Who responded: 78% Software/SaaS, 81% selling Mid-Market or Enterprise.

Question asked

“What sector is your startup in?”

Question asked

“How much venture capital have you raised to date in millions of USD?”

Question asked

“What is your average annual contract value (average ACV)?”

Question asked

“Derived: ACV bucket distribution compared to the 2024 survey (Q3).”

Question asked

“Which size of customer do you mostly target?”

Question asked

“Who is your primary buyer?”

Question asked

“What is your primary pricing structure?”

Question asked

“How many people work at your company?”

Part 2

Sentiment & growth.

Founders are more bullish on 2026 than they were on 2024.

Question asked

“On a scale of 0 to 10, how are you feeling about 2026?”

Question asked

“How much did your ARR grow in 2025 as a percent?”

Question asked

“How much will your ARR grow in 2026 as a percent?”

Part 3

Efficiency & sales metrics.

Median efficiency is flat year over year — but per-respondent, the distribution is bimodal: 32% improved, 40% declined. Cycles are flat overall.

Question asked

“What was your efficiency score in early 2025?”

Question asked

“What was your efficiency score in early 2026?”

Question asked

“Derived: per-respondent change between Q11 and Q12.”

Question asked

“How much did your sales cycles lengthen between early 2025 to 2026 in %?”

Question asked

“How much has lead-to-close conversion rate changed (percent) between early 2025 & 2026?”

Question asked

“How much have quotas changed (percent) from early 2025 to 2026?”

Question asked

“How much had NDR (Net Dollar Retention) changed from 2025 to 2026?”

Part 4

AI deployment & spend.

Almost everyone has AI in sales now — the differentiator is how it’s deployed.

Question asked

“How would you describe your AI deployment model for sales development?”

Question asked

“What is your estimated monthly spend on AI GTM tools?”

Question asked

“What percent productivity improvement do you PERCEIVE AI has delivered to your GTM team?”

Question asked

“What percent productivity improvement have you actually MEASURED from AI in your GTM team?”

Part 5

AI impact on headcount & strategy.

Most teams are holding SDR headcount steady — and only 7% see AI as a cost-cut play.

Question asked

“How has your SDR/BDR headcount changed in the last 12 months?”

Question asked

“What was the primary objective of your AI GTM investment?”

Question asked

“Which statement best describes your current view of AI’s impact on GTM?”

Part 6

Buyer-side AI.

Buyer AI doesn’t slow deals — it changes what sellers defend against.

Question asked

“How often do you encounter buyers using AI tools in the purchasing process (AI-generated RFPs, automated evaluations, AI negotiation)?”

Question asked

“Which AI-related objections are you hearing from buyers? (select all that apply)”

111 responses received, 105 analyzed after data-quality cleaning. Fielded Q2 2026. The cleaning dropped 5 responses with internal contradictions (e.g., 2-person companies reporting “Cut SDRs by >50%”) and standardized 7 entries with obvious data-entry errors (e.g., VC raised entered as raw dollars instead of millions, productivity gains entered >100%).

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Software/SaaS dominates the 2026 sample
Label	Value printed in chart
Software/SaaS/Other	79%
Infrastructure/IaaS	8%
Consumer Service	4%
Web3/Crypto	1%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

81% of respondents sell Mid-Market or Enterprise
Label	Value printed in chart
Very Small Businesses	4%
Small Businesses	18%
Mid-Market	40%
Enterprises	38%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Operations is the most common buyer at 22%
Label	Value printed in chart
Operations	21%
Other	18%
Engineering	10%
IT	10%
Marketing	9%
HR	8%
Sales	8%
Finance	8%
Product	4%
Customer Support	3%
Legal	1%
Customer Success	1%

N = 89. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Outcome-based pricing reaches a meaningful 13% tail
Label	Value printed in chart
Usage Based	30%
Seat Based	22%
Combination of Seat & Usage	22%
Outcome Based (pay per result)	11%

N = 79. Values transcribed from the original chart labels; horizontal axis: % of respondents. Subtitle: per-respondent efficiency change, early 2025 → early 2026; 33% improved; 39% declined.

Efficiency split: improvers, decliners, and a big "flat" middle
Label	Value printed in chart
Down >25%	14%
Down 10–25%	18%
Down 2–10%	8%
Flat (±2%)	28%
Up 2–10%	3%
Up 10–25%	13%
Up >25%	18%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Copilot is consensus; pure autonomous SDRs remain rare
Label	Value printed in chart
Copilot (AI + human)	48%
Hybrid (some AI autonomous)	35%
No AI in SDR	10%
Tried autonomous, reverted	4%
Fully autonomous	3%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents. Monthly spend in USD; subtitle says 50% sit in the 500–10K/mo range.

Most companies spend 500to10K per month on AI GTM tools
Label	Value printed in chart
< $500	18%
500 – 2,000	29%
2,000 – 10,000	24%
10,000 – 50,000	13%
$50,000 +	9%
Don't know	8%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Most teams held SDR/BDR headcount steady
Label	Value printed in chart
Cut by >50%	12%
Cut by 25–50%	8%
Cut by 10–24%	6%
Roughly the same	54%
Grew by 10–24%	13%
Grew by >25%	7%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

AI is a revenue and productivity bet — only 9% cite cost cuts
Label	Value printed in chart
Improve rep productivity without changing headcount	41%
Increase revenue / pipeline	38%
We haven't made a significant AI GTM investment	10%
Reduce headcount / costs	8%
Improve buyer experience	3%

N = 103. Values transcribed from the original chart labels; horizontal axis: % of respondents.

65% of founders call AI very useful or transformational; only 6% negative
Label	Value printed in chart
Transformational	16%
Very useful	49%
Incrementally helpful	27%
Overhyped	5%
Net negative	2%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Buyer-side AI is still a minority experience for sellers
Label	Value printed in chart
Never	15%
Rarely (<10% of deals)	41%
Sometimes (10–25% of deals)	23%
Often (25–50% of deals)	13%
Very often (>50% of deals)	8%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents. Multi-select question.

"We’re building this internally with AI" is now the dominant buyer friction
Label	Value printed in chart
"We’re building this internally with AI"	36%
"Show me the AI ROI / prove the AI works"	30%
None of the above	27%
"Does this integrate with our AI agent stack?"	18%
"We need to evaluate AI-native alternatives"	14%
"AI will replace this product"	12%

ACV distribution, 2024 vs 2026; under-$25K share shrank, mid-market grew.

Median ACV climbed from 35Kto50K between 2024 and 2026
ACV bucket	2024 (N=177)	2026 (N=94)
<$5K	17%	13%
5K–25K	29%	20%
25K–100K	26%	33%
100K–500K	24%	30%
$500K+	4%	4%

N = 105. Median 7.0; 2024 baseline 6.7. Sentiment: 0 = bearish, 10 = bullish. Counts printed above bars.

Founders are more confident about 2026 than they were about 2024
Sentiment score	Respondents
0	0
1	0
2	1
3	4
4	9
5	10
6	15
7	20
8	28
9	12
10	6

Median efficiency: 90 vs 42; mean AI spend: $17,060 vs $4,534 per month.

Top-quartile teams convert burn into ARR twice as efficiently, and spend ~4× more on AI to do it
Measure	Cohort	N	Value printed in chart
Median efficiency score	Top quartile	23	90
Median efficiency score	Bottom quartile	20	42
Mean monthly AI GTM spend	Top quartile	24	$17,060
Mean monthly AI GTM spend	Bottom quartile	23	$4,534

AI deployment mix by 2025 ARR growth quartile; bottom quartile is stuck on copilot.

Top growth companies are 2× more likely to run hybrid AI
Deployment	Top quartile	Bottom quartile
Copilot	38%	65%
Hybrid	50%	17%
No AI	0%	9%
Fully autonomous	4%	4%
Tried & reverted	8%	4%

High-frequency buyer AI (N=22) vs low-frequency (N=16); ROI proof + build-vs-buy surge.

Buyer objections shift dramatically when buyers are using AI in deals
Objection	High-frequency buyer AI	Low-frequency buyer AI
AI will replace this product	23%	25%
Integrate with our AI agent stack?	23%	12%
We’re building this internally with AI	50%	38%
Show me the AI ROI / prove it works	36%	12%
Evaluate AI-native alternatives	5%	19%
None of the above	14%	44%

N = 105. Values transcribed from the original chart labels; horizontal axis: % of respondents.

Only 7% of founders cite cost cuts as their primary AI objective
Label	Value printed in chart
Increase revenue / pipeline	38%
Rep productivity, no HC change	41%
No significant AI investment	10%
Improve buyer experience	3%
Reduce headcount / costs	8%

Median 2025 ARR growth, by SDR/BDR headcount change.

Companies cutting SDRs underperform companies growing them by 2×
Headcount change	N	Median ARR growth 2025
HC-Cutters	27	20%
HC-Same	57	32%
HC-Growers	21	40%