II. Introduction: The AI-powered GTM revolution

The current state of AI in startup GTM

Startups do more with less. In an increasingly competitive venture market where investors are making fewer and bigger bets, companies must find a way to grow while staying lean. 

In 2025, AI is a startup’s not-so-secret weapon. More than one-third of venture-backed startup professionals and founders (37%) report that AI lowered customer acquisition cost, while 72% say AI has improved their ability to upsell and cross-sell existing customers.

AI use cases with the greatest ROI for GTM

• Generative AI for content creation: 29%
• AI for startup productivity/workflow automation: 24%
• Generative AI for visual content creation: 23%

As more companies benefit from AI, they’re investing in it more. One-in-five startups say over 50% of their GTM tech stack incorporates AI tools or capabilities. And over two-thirds (69%) of founders have a dedicated AI specialist or team working on their GTM strategy.

Startups report that AI has driven the most improvement in GTM in customer service (30%), followed by sales (25%), and marketing (21%).

How AI has democratized technology

Large enterprises were the first to see value from AI. Today, it’s a near-universal part of tech stacks and is leveling the playing field for startups.

In our 2024 research, 76% of startups with dedicated AI teams saw significant or rapid growth—and 78% of founders believed AI would increase their growth in 2025. They were right. 

“We have an internal goal that we want to hit $100M revenue with less than 150 employees,” says Richard White, Founder and CEO of Fathom AI Meeting Assistant. “5-10 years ago, that would be a near impossible goal. But now with AI tools, it's gone from an insane goal to ‘That's doable.’”

Companies with more robust AI investments perform better. Startups with a dedicated AI team (34%) or lead (33%) are most likely to have ARR between $5M and $20M, while those that rely on existing teams (31%) or have no dedicated headcount (71%) are more likely to have an ARR of less than $1M. And 14% of startups with a dedicated AI team have an ARR of $20M to $50M, compared to only 11% of startups with an AI specialist, 9% of startups that rely on existing teams, and 2% of startups with no dedicated resources at all. 

What’s top of mind for startup leaders in 2025-2026

From their biggest challenges to their biggest opportunities, here’s what today’s startup founders are thinking about when it comes to AI. 

Challenges

While startups face many of the same challenges in 2025 as in 2024, their severity seems to have decreased. The proportion of startups facing integration issues between AI and existing systems dropped over 80%, and 62% fewer startups lack employees with AI expertise. 41% fewer startups struggle with cost in 2025 vs. 2024, reflecting the increasing ubiquity of AI tooling.

Challenges vary by startup stage as well. Seed-stage startups’ biggest challenge is choosing best-fit AI tools (23%), while Series A through D companies report that cost is their biggest concern (25%). Seed-stage startups are also 12% more likely than the average startup to face challenges with inadequate AI personnel when implementing AI tooling.

“There’s a debate—if you’re going to use AI, will it be more powerful to embrace and adopt in your product offering, or in your internal operations?” says Mark Roberge, co-founder and managing director at Stage 2 Capital. “There’s a huge argument for the latter. It’s hard for the incumbents to do it because it means moving people around and changing processes. If you’re an entrepreneur with a clean slate, you've got the chance of a lifetime to really leverage that.”

Opportunities

Startups have adopted AI to improve every part of GTM strategy.

“Breakthroughs in reasoning, multimodality, and agentic capabilities are opening new doors for creative applications of AI across the entire GTM stack. Startups are almost always first to experiment—and they often signal where the rest of the enterprise market is headed. We expect this type of innovation to continue, and to accelerate,” says Marc Manara, Head of Startups at OpenAI. 

This can pay dividends with investors, who are prioritizing quality investments over quantity. Average deal count has declined nearly 50% between 2021 and 2024, but deal size has grown significantly, as has total monthly capital invested (an average of $750M this year). 

In March, industry leader OpenAI closed a massive $40B funding round on a $300B post-money valuation. And in January, AI-powered customer intelligence platform Clay raised $40M at a $1.25B valuation, following 6x growth in 2024.

Laura McGinnis, principal at Balderton Capital, says that in the last 18 months, the firm is “doubling down on AI expertise, being hyper-selective, and ensuring portfolio companies are positioned to withstand competition from both startups and incumbents.

AI-enabled startups are innovating in other ways, too. Clay launched an employee tender at a $1.5B valuation, led by Sequoia Capital, which allows employees to exercise equity on their own timeline. Now, everyone at Clay can share in the company’s success.

III. AI implementation in startup GTM strategy

Here’s how companies resource and budget for AI, and founders’ top priorities for their AI investments.

AI team structure and organization

Seven in ten venture-backed startups have a dedicated team or lead responsible for AI initiatives.

Startups with 100-299 employees are 133% more likely to have a dedicated AI team than startups with under 10 employees. Series C and D startups are most likely to have a dedicated AI team (49%), while seed-stage startups are most likely to have a dedicated AI lead (27%).

AI investment and budgets

Nearly half of all venture-backed startups (46%) dedicate more than 25% of their GTM stack to AI tools. 

Startups with ARR over $20M (72%) are 218% more likely than startups with ARR under $1M (22%)—and Series C and D startups (63%) are 75% more likely than seed-stage startups (36%)—to meet this level of investment.

When it comes to overall budgets, we see similar trends.

Startups with ARR over $20M and Series C and D startups are more likely to invest a higher proportion of their tech stack in AI than lower-ARR and earlier-stage startups.

Top AI priorities for GTM

Software and services top the list of top AI investment areas.

Professional/business services and manufacturing startups are more likely to use AI for talent acquisition, training, and upskilling, as are Series C+D companies. Financial services and tech/AI startups, as well as seed-stage through Series B startups, are more likely to rank AI consulting and strategy services as their #2 area of AI investment.

How startups align AI with GTM strategy

Alignment starts at the top.

A dedicated governance framework that formalizes how to implement AI throughout a company is the top focus for tech/AI startups, as well as for startups that dedicate over 50% of their GTM tech stack to AI. 

As with all emerging technologies, clear usage guidelines and rules can ensure the responsible implementation of AI. To create an effective AI policy, look to your company’s core principles. We suggest thinking through security and privacy, accountability, fairness and inclusivity, and transparency around how, where, and what AI systems are used for. 

Implementation takeaways for startups

• Assess your team structure against peer benchmarks: Familiarize yourself with the skills you should hire for in the AI era.
• Analyze your AI investment in your tech stack: Check out the best AI tools for small businesses.
• Prioritize your AI investments in line with top performers: Top-performing startups allocate 20% or more of their budget to AI and invest in dedicated AI teams.
• Implement a structured approach to AI governance: Read our guide to creating an AI usage policy. 

IV. Global differences in AI implementation

Across the globe, GTM professionals use AI in different parts of their workflows and for different purposes.

AI implementation approaches

Startups in North America (NAM) find best-fit AI tools through word-of-mouth recommendations (24%), while startups in Europe, the Middle East and Africa (EMEA) and Asia-Pacific (APAC) rely on internal research and testing (29% and 27% respectively).

Marketing benefits

Startups in NAM and APAC cite increased lead generation and conversion rates (29% each) as the greatest marketing benefit from AI, while EMEA startups say it’s personalization and customer experience capabilities (30%).

Sales impact

NAM startups are 150% more likely than EMEA startups and 32% more likely than APAC startups to view improved lead conversion rates as their greatest sales benefit from AI (25% vs. 10% and 19%).

Customer service implementation

A greater proportion of startup customer service teams in NAM and APAC use AI to completely manage more than half of their customer service interactions (28% and 26% respectively) than those in EMEA (16%).

AI alignment strategy

While startups in EMEA and APAC primarily align their AI and GTM strategies through leadership-team collaboration (37% and 33%), NAM startups are more likely to build dedicated AI governance frameworks (30%).

AI investment increases

70% of APAC startups and 69% of NAM startups have moderately or significantly increased their AI investment in the past year, compared to 60% in EMEA.

Global AI takeaways for startups

• Prioritize investments in region-specific trends: Stay competitive with peers in your region by investing in key areas.
• Marketing: NAM and APAC prioritize lead gen/conversion rates, while EMEA emphasizes personalization.
• Sales: NAM overindexes on increased lead conversion rates, while EMEA sees value from CRM data enrichment and management, and APAC finds the most benefit from improved sales efficiency and productivity.
• Service: NAM sees the most value from using AI chatbots, while EMEA and APAC prioritize customer feedback analysis.
• AI governance: EMEA and APAC focus on leadership-GTM team collaboration, while NAM startups build dedicated governance frameworks.