Benchmarks

AI Strategy

Maturity, priorities, models, infrastructure, and ROI — the strategic baseline benchmarks for the full sample.

Georgian + NewtonX AI, Applied Benchmarks, Wave 3 · All respondents n=501 unless noted · March–April 2026

Benchmark 1.1

AI maturity distribution

Survey question

How would you describe your organization’s current AI adoption maturity?

Audience
All respondents · n=501
In depth
Ch 1 — AI Is Eating Your Strategy Ch 3 — What Makes a Runner
AI maturity distribution, Wave 3

63%

of B2B software executives surveyed are past the experimentation threshold — Jogger or Runner tier

All respondents · n=501 · Wave 3 · Runner n=110 / Jogger n=204 / Walker n=150 / Crawler n=37

Data for AI maturity distribution, Wave 3
Category% of organizations
Runner22%
Jogger41%
Walker30%
Crawler7%
0%10%20%30%40%50%RunnerRunner — % of organizations: 22%22%JoggerJogger — % of organizations: 41%41%WalkerWalker — % of organizations: 30%30%CrawlerCrawler — % of organizations: 7%7%

All respondents · n=501 · Wave 3 · Runner n=110 / Jogger n=204 / Walker n=150 / Crawler n=37

Benchmark 1.2

AI strategic priorities

Survey question

Which of the following are among your organization’s top 5 strategic priorities?

Audience
All respondents · n=501
In depth
Ch 1 — AI Is Eating Your Strategy
Top-5 strategic priorities, Wave 3

83%

selected AI for internal efficiency as a top-5 organizational priority — the most-selected priority in the survey

All respondents · n=501 · Wave 3 · sorted by share naming each priority

Data for Top-5 strategic priorities, Wave 3
Category% naming top-5
AI for efficiency83%
Build AI internally65%
Market expansion47%
Deploy external AI43%
Brand development37%
Customer experience36%
Tech stack upgrade34%
Revenue diversification31%
M&A activity20%
Cybersecurity19%
Restructuring14%
Agile transformation12%
Data privacy12%
Employee engagement10%
Supply chain5%
Sustainability3%
0%20%40%60%80%100%AI for efficiencyAI for efficiency — % naming top-5: 83%83%Build AI internallyBuild AI internally — % naming top-5: 65%65%Market expansionMarket expansion — % naming top-5: 47%47%Deploy external AIDeploy external AI — % naming top-5: 43%43%Brand developmentBrand development — % naming top-5: 37%37%Customer experienceCustomer experience — % naming top-5: 36%36%Tech stack upgradeTech stack upgrade — % naming top-5: 34%34%Revenue diversificationRevenue diversification — % naming top-5: 31%31%M&A activityM&A activity — % naming top-5: 20%20%CybersecurityCybersecurity — % naming top-5: 19%19%RestructuringRestructuring — % naming top-5: 14%14%Agile transformationAgile transformation — % naming top-5: 12%12%Data privacyData privacy — % naming top-5: 12%12%

All respondents · n=501 · Wave 3 · sorted by share naming each priority

Benchmark 1.3

Most advanced AI model in production

Survey question

What is the most advanced AI model approach your organization uses in production?

Audience
All respondents · n=501
In depth
Ch 3 — What Makes a Runner
Most advanced AI model approach in production, Wave 3

42%

use multi-step or multi-hop agentic API calls — the most common most-advanced approach in production

All respondents · n=501 · Wave 3

Data for Most advanced AI model approach in production, Wave 3
Category% of organizations
Multi-step / multi-hop calls (agentic)42%
Custom-trained small models (<3B)20%
Fine-tuned large models (>3B)16%
Single API calls11%
Custom-trained large models (>3B)7%
POCs only — none in production3%
No AI models1%
0%10%20%30%40%50%Multi-step / multi-hop calls (agentic)Multi-step / multi-hop calls (agentic) — % of organizations: 42%42%Custom-trained small models (<3B)Custom-trained small models (<3B) — % of organizations: 20%20%Fine-tuned large models (>3B)Fine-tuned large models (>3B) — % of organizations: 16%16%Single API callsSingle API calls — % of organizations: 11%11%Custom-trained large models (>3B)Custom-trained large models (>3B) — % of organizations: 7%7%POCs only — none in productionPOCs only — none in production — % of organizations: 3%3%No AI modelsNo AI models — % of organizations: 1%1%

All respondents · n=501 · Wave 3

Benchmark 1.4

AI infrastructure stack

Survey question

Which AI infrastructure components does your organization currently use?

Audience
Technical Decision Makers · n=252
In depth
Ch 3 — What Makes a Runner Ch 4 — The Agent Engineer
AI infrastructure components in use, Wave 3

48–53%

adoption across queues, data orchestration, vector databases and LLM observability — the four components that define a minimum viable production AI stack

Technical Leadership · n=252 · Wave 3

Data for AI infrastructure components in use, Wave 3
Category% adopted
Queues / background jobs53%
Data orchestration pipelines52%
Vector databases50%
LLM observability & evaluation48%
Serverless inference infra31%
Inference services31%
Durable workflow engines22%
Headless browser automation20%
Agentic auth19%
0%20%40%60%Queues / background jobsQueues / background jobs — % adopted: 53%53%Data orchestration pipelinesData orchestration pipelines — % adopted: 52%52%Vector databasesVector databases — % adopted: 50%50%LLM observability & evaluationLLM observability & evaluation — % adopted: 48%48%Serverless inference infraServerless inference infra — % adopted: 31%31%Inference servicesInference services — % adopted: 31%31%Durable workflow enginesDurable workflow engines — % adopted: 22%22%Headless browser automationHeadless browser automation — % adopted: 20%20%Agentic authAgentic auth — % adopted: 19%19%

Technical Leadership · n=252 · Wave 3

Benchmark 1.5

AI model types in use

Survey question

Which of the following AI model types does your organization currently use?

Audience
Technical Decision Makers · n=252
In depth
Ch 3 — What Makes a Runner
AI model types in use, Wave 3

84%

use large language models — now the baseline floor; where an organization sits above it defines the capability frontier

Technical Leadership · n=252 · Wave 3

Data for AI model types in use, Wave 3
Category% in use
Large language models84%
Multimodal models51%
Large reasoning models44%
Open-source / open-weight41%
Voice models25%
Model routers17%
Diffusion LLMs16%
Vision foundation models14%
Vision-language-action models14%
Video foundation models9%
World models9%
0%20%40%60%80%100%Large language modelsLarge language models — % in use: 84%84%Multimodal modelsMultimodal models — % in use: 51%51%Large reasoning modelsLarge reasoning models — % in use: 44%44%Open-source / open-weightOpen-source / open-weight — % in use: 41%41%Voice modelsVoice models — % in use: 25%25%Model routersModel routers — % in use: 17%17%Diffusion LLMsDiffusion LLMs — % in use: 16%16%Vision foundation modelsVision foundation models — % in use: 14%14%Vision-language-action modelsVision-language-action models — % in use: 14%14%Video foundation modelsVideo foundation models — % in use: 9%9%World modelsWorld models — % in use: 9%9%

Technical Leadership · n=252 · Wave 3

Benchmark 1.6

AI UX strategy

Survey question

How does your organization primarily incorporate AI into its user experience?

Audience
Technical Decision Makers · n=252
In depth
Ch 3 — What Makes a Runner
How AI is incorporated into the user experience, Wave 3

55%

use contextual assistance — AI embedded behind fixed UI elements — the most common UX pattern in production

Technical Leadership · n=252 · Wave 3

Data for How AI is incorporated into the user experience, Wave 3
Category% using
Contextual assistance55%
Background automation47%
Primary conversational interface46%
Adaptive / generative UI40%
Secondary conversational overlay30%
Voice22%
No formal UX strategy6%
0%20%40%60%Contextual assistanceContextual assistance — % using: 55%55%Background automationBackground automation — % using: 47%47%Primary conversational interfacePrimary conversational interface — % using: 46%46%Adaptive / generative UIAdaptive / generative UI — % using: 40%40%Secondary conversational overlaySecondary conversational overlay — % using: 30%30%VoiceVoice — % using: 22%22%No formal UX strategyNo formal UX strategy — % using: 6%6%

Technical Leadership · n=252 · Wave 3

Benchmark 1.7

AI product strategy

Survey question

Which best describes your organization’s current AI product development strategy?

Audience
Technical Decision Makers · n=252
In depth
Ch 1 — AI Is Eating Your Strategy Ch 3 — What Makes a Runner
AI product development strategy, Wave 3

27%

are predominantly building net-new AI products — for the first time this leads enhancement-only, at 16%

Technical Leadership · n=252 · Wave 3

Data for AI product development strategy, Wave 3
Net-new AI productsEqual emphasisEnhancing existing
Share of technical leaders27%57%16%
Share of technical leaders27%57%16%0%100%
Net-new AI productsEqual emphasisEnhancing existing

Technical Leadership · n=252 · Wave 3

Benchmark 1.8

AI ROI measurement

Survey question

Which best describes how your organization measures the ROI of its AI investment?

Audience
Technical Decision Makers · n=252
In depth
Ch 1 — AI Is Eating Your Strategy
How AI ROI is measured, Wave 3

55%

are unable to link AI investment ROI to hard revenue or cost savings

Technical Leadership · n=252 · Wave 3

Data for How AI ROI is measured, Wave 3
Category% of respondents
Directly tied to new revenue23%
Directly tied to cost savings22%
Believes it yields benefits but not quantified24%
Tied to revenue or cost but not tracked15%
Unsure about ROI14%
0%10%20%30%40%Directly tied to new revenueDirectly tied to new revenue — % of respondents: 23%23%Directly tied to cost savingsDirectly tied to cost savings — % of respondents: 22%22%Believes it yields benefits but notquantifiedBelieves it yields benefits but not quantified — % of respondents: 24%24%Tied to revenue or cost but not trackedTied to revenue or cost but not tracked — % of respondents: 15%15%Unsure about ROIUnsure about ROI — % of respondents: 14%14%

Technical Leadership · n=252 · Wave 3

Benchmark 1.9

AI sentiment (Tech)

Survey question

To what extent do you agree or disagree: “I would rather buy an AI agent that does a job than a SaaS tool that helps me do it.”

Audience
Technical Decision Makers · n=252
In depth
Ch 1 — AI Is Eating Your Strategy Ch 2 — The Agentic Divide Quantified
AI perception statements — technical leaders, Wave 3

81%

of tech leaders feel energized by AI’s rapid advancement — but only 57% would replace a SaaS tool with an agent that does the job

Technical Leadership · n=252 · Wave 3

Data for AI perception statements — technical leaders, Wave 3
Category% agree
I feel energized by the rapid advancement of AI81%
AI will revolutionize my function77%
AI tools have increased my team's expected work volume72%
There is too much hype around AI in my industry60%
I would rather buy an AI agent than a SaaS tool57%
Reliance on AI will stifle long-term innovation45%
AI will create more jobs than it displaces32%
AI will erode customer trust in our brand21%
0%20%40%60%80%100%I feel energized by the rapid advancementof AII feel energized by the rapid advancement of AI — % agree: 81%81%AI will revolutionize my functionAI will revolutionize my function — % agree: 77%77%AI tools have increased my team's expectedwork volumeAI tools have increased my team's expected work volume — % agree: 72%72%There is too much hype around AI in myindustryThere is too much hype around AI in my industry — % agree: 60%60%I would rather buy an AI agent than a SaaStoolI would rather buy an AI agent than a SaaS tool — % agree: 57%57%Reliance on AI will stifle long-terminnovationReliance on AI will stifle long-term innovation — % agree: 45%45%AI will create more jobs than it displacesAI will create more jobs than it displaces — % agree: 32%32%AI will erode customer trust in our brandAI will erode customer trust in our brand — % agree: 21%21%

Technical Leadership · n=252 · Wave 3

Full agree/disagree distribution for the agent-vs-SaaS statement: strongly agree 18% · somewhat agree 39% · neutral 29% · somewhat disagree 10% · strongly disagree 4%.

Benchmark 1.10

AI perceptions (full sample)

Survey question

To what extent do you agree or disagree with each of the following statements about AI in your organization?

Audience
All respondents · n=501 (Tech n=252 + GTM n=249)
In depth
Ch 1 — AI Is Eating Your Strategy Ch 8 — The Human Side of the AI Stack
AI perception statements — full sample vs function, Wave 3

80% & 78%

agree AI will revolutionize their function (80%) and feel energized by its advancement (78%) — both statistically higher than any other sentiment measured

All respondents · n=501 (Tech n=252 + GTM n=249) · Wave 3

Data for AI perception statements — full sample vs function, Wave 3
CategoryFull sampleTechGTM
AI will revolutionize my function80%77%84%
Energized by rapid AI advancement78%81%76%
AI increased expected work volume66%72%60%
Too much hype around AI57%60%53%
Would rather buy an agent than SaaS53%57%49%
Reliance on AI will stifle innovation44%45%43%
AI will create more jobs than it displaces27%32%21%
AI will erode customer trust18%21%15%
0%20%40%60%80%100%AI will revolutionize my functionAI will revolutionize my function — Full sample: 80%80%AI will revolutionize my function — Tech: 77%77%AI will revolutionize my function — GTM: 84%84%Energized by rapid AI advancementEnergized by rapid AI advancement — Full sample: 78%78%Energized by rapid AI advancement — Tech: 81%81%Energized by rapid AI advancement — GTM: 76%76%AI increased expected work volumeAI increased expected work volume — Full sample: 66%66%AI increased expected work volume — Tech: 72%72%AI increased expected work volume — GTM: 60%60%Too much hype around AIToo much hype around AI — Full sample: 57%57%Too much hype around AI — Tech: 60%60%Too much hype around AI — GTM: 53%53%Would rather buy an agent than SaaSWould rather buy an agent than SaaS — Full sample: 53%53%Would rather buy an agent than SaaS — Tech: 57%57%Would rather buy an agent than SaaS — GTM: 49%49%Reliance on AI will stifle innovationReliance on AI will stifle innovation — Full sample: 44%44%Reliance on AI will stifle innovation — Tech: 45%45%Reliance on AI will stifle innovation — GTM: 43%43%AI will create more jobs than it displacesAI will create more jobs than it displaces — Full sample: 27%27%AI will create more jobs than it displaces — Tech: 32%32%AI will create more jobs than it displaces — GTM: 21%21%AI will erode customer trustAI will erode customer trust — Full sample: 18%18%AI will erode customer trust — Tech: 21%21%AI will erode customer trust — GTM: 15%15%
Full sampleTechGTM

All respondents · n=501 (Tech n=252 + GTM n=249) · Wave 3

Benchmark 1.11

AI for internal efficiency vs customer-facing product

Survey question

Please indicate to what extent your organization uses AI for internal operational efficiency, and embeds AI within the product or service you sell to your customers.

Audience
All respondents · n=501
Where AI is in use — internal vs customer-facing, by maturity tier

96% and 88%

use AI for internal efficiency (96%) and embed it in a customer-facing product (88%) — B2B AI integration is nearing full penetration on both fronts

All respondents · n=501 · Wave 3

Data for Where AI is in use — internal vs customer-facing, by maturity tier
CategoryTotalRunnerJoggerWalkerCrawler
Internal operational efficiency96%99%97%95%81%
Embedded in customer-facing product88%97%95%81%48%
0%20%40%60%80%100%Internal operational efficiencyInternal operational efficiency — Total: 96%96%Internal operational efficiency — Runner: 99%99%Internal operational efficiency — Jogger: 97%97%Internal operational efficiency — Walker: 95%95%Internal operational efficiency — Crawler: 81%81%Embedded in customer-facing productEmbedded in customer-facing product — Total: 88%88%Embedded in customer-facing product — Runner: 97%97%Embedded in customer-facing product — Jogger: 95%95%Embedded in customer-facing product — Walker: 81%81%Embedded in customer-facing product — Crawler: 48%48%
TotalRunnerJoggerWalkerCrawler

All respondents · n=501 · Wave 3

Benchmark 1.12

Use of AI by function

Survey question

In which of the following areas are you currently using AI? Select all that apply.

Audience
All respondents · n=501
Functions currently using AI, Wave 3

80%

use AI for data analysis, collection and insight extraction — the most frequent area of AI integration

All respondents · n=501 · Wave 3 · multiple selections permitted

Data for Functions currently using AI, Wave 3
Category% using AI
Data — analysis, collection, insights80%
Sales and marketing79%
Engineering, product, R&D78%
Market research and insights68%
Customer service / success67%
IT and security58%
Operations and field operations49%
HR & recruitment47%
Legal and compliance32%
Finance31%
Supply chain and logistics16%
0%20%40%60%80%Data — analysis, collection, insightsData — analysis, collection, insights — % using AI: 80%80%Sales and marketingSales and marketing — % using AI: 79%79%Engineering, product, R&DEngineering, product, R&D — % using AI: 78%78%Market research and insightsMarket research and insights — % using AI: 68%68%Customer service / successCustomer service / success — % using AI: 67%67%IT and securityIT and security — % using AI: 58%58%Operations and field operationsOperations and field operations — % using AI: 49%49%HR & recruitmentHR & recruitment — % using AI: 47%47%Legal and complianceLegal and compliance — % using AI: 32%32%FinanceFinance — % using AI: 31%31%Supply chain and logisticsSupply chain and logistics — % using AI: 16%16%

All respondents · n=501 · Wave 3 · multiple selections permitted

Benchmark 1.13

Motivation to apply AI, by function

Survey question

What’s your main motivation for applying AI to this area?

Audience
All respondents · n=501
Main motivation for applying AI — average across 11 functions

31%

name increasing team productivity as their main motivation, averaged across 11 functions — a 13-point gap over the next most-cited reason, cost savings

All respondents · n=501 · Wave 3

Data for Main motivation for applying AI — average across 11 functions
MetricValueDetail
Increasing productivity of my team31%High — Legal (42%), R&D (40%) · Low — Supply chain (15%)
Cost savings18%High — Supply chain (40%), Finance (37%) · Low — Market research (4%)
Revenue growth / generation14%High — Sales & marketing (49%) · Low — IT security & HR (1–2%)
Creating a competitive advantage13%High — Market research (29%) · Low — Finance (2%)
Improving scalability8%High — IT security (17%) · All remaining 4–11%
Improving customer experience / retention8%High — Customer service (49%) · Low — IT security & HR (1–2%)
Executive mandate to explore or implement AI4%Range 2–9% across functions
Upskilling and training my people3%High — HR (14%) · All remaining 1–3%
Competitive pressure2%Range 1–5% across functions
Increasing productivity of my team
High — Legal (42%), R&D (40%) · Low — Supply chain (15%)
31%
Cost savings
High — Supply chain (40%), Finance (37%) · Low — Market research (4%)
18%
Revenue growth / generation
High — Sales & marketing (49%) · Low — IT security & HR (1–2%)
14%
Creating a competitive advantage
High — Market research (29%) · Low — Finance (2%)
13%
Improving scalability
High — IT security (17%) · All remaining 4–11%
8%
Improving customer experience / retention
High — Customer service (49%) · Low — IT security & HR (1–2%)
8%
Executive mandate to explore or implement AI
Range 2–9% across functions
4%
Upskilling and training my people
High — HR (14%) · All remaining 1–3%
3%
Competitive pressure
Range 1–5% across functions
2%

All respondents · n=501 · Wave 3

Benchmark 1.14

Top reason for agentic AI

Survey question

What are the main reasons your organization is investing in Agentic AI? % who selected each reason.

Audience
Technical Decision Makers · n=252
Reasons for investing in agentic AI, by maturity tier

53%

of Runners name creating new revenue streams or business models as a reason for investing in agentic AI — 10 points above Walkers, and the clearest signal that Runners treat AI as a revenue lever rather than a cost lever

Technical Leadership · n=252 · Wave 3 · % selecting each reason

Data for Reasons for investing in agentic AI, by maturity tier
CategoryTotalRunnerJoggerWalkerCrawler
Increase efficiency, automate workflows46%40%49%48%43%
Create new revenue streams / business models43%53%35%43%20%
Improve customer experience and engagement32%28%31%31%57%
Reduce costs, improve resource allocation30%28%27%33%42%
Enhance decision-making and strategic insights21%14%26%24%0%
0%20%40%60%Increase efficiency, automate workflowsIncrease efficiency, automate workflows — Total: 46%46%Increase efficiency, automate workflows — Runner: 40%40%Increase efficiency, automate workflows — Jogger: 49%49%Increase efficiency, automate workflows — Walker: 48%48%Increase efficiency, automate workflows — Crawler: 43%43%Create new revenue streams / businessmodelsCreate new revenue streams / business models — Total: 43%43%Create new revenue streams / business models — Runner: 53%53%Create new revenue streams / business models — Jogger: 35%35%Create new revenue streams / business models — Walker: 43%43%Create new revenue streams / business models — Crawler: 20%20%Improve customer experience and engagementImprove customer experience and engagement — Total: 32%32%Improve customer experience and engagement — Runner: 28%28%Improve customer experience and engagement — Jogger: 31%31%Improve customer experience and engagement — Walker: 31%31%Improve customer experience and engagement — Crawler: 57%57%Reduce costs, improve resource allocationReduce costs, improve resource allocation — Total: 30%30%Reduce costs, improve resource allocation — Runner: 28%28%Reduce costs, improve resource allocation — Jogger: 27%27%Reduce costs, improve resource allocation — Walker: 33%33%Reduce costs, improve resource allocation — Crawler: 42%42%Enhance decision-making and strategicinsightsEnhance decision-making and strategic insights — Total: 21%21%Enhance decision-making and strategic insights — Runner: 14%14%Enhance decision-making and strategic insights — Jogger: 26%26%Enhance decision-making and strategic insights — Walker: 24%24%Enhance decision-making and strategic insights — Crawler: 0%0%
TotalRunnerJoggerWalkerCrawler

Technical Leadership · n=252 · Wave 3 · % selecting each reason