All AI Statistics

of organisations report regular AI use in at least one business function

Up from 78% in 2024. From McKinsey's 2025 Global Survey on the State of AI, covering 1,993 respondents across 105 countries.

of global knowledge workers use AI at work

From the 2024 Work Trend Index surveying 31,000 workers across 31 countries. Usage nearly doubled in six months.

of companies have begun scaling AI programs across the enterprise

While AI use is widespread, most organisations are still in experimenting or piloting stages. Only about one-third report scaling.

of organisations now use AI in three or more business functions

More than two-thirds use AI in more than one function. Half report use in three or more.

of companies with >$5B revenue have reached the AI scaling phase

Compared with only 29% of companies with less than $100M in revenue. Larger companies lead scaling efforts.

of organisations are at least experimenting with AI agents

23% report scaling agentic AI in at least one function, and an additional 39% have begun experimenting.

of organisations are scaling an agentic AI system in their enterprise

Most of those scaling agents are only doing so in one or two functions. In any given function, no more than 10% are scaling agents.

of AI users at work started using it less than six months ago

Demonstrates the rapid recent acceleration of gen AI adoption in the workplace.

of organisations say AI is enabling their innovation

A majority report improved innovation, and nearly half report improvement in customer satisfaction and competitive differentiation.

of respondents report any enterprise-level EBIT impact from AI

Most of those say less than 5% of their organisation's EBIT is attributable to AI. Enterprise-wide financial impact remains limited.

higher growth in revenue per employee in industries most exposed to AI

From PwC's 2025 Global AI Jobs Barometer, analysing close to a billion job ads across six continents.

of organisations set efficiency as an objective of their AI initiatives

High performers also set growth and innovation as objectives, not just cost reduction.

of leaders say they wouldn't hire someone without AI skills

From the 2024 Work Trend Index. 71% also prefer a less experienced candidate with AI skills over a more experienced one without.

wage premium for workers with AI skills vs. the same role without

Up from 25% the previous year. Every industry analysed pays wage premiums for AI skills.

of AI users bring their own AI tools to work (BYOAI)

Even more common at small and medium-sized companies (80%). Cuts across all generations, not just Gen Z.

faster skill change in AI-exposed jobs compared to other jobs

More than 2.5x faster than last year. Change is fastest in automatable jobs.

of leaders worry about quantifying the productivity gains of AI

While 79% agree their company needs AI to stay competitive, pressure to show immediate ROI is stalling action.

of organisations using AI have experienced at least one negative consequence

Nearly one-third report consequences from AI inaccuracy. Inaccuracy is the most common risk both experienced and mitigated.

of organisations have not yet begun scaling AI across the enterprise

The transition from pilots to scaled impact remains a work in progress at most organisations.

of workers currently use generative AI at work

From a Salesforce survey of 14,000+ workers across 14 countries. Over half of those using gen AI do so without formal employer approval.

of people who use AI are now doing so at work — only 1% use AI exclusively at work with no personal usage

From Exploding Topics' original survey of 1,003 AI users. The vast majority who adopt AI at work also use it at home.

of tech workers are using AI tools at work — up from just 14% in 2024

Generating code is a major use case for AI in the tech sector. Cited by Exploding Topics from a CNN/Google study published September 2025.

of AI users use AI tools every single day — a further 39.38% use them a few times per week

84.84% of AI users use AI at least once per week. Only a tiny percentage use it less than monthly.

of AI users have increased their usage in the past 12 months — 48.49% say they use it 'a lot more'

Only 3.13% have reduced their AI usage. The steepest increase is among the highest earners — 72.84% of those earning $200K+ now use AI 'much more'.

of workers say AI has at least 'somewhat' improved their productivity — over a third report a 'significant' improvement

Only 2.72% say AI has decreased their productivity. 16.43% report no change.

more weekly projects completed by programmers when assisted by AI — the largest measured gain across all professions

NN Group research found AI-assisted programmers more than doubled their weekly project completion rate.

more documents produced per hour by business professionals using AI

Business professionals — including analysts, consultants, and writers — produced 59% more documents per hour with AI drafting, summarisation, and formatting tools.

of workers describe their overall AI experience at work as at least 'somewhat positive'

52.84% call it entirely positive, 39.01% say sometimes positive. Only 1.65% say AI at work is not positive at all.

saved per week by marketers using AI tools — equivalent to over a month per year

5 hours/week × 52 weeks ÷ 8-hour days = 32.5 days per year. Salesforce surveyed over 1,000 marketers.

of PR professionals say AI speeds up their work — up from 28% using GenAI in 2023 to 75% in 2025

From the Muck Rack State of AI in PR 2025 report. Brainstorming (82%), first drafts (72%), and editing (70%) are the top AI use cases.

of customer service reps at organisations using AI say it saves them time

From Salesforce's State of Service report. Service reps spend 66% of their time on non-customer-facing tasks — AI automates many of these.

of AI users say the technology improves the quality of their work

Beyond speed, AI is also improving output quality. 51% of the publishing industry now uses AI daily, 33.8% weekly.

of people who use AI at work use it for writing reports, emails, and presentations

A similar number (63.48%) use AI for editing. Women are slightly more likely than men to use AI for writing (67.87% vs 61.98%).

of marketers using GenAI deploy it for basic content creation and writing copy

Inspiring creative thinking (71%), analysing market data (63%), and generating image assets (62%) round out the top use cases among 1,000+ marketers surveyed.

of workplace AI users turn to the technology for data analysis

Men (52.09%) are far more likely than women (33.93%) to use AI for data analysis. Code writing is used by 26% of workplace AI users.

of marketers expect GenAI to free them from busy work so they can focus on more strategic tasks

The promise of AI in knowledge work is shifting from raw speed to strategic value — letting professionals focus on higher-order work.

of workers receive little or no AI training from their employer

Less than half (47.04%) feel they've received 'excellent' training. 19.5% say they have received no support at all.

of employees pay for their own AI tools at work — 50.2% use at least one personal AI account for work

With training inconsistent, employees are going rogue. A further 11.58% use a mix of employer-funded and personal tools.

of workplace AI users cite privacy and security as a concern — the biggest fear at work

Being made to look replaceable (43.31%) and quality/accuracy worries (42.12%) are the next-biggest fears. Only 9.78% have no concerns at all.

of companies still lack a formal AI policy — though improving from 72% in 2024

40% of companies don't offer AI training, while 76.3% of publishers report ethical concerns about AI usage.

of AI-using businesses in software engineering & manufacturing report cost decreases — the highest of any function

McKinsey's 2025 State of AI survey found software engineering and manufacturing share the highest rate of reported cost savings among all business functions, with 7% of software engineering firms reporting savings of 20% or more.

of C-suite leaders expect AI to deliver cost savings — and roughly half of those expect savings exceeding 10%

BCG surveyed 1,400+ C-suite executives across 50 markets and 14 industries. More than half expected AI-driven cost savings in 2024, with the most ambitious anticipating over 10% — equivalent to $1 billion for a company earning $10 billion in revenue.

of UK businesses using AI report improved workforce productivity

A survey of 3,500 UK businesses by the Department for Science, Innovation & Technology found three-quarters of AI adopters reported productivity gains. Over half (57%) also developed new or improved processes or operations.

as many leaders report transformative AI impact in 2026 compared to the year prior

Deloitte surveyed 3,235 leaders across 24 countries and found the share reporting transformative business impact from AI doubled year-over-year. However, only 34% say they are truly reimagining their business with AI, and the AI skills gap remains the top barrier.

of AI-using businesses in IT report cost decreases from AI activities

IT was the third-highest function for reported AI cost savings, behind software engineering and manufacturing (both 56%). The data comes from McKinsey's 2025 global State of AI survey across 105 nations.

in strategy & corporate finance report AI-driven cost decreases

Strategy and corporate finance was also one of the top functions for revenue increases at over 60%, making it one of the strongest dual-benefit functions for AI adoption.

of businesses report cost decreases in service operations and HR from AI

Service operations and HR round out the top functions for AI-driven cost savings. Automated workflows for support ticket routing, scheduling, and employee onboarding drive the gains.

of AI investments at leading companies go toward reshaping key functions and inventing new offerings — yielding 2.1× greater ROI

BCG surveyed 1,800+ executives and found that AI leaders allocate over 80% of their investment to high-impact function reshaping rather than scattered experimentation. These leaders also focus on fewer use cases (3.5 vs 6.1) and anticipate 2.1× greater ROI than peers.

more weekly projects completed by programmers when assisted by AI

NN Group research found AI-assisted programmers more than doubled their weekly project completion rate — the largest measured productivity gain across all professions studied.

more documents produced per hour by business professionals using AI

Business professionals — including analysts, consultants, and writers — produced 59% more documents per hour with AI assistance. AI drafting, summarisation, and formatting tools drove the gains.

of UK businesses using AI reported increased employee productivity

Among the 3,500 businesses surveyed by DSIT, 56% of AI adopters saw increased productivity, 35% reported no change, and just 1% reported a decrease. Businesses cited the reduction of repetitive and admin tasks as the primary driver.

rise in worker access to AI tools during 2025 alone

Deloitte's survey of 3,235 leaders across 24 countries found that the share of workers with access to AI tools rose by half in a single year. The number of companies with 40%+ of AI projects in production is also set to double within six months.

reduction in operations costs at major US banks through AI automation

AI-powered automation reduced operations costs at major US banks by 13%, with processing errors falling by 40%. Compliance monitoring AI also cut audit preparation times by 50%.

reduction in banking call-centre costs via AI chatbots

AI chatbots can cut banking call-centre costs by up to 70% by handling routine enquiries, account lookups, and transactional queries without human intervention.

of retailers cite reduced operational costs from AI

Nearly all retailers using AI report lower operational costs. Additionally, 87% say AI positively impacted revenue, and AI chatbots are boosting conversions by up to 35%.

of healthcare leaders report positive ROI in the first year of GenAI adoption

Nearly three-quarters of healthcare and life-science leaders saw positive returns within their first year of generative AI adoption. 72% also cited increased productivity from GenAI tools.

of organisations say AI has improved innovation — the most-cited company-wide benefit

Innovation was the top qualitative benefit reported in McKinsey's 2025 survey. Nearly half of respondents also cited improved customer satisfaction and competitive differentiation.

of AI-using businesses in marketing & sales report increased revenue

Marketing and sales was the top business function for AI-driven revenue gains. Strategy/corporate finance and product development both exceeded 60%, while IT was the lowest at 53%.

of higher-growth companies expect significant or substantial ROI from AI within 2 years

Forbes Research's 2025 survey of 1,075 C-suite leaders at companies with over $1B in annual revenue found high-growth firms (≥10% revenue growth) far more optimistic about AI ROI — 62% vs. 49% of companies overall.

average improvement in customer engagement and satisfaction where GenAI has been deployed

Capgemini surveyed 1,100 executives at companies with $1B+ revenue across 14 countries and 11 industries. Beyond the 6.7% engagement lift, 80% of organisations increased GenAI investment since 2023, and 24% have integrated GenAI at scale — up from just 6% the year prior.

of CEOs report neither increased revenue nor decreased costs from AI in the last 12 months

PwC surveyed 4,454 CEOs across 95 countries. While experimentation is widespread, the majority have yet to translate AI investment into measurable financial gains. Only 33% report gains in either cost or revenue.

of CEOs say AI has delivered both cost and revenue benefits — a defining divide between leaders and laggards

Forbes analysis of PwC's 2026 CEO Survey found that CEOs in the top 12% are two to three times more likely to have embedded AI extensively across products, services, demand generation, and strategic decision-making — they rewired operations, not just bought licences.

of enterprises say AI has had an impact on increasing annual revenue in some or all parts of the business

NVIDIA's annual surveys garnered data from over 3,200 respondents across financial services, retail, healthcare, telecom, and manufacturing. Nearly a third (30%) said the revenue increase was greater than 10%, with 33% reporting 5–10% growth.

gap between ambition and reality — 74% of organisations want AI to grow revenue but only 20% have seen it

Analysis of Deloitte's State of AI 2026 survey of 3,235 leaders across 24 countries found a massive ambition-to-reality gap. The 20% reporting revenue growth had tied AI deployments to specific business KPIs from day one and built governance structures to scale from pilot to production.

of enterprises report AI-driven annual revenue increases exceeding 10% — rising to over 40% among C-suite executives

NVIDIA's 2026 survey found that executives at the C-suite or VP level were significantly more likely to report large revenue gains (>10%), suggesting that leadership engagement directly correlates with higher financial returns from AI.

of enterprises say AI has helped reduce annual costs — with 25% reporting reductions greater than 10%

Cost reductions from AI were nearly as widespread as revenue gains. Among industry verticals, retail and CPG led with 37% reporting costs reduced by more than 10%, driven by AI-powered inventory management, demand forecasting, and digital twins.

higher profit margins at companies applying AI widely to products, services, and customer experiences

Separate PwC analysis found that companies which embedded AI extensively across their offerings achieved nearly four percentage points higher profit margins than those that had not — demonstrating that broad, deep integration is key to financial outperformance.

of organisations say AI has improved innovation — the most-cited enterprise-wide qualitative benefit

McKinsey's 2025 global survey of 1,993 participants found innovation was the top qualitative benefit. Nearly half also cited improved customer satisfaction and competitive differentiation. However, only 39% attributed any level of EBIT impact to AI at the enterprise level.

of respondents say their AI budget will increase in 2026 — with nearly 40% expecting growth of 10% or more

North American organisations are especially keen, with 48% expecting budget increases of 10%+. The top spending priority for 42% of respondents is optimising current AI workflows and production cycles, followed by finding additional use cases (31%) and building AI infrastructure (31%).

of executives name AI a top-three strategic priority for 2025

BCG surveyed over 1,800 C-suite executives and found three-quarters consider AI among their top strategic priorities. Companies also plan to invest more in GenAI in 2025 than in the prior year, even as they recognise the discipline required to move from pilots to results.

of organisations increased their generative AI investment since 2023

Capgemini surveyed 1,100 executives at companies with $1B+ revenue across 14 countries and 11 industries. A further 20% maintained their investment level. The share that integrated GenAI into some or most locations quadrupled from 6% to 24% in just 12 months.

of CEOs cite 'transforming fast enough to keep pace with technological change' as their number-one concern

This concern ranked well ahead of worries about innovation capability or medium-to-long-term business viability (both 29%). CEO confidence in revenue growth has also fallen to a five-year low — just 30% are confident, down from 38% in 2025 and 56% in 2022.

of AI investments at leading companies go toward reshaping functions — yielding 2.1× greater anticipated ROI than peers

BCG found that leading companies solve the 'impact gap' by allocating the vast majority of investment to reshaping key functions and inventing new offerings, rather than smaller-scale productivity initiatives. They also focus on an average of 3.5 use cases vs 6.1 for others.

more likely to report meaningful financial returns — CEOs whose organisations have strong AI foundations

PwC found that organisations with established Responsible AI frameworks and technology environments enabling enterprise-wide integration are three times more likely to report meaningful returns. Foundations matter as much as scale.

more likely to have fundamentally redesigned workflows — the top factor distinguishing AI high performers

McKinsey's survey of 1,993 participants found that AI high performers (~6% of respondents) are nearly three times as likely as others to say their organisations have fundamentally redesigned individual workflows. This intentional redesign has one of the strongest contributions to achieving meaningful business impact.

median ROI on generative AI reported by product development teams that followed top AI best practices

IBM research found that product development teams that followed the top four AI best practices 'to an extremely significant extent' achieved this median ROI. The practices include celebrating feedback, working iteratively, learning from user data, and building multidisciplinary teams.

of organisations have moved 40% or more of their AI pilots into production — the rest remain in 'pilot purgatory'

Three out of four enterprises have the majority of AI initiatives still sitting in pilot mode — consuming budget, occupying engineering time, and delivering nothing to the bottom line. Without measurement frameworks to justify scaling investment, pilots stall and teams move on to the next experiment.

of executives say they can measure AI ROI confidently — while 79% report seeing productivity gains

IBM's Q4 2025 Think Circle found a stark gap between perceived productivity value and the ability to translate it into financial impact. Culture, governance, workflow design, and data strategy — not technology — are the main constraints on realising ROI.

of AI initiatives deliver expected ROI, and just 16% have scaled enterprise-wide

An IBM CEO study found CEOs are balancing pressure for short-term ROI with longer-term innovation goals. This supports the broader narrative that AI often starts as experimentation first and value realisation second — typical of emerging technology adoption cycles.

of respondents attribute any level of EBIT impact to AI at the enterprise level — and most of those say less than 5%

While AI use-case-level benefits are common (cost, revenue, innovation), translating them to enterprise-wide financial impact remains rare. McKinsey's survey of 1,993 participants across 105 nations shows most organisations have not yet embedded AI deeply enough to move the enterprise P&L.

of service professionals use at least one form of AI — 39% use agentic AI specifically

From Tidio's analysis of Salesforce industry data. AI has moved well beyond pilot in customer service teams.

of customer service workers said they were using generative AI for work — the lowest of all departments surveyed

Only 15% said they plan to use genAI in the future. Service workers lagged behind marketers (51%) and salespeople (~33%) in adoption.

of service leaders say AI agent investment is essential for their organisation

Despite slower adoption rates among individual service reps, leadership sees AI agents as a strategic priority.

of customer interactions will eventually involve AI in some form, predicts the Zendesk CEO

59% of customers already believe AI will change how they interact with companies within the next two years.

of service teams with AI say it reduces their costs

From Salesforce's State of Service report. Cost reduction is one of AI's clearest benefits in service operations.

reduction in customer service costs through AI chatbots

Businesses report up to $20M in cost savings from chatbot deployments. The average chatbot ROI is 1,275% based on support cost savings.

of service reps' time is spent on non-customer-facing tasks like admin and data entry

AI can automate these tasks — freeing reps to focus on what matters: customer relationships. 85% of reps at AI-using orgs say it saves time.

average longest time US consumers have spent trying to resolve a single issue with customer service

Longer than a full workday. AI agents can dramatically reduce this by providing instant, consistent resolution across channels.

service professionals using generative AI say it helps them serve customers faster

Despite lower adoption rates, service pros who do use AI overwhelmingly report faster resolution and improved performance.

of customer service cases expected to be resolved by AI by 2027 — up from 30% in 2025

Service professionals themselves forecast rapid growth in AI case resolution within just two years.

increase in case resolution seen by Wiley after deploying Salesforce's Agentforce — outperforming their old bot

A real-world example from one of the first major Agentforce deployments, demonstrating tangible improvements over previous-gen chatbots.

of US customer service interactions involve at least one transfer, consumers estimate

Customers walk away from nearly one-third of service interactions without getting what they need. AI agents can reduce transfers by resolving queries at first contact.

of consumers are frustrated when customer service can't resolve their issues instantly

Two-thirds of consumers expect instant resolution — a bar that AI agents are uniquely positioned to meet.

of consumers don't care how they interact with a company as long as their problems are fixed fast

Speed of resolution matters more than channel. One-third of consumers would rather purchase through AI agents than with a person.

of consumers would work with an AI agent instead of a human to avoid repeating themselves

Repetition is a top frustration. AI agents retain full context across the interaction, eliminating this pain point.

of service professionals worry they will lose their job if they don't learn to use generative AI

Ironically, service workers are both the most worried about AI and the least likely to be using it. 60% say they don't know how to get value from GenAI at work.

of service professionals use at least one form of AI — from chatbots to real-time agent assist tools

39% specifically use agentic AI. AI is no longer limited to simple IVR scripts — it now handles routing, summarisation, and live coaching.

of customer service cases expected to be resolved entirely by AI by 2027 — up from 30% in 2025

Service professionals themselves forecast rapid growth in AI-only case resolution within just two years.

of US customer service interactions involve at least one transfer, consumers estimate

AI routing can dramatically reduce transfers by resolving queries at first contact or directing callers to the right specialist immediately.

increase in case resolution at Wiley after deploying AI agents — outperforming their previous bot

A real-world enterprise deployment showing next-gen AI agents significantly outperforming legacy chatbot systems in a contact centre.

of service teams using AI say it reduces their costs

From Salesforce's State of Service report. Cost reduction is AI's most consistent benefit in contact centre operations.

reduction in banking call-centre costs through AI chatbots

AI chatbots handle routine enquiries, account lookups, and transactional queries without human intervention. 43% of banking customers prefer chatbot resolution.

average chatbot ROI based on support cost savings

Businesses report up to $20M in cost savings. Up to 30% reduction in overall customer service costs through chatbot deployment.

efficiency gain for banks embracing AI — driven by 2× customer retention, 30% lead conversion uplift, and 50% productivity boost

PwC data shows banks gain 15 percentage points in efficiency, with 50% of staff shifting to higher-value roles as middle-office tasks are automated.

of service reps' time is spent on non-customer-facing tasks — AI can automate most of these

Administrative tasks and manual data entry consume two-thirds of agent time. AI automates these, letting reps focus on complex customer issues.

of customer service reps at AI-using organisations say the technology saves them time

From Salesforce's State of Service report. Time savings is the most universally reported benefit among agents actually using AI.

service professionals using GenAI say it helps them serve customers faster

Among the subset of service workers who have adopted GenAI, the productivity impact is nearly unanimous.

of service workers worry about job loss if they don't learn to use GenAI — despite being the department least likely to use it

60% say they don't know how to get the most value from GenAI; 55% don't know how to use it effectively. A training gap compounds the anxiety.

average longest time US consumers report spending to resolve a single customer service issue

Longer than a full workday. AI agents can dramatically compress resolution time by providing instant, context-aware support.

of consumers walk away from customer service interactions without getting what they need

Failed resolution represents lost revenue and damaged loyalty. AI agents can reduce abandonment with 24/7 availability and instant response.

of consumers are frustrated when customer service can't resolve their issues instantly

Two-thirds of consumers expect instant resolution — setting a bar that only AI-powered operations can consistently meet.

of consumers don't care how they interact with a company as long as their problems are fixed fast

Speed of resolution outweighs channel preference. One-third would prefer purchasing through AI agents over interacting with a human.

global chatbot market size in 2023 — projected to reach $15.5 billion by 2028

Growing at approximately 23% CAGR, driven by enterprise customer service, e-commerce, and healthcare adoption.

increase in chatbot adoption between 2020 and 2025

The pandemic and subsequent LLM breakthroughs accelerated growth from niche deployments to mainstream business tool.

of B2B companies and 42% of B2C companies now use chatbot software

B2B adoption leads because chatbots handle high-volume, repetitive enquiries common in enterprise sales and support.

most visited online destination in the US and Europe — that's where ChatGPT ranked by Q4 2025

ChatGPT became one of the top web destinations after search engines, reflecting the mainstream adoption of conversational AI.

of people have had a conversation with a chatbot in the past year

The prevalence of chatbots continues to grow, and so does people's willingness to use them across websites, apps, and messaging platforms.

of businesses using chatbots for marketing report a rise in high-quality leads

Chatbots don't just handle support — they actively generate and qualify leads through conversational engagement.

more leads on career sites using HR chatbots — with 12,000+ hours saved annually

HR chatbots automate initial screening, FAQs, and scheduling, dramatically increasing applicant engagement.

of banking customers prefer chatbot resolution for their queries

For routine banking tasks like balance checks and transaction queries, nearly half of customers now prefer chatbot over human interaction.

of consumers think companies using chatbots take good care of their customers

Having a chatbot is now seen as a positive signal of customer care and digital maturity.

of consumers trust information provided by chatbots

Nearly two-thirds of users trust chatbot responses, though trust varies by context and industry.

of consumers would talk to a chatbot rather than wait for a human representative

Speed and availability drive preference — most consumers choose instant bot response over queuing for a human.

of consumers say chatbots influence their purchasing decisions

Chatbots are not just support tools — they actively shape buying behaviour through recommendations and guided experiences.

average chatbot ROI based on support cost savings

Businesses report up to $20M in total savings. Up to 30% reduction in overall customer service costs from chatbot deployments.

of chatbot queries resolved in fewer than 11 messages

Modern AI chatbots resolve the vast majority of conversations quickly, without long back-and-forth exchanges.

reduction in customer service costs through chatbot deployment

Cost savings come from deflecting routine enquiries, reducing agent workload, and enabling 24/7 coverage without additional headcount.

of service leaders say AI agent investment is essential for their organisation

Leadership sees chatbots and AI agents as a strategic imperative rather than an optional efficiency play.

of Gen Z consumers find customer service interactions stressful — 20% prefer starting with a chatbot

Younger consumers who grew up with digital interfaces often prefer chatbot-first interactions over phone or in-person service.

of Gen Z consumers are already comfortable with AI agents shopping for them

Higher than the overall average of 24%. Gen Z is the most receptive generation to letting AI agents handle purchasing decisions.

of Gen Z consumers are comfortable with AI agents creating personalised content for them

Compared to 37% across all age groups. Gen Z expects AI-powered personalisation as the default experience.

of consumers would rather purchase a product through AI agents than with a human

Efficiency and convenience are driving a shift toward automated purchasing — especially for routine or repeat buys.

of consumers are already comfortable with AI agents scheduling appointments for them

Appointment scheduling is one of the most natural early use cases for voice AI — structured, time-sensitive, and low-risk.

of consumers are already comfortable with AI agents shopping for them — rising to 32% among Gen Z

Nearly a quarter of all consumers and a third of Gen Z are comfortable delegating purchasing decisions to AI agents.

of consumers don't care how they interact with a company as long as their problems are fixed fast

Channel agnosticism means voice AI doesn't need to pass the Turing test — it just needs to resolve issues quickly.

of consumers are comfortable with AI agents creating more personalised and useful content for them

Rises to 44% among Gen Z consumers. Comfort with AI-generated personalisation extends beyond chat to voice and proactive outreach.

of service leaders say AI agent investment is essential for their organisation

Voice agents are a key part of the agentic AI strategy. Leaders see them as essential for 24/7 coverage and scale.

of service professionals use at least one form of AI — 39% specifically use agentic AI

Agentic AI — which includes autonomous voice agents — is already used by nearly 4 in 10 service professionals.

of salespeople's time is spent on non-selling tasks — voice AI can automate outbound call scheduling and follow-ups

Voice AI handles appointment setting, reminders, and initial outreach so reps can focus on high-value conversations.

of customer service cases expected to be resolved by AI by 2027 — spanning both chat and voice channels

Voice AI resolution will be a major driver of this growth as speech recognition and synthesis continue to improve.

of customer service interactions involve at least one transfer — voice AI can route and resolve at first contact

Transfers are a top frustration for customers. AI voice agents that resolve queries at first contact eliminate this entirely.

service professionals using GenAI say it helps them serve customers faster

GenAI-powered voice agents provide real-time suggestions to human agents or handle calls autonomously with faster resolution.

reduction in banking call-centre costs with AI-powered voice and chat bots

Voice AI handles routine enquiries, account lookups, and transactional queries in banking — one of the most mature voice AI verticals.

increase in case resolution at Wiley after deploying next-generation AI agents

Demonstrates the leap in resolution quality from legacy bots to modern agentic AI across both voice and text channels.

of service teams with AI say it reduces their costs — voice AI is a key contributor

Voice agents handle overflow, after-hours, and routine calls that would otherwise require additional staffing.

average ROI from AI agent deployments based on support cost savings

Combined chat and voice bot savings can reach $20M+ for large enterprises. Voice AI extends these savings to phone-based customer bases.

of consumers would work with an AI agent instead of a human to avoid repeating themselves

Voice AI agents that maintain full context across the conversation solve one of the biggest frustrations in phone-based support.

of consumers would rather purchase a product through AI agents vs. with a human representative

Voice-based purchasing through AI agents is emerging as a new commerce channel, particularly for repeat orders and subscriptions.

of PR professionals use generative AI — up from 28% in 2023

From the Muck Rack State of AI in PR 2025 report. Nearly triple the adoption rate in just two years.

of marketing and sales departments regularly use generative AI — the highest of any business function

That figure rises to 55% in the marketing/sales departments of tech companies. Product/service development is next at 28%.

of marketers are already using or experimenting with generative AI at work — another 22% plan to soon

If plans materialise, nearly three-quarters of marketers surveyed could be using GenAI. Survey covered over 1,000 marketers.

of the publishing industry uses AI daily — 33.8% use it weekly

ChatGPT is used by 77.8% of Substack publishers; Claude by 28.2% and Grammarly by 27.9%.

of AI-using marketers say brainstorming is their top AI use case, followed by first drafts (72%) and editing (70%)

AI is used across the full content creation pipeline — from ideation to refinement.

of marketers using GenAI deploy it for basic content creation and writing copy

Inspiring creative thinking (71%), analysing market data (63%), and generating image assets (62%) are also major use cases.

of email marketers believe AI-supported email marketing is more effective than manual efforts

A further 20% think AI is at least as effective as traditional methods. Less than a third believe manual email marketing remains superior.

of service professionals using GenAI apply it to personalised customer communications

AI-driven personalisation of marketing and service communications is now the standard approach at GenAI-using organisations.

saved per week by marketers using AI tools — equivalent to over a month of work per year

5 hours/week × 52 weeks ÷ 8-hour days = 32.5 days saved per year. Content drafting, reporting, and segmentation drive the time savings.

of PR professionals say AI speeds up their work

Speed is the most universally reported benefit. 78% also say AI improves the quality of their work.

of marketers expect generative AI to eliminate busy work and free them for more strategic tasks

The promise of AI in marketing is shifting from raw throughput to enabling strategic, creative work.

of sales teams with AI saw revenue growth in the past year — versus 66% of teams without AI

Marketing-driven revenue also benefits: 76% of ecommerce teams with AI credit it for revenue growth.

of companies still lack a formal AI policy — though improving from 72% in 2024

Despite rapid adoption, most organisations haven't established governance frameworks for AI use in marketing.

of publishers have ethical concerns about AI usage in content creation

Three-quarters of publishers express ethical reservations even as they increasingly adopt AI tools for their workflows.

of marketers don't know how to use generative AI safely — 43% don't know how to get the most value from it

Despite rapid adoption, a significant skills gap persists. 7 in 10 marketers say their employer doesn't yet offer GenAI training.

of companies don't offer AI training to their employees — despite widespread adoption

The training gap between AI enthusiasm and AI competence is widening across marketing departments.

of salespeople are using or plan to use generative AI — compared to 51% of marketers

Sales teams lag behind marketing in GenAI adoption. 61% of sales pros say they believe GenAI will help them better serve customers.

of marketing and sales departments regularly use generative AI — the highest of any business function

Rises to 55% in tech company sales departments. Product/service development trails at 28%.

of salespeople using GenAI apply it to basic content creation — 74% for analysing market data

Automating personalised sales communications (71%) rounds out the top three use cases among AI-equipped sales teams.

of sales professionals believe generative AI will help them sell more efficiently

The same percentage also believe it will help them better serve their customers — yet most haven't begun using it.

of sales teams with AI saw revenue growth in the past year — versus 66% of teams without AI

From Salesforce's State of Sales report. A 17-percentage-point revenue growth gap between AI and non-AI teams.

of salespeople currently using GenAI say it has helped increase sales by enhancing customer interactions

Among the smaller pool of sales reps who have adopted GenAI, the impact on actual sales is dramatic and near-universal.

increase in lead conversion rates through AI-powered lead generation

AI in lead generation also reduces manual work by 15% or more while offering higher ROI and lower customer acquisition cost.

of AI-using businesses in marketing & sales report increased revenue — the highest of any business function

Marketing and sales leads all functions for AI-driven revenue gains. Strategy/corporate finance and product development both exceed 60%.

of salespeople's time is spent on non-selling tasks like admin and manual data entry

From Salesforce's State of Sales report. AI can automate the majority of these tasks, freeing reps for high-value conversations.

of sales professionals expect GenAI to help them generate sales reports — 48% for content creation

Report generation, content creation, and market data analysis are the top three areas where reps expect AI to transform their role.

saved per week by professionals using AI — equivalent to over a month of productive time per year

Salesforce's marketers data shows 5 hours/week = 32.5 days/year. Sales professionals report similar time savings across CRM updates, email drafting, and research.

more valuable — website visitors from AI search compared to those from organic search

Traffic from ChatGPT search is projected to overtake organic search by 2028. Sales teams that optimise for AI search visibility gain higher-intent leads.

of salespeople say they don't know how to get the most value from generative AI at work

Nearly half also say they don't know how to use GenAI safely (49%) or effectively (47%). A clear skills gap is limiting adoption.

of sales professionals worry they will lose their job if they don't learn to use generative AI

Job loss anxiety is higher than actual adoption rates — a sign that the pressure to adopt is building even among non-users.

marketers and salespeople say their employer does not yet provide generative AI training

The training deficit is the biggest barrier. 54% of marketers say training programmes are important for successfully using GenAI.

of companies lack a formal AI policy — down from 72% in 2024 but still a majority

Without clear policies, sales teams adopt AI inconsistently. 29% of employees pay for their own AI tools at work.

increase in lead conversion rates through AI-powered lead targeting

AI in lead generation offers higher ROI and lower customer acquisition cost than traditional methods.

of businesses using chatbots for marketing report a rise in high-quality leads

Chatbots don't just handle support — they actively generate and qualify leads through conversational engagement on websites.

more leads on career sites using HR chatbots — with 12,000+ hours saved annually

HR chatbots automate initial applicant screening, answer FAQs, and schedule interviews, dramatically increasing engagement.

uplift in lead conversion for banks embracing AI — alongside 2× customer retention rates

PwC data shows AI delivers 15 percentage points of efficiency gain for banks, driven by multiple improvements including lead conversion.

reduction in manual work from AI-powered lead generation

AI reduces manual prospecting, data entry, and qualification work while delivering higher-quality leads.

more valuable — website visitors from AI search compared to organic search visitors

Traffic from ChatGPT search is projected to overtake organic search by 2028. AI search visitors convert at dramatically higher rates.

average chatbot ROI based on support cost savings — the same bots that generate and qualify leads

Chatbot ROI includes both cost savings and revenue gains from lead generation. Businesses report up to $20M in savings.

of consumers say chatbots influence their purchasing decisions

Chatbot-driven lead nurturing actively shapes buying behaviour through personalised recommendations and guided product discovery.

of salespeople using GenAI say it helped increase sales by enhancing and speeding up customer interactions

AI-powered prospecting — from initial outreach to needs analysis — directly contributes to faster deal progression.

of AI-using salespeople use it to automate personalised sales communications

Personalised outreach at scale was previously impossible for individual reps. AI generates tailored emails, proposals, and follow-ups.

of AI-using sales professionals leverage it for analysing market data and identifying prospects

AI analyses large datasets to identify patterns in customer behaviour, buying signals, and market opportunities for prospecting.

of consumers are already comfortable with AI agents scheduling appointments for them

AI-driven appointment scheduling converts inbound interest to meetings without human intervention — a key lead gen milestone.

of people have had a conversation with a chatbot in the past year — every conversation is a lead gen opportunity

Chatbot interactions are touchpoints for capturing contact info, qualifying intent, and routing leads to sales.

of consumers would talk to a chatbot rather than wait for a human representative

This willingness means more leads engage in real-time rather than bouncing — reducing website abandonment and improving capture.

of banking customers prefer chatbot resolution — indicating chatbot comfort extends beyond support to engagement

In financial services, chatbot-first interactions open cross-sell and upsell opportunities during routine account enquiries.

of consumers think companies using chatbots take good care of their customers

Having a chatbot is a positive brand signal — improving trust and willingness to share contact information and purchase intent.

jobs expected to be displaced by AI and machine learning by 2030 — but 170 million new roles will be created

From the WEF's Future of Jobs Report 2025. Net job creation is 78 million, but significant workforce disruption is expected.

cashier and ticket clerk jobs projected to be lost by 2030 — the most of any single role

Administrative assistants (6M+), cleaners (2.5M+), and stock-keeping clerks (2M+) are also among the most at-risk roles.

of generative AI users are looking to automate tasks at work

Automation of repetitive work tasks is the top motivation for workplace AI adoption, ahead of communication and learning.

faster skill change in AI-exposed jobs compared to jobs not exposed to AI

From PwC's 2025 Global AI Jobs Barometer. Change is fastest in automatable jobs — more than 2.5× faster than last year.

of companies now use AI in at least one business function — up from 78% the year prior

Almost 9 in 10 organisations have adopted at least one AI tool. 90% are either already using AI or plan to introduce it.

of industries are increasing their use of AI — including less-obvious sectors like mining and agriculture

Every single industry is increasing AI adoption. AI automation is not limited to tech — it spans the entire economy.

of organisations are at least experimenting with AI agents — 23% are already scaling them

From McKinsey's 2025 State of AI survey. Agentic AI — which can take autonomous multi-step actions — is the next frontier of automation.

of AI-using organisations have adopted fully centralised AI hubs or centres of excellence

Centralisation is most common in risk and compliance (57%) and data governance (46%). Decentralised models are more common for tech talent.

of salespeople's time is spent on non-selling tasks that AI can automate — service reps spend 66% on admin

AI automation frees the majority of sales and service workers' time for customer-facing, high-value activities.

of workers say AI has improved their productivity — over a third report a 'significant' improvement

Productivity gains from AI automation are nearly universal among users. Only 2.72% report decreased productivity.

more weekly projects completed by AI-assisted programmers — the largest measured automation gain

AI coding assistants automate boilerplate, testing, and documentation, more than doubling developer output.

of employees pay for their own AI tools at work — taking automation into their own hands

With 50.11% receiving little or no training, employees are self-funding AI automation. 50.2% use personal AI accounts for work.

potential revenue contribution from AI technology by 2030 — boosting global GDP by 26%

PwC estimates AI could generate $15.7 trillion in revenue by the end of the decade through automation and augmentation.

AI contribution to manufacturing by 2035 — the largest of any single industry

Manufacturing, wholesale & retail ($2.23T), and professional services ($1.85T) stand to gain the most from AI automation.

of retailers cite reduced operational costs from AI automation

Nearly all retailers using AI report lower operational costs. AI chatbots boost conversions by up to 35%; 87% say AI positively impacts revenue.

reduction in operations costs at major US banks through AI automation — with 40% fewer processing errors

Banking AI automation also cut audit preparation times by 50%. Compliance monitoring is one of the fastest-growing AI automation use cases.

net new jobs expected from AI by 2030 — the creation of 170M new roles minus 92M displaced

Enterprise sign-ups to AI courses on Coursera have exceeded 200,000 as organisations race to re-skill their workforces.

of tech workers are using AI tools — up from just 14% in 2024

The tech sector's near-universal AI adoption previews what other industries will experience as automation matures.

of customer service cases expected to be resolved by AI by 2027 — up from 30% in 2025

Automation is moving from task-level to full case resolution. By 2027, half of all service interactions may require no human input.

wage premium for workers with AI skills vs. the same role without

Up from 25% the year prior. As automation reshapes roles, AI-skilled workers command a growing premium across every industry.

jobs expected to be displaced by AI and automation globally by 2030

From the WEF Future of Jobs Report 2025. However, 170 million new roles are expected to be created — a net gain of 78 million jobs.

cashier and ticket clerk jobs projected to be lost by 2030 — the most of any single role

Administrative assistants (6M+), cleaners (2.5M+), and stock-keeping clerks (2M+) are also among the most at-risk roles.

of businesses expect AI adoption to decrease their employee headcount over the next year

20% expect a 3–10% reduction, while 4% anticipate drops exceeding 20%. Only 14% expect headcount increases.

of companies expect no or very little change in employee numbers due to AI from 2025 to 2026

Despite headline fears, the largest group of businesses expects stability — 11 percentage points more than those expecting reductions.

of common tasks in interpreter and translator roles can already be performed by AI

Interpreters top the list. Historians (91%), mathematicians (91%), and proofreaders (91%) are also highly exposed.

of common tasks in writer and author roles can be replicated by AI

Statistical assistants (85%), sales representatives (84%), and technical writers (83%) are similarly exposed.

of common tasks in data scientist roles can be replicated by AI

Even traditionally 'safe' technical roles face high automation exposure. Political scientists and geographers also score 77%.

of marketing professionals think AI will eliminate more jobs than it creates in the next three years

Concern is especially high among strategic and communications-based roles where AI content generation is already replacing entry-level work.

of job listings in translation and localisation reference AI — the highest of any industry

Two-thirds of translation job postings now mention AI skills, signalling massive transformation of the profession.

faster rate of skill change in AI-exposed jobs compared to non-exposed jobs

More than 2.5× faster than last year. The skills demanded in AI-exposed roles are changing at an accelerating pace.

of industries are increasing their use of AI — including less obvious sectors like mining and agriculture

No industry is immune from AI displacement pressure. Every single sector is increasing adoption.

of real estate job listings reference AI — the lowest of any industry

Construction and manufacturing are also below 15%. Physical, site-based roles remain least AI-exposed for now.

of workplace AI users worry that AI will make them look replaceable

Replaceability is the second-biggest AI workplace fear, behind only privacy and security concerns (48.8%).

of workers earning $200K+ fear AI could make their role redundant — more than double the rate of lower earners

High earners are the most anxious about AI displacement. Only 26.67% of workers earning $25K–$50K share this concern.

Gen Z workers are 'very concerned' about losing their job to AI within the next 5 years

24% of workers aged 18–34 rated their job loss concern at 8/10 or higher, reflecting entry-level vulnerability.

of workers have zero concerns about using AI at work

Over 90% of workers have at least some AI-related concern. Privacy, replaceability, and accuracy are the top three.

higher revenue-per-employee growth in industries most exposed to AI compared to least exposed

Despite displacement fears, AI-exposed industries are generating far more value per worker. Revenue growth has nearly quadrupled since 2022.

faster wage growth in industries most exposed to AI vs. least exposed

Wages are rising even in the most automatable roles, suggesting AI is making workers more valuable, not less.

growth in job availability for roles most exposed to AI — but 65% growth in less-exposed roles

Job growth is slower in AI-exposed roles but still positive. The gap suggests augmentation rather than mass replacement.

of the global workforce will require significant upskilling by 2030 due to AI

Nearly 6 in 10 workers globally need to acquire new skills to remain employable in an AI-transformed economy.

new jobs expected to be created by AI globally by 2030 — while 92 million are displaced

The net effect is 78 million new jobs. Enterprise AI course sign-ups on Coursera have already exceeded 200,000.

net new jobs from AI by 2030 after accounting for displacement — per the WEF's Future of Jobs Report

The creation-to-displacement ratio is nearly 2:1, meaning AI is a net job creator even in its most disruptive period.

of all US job listings are now specifically in the AI sector

Almost 1 in 50 job postings mention skills like NLP, neural networks, machine learning, or robotics. Up from just 0.7% in 2015.

of businesses expect AI to increase their employee headcount over the next year

While modest, this represents net new hiring driven specifically by AI capabilities — including 3% expecting 11–20% growth.

fastest-growing role category: big data specialists — followed by AI specialists and data warehousing specialists

From WEF's 2025 Future of Jobs Report. Autonomous vehicle specialists also feature in the top 10 fastest-growing roles.

AI companies operating in the UK in 2024 — a year-on-year rise of 58%

UK AI employment reached 86,000+ in 2024, up 33% from the prior year. Each firm represents multiple new AI-specific roles.

of IT and telecoms businesses are fully embracing AI adoption — the highest of any industry

98% of IT/telecoms firms use AI to some extent. This near-universal adoption creates continuous demand for new AI-skilled workers.

of organisations are at least experimenting with AI agents — 23% are already scaling them

Agentic AI is spawning entirely new job categories: agent designers, orchestration engineers, and AI operations specialists.

wage premium for workers with AI skills — comparing workers in the same job with and without AI skills

Up from 25% just one year prior. Every industry PwC analysed pays wage premiums for AI skills.

average salary uplift for AI skills in non-profit and NGO roles — the highest of any sector

Non-profits see the biggest AI salary premium at £21,000 more on average. Quality assurance (28.53%) and IT/networking (20%+) follow.

average salary premium for jobs that mention AI skills vs. those that don't — across all industries

Jobs citing AI skills pay approximately £2,930 more on average. The premium exists in nearly every sector.

higher revenue per employee in industries most exposed to AI vs. least exposed

AI-exposed industries generate 3× more revenue growth per worker. This value creation drives demand for AI-skilled employees.

of translation and localisation job listings now reference AI skills — the most of any industry

Software engineering and data science are the only other sectors where over 50% of roles mention AI. The industry is rapidly reshaping.

of jobs across all analysed industries now feature AI-related terms in their listings

AI Assistant, AI, and AI Automation are the three most commonly used AI-related terms in job descriptions across all sectors.

of tech workers are now using AI tools — up from just 14% in 2024

Tech sector adoption surged from 14% to 90% in one year. Code generation is a major use case, creating demand for AI-literate developers.

of product management, cybersecurity, and government job listings mention AI skills

AI skills are no longer limited to engineering. Non-technical industries are rapidly creating AI-adjacent roles.

growth in job availability for roles most exposed to AI — showing net creation, not destruction

Even highly AI-exposed roles are seeing job growth. Less-exposed roles grew 65%, but AI-exposed roles are still expanding.

enterprise sign-ups to AI courses on Coursera as organisations race to fill new AI roles

The surge in corporate AI training reflects the scale of new AI-related positions being created across enterprises.

people employed in the UK AI sector in 2024 — a 33% increase year-on-year

Up from 65,000 the prior year. The UK's AI workforce is growing faster than almost any other technology sector.

in global AI startup funding in 2025 — accounting for 49.9% of all startup funding globally

Nearly half of all global startup funding now goes to AI. This capital creates thousands of new companies and hundreds of thousands of new roles.

of people who use AI are now using it at work — across a wide range of tasks

The vast majority of AI users have brought the technology into their professional lives. Only 1% use AI exclusively at work.

of AI users use the tools every single day — a further 39.38% use them a few times per week

84.84% of AI users use it at least once a week. Daily usage is now the single most common frequency.

of workers who use AI at work use it for writing reports, emails, and presentations

Writing is the #1 AI use case at work. 63.48% also use it for editing, and 43.62% for data analysis.

of AI users have increased their usage in the past 12 months

48.49% say they use AI 'a lot more' than a year ago. Only 3.13% have decreased usage.

of businesses worldwide use AI in at least one business function — up from 78% the year prior

Almost 9 in 10 organisations have adopted at least one AI tool. 90% are either using AI or plan to introduce it.

of workplaces actively encourage AI use — a further 41.37% allow it in some situations

Only 3.78% of employers discourage or prohibit AI. 9.57% have no official stance at all.

of AI-using businesses have fully integrated AI across the entire organisation

62% of AI-using businesses haven't moved beyond the piloting phase. Just 28% are at the scaling stage.

of companies with 250–500 employees have embraced some level of AI adoption

Adoption increases with company size. This drops to 58% for sole traders — mid-sized firms (37%) are most likely to fully embrace AI.

of workers say AI has improved their productivity — over a third report a 'significant' improvement

Only 2.72% report decreased productivity. 16.43% see no change. The vast majority experience clear productivity gains.

average increase in workplace task output when using generative AI tools

At normal productivity growth rates, this would take 47 years in the US and 88 years in the EU to achieve naturally.

of companies say AI has improved their innovation efforts

Innovation was the most positively affected measure — 19 percentage points above any other. Market share was least impacted (25%).

of businesses using AI in marketing and sales report increased revenue — the highest of any function

Marketing/sales is the business function where AI most consistently drives revenue growth. Strategy and product development also exceed 60%.

of employees pay for their own AI tools at work — without employer support

A further 11.58% use a mix of personal and employer-funded tools. Over 4 in 10 workers are at least partially self-funding.

of employees receive little or no AI training from their employers

Only 47.04% feel they've received 'excellent' training. 19.5% report no support at all. 30.61% describe it as 'limited'.

of employees use at least one personal AI account for work tasks

Shadow AI is widespread. Half the workforce is using personal AI accounts professionally, often without oversight or approved data policies.

of workplace AI users use ChatGPT — making it the dominant tool in offices

Google AI Mode and Gemini maintain second and third place. Claude and Cursor are more popular at work than at home.

of workers say their experience with AI at work has been at least 'sometimes positive'

52.84% say it's entirely positive. Only 1.65% say it's not at all positive. Workplace AI satisfaction is overwhelmingly high.

of workplace AI users cite privacy and security as their #1 concern

Privacy beats replaceability fears (43.31%) and quality/accuracy concerns (42.12%) as the top workplace AI worry.

of AI users do not use the technology at work — 44.9% cite company policy, 55.1% opt out by choice

Women (11%) are more likely than men (6.2%) to opt out of AI at work. Workers aged 18–29 are also more likely to abstain.

of people describe their overall attitude to AI as at least 'somewhat positive'

Only 7.89% hold at least somewhat negative views. 91.4% enjoy using AI in everyday products and services.

of the global workforce will require significant upskilling by 2030 due to AI

Nearly 6 in 10 workers need new skills to remain competitive. The WEF calls the skills gap the most significant obstacle to AI transformation.

faster rate of skill change in AI-exposed jobs — more than 2.5× faster than last year

The skills required for AI-exposed jobs are transforming at an accelerating pace, making continuous learning non-negotiable.

of AI-using businesses have not moved beyond the piloting phase of AI adoption

Only 7% have fully integrated AI. The gap between adoption intent and execution is largely a skills and capability problem.

of people still report not using AI at all — either personally or professionally

One in five people remain entirely outside the AI revolution. A further 4.58% are unsure if they've ever used AI.

wage premium for workers with AI skills vs. the same role without — up from 25% the year prior

The premium has more than doubled in one year. Every industry PwC analysed pays higher wages for AI-skilled workers.

salary uplift for AI skills in non-profit/NGO roles — the largest of any sector at ~£21,000 extra

Non-profits and NGOs offer the biggest salary boost for AI skills, likely reflecting scarcity of tech talent in the sector.

average salary premium across all industries for jobs that mention AI skills

Jobs citing AI skills pay ~£2,930 more on average. The premium is positive in the majority of industries.

salary drop for healthcare roles that mention AI skills — the largest negative premium of any sector

Nine industries show lower pay for AI-skill roles. In healthcare and education, AI skills are more common in lower-paid, task-oriented positions.

of employees receive little or no AI training and support from their employers

Only 47.04% report 'excellent' training. 19.5% say they've received no support at all from leadership.

enterprise sign-ups to AI courses on Coursera as organisations race to re-skill their workforces

Corporate-sponsored AI training is surging. Coursera has become a primary channel for structured AI upskilling at scale.

of workplaces actively encourage AI use — but only 47% provide adequate training to match

There's a significant gap between employer encouragement and actual support. Enthusiasm outpaces investment in skills.

of employees pay for their own AI tools at work — self-funding their skill development

With inadequate employer support, workers are taking the initiative. A further 11.58% use a mix of personal and employer-funded tools.

of translation and localisation job listings now reference AI skills — the highest of any industry

Software engineering and data science are the only other sectors where over 50% of roles mention AI skills.

of all job listings across all industries now feature AI-related terms

AI skills have crossed from specialist to mainstream. Nearly a third of all postings reference some form of AI capability.

of real estate job listings mention AI — the lowest of any industry

Construction (<15%) and manufacturing (<15%) also lag. Physical, site-based industries are slowest to demand AI skills.

of tech workers now use AI tools — up from just 14% in 2024

The tech sector's near-universal AI adoption previews the skills transformation every other industry will experience.

of employers discourage or prohibit AI use — creating a skills-hostile environment

A small but significant minority of employers actively block AI skill development. 9.57% have no policy at all.

of high earners ($200K+) now use AI 'much more' than a year ago — leading the adoption curve

AI skill acquisition correlates strongly with income. Nobody earning above $100K reports using AI 'much less' than before.

of workers aged 18–29 who use AI at home actively choose not to use it at work

Young workers are the most AI-skeptical generation in the workplace — despite being comfortable with the technology personally.

of GenAI users still feel they're not familiar enough with the technology

88% are unclear about how AI will impact their lives, and a third feel it's not useful for them. The confidence gap remains wide.

worldwide AI spending forecast for 2026

A 44% increase year-over-year. AI infrastructure alone accounts for more than half of total spending.

projected worldwide AI spending in 2027

Continued rapid growth from $1.76T in 2025 to $2.53T in 2026 to $3.34T in 2027.

global AI market revenue in 2026

Growing from $294.16B in 2025. Covers AI hardware, software, and services vendor revenue. Forecast to reach $2,480B by 2034 at a CAGR of 26.60%.

projected generative AI market by 2032

Growing from $40B in 2022 at a CAGR of 42%. Driven by training infrastructure, inference devices, digital ads, and specialised software.

AI infrastructure spending in 2026

Up from $965B in 2025. AI infrastructure represents 54% of total worldwide AI spending and remains the largest segment.

in new AI infrastructure spending added in 2026

Technology providers continue to build out AI foundations. This incremental spend alone exceeds most entire technology categories.

increase in spending on AI-optimised servers in 2026

AI-optimised servers represent 17% of total AI spending. Demand from hyperscale cloud providers is the primary driver.

total data center systems spending in 2026

Up 31.7% from nearly $500B in 2025. Server spending alone is growing 36.9% year-over-year, driven by AI workloads.

worldwide AI services spending in 2026

Up from $439B in 2025 and projected to reach $761B by 2027. AI services is the second-largest spending segment after infrastructure.

worldwide AI software spending in 2026

Up from $283B in 2025 and projected to reach $636B by 2027. Software's share of the AI market continues to rise.

growth in generative AI model spending in 2026

GenAI models continue to experience the strongest growth of any AI spending segment. Their share of the software market is rising by 1.8% in 2026.

of the AI market accounted for by the software segment in 2026

AI software platforms are now accessible to non-data scientists, reducing reliance on hiring specialists and speeding up time-to-market.

North America AI market revenue in 2026

Up from $93.5B in 2025. North America captured 31.80% of the global market, driven by hyperscalers like Microsoft, IBM, and Google.

Asia Pacific AI market revenue in 2026

Growing from $83.75B in 2025 (28.50% global share). China is projected at $37.16B, Japan at $20.9B, and India at $18.08B in 2026.

Europe AI market revenue in 2026

Up from $65.48B in 2025 (22.30% share). The EU launched a $225B 'AI Continent Action Plan' in February 2025 to position Europe at the forefront of the AI revolution.

total worldwide IT spending in 2026

Up 10.8% from 2025. AI-related investment is the primary growth driver across data centre, software, and services segments.

CAGR for AI in healthcare — the fastest-growing vertical

AI is being used for diagnostics, personalised treatment, and administrative automation. Approximately 64% of patients are open to AI-powered 24/7 health support.

of the AI market held by BFSI — the largest industry vertical in 2025

AI enables personalised financial advice, real-time fraud detection, and 24/7 chatbot support in banking, financial services, and insurance.

CAGR for AI adoption among small and medium enterprises

SMEs are expected to register the highest growth rate of any enterprise segment. AI can drive 6–10% revenue increases for SMEs according to SAP research.

overall AI market CAGR from 2025 to 2034 — reaching $2,480 billion

The AI market is projected to grow more than 8× from $294B in 2025 to nearly $2.5T in 2034, making it one of the fastest-expanding technology sectors in history.

in global venture investment in Q1 2026 — an all-time record

Investors poured $300 billion into 6,000 startups globally in Q1 2026, up over 150% quarter over quarter and year over year. Q1 alone totaled close to 70% of all venture capital spending in 2025.

went to AI startups in Q1 2026 — 80% of total global venture funding

AI shattered records in Q1 2026 with $242 billion, representing 80% of all global venture funding. The previous record was set in Q1 2025 when AI accounted for 55% of global VC.

in total AI investment in 2025 — 50% of all global venture capital

Full-year 2025 AI investment represented the most concentrated technology investment in history — approximately 50% of all global venture funding flowed to AI companies, up from 34% in 2024.

of global Q1 2026 venture capital went to US-based companies ($250B)

U.S.-based companies raised $250 billion, or 83% of global VC in Q1 2026 — up significantly from 71% in Q1 2025. China followed with $16.1 billion and the U.K. with $7.4 billion.

consensus estimate for 2026 hyperscaler AI capex

Wall Street consensus for 2026 hyperscaler capital spending is now $527 billion, up from $465 billion at the start of Q3 2025 earnings season, continuing a trend of upward revisions.

in global data center construction projected through 2028

Morgan Stanley Research estimates ~$2.9 trillion in global data center construction cost through 2028, with more than 80% of that spending still ahead. This feeds directly into industrial output and power investment.

in AI-related spending projected for 2026

Hyperscalers such as Microsoft, Alphabet, Amazon, and Meta are engaged in an AI infrastructure arms race. Capital spending is expected to rise by more than 34% again in 2026.

of US GDP growth this year is estimated to come from AI-related investment

AI-related investment now looks more like industrial buildout than speculative tech spending. Data center construction, power investment, and services spend provide real macro support.

raised by just 4 companies in Q1 2026 — 65% of all global VC that quarter

Four of the five largest venture rounds ever recorded were closed in Q1 2026: OpenAI ($122B), Anthropic ($30B), xAI ($20B), and Waymo ($16B) collectively raising $188 billion.

added to unicorn valuations in Q1 2026 — largest single-quarter bump ever

The Crunchbase Unicorn Board added $900 billion in value during Q1, marking the largest valuation bump in a single quarter. Outsized mega-rounds pushed overall startup valuations higher.

captured by foundation model companies in 2025 — 40% of all global AI funding

Foundation model companies — those building the large language models that serve as infrastructure for AI applications — captured $80 billion in 2025. OpenAI and Anthropic combined captured 14% of all global VC across all sectors.

of mega-rounds (deals >$500M) in late 2025 went to AI companies

The concentration of mega-round activity into AI is unprecedented. Geographic concentration is equally pronounced: the San Francisco Bay Area alone received $122 billion (76% of all US AI funding) in 2025.

in late-stage funding in Q1 2026 — up 205% year over year

The Q1 funding surge was concentrated in late-stage funding across 584 deals. A total of $235 billion was invested in 158 companies that raised rounds of $100 million and more.

in early-stage funding in Q1 2026 — up 41% year over year

Early-stage funding totaled $41.3 billion across 1,800 deals. Much of the increase went to Series A rounds.

in seed funding in Q1 2026 — up 31% year over year

Seed funding grew 31% year over year, though the increase was entirely due to larger rounds — deal counts fell 30% YoY to 3,800 as capital concentrated into fewer, bigger bets.

valuation premium for seed-stage AI startups vs. non-AI peers

Seed-stage AI startups command median pre-money valuations of approximately $17.9 million — 42% higher than non-AI startups. Series A AI valuations average $51.9M, 30% above non-AI peers.

of companies plan to increase their AI budgets in 2026

Among 3,200+ survey respondents, 86% said their AI budget will increase in 2026 and 12% said budgets will stay the same. Nearly 40% said budgets will increase by 10% or more.

of companies report AI has increased their annual revenue

Nearly a third (30%) said the revenue increase was greater than 10%. Similarly, 87% said AI helped reduce annual costs, with 25% reporting cost decreases greater than 10%.

in enterprise AI spending in 2025 — a threefold year-over-year increase

Enterprise AI spending reached $37 billion in 2025 according to Menlo Ventures, with roughly equal splits: $19 billion in user-facing AI products and $18 billion in AI infrastructure.

faster cash-flow margin expansion for AI adopters vs. the global average

21% of S&P 500 companies now cite AI benefits (up from 10% in 2024). Those delivering measurable results are seeing cash-flow margin expansion at roughly double the global average.

AI companies operate worldwide as of 2025

Approximately 90,904 artificial intelligence companies now operate globally. Roughly 32.5% (29,618) are based in the United States, home to OpenAI, Anthropic, and Hugging Face.

newly funded AI startups in the United States — more than the next 14 countries combined

The US leads with 6,956 newly funded AI startups, followed by China (1,605), the UK (885), Israel (492), and Canada (481). US private AI investment reached $109.1 billion in 2024 — 12× China's.

of tech unicorns in 2026 are AI-related startups

Half of all tech unicorns in 2026 are AI-related, showing strong investor confidence in the sector. AI-native startups reached unicorn status roughly one year faster than traditional SaaS peers.

of entrepreneurs now incorporate AI as a core component of their startup

According to Techstars' 2024 Startup Outlook Survey, nearly three-quarters of entrepreneurs have AI as a key component or enabler of their business model, up sharply from prior years.

in total AI startup investment in 2025 — 50% of all global VC

AI startups attracted $202.3 billion in 2025, approximately half of all global venture funding. This represented a 75%+ year-over-year increase from $114 billion in 2024.

of global AI venture capital went to US-based startups in 2025

US-based AI startups received $159 billion of the $202.3 billion total. The San Francisco Bay Area alone captured $122 billion — 60% of global AI funding in a single metro.

of AI funding went to megarounds of $500M or more

Megarounds dominated AI funding. SoftBank led with a $40 billion investment in OpenAI — the single largest venture deal. PE-led deals totaled $63 billion across ~300 rounds, while VCs led 75% of all deals.

flowed to generative AI startups in H1 2025 alone — surpassing the entire 2024 total

Global venture funding in generative AI reached $49.2 billion in the first half of 2025, already exceeding the full-year 2024 figure. Investors shifted focus toward companies demonstrating measurable operational leverage.

for AI-native startups to hit $30M ARR — vs. 60+ months for traditional SaaS

Investor roundtable data shows AI companies reaching $30M ARR within 20 months compared to over 60 months for traditional SaaS. Top-performing 'AI supernova' startups hit $125M ARR by year two with $1.13M ARR per FTE — 4–5× above typical SaaS benchmarks.

revenue per employee at top AI startups — 6× the SaaS average

The most successful AI-native startups generate $3.48 million in revenue per employee, approximately six times the average for traditional SaaS companies. AI automation of routine tasks enables exceptionally lean operations.

in annualized revenue for AI-native startups as of May 2025

AI-native startups collectively surpassed $15 billion in annualized revenue by May 2025. Enterprise AI spending reached $37 billion in 2025, a threefold year-over-year increase per Menlo Ventures.

of AI-using SaaS startups are profitable — vs. 54% for non-AI peers

SaaS Capital data shows 61% of AI-using SaaS startups are profitable compared to 54% of their non-AI peers. AI-first startups also report higher post-money caps: 62% above $10M, and 12% above $20M.

average employees at the top 10 AI startups by revenue per head

The highest-performing AI startups by revenue per employee operate with remarkably lean teams. Top-performing AI-native startups operated with teams 40%+ smaller than comparable traditional SaaS peers.

open AI roles worldwide — vs. only 518K qualified candidates

The AI talent gap is severe: 1.63 million open AI-related roles globally face a supply of just 518,000 qualified candidates. About 80% of companies are prioritizing upskilling existing staff to bridge this gap.

pay premium for AI roles compared to typical software engineering positions

AI-specialized roles command roughly 67% higher compensation than traditional software engineering positions. AI engineer hiring grew ~25% year over year as demand intensified across all sectors.

barrier to AI integration: the AI skills gap, per Deloitte's 2026 enterprise AI survey

Deloitte surveyed 3,235 senior leaders across 24 countries and found the AI skills gap is the single biggest barrier to integration. Education — not role or workflow redesign — was the #1 way companies adjusted their talent strategies.

of enterprise generative AI pilots stall or fail to deliver measurable P&L impact

MIT's NANDA initiative found that about 95% of enterprise AI pilot programs fail to achieve rapid revenue acceleration. The core issue is not model quality but flawed enterprise integration — generic tools stall because they don't learn from or adapt to existing workflows.

of YC's Spring 2025 batch is building AI agents

Nearly half of Y Combinator's Spring 2025 cohort is focused on agentic AI — autonomous systems that can learn, remember, and act independently within set boundaries. This signals a major shift from copilot-style tools to fully autonomous AI workflows.

of startups are now paying for at least one AI tool

Roughly 70% of startups are paying for at least one AI tool, with 76% of SaaS companies embedding AI directly into their products and 69% using it in operations. Spending on AI-native tools grew 75.2% year over year.

of top AI applications can be adopted without an enterprise license

Analysis of the top 50 AI application-layer companies by startup spend shows nearly 70% can be adopted by individuals and brought into teams without enterprise contracts. Products are moving from consumer → prosumer → enterprise faster than any prior software era.

global generative AI market size in 2025 — projected to reach $1,005 billion by 2034

The gen AI market is growing at a CAGR of 44.2% from 2025 to 2034, one of the fastest technology market expansions ever recorded.

CAGR for the generative AI market between 2025 and 2034

Gen AI growth far outpaces the broader AI market (18.73% CAGR) as enterprises accelerate adoption across every function.

of generative AI revenue comes from North America — the dominant region globally

North America leads gen AI revenue, followed by Europe (28%) and Asia Pacific (22%), which is the fastest-growing region at 27.6% CAGR.

of the gen AI market belongs to the media and entertainment industry — the largest vertical

Media and entertainment leads gen AI adoption, followed by automotive and transportation (22%) and financial services (14%).

weekly active users on ChatGPT as of February 2026 — more than double the 400M reported a year earlier

ChatGPT's user base doubled in 12 months. India alone reached 100 million weekly active users, making it the second-largest market after the U.S.

in annual recurring revenue for OpenAI as of June 2025 — spanning consumer, business, and API products

OpenAI's revenue tripled from $3.7B in 2024, fuelled by 10M ChatGPT Plus subscribers and 3M paying business users.

market share held by ChatGPT in the generative AI chatbot market

ChatGPT dominates with ~80% share. The nearest competitors are Gemini (~8%), DeepSeek (~7.5%), and Perplexity (~3.3%).

queries processed per day on ChatGPT as of July 2025

From 10M queries/day at launch to 2.5B daily — a 250× increase reflecting ChatGPT's integration into daily workflows worldwide.

of organisations increased their generative AI investment since 2023 — with zero decreasing investment

Capgemini surveyed 1,100 executives at $1B+ revenue companies across 14 countries. The remaining 20% maintained their investment level.

of large organisations have integrated gen AI into some or most functions — a 4× increase in 12 months

Up from just 6% the prior year, showing rapid movement from pilots to production across enterprise operations.

of marketing and advertising companies have adopted gen AI — the highest sector adoption rate

Marketing leads at 37%, followed by technology (35%), consulting (30%), teaching (19%), accounting (16%), and healthcare (15%).

as many leaders report transformative AI impact in 2026 compared to the prior year

Deloitte surveyed 3,235 leaders across 24 countries. However, only 34% say they are truly reimagining their business with AI.

rise in worker access to AI tools during 2025 alone

Deloitte's survey of 3,235 leaders found that worker AI access rose by half in a single year, and companies with 40%+ of AI projects in production are set to double within six months.

of Gen Z workers have adopted generative AI in their workplace — the highest of any generation

Gen Z leads at 29%, closely followed by Gen X (28%) and Millennials (27%), showing broad generational adoption.

monthly visits to ChatGPT.com in February 2026 — with 73% from desktop and 27% from mobile

ChatGPT's monthly traffic rivals major social platforms. The U.S. accounts for 18.86% of visits, followed by India (9.76%) and Brazil (5.08%).

total ChatGPT app downloads across iOS and Android since launch in May 2023

Monthly app downloads peaked at 73.4 million in December 2025, reflecting gen AI's shift from web-only to mobile-first usage.

of organisations enforce a complete ban on public gen AI tools — virtually all now allow controlled adoption

The era of gen AI bans is over. Even conservative industries have moved to governance-based adoption rather than prohibition.

of organisations plan to integrate AI agents within the next 1–3 years

The shift from generative AI copilots to autonomous AI agents is the next frontier, with overwhelming enterprise intent to adopt.

decrease in inference costs for GPT-3.5-level models — making gen AI economically accessible at scale

Plummeting costs mean gen AI can now be deployed for low-margin tasks that were previously uneconomical to automate.

of companies say they are truly reimagining their business with AI — the majority still use it at surface level

Despite surging investment, most organisations are still at the productivity stage rather than transformative reinvention.

of organisations plan to integrate AI agents within the next 1–3 years

Capgemini surveyed 1,100 executives at $1B+ revenue companies. The intent to deploy autonomous agents spans every industry.

of companies use agentic AI at least moderately today — poised to rise sharply in two years

Deloitte surveyed 3,235 leaders across 24 countries. Agentic AI usage is expected to increase significantly within the next two years.

of executives are actively using AI agents in production workflows

Nearly half of executives have moved beyond experimentation to deploying agents in real-time operations.

of companies are already deploying AI solutions — with a growing interest in agentic capabilities

McKinsey found that companies deploying AI solutions are increasingly exploring frontier technologies like agents for autonomous workflows.

companies has a mature model for governance of autonomous AI agents

Despite surging adoption, oversight is lagging. Most organisations lack frameworks for monitoring and controlling autonomous agent behaviour.

of companies believe their strategy is highly prepared for AI adoption — up from prior year

Strategy confidence is rising, but companies feel less prepared on infrastructure, data, risk management, and talent readiness.

of organisations enforce a complete ban on public gen AI tools — virtually all now allow AI use

The shift from prohibition to governance is near-complete. Organisations are building guardrails rather than blocking AI entirely.

of companies say they are truly reimagining their business with AI — the rest use it at surface level

37% are redesigning key processes, while 30% are using AI at only a surface level. Agentic AI may accelerate the move to deeper transformation.

of organisations report that generative AI has improved productivity in their operations

AI agents amplify this by automating multi-step workflows end-to-end rather than just assisting individual tasks.

of organisations report positive ROI from AI within the first year of deployment

Agentic AI accelerates ROI by handling complete workflows autonomously — from customer inquiries to supply chain decisions.

average improvement in customer engagement and satisfaction from gen AI deployment

AI agents that handle customer interactions 24/7 drive measurable improvements in satisfaction and engagement scores.

of organisations report direct cost savings from AI deployment

Agents that autonomously handle processes deliver compounding cost savings as they eliminate manual handoffs.

of companies report at least limited use of physical AI today — set to reach 80% in two years

Physical AI includes robots, digital twins, and IoT-driven systems that operate autonomously with AI agents.

of organisations increased their generative AI investment since 2023

Investment growth is fuelling the transition from gen AI copilots to fully autonomous agents across enterprise workflows.

of large organisations have integrated gen AI into some or most functions — a 4× increase in 12 months

Up from 6% a year prior. Integrated gen AI provides the foundation on which autonomous agents are built.

rise in worker access to AI tools during 2025 alone — expanding the agent deployment surface

As more workers gain AI access, organisations are deploying agents that handle routine tasks so humans can focus on strategic work.

of companies expect to use physical AI within two years — with Asia Pacific leading

Physical AI — robots, autonomous logistics, digital twins — will extend agentic capabilities from software into the physical world.

of AI projects expected in production within six months — double the current rate

The number of companies with 40%+ of experiments in production is set to double, signalling a mass shift from pilots to deployment.

of interpreter and translator tasks can be replicated by AI — showcasing agent capability breadth

AI agents can now handle complex language tasks at near-human quality, enabling autonomous multilingual customer service and documentation.

decrease in AI inference costs — making always-on agents economically viable

Plummeting costs mean organisations can run AI agents continuously without prohibitive compute expenses.

global machine learning market size in 2025 — projected to reach $1,710 billion by 2035

The ML market is expanding at a CAGR of 33.66% from 2026 to 2035, driven by automation, cloud adoption, and data-driven decision-making.

of the total AI market belongs to machine learning — the largest technology segment

ML leads all AI technology segments, ahead of deep learning, NLP, machine vision, and generative AI.

in ML revenue within the broader $757.58B AI market — reflecting ML's foundational role

ML is the backbone of most AI applications: recommendation engines, fraud detection, predictive analytics, and autonomous systems.

U.S. machine learning market size in 2025 — expected to reach $380.59B by 2035

The U.S. ML market alone is growing at a 34% CAGR, driven by tech giants, government initiatives, and surging computing power.

of organisations increased their AI and ML investment since 2023 — with zero decreasing spending

Investment growth spans infrastructure, talent, and ML platform licensing. The remaining 20% maintained their spending level.

of large organisations have integrated ML/AI into some or most business functions

A 4× increase from just 6% a year prior, showing rapid movement from ML experiments to production deployment.

rise in worker access to AI and ML tools during 2025 alone

ML-powered tools — from analytics dashboards to predictive models — are becoming standard across business functions.

of the global ML market is held by North America — the largest regional share

North America leads due to tech giant R&D, advanced cloud infrastructure, and favourable government AI initiatives.

of the AI market revenue comes from software — the primary delivery vehicle for ML models

ML software — SageMaker, Vertex AI, Azure ML — dominates AI spending as enterprises move from custom builds to managed platforms.

of AI projects expected in production within six months — double the current rate

MLOps maturity, automated pipelines, and managed platforms are closing the gap between ML experimentation and production.

decrease in inference costs for GPT-3.5-level models — making ML deployment dramatically cheaper

Falling compute costs enable organisations to deploy ML models at scale without prohibitive infrastructure spending.

annual decline in AI hardware costs — accelerating ML adoption across industries

Cheaper GPUs, TPUs, and edge devices make it practical to run ML inference continuously in production environments.

of AI market end-use revenue comes from BFSI — the largest vertical for ML applications

Banking, financial services, and insurance lead ML adoption for fraud detection, credit risk assessment, and algorithmic trading.

CAGR for healthcare AI — the fastest-growing end-use segment

ML in healthcare is accelerating for medical imaging, drug discovery, clinical decision support, and personalized medicine.

in AI revenue from manufacturing — driven by ML-powered quality control and predictive maintenance

Manufacturing AI revenue grew from $43.44B in 2022 to $61.49B in 2024, with ML enabling defect detection and supply chain optimisation.

of organisations report positive ROI from ML/AI within the first year of deployment

ML models in production deliver measurable business returns quickly — with $3.20 returned for every $1 invested within 14 months.

CAGR for the generative AI segment — the fastest-growing AI technology built on ML foundations

Generative AI is the ML frontier, but all gen AI models are built on machine learning foundations — transformers, attention mechanisms, and training at scale.

CAGR for AI in cybersecurity — the fastest-growing function for ML deployment

ML-powered threat detection, anomaly identification, and automated response are becoming essential as cyber threats grow more sophisticated.

of organisations plan to deploy AI agents within 1–3 years — the next evolution of ML in production

Autonomous ML-powered agents represent the next frontier: systems that plan, execute, and iterate without constant human oversight.

annual improvement in AI energy efficiency — making large-scale ML training more sustainable

Improving energy efficiency addresses a key ML concern: the environmental impact of training and running large models at scale.

global NLP market size in 2025 — projected to reach $791.16 billion by 2034

The NLP market is growing at a 38.40% CAGR (2025–2034), fuelled by chatbots, search, document processing, and voice assistants.

in NLP revenue within the broader $757.58B AI market — reflecting NLP's strategic importance

NLP accounts for the third-largest AI technology segment behind deep learning and ML, powering conversational AI and text analytics.

U.S. NLP market size in 2024 — the largest single-country market globally

The U.S. leads NLP adoption due to tech giants' massive investment, English-language dominance in training data, and enterprise demand.

of the global NLP market is held by North America — the largest regional share

Asia-Pacific is the fastest-growing region, but North America still leads due to first-mover advantage and cloud infrastructure.

weekly active users on ChatGPT as of February 2026 — the most-used NLP application ever built

ChatGPT grew from 100M users in early 2023 to 900M weekly actives, demonstrating unprecedented consumer adoption of NLP technology.

queries processed daily by ChatGPT — roughly 10× Google's search volume growth rate

The volume of natural language queries illustrates how NLP has become the primary interface between humans and AI.

of the generative AI chatbot market held by ChatGPT — dominant market share

Competitors like Gemini, Claude, and Copilot share the remaining 20%, reflecting ChatGPT's first-mover and network effects.

annualised revenue run rate for ChatGPT — proving NLP's commercial viability

OpenAI reached $10B ARR by June 2025, up from roughly $2B in late 2023, demonstrating rapid monetisation of NLP capabilities.

of the NLP market comes from solution software — platforms, APIs, and prebuilt models

Enterprises prefer NLP-as-a-service solutions over building custom models, driving the solution segment's dominance.

rise in worker access to AI/NLP tools during 2025 — democratising text intelligence

NLP tools for summarisation, translation, and content generation are now standard offerings in enterprise productivity suites.

of organisations deploying AI solutions — many powered by NLP for customer service and analytics

NLP is the most user-facing AI technology: chatbots, document analysis, sentiment tracking, and voice assistants all depend on it.

of enterprises are truly reimagining their business with AI — NLP is a key enabler

NLP capabilities like semantic search, intelligent document processing, and conversational interfaces are central to business reinvention.

decrease in inference costs for GPT-3.5-level NLP models — enabling ubiquitous deployment

Dramatically lower costs mean NLP is now economically viable for tasks that were prohibitively expensive just two years ago.

of interpreter tasks can be replicated by NLP systems — human-level language comprehension

NLP models now match human performance on translation, summarisation, and language understanding benchmarks.

monthly visits to ChatGPT — making it the most-trafficked NLP interface globally

Traffic volume reflects NLP's transition from a niche technology to a mainstream consumer and enterprise tool.

cumulative ChatGPT mobile app downloads — NLP in every pocket

Mobile adoption shows NLP is being used for on-the-go tasks: quick answers, voice interaction, image analysis, and real-time translation.

CAGR for the NLP market through 2034 — among the fastest-growing AI segments

NLP growth outpaces the broader AI market (19.1% CAGR), driven by conversational AI, enterprise search, and multilingual demand.

of organisations plan to deploy NLP-powered AI agents within 1–3 years

AI agents — autonomous systems that understand and generate language — represent the next frontier for enterprise NLP deployment.

of enterprises already using agentic AI at least moderately — built on NLP foundations

Agentic AI systems require NLP for understanding instructions, generating responses, and coordinating with human users.

average improvement in customer engagement from NLP-powered AI — proving business value

NLP-powered chatbots, sentiment analysis, and personalised content generation deliver measurable customer experience improvements.

global computer vision market size in 2025 — projected to reach $58.29 billion by 2030

The CV market is growing at a 19.8% CAGR (2025–2030), driven by quality inspection, autonomous systems, and medical imaging.

in machine vision revenue within the broader $757.58B AI market — reflecting CV's industrial scale

Machine vision within the AI market is much larger than the standalone CV market because it includes embedded vision in AI systems.

global CV market size in 2024 — establishing the baseline for 19.8% CAGR growth

Year-over-year growth from $19.82B (2024) to $23.62B (2025) represents a 19.2% increase, consistent with forward projections.

CAGR for the computer vision market from 2025 to 2030

Steady double-digit growth reflects expanding use cases in automotive, healthcare, retail, and industrial automation.

of the computer vision market revenue comes from hardware — cameras, sensors, GPUs, and processors

Hardware dominance reflects the sensor-intensive nature of CV: industrial cameras, LiDAR, depth sensors, and edge AI processors.

of the CV market is software — computer vision platforms, APIs, and analytics tools

Software is the faster-growing segment as cloud-based CV APIs and edge inference platforms reduce the need for custom hardware.

annual decline in AI hardware costs — making computer vision deployment more affordable

Cheaper GPUs (NVIDIA, AMD) and purpose-built vision processors (Google TPUs, Intel Movidius) lower the barrier for CV adoption.

annual improvement in AI energy efficiency — critical for always-on computer vision systems

CV systems run continuously in factories, hospitals, and vehicles — energy efficiency directly impacts operational cost and feasibility.

of the global computer vision market revenue comes from Asia Pacific — the largest regional share

Asia Pacific leads due to massive manufacturing scale in China, Japan, and South Korea, plus automotive and electronics demand.

of the CV market is held by North America — driven by tech giants and autonomous vehicle R&D

North America is the second-largest region, led by NVIDIA, Google, Intel, and autonomous vehicle companies like Waymo and Tesla.

of the broader AI market held by North America — providing infrastructure for CV innovation

North America's overall AI leadership creates the R&D ecosystem, cloud infrastructure, and talent pipeline that CV companies depend on.

total AI market size in 2025 — of which computer vision captures a growing share

CV's $23.62B market is roughly 3% of total AI spending, but growing as visual AI moves from niche to mainstream.

is the dominant application segment for computer vision — powered by manufacturing demand

Quality assurance and inspection drive the most CV revenue, with automated visual inspection replacing manual quality checks.

in AI revenue from manufacturing in 2024 — a major driver of industrial computer vision

Manufacturing's large AI spend includes CV for defect detection, robot guidance, packaging verification, and safety monitoring.

CAGR for healthcare AI — propelling CV in medical imaging, pathology, and diagnostics

Medical imaging is one of CV's highest-value applications: radiology, retinal screening, pathology slide analysis, and surgical guidance.

of organisations report positive ROI from AI within the first year — including CV deployments

CV projects in visual inspection, security, and healthcare deliver fast payback due to replacing labour-intensive manual processes.

of organisations expect to use physical AI (including CV) — growing from 58% today to 80% within 2 years

Physical AI — robots, drones, autonomous vehicles — all depend on computer vision as their primary sensing modality.

of organisations plan to deploy AI agents within 1–3 years — many requiring vision capabilities

Multimodal AI agents that can see, understand, and act on visual information represent the convergence of CV and agentic AI.

decrease in AI inference costs — making real-time computer vision economically viable at scale

Plummeting inference costs mean CV can be deployed continuously on video streams rather than selectively on still images.

as many leaders report AI is having transformative impact — including visual AI applications

Computer vision's move from experimental to transformative mirrors the broader AI maturity curve across enterprises.

global AI in healthcare market size in 2025 — projected to reach $1.03 trillion by 2034

The market is growing at a CAGR of 43.96% from 2026 to 2034, one of the fastest growth rates among all AI verticals.

of the AI healthcare market is held by North America — the dominant region globally

North America's market was valued at $17.51B in 2025, driven by advanced healthcare infrastructure and high AI integration.

CAGR for the global AI in healthcare market from 2026 to 2033

Grand View Research estimates the market at $36.67B in 2025, reaching $505.59B by 2033. Both major research firms agree on explosive growth.

of healthcare, pharma, and medical companies use generative AI — the lowest of the top 8 industries

Despite being the lowest among top sectors, 63% adoption still represents massive penetration. Technology leads at 88%.

of healthcare organisations are currently utilising AI technology

A March 2024 Microsoft-IDC study found that AI is already embedded across diagnostics, imaging, patient monitoring, and administration.

returned for every $1 invested in healthcare AI — ROI realised within 14 months on average

Healthcare AI delivers rapid, measurable returns. The 14-month payback period is among the fastest of any enterprise AI investment.

of healthcare and life sciences leaders report positive ROI from AI within the first year

The second-annual Google Cloud ROI of AI report confirms that gen AI initiatives continue to deliver compounding returns.

of healthcare and life sciences executives are actively using AI agents in production

34% have launched more than 10 agents. Use cases span tech support, patient experience, and inventory management.

of pharma and life sciences professionals use AI in drug discovery

AI can reduce drug discovery from 5–6 years to just one year. Robot-assisted surgery held the largest application segment share at 22.94%.

of healthcare organisations report that generative AI has improved productivity

Productivity is the most commonly cited improvement. 61% also report enhanced patient experience and 52% cite business growth.

of interpreter and translator tasks in healthcare can be replicated by AI

AI's ability to handle language-based tasks has major implications for multilingual patient care and medical record processing.

salary drop for healthcare roles mentioning AI — the largest negative premium of any sector

AI skills are more commonly referenced in lower-paid, task-oriented healthcare roles. Higher-paid clinical positions remain more resistant to automation.

of health practitioners' weekly work hours spent using generative AI — saving 1.3% of total time

Health practitioners have higher AI usage than education or sales roles, though time savings are lower than management or computer/math occupations.

of healthcare organisations report AI improvements in security — a critical area for patient data

Data privacy is the top concern when choosing an LLM provider (37%), followed by costs (30%) and ease of use (27%).

of the AI healthcare market goes to hospitals and clinics — the largest end-user segment

Hospitals drive adoption through AI-powered physician workflows, documentation automation, and clinical decision support.

machine learning share of healthcare AI technology — the dominant technology segment in 2025

Machine learning excels at extracting insights from EHRs, imaging, genomic data, and wearables to improve patient outcomes.

of healthcare leaders plan to allocate 50%+ of future AI budgets specifically to AI agents

Agentic AI is the new strategic differentiator — agents automate prior authorizations, clinical documentation, and patient scheduling.

global health worker deficit projected by 2033 — accelerating AI adoption for capacity

The shortage of healthcare workers is a primary driver of AI adoption. AI algorithms help with rapid diagnosis and treatment planning.

of North America's AI healthcare market is in the U.S. — the world's largest single country market

The U.S. market is projected to reach $22.7B by 2026. GE HealthCare's partnership with AWS for AI diagnostics exemplifies the growth.

of healthcare executives cite data privacy as their top consideration when choosing an AI provider

In a highly regulated industry, data security outranks cost (30%) and ease of use (27%) when selecting AI technology partners.

global AI in banking market size in 2025 — projected to reach $379.41B by 2034

The market is growing at a CAGR of approximately 30.5%, driven by fraud detection, loan processing, and risk management.

of the financial sector has fully embraced AI — the second-highest industry after IT/telecoms

Financial services trails only IT/telecoms (51%) in full AI adoption. 28% of admin/support and 30% of legal have embraced AI.

potential annual value AI could add to global banking — per McKinsey estimates

McKinsey concluded that generative AI alone could add $200–340B per year in value across the banking sector.

salary uplift for finance and banking roles that mention AI — the third-highest premium of any sector

Finance AI roles command a small but positive salary premium, behind only IT/tech (+1.89%) and engineering (+0.93%).

saved by the banking industry through AI-powered fraud detection

AI can detect fraud attempts in real time by analysing patterns across millions of transactions instantly.

fewer processing errors in banking operations due to AI automation

AI reduces manual data entry errors and improves compliance checking across loan processing and KYC onboarding.

reduction in audit preparation time with AI-assisted compliance tools

AI automates document gathering, cross-referencing, and anomaly detection that traditionally consumed weeks of human effort.

average cost reduction across banking operations achieved through AI deployment

Operational cost savings span customer service, compliance, fraud detection, and back-office processing.

of banks worldwide are actively investing in AI technology

The near-universal adoption reflects AI's role as a competitive necessity rather than a differentiator in financial services.

of banking industry leaders have already implemented generative AI

Three-quarters of banking leaders have moved beyond pilots to active gen AI deployment in customer-facing and internal operations.

of North American banks are either using or planning to use AI

North America leads global banking AI adoption, with virtually all banks committed to AI integration.

of banking and finance professionals reported using AI tools in their work

Banking/finance is the third-highest AI-using industry after advertising (37.7%) and retail/ecommerce (35.2%).

reduction in call centre costs achieved by banks deploying AI chatbots

AI chatbots handle routine enquiries — balance checks, transaction history, card activations — that previously required human agents.

improvement in loan processing efficiency through AI underwriting automation

AI accelerates credit assessment, income verification, and document analysis, reducing approval times from weeks to hours.

net cost reduction possible across banking operations when AI is fully scaled

Full-scale AI deployment across front-office, middle-office, and back-office functions delivers the highest aggregate savings.

average improvement in customer engagement and satisfaction where gen AI has been deployed

Financial services organisations using gen AI report measurable customer satisfaction gains across digital channels.

of organisations plan to integrate AI agents within 1–3 years

The shift from chatbots to autonomous AI agents is accelerating — financial services is among the most aggressive adopters.

of organisations increased their gen AI investment since 2023 — with 20% maintaining levels

No major organisation has decreased gen AI investment, signalling sustained confidence in the technology's financial returns.

of large organisations have integrated gen AI into some or most locations — up from 6% a year ago

The 4× increase in gen AI integration in just 12 months demonstrates the speed at which financial institutions are scaling AI.

of organisations enforce a complete ban on public gen AI tools — down significantly from 2023

The era of blanket AI bans is effectively over. Even highly regulated financial firms now allow controlled gen AI use.

of real estate job listings mention AI — the lowest proportion of any industry surveyed

Real estate lags behind IT/telecoms (51%), finance (31%), and legal (30%) in AI-related hiring, indicating early-stage adoption.

of large organisations have increased their gen AI investment since 2023

Real estate firms are part of this cross-industry investment wave, with gen AI applied to listings, valuations, and client communications.

of large organisations have integrated gen AI into some or most functions — up from just 6% a year earlier

This 4× increase in just 12 months signals that real estate companies investing in AI are scaling quickly beyond pilots.

of organisations plan to integrate AI agents within the next 1–3 years

AI agents that handle property enquiries, schedule viewings, and qualify leads autonomously are a key focus for proptech.

national median error rate for Zillow's AI-powered Zestimate property valuations

Zillow's neural network, trained on millions of photos and home values, delivers valuations with remarkable accuracy across the U.S.

property parcels processed by Quantarium's deep learning valuation model in the U.S.

Mortgage lenders and construction companies rely on AI-powered automated valuation models (AVMs) that process massive property datasets.

in deals closed through AI-powered Crexi commercial real estate platform

Crexi's AI solutions helped users market over $5 trillion in property value, demonstrating AI's scale in commercial real estate.

residential real estate transactions per month processed on Entera's AI investing platform

Entera uses AI for intelligent analytics across 24+ U.S. markets to help investors find, buy, and operate single-family homes.

of advertising professionals use AI — the highest of any industry, driving real estate marketing growth

Real estate marketing increasingly leverages AI tools pioneered in advertising — automated listing descriptions, targeted ads, and lead scoring.

boost in conversion rates from AI chatbots in customer-facing applications

Property management and brokerage firms deploying AI chatbots for lead qualification report conversion improvements comparable to ecommerce.

average improvement in customer engagement where organisations have deployed gen AI

Real estate firms using gen AI for personalised property recommendations and automated follow-ups see measurable engagement gains.

of consumers are more likely to shop with brands offering personalised recommendations

In real estate, personalised property suggestions based on browsing behaviour and preferences are becoming standard on major platforms.

of interpreter and translator tasks can be replicated by AI — critical for multilingual property services

AI translation enables real estate platforms to serve international buyers and tenants without human translators.

of organisations in manufacturing and software engineering report AI cost savings — real estate follows the same pattern

Property management firms adopting AI for maintenance requests, tenant screening, and lease management report comparable operational savings.

of organisations enforce a complete ban on public gen AI tools — virtually all now allow controlled use

Real estate firms have moved from blanket AI bans to controlled adoption, with agents using gen AI for listings, emails, and market analysis.

of retailers report AI reduced operational costs — a benchmark for property management firms

Property management shares many operational parallels with retail — inventory (listings), customer service, and transaction processing.

major AI-powered real estate companies actively reshaping the industry in 2025

Companies like Zillow, Redfin, Opendoor, Cherre, and EliseAI are deploying AI across valuation, lead gen, and property management.

in property value marketed through Crexi's AI-powered commercial real estate platform

The sheer volume of property value flowing through AI-enhanced platforms demonstrates the technology's growing role in transactions.

of weekly work time in real estate-adjacent services being spent using generative AI

While lower than tech-heavy industries, the percentage is growing rapidly as agents adopt gen AI for listings, comparables, and client comms.