State of AI in business & GTM.
Discover how AI is transforming global go-to-market strategies and plot where your company sits on the AI adoption bellcurve.
AI is everywhere in business right now, but widespread use doesn't equal meaningful impact.
Across the 6,000 marketing, sales, and service professionals surveyed in this year's State of AI research, ~90% said their company was using AI, but less than 10% cited transformative adoption (where AI genuinely evolves how their workplace works).
Teams winning with AI are not necessarily the ones with the most tools, the biggest budgets, or the longest head start. They are the ones who moved from experimentation to integration to team enablement, built the data foundations that make AI actually work, and focused on AI use cases and tools that solved their biggest pain point, strategic opportunity, or KPI target.
Why does this matter? While companies starting their AI journey will surely see fast benefits like time savings and increased output, those transforming their GTM with AI are already exceeding their increasingly competitive, mission-critical revenue, deal, and lead targets.
This report breaks down where go-to-market teams stand on AI in 2026, what sets the top users apart from the rest, and what any team, regardless of size or maturity, can do to close the gap.
Who is driving AI adoption?
Despite the wide use of AI across companies and functions, the majority of respondents (34%) say their companies are only at an "emerging" maturity level.
Mid-market companies lead in advanced ("established" or "transformational") adoption (37%), while employees at enterprise companies are 2-5x more likely to rate their company's AI maturity as "transformational" than employees in SMB sectors.
In fact, SMBs lag across every advanced maturity stage, as they're 2x more likely not to use AI at all compared to other company sizes, and only 1 in 5 employees say their SMB has reached advanced AI use.
| Stage | Overall | SMB (1-50ee) | Mid SMB (51-200ee) | Mid-market (201-2000ee) | Enterprise (2001+ee) |
|---|---|---|---|---|---|
| Early | 24% | 30% | 24% | 19% | 24% |
| Emerging | 34% | 28% | 38% | 36% | 36% |
| Advanced Established | 23% | 15% | 24% | 29% | 20% |
| Advanced Transformational | 6% | 5% | 2% | 8% | 10% |
Adoption by GTM department
Overall, respondents (especially marketers) rated their department's adoption level higher than their company's, suggesting that GTM teams are often the AI leaders within their organizations and drive their company-wide AI maturity.
Marketing is ahead of the GTM adoption curve, with the highest rate of advanced maturity among GTM departments at 42%.
When we asked marketers to rate their personal level of AI adoption in their role, they were also ahead of other GTM departments, with 48% citing advanced AI adoption in their work. Sales and service employees were more likely to rate their adoption level as "emerging."
| Marketing | Sales &/or RevOps | Service | |
|---|---|---|---|
| Advanced adoption (established + transformational) | 48% | 29% | 28% |
Marketing also leads in these other AI usage areas:
| Metric | % marketing respondents | % sales &/or rev ops respondents | % service respondents |
|---|---|---|---|
| Active agent use | 54% | 40% | 37% |
| AI budget increased in 2026 | 69% | 59% | 55% |
| AI makes revenue goals easier to hit | 72% | 60% | 57% |
HubSpot customers are also more likely than non-customers to say that AI implementation at their companies has made it easier to meet overarching company goals. For example, 77% of HubSpot customers say that it's easier to meet company revenue goals compared to 56% of non-customers, and customers are also performing better than revenue targets (67% vs 45%).
AI adoption by role level
A majority of survey respondents have progressed beyond beginner or "early" levels of adoption and rate themselves as emerging users.
| Individual contributor (IC) | Manager | Director | VP | C-Suite/Owner |
|---|---|---|---|---|
| Emerging | Emerging | Established | Established | Emerging |
Who's got the strongest AI skills?
Our survey also asked respondents to rate their AI skill level from from a scale of three experience tiers defined in the box below:
Most (and least) fluent roles
There is a wide fluency gap between leadership and ICs that may slow AI advancement, as fluency is concentrated at the top. VPs are nearly 2x more likely to describe themselves as AI fluent than C-Suite and Directors, and more than 7x more likely than individual contributors:
Senior-level professionals show greater confidence in AI-assisted work because they push back on AI outputs rather than passively accept them. This engagement is a distinguishing factor in AI fluency between role levels.
Company expectations for AI skills are increasing, though, so as the year progresses, we will likely see employees rise to proficient or fluent levels with greater use. Still, only 13% of companies have made AI skills a formal requirement.
As expected, transformational companies set a higher bar and are more likely to formally require AI proficiency and expect employees to at least reach a proficient skill level.
AI skill-level by department
While marketing leads the way in advanced adoption rates across departments and employee-level roles, marketers are also ahead in AI skills and knowledge, with 53% describing themselves as "proficient" and 22% citing "fluent."
| Marketing | Sales/rev ops | Service | |
|---|---|---|---|
| Average Level of fluency | Proficient (53%) | Proficient (45%) | Beginner (47%) |
| % respondents describing themselves as fluent | 22% | 11% | 9% |
How businesses use AI
As GTM teams mature, they move away from generative and prompt-based AI toward data-powered AI. The highest-impact use cases across GTM departments depend on shared, well-connected context.
Our survey dug into the three key areas of GTM:
Top AI use cases in marketing
Using AI to analyze campaign performance and website lead capture are the top marketing use cases with the highest outcomes, and they're benefitting from doing so: "The AI lead capture agent on our website engaged 1,200 visitors in one month, capturing 340 qualified leads that converted at 22% rate," reported an anonymous respondent.
| Highest outcomes AI use cases | % reporting measurable outcomes | AI's average level of involvement | Time saved per week |
|---|---|---|---|
| Campaign performance insights | 83% | heavy (AI carries out the workflow, humans monitor) | 3-5 hours |
| AI lead capture (website) | 80% | heavy | 1-5 hours |
| Real-time campaign refinement | 79% | heavy | 3-5 hours |
| Target buyer identification | 79% | heavy | 3-5 hours |
| Brand content alignment | 77% | heavy | 3-5 hours |
| Lead handoff automation | 77% | moderate (AI assists, humans oversee) to heavy | 3-5 hours |
Maturity triggers marketing momentum
While many marketers start their AI adoption journey with quick-hit generative content or productivity hacks, those who reach advanced maturity use AI for more complex, strategic, and data-driven use cases.
In other words, their goal of using and investing in AI shifts from increasing output to increasing KPIs – with advanced AI adopters primarily using AI to learn and act faster on performance insights and campaign intelligence.
Transformational teams also tend to be more picky about how they leverage AI and have an average of ~4 use cases.
Top AI use cases of sales & rev ops respondents
Sales, as well as respondents on revenue operations teams that work closely with or within sales,, see the strongest outcomes from AI use cases related to pipeline prioritization and automating tasks to accelerate deals.
"Our sales team closed deals 20% faster because AI flagged high-probability leads. It took the guesswork out of prioritizing outreach, so reps focused energy where it actually mattered," says one anonymous survey respondent.
| Highest outcomes AI use cases | % reporting measurable outcomes | AI's average level of involvement | Time saved per week |
|---|---|---|---|
| Pipeline prioritization | 76% | moderate (AI assists, humans oversee) | 3-5 hours |
| At-risk deal identification | 73% | moderate | 1-5 hours |
| Approval routing automation | 70% | moderate | 3-5 hours |
| Invoice automation | 70% | moderate | 1-3 hours |
| Demo creation | 69% | moderate | 1-3 hours |
| Lead scoring | 69% | moderate | 1-3 hours |
AI + Context = Sales revenue streams
Transformational sales and/or revenue operations teams have an average of ~5 use cases, and they differ in that they move away from just using AI as an assistant to help reps work faster to creating more intelligent revenue engines.
HubSpot customers responding to our survey report some of the most measurable results (88%) from using AI to identify at-risk deals and provide next-best actions. Learn more about how to get those same results for your business with HubSpot Agents here.
Top AI use cases in customer service, experience, and fulfillment departments
Service teams' strongest outcome-ready AI use cases combine automation with customer context, resolution support, and retention signals. The benefits of this are clear as well: In six months after adopting HubSpot Agents, service teams handled 13% more conversations per seat.
| Highest outcomes AI use cases | % reporting measurable outcomes | AI's average level of involvement | Time saved per week |
|---|---|---|---|
| Refund automation | 71% | moderate (AI assists, humans oversee) | 1-3 hours |
| Ticket resolution (AI + data) | 68% | moderate | 1-3 hours |
| Offboarding automation | 68% | moderate | 3-5 hours |
| Resolution time insights | 66% | moderate | 1-3 hours |
| Call routing automation | 66% | moderate | 1-3 hours |
| Rep coaching | 65% | moderate | 1-3 hours |
Transformational service teams use AI to act on key customer signals.
Transformational service AI use cases (an average of ~4) with the strongest outcomes combine automation with customer context that gives reps a well-rounded view of the customer, which helps them act on signals earlier. Where early use is about foundational support efficiency, transformational use is about resolution efficiency, such as using contextual data to solve support tickets.
Using AI to surface insights or data to gain full customer context and reduce resolution times is one of the strongest, measurable outcomes for HubSpot customers. With HubSpot Agents, you can quickly and easily draw on customer history and past conversations to lower your own resolution times.
Agentic adoption across GTM
We define AI agents as systems that perform multi-step work powered by a company or business context. A notable difference between traditional AI tools and agentic AI is that agents can take action across tools or workflows with limited human intervention.
The shift from simple, one-off AI tools (like prompt and generative-based tools) to multi-step agents is a defining transition of 2026 GTM AI. The move is recent: most teams report starting to use them 6-8 months ago, and usage has increased over the last six months.
Agentic use also helps companies punch above their AI maturity weight class. For example, our data shows that an emerging-stage company that uses agents regularly (64%) outperforms an established-stage company that does not (52%).
Marketers are again leading the charge in agentic adoption, with 54% reporting active agent use.
Salespeople say that autonomous-led pipeline prioritization processes are the highest-performing use case offering measurable outcomes, and service respondents say the same about refund automation.
How AI-forward GTM teams are building their AI tech stack
GTM teams are already using AI embedded in the tools they use daily, signaling that AI connected to familiar systems (like CRM data or deal context) drives measurable outcomes.
Regardless of the tools they use, departments are achieving measurable outcomes with AI. Most significantly, they report faster work and reduced manual effort.
Marketers, salespeople, and service reps with more time on their hands can focus on the aspects of their jobs that require a human touch, which are often the ones that increase customer satisfaction scores and keep customers coming back.
This is especially important for servicepeople, for example, as our data shows that customers who react most negatively to AI when seeking support want the option to talk to a human (51%).
Top five GTM AI tools
ChatGPT is the most popular AI tool overall, and across the marketing, sales, and service fields. However, usage has diversified over the past year as new platforms have entered the market or become more readily available.
*Default starting point, but usage has fallen because of diversified tool options
Top five AI tools by GTM department
Marketers' second-most-used tool is Canva AI, which aligns with a significant marketing task (content creation), and Gemini (Google) takes second place for sales, rev ops, and service teams.
For marketers specifically, the most popular automation platforms are HubSpot and Microsoft Dynamics Customer Insights.
Most popular AI agents
Most departments already using agents are running 2-3, and they are deployed strategically for a limited number of workflows.
Top AI Agents
- ChatGPT Agents: The most commonly-used agentic platform in marketing, sales, and service
- HubSpot Agents: The second-most used agentic tool in marketing and the third for both sales and service)
- Agentforce by Salesforce: The second-most-used platform of sales and service departments
What do GTM decision makers look for in AI tools?
When recommending or approving AI tools for purchase, GTM decision-makers prioritize ease of use and privacy above raw tool capacity.
| Influencing factor | % GTM decision makers ranking as a top 3 factor |
|---|---|
| Easy for employees to learn, set up, and use | 32% |
| Data privacy, security, and legal policies | 29% |
| Integration with existing tools/platforms | 27% |
| Affordability/cost savings potential | 27% |
There's a bit of variation if we take a deeper look:
- Managers and C-Suite weigh ease of use for employees to learn, set up, and use
- VPs and Directors weigh data privacy, security, and/or legal policies most heavily
- Decision-makers at enterprise companies prioritize demonstrated ROI and ease of governance more than those at SMBs, likely because they face higher accountability and oversight demands.
Despite agentic benefits, companies aren't prioritizing software choices based on agentic features; they're prioritized least often compared to other decision factors.
AI performance outcomes
Marketing, sales, and service teams are already benefitting from implementing AI at their companies this year.
- 60% report that implementing AI has made it significantly easier to meet company revenue goals
- 60% say they're achieving measurable outcomes from implementing AI
- 80% of companies where AI is foundational to operations beat revenue targets
What's more is that HubSpot customers are 2x more likely to report significant measurable outcomes from using AI.
AI impact and outcomes against KPIs
To pinpoint if and where AI use could be leading to the most impact, we asked relevant respondents to note how they were currently performing (on a scale of much worse than to much better than target) against these targets:
- Revenue
- Marketing Qualified Leads (MQLs)
- Customer Acquisition Cost (CAC)
- Deals Won-Closed
- Deal Win Rate
- Customer Retention
- Tickets Resolved
We then dissected these responses by their level of AI usage, finding that respondents in departments with advanced AI adoption were far more likely to report passing or outperforming against key targets:
| Key Performance Indicator | % of respondents in early adoption departments | % of respondents in advanced (established + transformational) adoption departments |
|---|---|---|
| MQLs | 37% performing better than target | 71% performing better than target |
| CAC | 39% performing better than target | 70% performing better than target |
| Closed-won revenue | 24% performing better than target | 60% performing better than target |
| Deal win rate | 28% performing better than target | 61% performing better than target |
| Customer retention | 34% performing better than target | 58% performing better than target |
| Tickets closed | 32% performing better than target | 57% performing better than target |
On top of this correlation, marketing, sales, and service respondents all reported improved KPI performance as a result of AI.
| Key Performance Indicator | % of respondents seeing better measurable performance |
|---|---|
| Marketing Qualified Leads (MQL) | 59% of marketers*72% also say AI makes hitting this target easier |
| Customer Acquisition Cost (CAC) | 58% of marketers |
| Closed-won revenue | 41% of sales and revops professionals |
| Deal win rate | 44% of sales and revops professionals*55% also say AI makes hitting this target easier |
| Customer retention rate | 41% of service professionals*53% also say AI makes hitting this target easier |
| Tickets resolved | 41% of service professionals |
Even if they haven't yet seen or reported measurable KPI improvements, more than half of GTM respondents say AI makes meeting at least one of their core business KPIs easier, suggesting AI could be poised to drive future measurable impacts.
How teams drive impact with AI
When looking at which use-cases were linked to higher AI ROI, we discovered untapped opportunities for businesses, as the AI use-cases driving the highest ROI for some teams were among the least used by others.
For example, most respondents using AI for data and intelligence tasks say those use cases drive the highest ROI. Yet, only a minority of respondents actually use AI for data and intelligence solutions at this point – despite its potential unlock.
Meanwhile, only 10% of respondents use AI for pipeline prioritization, even though 75% of those who do say it drives measurable results. Instead, most sales professionals (25%) use AI for personalized communications, but only 58% report measurable outcomes.
The good news? GTM teams still in the early stages of AI use may see a significant lift in impact as they continue to climb the maturity ladder through more experimentation and implementation of new use cases — particularly those that combine AI and high-quality business data.
Another thing to remember? A whopping 60% of respondents report that AI has made it significantly easier to meet company revenue goals, regardless of the impact they see from each use case they've piloted so far.
Here are some key AI themes and use cases directly tied to teams that report exceeding goals.
1. Companies where AI is foundational are more likely to beat revenue targets.
80% of companies where AI is foundational (or regularly ingrained into systems) beat revenue targets, compared with 33% of early-stage AI companies.
Moving from early experimentation to established, consistent use of AI yields the biggest gains. Across all departments, more marketers reported that AI makes it easier to hit company revenue targets.
2. AI agents give departments a significant boost, regardless of maturity stage.
As mentioned, departments using AI agents at every maturity stage outperform those that aren't, even at companies with emerging AI maturity.
According to our data, respondents are 20-34% more likely to report measurable outcomes for autonomous agent use cases than human-assisted use cases.
3. Context is a significant driver of measurable results.
Data and context access (and ensuring data is current) are directly related to business performance and help companies overperform. Even when we control for company and department AI maturity, data context remains an independent factor in what helps companies outperform.
For most teams, the peak context level at which AI can offer a revenue boost is 3-4 areas with full access to context. Case in point: when HubSpot customers layer HubSpot AI onto high-quality context, outcomes improve significantly: for a marketing outcome like CTA clicks, customers with good context see a +180% boost, while those with great context see a +420% boost.
4. Data quality helps companies beat revenue targets.
Aside from simply giving AI access to context, data quality matters too.
Companies with excellent data quality (current, relevant data) are 2.2x more likely to beat revenue targets than those that aren't:
Other AI benefits & impacts
The main non-KPI benefit GTM teams get from implementing AI is saved time, with 27% of marketers, 29% of sales, and 27% of service respondents saying speed and agility is the number one area of improvement.
Any time saved has a positive, cascading effect. With extra time;
- Marketers have more time to supervise AI outputs, expand their responsibilities, and learn new skills.
- Service teams have time to learn new skills and expand individual business impact.
- Salespeople can learn new skills and expand the scope of their responsibilities.
While time saved is the top benefit across GTM, other positive AI impacts vary by department.
| Area of improvement | % marketing selecting | % service selecting | % sales/rev ops selecting |
|---|---|---|---|
| Speed and agility | 27% | 27% | 29% |
| Creativity | 23% | 18% | 20% |
| Personal productivity | 21% | 24% | 24% |
| Volume of completed work | 20% | 20% | 18% |
| Accuracy | 18% | 21% | 22% |
The human side of AI: career outlooks, emotions, and fears
While AI gives teams substantial speed and agility, as well as time to focus on bigger opportunities that require a human touch, leaders may also see AI fatigue or employee concerns about job security.
While you might think that companies that heavily limit or minimize AI use see boosted morale and motivation, our research revealed the opposite.
Companies that invest in helping employees move up the maturity and AI fluency ladders (which includes enabling AI access and usage for each employee) are the ones seeing real team growth, talent momentum, and business returns from human staff.
In fact, transformational companies are more likely to grow headcount than cut it: 50% of company decision-makers say their business has grown total headcount because of AI, while only 20% reported decreases. The biggest workforce shift is actually redeploying or restructuring roles rather than eliminating those employed in them.
Also, employees who have made AI a foundational part of their workday are more likely to say that AI has personally benefitted their career:
Senior leaders specifically are benefiting from embedded AI at their companies, with 16% of VPs saying AI has improved their career trajectory compared to just 5% of ICs.
If we narrow it down by GTM function:
- Marketing is the most AI-advanced department and the most likely to report career advancement from AI use, but marketers are also the most worried about job security.
- AI-advanced sales reps report stronger performance reviews tied to AI-assisted close rates, and they're the least worried about job security compared to other departments.
- For Service teams that have adopted AI agents, managers can spend more time on team development, with 9% reporting having more time to review service performance and 33% reporting they can spend more time coaching reps.
Employee AI anxiety
AI-related anxieties and job security concerns haven't left the workforce (and likely never will) – but they have greatly evolved.
Who's most at-risk for AI anxiety?
First off, those who optimistically dove headfirst into AI a year or more ago may be bearing the weight of AI anxiety or fatigue today.
Respondents who described their AI skill level as "fluent" were 1.5x more likely than AI beginners to have AI-related concerns. Similarly, respondents at companies with advanced AI maturity were more likely to cite concerns with AI.
This trend suggests that extensive users have a clear view of AI's potential for both good and bad disruption.
Where employees stand on job security
While fears of AI role replacement once led employees to avoid, limit, or push back on AI use, today's workforce is now anxious about whether they're learning, implementing, and scaling AI use enough to protect their roles, career growth, and competitive edge against others in their field.
In fact, more than half of marketers and 47% of sales or revops professionals say job security concerns led them to use AI more this year.
What holds AI adoption back?
The main barrier to AI adoption across all maturity levels is trust, privacy, and security, which makes sense: using AI often requires giving AI sensitive data that you want to ensure is safe.
Barriers do differ meaningfully by maturity level, as early-stage teams face fundamentally different obstacles than transformational teams.
Top challenges of non-adopters and early-stage adopters
For companies not yet using AI or just starting out, the primary barriers are trust, clarity, and enablement.
| % selecting | |
|---|---|
| Trust, privacy, or security concerns | 27% |
| Nothing is a major barrier | 21% |
| Concerns about job security, morale, or loss of human connection | 20% |
| Unclear how to use AI effectively or which tools to use | 18% |
| % selecting | |
|---|---|
| Trust, privacy, or security concerns | 31% |
| Privacy, compliance, or legal restrictions limit what data we can use with AI | 22% |
| Lack of time to configure, train, or troubleshoot AI | 22% |
| Concerns about job security, morale, or loss of human connection | 20% |
| Governance, legal, or ethical concerns | 19% |
Top challenges of emerging to transformative adopters
The barrier set shifts for more advanced adopters because they have already moved past early challenges, such as uncertainty about how even to use AI.
Trust, privacy, and security concerns about AI is the top barrier for emerging, established, and transformational adopters.
Emerging and established adopters are also concerned with privacy, compliance, and legal restrictions that limit what data can be used. Transformational adopters specifically struggle with AI that cannot reliably access, connect to, or use the data it needs.
Top barriers to personal (employee-level) adoption
Marketers and sales & rev ops employees are proficient in AI, while servicepeople are beginners. Here's a breakdown of barriers to personal AI adoption by department:
What enables AI adoption?
Transformational AI companies don't reach that maturity level by accident, and our data reveals four distinguishing enabling factors.
These factors indicate that maturity is not just about enthusiasm or usage but also about allocating resources to scale AI.
At transformational companies, top management explicitly champions AI adoption. They allocate budget, issue company-wide directives, and make AI a stated strategic priority.
One anonymous survey respondent employed at a transformational company told us that "Strong leadership support was crucial. Our executives pushed for AI integration early on and allocated enough budget to make it happen. Without their vision, we wouldn't be this far along."
Investment in scalable cloud platforms, modern tech, and deep integration into business processes. This includes building data pipelines, CRM integrations, and real-time data streams that enable AI to access the context it needs to integrate with business processes.
Transformational companies invest in scalable cloud platforms, modern tech, and integrate AI into business processes by providing it with the data pipelines, CRM integrations, and real-time data streams that help AI access the context it needs to integrate into business processes.
Transformational companies have documented, explicit AI strategies tied to KPIs and long-term company objectives, not just AI experimentation. Transformational companies prioritize use cases based on ROI and internal fluency, and have AI as a measurable priority.
Transformational companies invest in getting their data in order, and they're 5x more likely to rate data quality as excellent. How do they do this? They clean up their records, implement governance policies, and build data pipelines that ensure data availability.
"Inputting junk data can be incredibly costly. So we spent six months fixing our student engagement data. It was tedious but crucial. Once our AI picked up on a clear signal, everything fell into place," says an anonymous respondent.
Themes of GTM departments with advanced AI adoption
Department-level transformation doesn't require company-wide transformation, and departments can actually lead their companies in AI adoption, as our data shows that many marketing teams have.
What distinguishes advanced GTM departments is:
- Transformational departments are much more likely to report meaningful increases in AI budgets.
- They have moved from content generation to strategic data use cases, such as CRM enrichment and optimization.
- They use agents in regular multi-workflow deployments (52% of transformational departments use agents regularly across workflows vs. 13% of emerging depts).
- They maintain fuller context access for AI tools, like business guidelines, customer history, deal data, and process workflows.
- They have dedicated enablement roles, like AI coaches, implementation specialists, and governance leads.
- Transformational organizations are strategic in applying AI and use it across more use cases.
Agent use is another clear differentiator and transformational teams are consistently running more of them: 94% of transformational teams saw agent use increase, and 55% say their use increased significantly.
What employee-level AI adopters have in common
Speed and productivity gains are universal for AI adopters regardless of maturity, but the most significant gaps between early and transformational adopters at the employee level are the human aspects of their jobs, like career trajectory benefits and individual business impact (how you grow), communication and motivation (how you're perceived), and workplace relationships (how you contribute beyond yourself).
Some traits shared by advanced personal AI adopters:
- They push back on AI responses rather than accepting them passively.
- They use AI for strategic tasks (prioritization, planning, decision making), not just executional ones.
- They report more time for learning new skills, which creates a compounding advantage.
- They're more likely to have had formal or informal skill development at their company.
How HubSpot customers get ahead of AI adoption curves
When we segmented respondents by CRM and business software, HubSpot customers showed a consistent pattern of stronger AI outcomes, faster adoption, and deeper agent use across the GTM function.
Here are four key findings from HubSpot customer data that separate customers from non-customers.
What's driving the HubSpot customer advantage?
Context quality. HubSpot customers give AI tools fuller access to business context at every level (business guidelines, team context, network context, etc.) than non-customers.
This suggests that better-maintained context makes companies more likely to outperform, regardless of maturity. Case in point: 50% of companies that give AI access to just one level of context outperform revenue targets. That number increases to 69% for two levels of context and peaks at 75% for three levels.
For HubSpot customers, the CRM is the intelligence layer that connects AI to real customer data. When HubSpot Agents have access to contact history, deal stages, email engagement, and support tickets, they can do the data-powered work that the highest-maturity teams are doing manually today.
Build your AI playbook
The data in this report points to a clear pattern: the distance between companies that are experimenting with AI and companies where AI is genuinely driving results is large, and it reflects the structural differences in how these organizations are built: the quality of their data, the depth of their integrations, and the fluency of their people.
The good news is that none of these factors is fixed. The four enablers we identified in our data (leadership commitment, infrastructure investment, documented strategy, and a strong data foundation) are all things you can build toward, and our data shows that individual departments can lead the way even when the companies they sit within have not yet caught up.
For GTM teams specifically, the opportunity lies in use cases that reward intelligence over output, automation over generation, and context over volume.
Where you are today on the maturity curve matters less than the direction you are moving and how deliberately you are moving there.