2026 AI in ABM Benchmark Report | ForgeX

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2026 AI in ABM Benchmark Report

BENCHMARK REPORT

How ABM teams are adopting AI, where it delivers value, and what separates top performers from the rest.

Brought to you by:

Lead Author: Eric Wittlake, GTM Advisor and Research Analyst

Introduction

Key Takeaways

Table of Contents

Related Research

ABM Maturity Report: What Sets Advanced Programs Apart

AI+ABM Inflection Point Report

ForgeX Research 2026 State of AI in ABM Benchmark Report

06Respondent Profile03

08Highlights by Segment04

09AI’s Impact on Results05

14The Role of AI in Execution06

18AI Operations and Governance07

The AI Tech Stack

Conclusion

https://research-hub.forgex.ai/abm-maturity-report/ https://research-hub.forgex.ai/the-ai-abm-inflection-point-report/

AI has quickly moved from experimental to expected within account-based marketing teams. Most B2B organizations with active ABM programs already use AI, and the productivity and impact expectations of executives, boards, and investors continue to increase.

Yet for many teams, adoption alone has not translated into significant impact. Some organizations report little measurable improvement from their AI investments, while others have already moved past early experimentation into structured, multi-channel deployment that is directly impacting pipeline and revenue.

To understand what separates these top performing AI adopters, ForgeX surveyed 189 B2B marketing practitioners with active ABM programs about their AI usage, outcomes, barriers, and strategic approaches. This report provides benchmarks and operational insights ABM and marketing leaders can use to increase the impact AI has in their own ABM programs.

ForgeX Research 2026 State of AI in ABM Benchmark Report

Introduction01

ForgeX Research 2026 State of AI in ABM Benchmark Report

Companies with an AI roadmap are 2.5x as likely to be top performers

Early wins create a conviction flywheel

An AI Roadmap is one of the most critical investments you can make to drive enduring impact of AI. A strong roadmap prioritizes high impact use cases and drives enablement and adoption across the team. More than half of respondents do not have an AI roadmap yet, making this a high priority investment for teams looking to expand the impact of AI.

Top performing companies aren’t just getting more value today, they are 8x more likely to be confident that AI will significantly improve performance again in the next year. For companies that have been slow to adopt AI, the oft-stated advice of “just get started” may be more important here than almost anywhere else.

AI makes nearly everyone faster, but only makes top performers better

The results are clear, AI can make almost anyone faster, but only top performers successfully convert that speed into performance improvements. Fully 80% of our underperforming segment failed to convert speed improvements to impact.

Top performers extend AI to high-touch channels

Using AI to write marketing emails is easy, and nearly universal. Top performers are significantly more likely to deploy AI to more complex and orchestration heavy tactics, including personalized landing pages (+25 percentage points), in-person events (+18pp), and direct mail (+16pp).

Integrations become a critical challenge as the role and impact of AI grows

Top performers distinctive challenges are barriers to expanding their use of AI, including integration challenges and expertise limitations. Conversely, underperforming companies’ biggest barrier is ROI, they are struggling to justify expanding investments.

Key Takeaways02

ForgeX Research 2026 State of AI in ABM Benchmark Report

More than half of organizations cannot directly measure the ROI of AI investments

The majority of respondents have little to no ability to directly quantify the ROI of AI, and yet many of those same respondents are confident that AI has a significant impact on results. The impact often comes from productivity and process improvements that are difficult to directly measure. Top performing companies are willing to act on imperfect signals without waiting for refined attribution.

AI drives cost efficiency in high-volume channels, outcomes in high-touch ones

In email and content syndication, respondents are two to three times more likely to report that cost savings are bigger than business impacts. In executive engagement and in-person events, the pattern reverses: AI has a bigger impact on outcomes than costs.

AI is putting the Build vs Buy decision back on the table

How we define top performers:

Teams building proprietary AI solutions are the only group that expects their ABM tech spending to fall, while those focused on a mix of AI-native vendors and AI features from existing vendors are the most likely to expect increased tech spending. Companies need to weigh the tradeoff between cost, speed, and ongoing maintenance.

Respondents who agree or strongly agree that AI has significantly improved their ABM program performance (n=110, 58% of sample). All others are classified as below average (n=79, 42%).

Top performers report different behaviors, priorities, different, and outlooks than the rest of the sample across nearly every question asked. While objective measures of the value of AI continue to be difficult for some companies to measure, as the data here shows, the belief that AI is delivering value drives behavior and ultimately leads to more directly measurable value as well.

ForgeX surveyed 189 B2B marketing practitioners with active ABM programs between Q1 2026. All respondents operate at least one ABM deployment model (1:1 ABM, 1:Few ABM, 1:Many ABM, or deal- based ABM). The sample is overwhelmingly B2B (87%), with the remainder operating hybrid B2B/B2C models.

Companies with 501-5,000 employees are the largest segment at 32% of respondents.

ABM practitioners are the largest title group, representing 38% of respondents.

ForgeX Research 2026 State of AI in ABM Benchmark Report

COMPANY SIZE

JOB FUNCTION

Respondent Profile03

ForgeX Research 2026 State of AI in ABM Benchmark Report

GEOGRAPHY

ABM DEPLOYMENT MODELS IN USE

INDUSTRY

Two-thirds of respondents are based in North America.

87% of respondents run two or more ABM deployment models simultaneously.

Seven in ten respondents work in technology companies.

What is the status of the following types of ABM deployments within your organization?

Beyond the top performers segment that is used for analysis throughout much of the report, company size in particular shows noticeable differences across many of the question asked.

ForgeX Research 2026 State of AI in ABM Benchmark Report

Technology companies are not outpacing their non-tech peers

Tech and non-tech companies have nearly identical representation in the top performer segment, at 57% and 60% respectively. Across most questions asked, non-tech respondents were on par with their tech peers.

On the surface, this breaks with historical tech adoption trends in marketing, where SaaS companies have been the early adopters of new technologies, including marketing automation and ABM, over the last two decades. However, all respondents have significant ABM adoption, which likely removes later adopting non-tech companies from the sample.

Small companies are getting the most from AI but face scaling challenges

Mid-Market companies are balancing speed and structure

Enterprise has resources but needs to become more agile

76% qualify as top performers, the highest rate of any size segment. These teams have full control over AI tool decisions, face the least governance friction, and lead on several use cases. However they are also hitting structural barriers: data quality is their top barrier, they deploy AI across fewer channels, and they are less likely to have an AI roadmap. Although they are generating real value from AI, but their ability to scale is limited by the same leanness that gives them speed.

The 51-500 employee segment leads on AI roadmap adoption, deploys AI across the most channels, and is roughly three times more likely than large organizations to be able to quantify their ROI. Budget is their most notable barrier to further adoption. Overall this is the best-positioned to continue expanding their AI-driven gains.

Just 51% qualify as top performers, the lowest rate of any segment. Enterprises are the most likely to have AI tool decisions made outside the ABM team and to cite privacy and compliance as a top barrier. They have the infrastructure and budget, but governance overhead, integration complexity, and distributed decision-making have significantly slowed adoption.

Highlights by Segment04

ForgeX Research 2026 State of AI in ABM Benchmark Report

Top performers are 3x more likely to drive higher conversion with AI

Faster execution is the most universally felt AI benefit. Even 46% of other respondents report moderate or significant improvement in execution speed.

Speed often doesn’t translate to outcomes. 57% of top performers reported improvements in conversion and pipeline impacts, more than 3x the 18% of other respondents reporting increases. Improvements in targeting and cost efficiency followed a similar, although slightly lower multiple, pattern.

While moving faster is often the first benefit seen when rolling out AI in ABM, that speed often doesn’t translate to outcomes that matter the most to the business.

AI’s Impact on Results05

Respondents seeing improvement in results from AI in each of four areas (Figure 1)

Q: “In which of the following areas has AI delivered moderate or significant improvement to your ABM program?”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

59% of top performers have a clearly defined AI roadmap for ABM. Among all other respondents, only 23% do. This is the largest gap between top performers and others. While a roadmap does not guarantee results, the absence of one is strongly associated with lower AI value across nearly every dimension measured.

The overall results reinforce how early businesses are in creating rolling out AI in ABM teams. Most organizations (56%) still do not have a shared AI roadmap. Despite early wins, teams are not investing in intentionally planning where to focus AI and equipping teams for success beyond naturally early-adopting team members.

A defined AI roadmap is the clearest differentiator of top performers

Percentage of respondents with a defined AI roadmap (Figure 2)

Q: “Does your organization have a clearly defined AI roadmap for ABM that you are aware of?”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

The survey results make it clear that building early momentum is one of the most important steps teams can take, particularly if they are struggling to win support for AI investments.

Organizations already seeing value from AI are overwhelmingly optimistic about what comes next. 88% of top performers expect AI to significantly improve their ABM programs in the next 8 to 12 months, and half selected “strongly agree.” Across all other respondents, only 51% expect significant improvement, and just 6% have a strong conviction.

Success is reinforcing expectations of future success. Without wins to point to, securing budget, executive attention, and cross-functional cooperation becomes harder, lowering expectations and delaying meaningful results.

Early wins create a conviction flywheel

Respondents expecting AI to significantly improve ABM performance in the next 8 to 12 months (Figure 3)

Q: “Looking forward, AI will significantly improve the performance of our ABM program over the next 8 to 12 months?”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

Half of all respondents report little or no ability to quantify the ROI of their AI investments. That includes 43% of top performers, the same group reporting meaningful improvements in targeting, execution speed, and pipeline impact.

This shows successful organizations are confident in the impact AI is having even when they can only directly measure a small portion of that impact.

Anecdotal evidence is still more important than measured ROI

Ability to quantify ROI of AI investments, by value achieved segments (Figure 4)

Q: “How would you rate your team’s ability to quantify the ROI of AI investments in your ABM program?”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

Instead of direct ROI, value is being assessed through anecdotal indicators, team feedback, and observable improvements in output quality or speed, not through AI-specific attribution or results.

This shouldn’t be surprising, as AI is often embedded into the work that was already happening (writing emails, building audiences, personalizing pages), isolating its incremental contribution is much more difficult than measuring an individual channel overall. However, this is a key area of opportunity for many teams to devise new ways to measure the impact of AI, not only in directly attributed ROI, but also in the value of increased productivity and the impact of that productivity on the business.

— Zach Diamond, Cognition

“We’re definitely walking before we run. You need the basic load down before you figure out what you automate. You can’t go in and say "I’m going to automate this" without knowing all the steps.”

ForgeX Research 2026 State of AI in ABM Benchmark Report

Top performers apply AI across an average of 3.7 channels, compared to 2.4 for below-average respondents. Both groups use AI for email marketing and digital advertising at similar rates. The key difference is in the application of AI in higher touch channels.

The biggest adoption gaps are in in-person events, executive engagement, and direct mail. These are channels where AI isn’t easily applied as a feature. Instead AI is typically applied via orchestrations or multi-channel workflows. This points to more sophisticated AI implementation among top performing teams, not just implementation across more channels.

The Role of AI in Execution06 Top performers extend AI into complex, high-touch channels

AI channel adoption rates by value achieved segment (Figure 5)

Q: “Is your organization currently using AI-powered tools or capabilities to support each of the following channels or ABM program components?”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

AI is impacting cost efficiency more than business outcomes

Where AI has a larger effect: business impact vs. cost efficiency by channel (Figure 7)

n = 189

Q: “Focusing specifically on business impact, to what extent has your use of AI improved outcomes in each of the following ABM channels or program components? (Business impact includes stronger engagement, higher conversion rates, increased pipeline influence, or higher-quality outputs.)”

Q: Focusing specifically on cost efficiency (including reductions in spend, labor hours, or production time at a similar volume of work), to what extent has your use of AI improved efficiency in each of the following ABM channels or program components?

ForgeX Research 2026 State of AI in ABM Benchmark Report

Overall, AI is moving the needle on cost efficiency more than business impact in 6 out of the 8 channels covered in the survey.

In relationship-driven channels, respondents are much more likely to report stronger impacts on business outcomes. For executive engagement, respondents are nearly three times more likely to report that AI is affecting business impact more than cost savings (29% vs. 10%).

Conversely, in high-volume channels like email marketing and digital advertising, respondents are two to three times more likely to indicates stronger improvements to cost efficiency than business impact.

ForgeX Research 2026 State of AI in ABM Benchmark Report

AI Operations and Governance07 Top performers are facing new challenges

Top 3 selected barriers to AI adoption in ABM programs (Figure 8)

Q: “What are the 3 most significant barriers to AI adoption in your organization’s ABM program(s)? Select up to three.”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

Top performers have already deployed AI more broadly, now they are constrained by limited integrations, budget for additional rollout, and expertise across teams.

Other respondents are held back by the lack of clear ROI and poor data quality. Both are likely limiting adoption to early use cases or sporadic applications. Their limited adoption means they are less likely to be hitting budget constraints.

Company size also impacts barriers. Small companies face a disproportionate data quality problem. Mid- market and mid-enterprise organizations share an expertise gap as their primary distinctive challenge. Enterprises are much more likely to be constrained by governance and integration than smaller companies.

Governance is not limiting AI’s value in most ABM programs

Agreement that AI governance restricts ABM potential (Figure 9)

n = 189

Q: “To what extent do you agree with the following statement: ‘Our organization’s AI governance or policies restricts us from fully realizing the potential of AI in ABM.’”

ForgeX Research 2026 State of AI in ABM Benchmark Report

Top performers are more likely to feel constrained by governance (32%) than others, as they push closer to the boundaries of what is allowed.

Non-technology companies feel governance restricts them at a notably higher rate than tech companies (38% vs. 25% agree), likely reflecting heavier regulatory environments in financial services, healthcare, and professional services. This is one of the widest industry gaps in the dataset.

ForgeX Research 2026 State of AI in ABM Benchmark Report

AI TOOL SELECTION APPROACH 1-50 (N=21) 51-500 (N=53) 501-5,000 (N=60) 5,000+ (N=55)

Hybrid

Existing vendor AI

Proprietary

Corporate Decision

AI-native tools

Unsure

52%

19%

10%

55%

23%

11%

45%

22%

12%

42%

15%

16%

18%

Columns may not sum to 100% due to rounding.

The AI Tech Stack08 Incumbent tech providers have an advantage

AI tool selection approach by company size (Figure 10)

Q: “When selecting AI tools to support ABM, which approach best describes the approach your ABM team takes today?”

n = 189

ForgeX Research 2026 State of AI in ABM Benchmark Report

Incumbent technology providers rolling out AI capabilities have a significant advantage over AI-native solutions. AI-native solutions are not the preferred approach across any size or high performer segment.

Enterprises are most likely to be building proprietary AI solutions, but even enterprises are 3x more likely to pursue a hybrid approach than a proprietary build. While AI is making the Build option more accessible and vibe-coded tools drive LinkedIn sharing, Buy is still the primary tech stack approach across segments.

ForgeX Research 2026 State of AI in ABM Benchmark Report

How organizations build their AI stack determines where ABM budgets are headed

Expected ABM tech spending changes by AI tool selection approach (Figure 11)

Q: “Do you expect AI to drive a decrease or increase in your organization's spending on ABM technology over the next 12-24 months?”

n = 146

The way organizations structure their AI tech stack is the strongest predictor of ABM tech spending expectations. Hybrid adopters, those combining existing platform AI features with new AI-native tools, are most likely to expect increased tech spending. They are adding tools on top of what they already pay for, expanding both capability and cost.

Organizations prioritizing existing vendors expect moderate growth, likely reflecting an assumption that AI capabilities will be bundled into current contracts at incremental cost.

Proprietary builders are a significant outlier. 42% expect decreases and only 26% expect increases, the only net-negative spending outlook in the dataset. Companies building internal AI capabilities are the ones most likely to be churning from ABM tech solutions.

AI adoption in ABM has passed the early-adoption phase. The question is no longer whether teams are using AI, but whether they are creating an environment where AI is adopted across the team and producing meaningful results.

The difference in performance between top performers and others is stark. But notably, there is little difference in the way they look to add AI to their tech stack capabilities. Instead the differences are in their behavior, including experimentation, adoption, and planning.

ABM and AI leaders need to focus on systematic applications of AI that allow them to drive and measure the benefit of AI in their organizations. The behaviors of the top performing respondents highlighted throughout this report provide additional insight into what that will require.

ForgeX Research 2026 State of AI in ABM Benchmark Report

Conclusion09

Eric Wittlake is a GTM advisor and analyst for ForgeX. He has spent more than two decades working with B2B SaaS and enterprise technology companies as a practitioner, analyst, and agency leader. A former senior analyst at TOPO and Gartner, he led TOPO’s ABM practice and authored foundational ABM research including the TOPO Account Based Funnel and Account Based Benchmark reports. He most recently led initiatives across product marketing, analyst relations, and services delivery at 6sense through a period of more than 5x revenue growth.

ForgeX is a research and advisory firm focused on modernizing ABM, accelerated with AI. We run primary research, build benchmark data on performance, and advise B2B teams on operating models, measurement, and AI enablement.

— Additional contributor.

Research inquiries: yael@forgex.ai Web: forgex.ai

ForgeX Research 2026 State of AI in ABM Benchmark Report

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BENCHMARK REPORT 2026 AI in ABM Benchmark Report Lead Author: Eric Wittlake, GTM Advisor and Research Analyst

Table of Contents Introduction Key Takeaways Respondent Profile Highlights by Segment AI’s Impact on Results The Role of AI in Execution AI Operations and Governance The AI Tech Stack Conclusion Related Research AI+ABM Inflection Point Report ABM Maturity Report: What Sets Advanced Programs Apart ForgeX Research 2026 State of AI in ABM Benchmark Report

Introduction AI has quickly moved from experimental to expected within account-based marketing teams. Most B2B organizations with active ABM programs already use AI, and the productivity and impact expectations of executives, boards, and investors continue to increase. Yet for many teams, adoption alone has not translated into significant impact. Some organizations report little measurable improvement from their AI investments, while others have already moved past early experimentation into structured, multi-channel deployment that is directly impacting pipeline and revenue. To understand what separates these top performing AI adopters, ForgeX surveyed 189 B2B marketing practitioners with active ABM programs about their AI usage, outcomes, barriers, and strategic approaches. This report provides benchmarks and operational insights ABM and marketing leaders can use to increase the impact AI has in their own ABM programs. ForgeX Research 2026 State of AI in ABM Benchmark Report

Key Takeaways Companies with an AI roadmap are 2.5x as likely to be top performers An AI Roadmap is one of the most critical investments you can make to drive enduring impact of AI. A strong roadmap prioritizes high impact use cases and drives enablement and adoption across the team. More than half of respondents do not have an AI roadmap yet, making this a high priority investment for teams looking to expand the impact of AI.

Early wins create a conviction flywheel Top performing companies aren’t just getting more value today, they are 8x more likely to be confident that AI will significantly improve performance again in the next year. For companies that have been slow to adopt AI, the oft-stated advice of “just get started” may be more important here than almost anywhere else.

AI makes nearly everyone faster, but only makes top performers better The results are clear, AI can make almost anyone faster, but only top performers successfully convert that speed into performance improvements. Fully 80% of our underperforming segment failed to convert speed improvements to impact.

Top performers extend AI to high-touch channels Using AI to write marketing emails is easy, and nearly universal. Top performers are significantly more likely to deploy AI to more complex and orchestration heavy tactics, including personalized landing pages (+25 percentage points), in-person events (+18pp), and direct mail (+16pp).

Integrations become a critical challenge as the role and impact of AI grows Top performers distinctive challenges are barriers to expanding their use of AI, including integration challenges and expertise limitations. Conversely, underperforming companies’ biggest barrier is ROI, they are struggling to justify expanding investments. ForgeX Research

More than half of organizations cannot directly measure the ROI of AI investments The majority of respondents have little to no ability to directly quantify the ROI of AI, and yet many of those same respondents are confident that AI has a significant impact on results. The impact often comes from productivity and process improvements that are difficult to directly measure. Top performing companies are willing to act on imperfect signals without waiting for refined attribution.

AI drives cost efficiency in high-volume channels, outcomes in high-touch ones In email and content syndication, respondents are two to three times more likely to report that cost savings are bigger than business impacts. In executive engagement and in-person events, the pattern reverses: AI has a bigger impact on outcomes than costs.

AI is putting the Build vs Buy decision back on the table Teams building proprietary AI solutions are the only group that expects their ABM tech spending to fall, while those focused on a mix of AI-native vendors and AI features from existing vendors are the most likely to expect increased tech spending. Companies need to weigh the tradeoff between cost, speed, and ongoing maintenance.

How we define top performers: Respondents who agree or strongly agree that AI has significantly improved their ABM program performance (n=110, 58% of sample). All others are classified as below average (n=79, 42%). Top performers report different behaviors, priorities, different, and outlooks than the rest of the sample across nearly every question asked. While objective measures of the value of AI continue to be difficult for some companies to measure, as the data here shows, the belief that AI is delivering value drives behavior and ultimately leads to more directly measurable value as well.

Respondent Profile ForgeX surveyed 189 B2B marketing practitioners with active ABM programs between Q1 2026. All respondents operate at least one ABM deployment model (1:1 ABM, 1:Few ABM, 1:Many ABM, or deal-based ABM). The sample is overwhelmingly B2B (87%), with the remainder operating hybrid B2B/B2C models. Companies with 501-5,000 employees are the largest segment at 32% of respondents. ABM practitioners are the largest title group, representing 38% of respondents.

Two-thirds of respondents are based in North America. Seven in ten respondents work in technology companies. What is the status of the following types of ABM deployments within your organization? 87% of respondents run two or more ABM deployment models simultaneously. ForgeX Research 2026 State of AI in ABM Benchmark Report

Highlights by Segment Beyond the top performers segment that is used for analysis throughout much of the report, company size in particular shows noticeable differences across many of the question asked. Technology companies are not outpacing their non-tech peers Tech and non-tech companies have nearly identical representation in the top performer segment, at 57% and 60% respectively. Across most questions asked, non-tech respondents were on par with their tech peers. On the surface, this breaks with historical tech adoption trends in marketing, where SaaS companies have been the early adopters of new technologies, including marketing automation and ABM, over the last two decades. However, all respondents have significant ABM adoption, which likely removes later adopting non-tech companies from the sample. Small companies are getting the most from AI but face scaling challenges 76% qualify as top performers, the highest rate of any size segment. These teams have full control over AI tool decisions, face the least governance friction, and lead on several use cases. However they are also hitting structural barriers: data quality is their top barrier, they deploy AI across fewer channels, and they are less likely to have an AI roadmap. Although they are generating real value from AI, but their ability to scale is limited by the same leanness that gives them speed.

Mid-Market companies are balancing speed and structure The 51-500 employee segment leads on AI roadmap adoption, deploys AI across the most channels, and is roughly three times more likely than large organizations to be able to quantify their ROI. Budget is their most notable barrier to further adoption. Overall this is the best-positioned to continue expanding their AI-driven gains.

Enterprise has resources but needs to become more agile Just 51% qualify as top performers, the lowest rate of any segment. Enterprises are the most likely to have AI tool decisions made outside the ABM team and to cite privacy and compliance as a top barrier. They have the infrastructure and budget, but governance overhead, integration complexity, and distributed decision-making have significantly slowed adoption. ForgeX Research 2026 State of AI in ABM Benchmark Report

AI’s Impact on Results Top performers are 3x more likely to drive higher conversion with AI Respondents seeing improvement in results from AI in each of four areas (Figure 1) Q: “In which of the following areas has AI delivered moderate or significant improvement to your ABM program?” n = 189 Faster execution is the most universally felt AI benefit. Even 46% of other respondents report moderate or significant improvement in execution speed. Speed often doesn’t translate to outcomes. 57% of top performers reported improvements in conversion and pipeline impacts, more than 3x the 18% of other respondents reporting increases. Improvements in targeting and cost efficiency followed a similar, although slightly lower multiple, pattern. While moving faster is often the first benefit seen when rolling out AI in ABM, that speed often doesn’t translate to outcomes that matter the most to the business. ForgeX Research 2026 State of AI in ABM Benchmark Report

A defined AI roadmap is the clearest differentiator of top performers Percentage of respondents with a defined AI roadmap (Figure 2) Q: “Does your organization have a clearly defined AI roadmap for ABM that you are aware of?” n = 189 59% of top performers have a clearly defined AI roadmap for ABM. Among all other respondents, only 23% do. This is the largest gap between top performers and others. While a roadmap does not guarantee results, the absence of one is strongly associated with lower AI value across nearly every dimension measured. The overall results reinforce how early businesses are in creating rolling out AI in ABM teams. Most organizations (56%) still do not have a shared AI roadmap. Despite early wins, teams are not investing in intentionally planning where to focus AI and equipping teams for success beyond naturally early-adopting team members. ForgeX Research 2026 State of AI in ABM Benchmark Report

Early wins create a conviction flywheel Respondents expecting AI to significantly improve ABM performance in the next 8 to 12 months (Figure 3) Q: “Looking forward, AI will significantly improve the performance of our ABM program over the next 8 to 12 months?” n = 189 The survey results make it clear that building early momentum is one of the most important steps teams can take, particularly if they are struggling to win support for AI investments. Organizations already seeing value from AI are overwhelmingly optimistic about what comes next. 88% of top performers expect AI to significantly improve their ABM programs in the next 8 to 12 months, and half selected “strongly agree.” Across all other respondents, only 51% expect significant improvement, and just 6% have a strong conviction. Success is reinforcing expectations of future success. Without wins to point to, securing budget, executive attention, and cross-functional cooperation becomes harder, lowering expectations and delaying meaningful results. ForgeX Research 2026 State of AI in ABM Benchmark Report

Anecdotal evidence is still more important than measured ROI Ability to quantify ROI of AI investments, by value achieved segments (Figure 4) Q: “How would you rate your team’s ability to quantify the ROI of AI investments in your ABM program?” n = 189 Half of all respondents report little or no ability to quantify the ROI of their AI investments. That includes 43% of top performers, the same group reporting meaningful improvements in targeting, execution speed, and pipeline impact. This shows successful organizations are confident in the impact AI is having even when they can only directly measure a small portion of that impact. ForgeX Research 2026 State of AI in ABM Benchmark Report

Instead of direct ROI, value is being assessed through anecdotal indicators, team feedback, and observable improvements in output quality or speed, not through AI-specific attribution or results. This shouldn’t be surprising, as AI is often embedded into the work that was already happening (writing emails, building audiences, personalizing pages), isolating its incremental contribution is much more difficult than measuring an individual channel overall. However, this is a key area of opportunity for many teams to devise new ways to measure the impact of AI, not only in directly attributed ROI, but also in the value of increased productivity and the impact of that productivity on the business.

“We’re deﬁnitely walking before we run. You need the basic load down before you ﬁgure out what you automate. You can’t go in and say "I’m going to automate this" without knowing all the steps.” — Zach Diamond, Cognition ForgeX Research 2026 State of AI in ABM Benchmark Report

The Role of AI in Execution Top performers extend AI into complex, high-touch channels AI channel adoption rates by value achieved segment (Figure 5) Q: “Is your organization currently using AI-powered tools or capabilities to support each of the following channels or ABM program components?” n = 189 Top performers apply AI across an average of 3.7 channels, compared to 2.4 for below-average respondents. Both groups use AI for email marketing and digital advertising at similar rates. The key difference is in the application of AI in higher touch channels. The biggest adoption gaps are in in-person events, executive engagement, and direct mail. These are channels where AI isn’t easily applied as a feature. Instead AI is typically applied via orchestrations or multi-channel workflows. This points to more sophisticated AI implementation among top performing teams, not just implementation across more channels. ForgeX Research 2026 State of AI in ABM Benchmark Report

AI is impacting cost efficiency more than business outcomes Where AI has a larger effect: business impact vs. cost efficiency by channel (Figure 7) n = 189 Q: “Focusing specifically on business impact, to what extent has your use of AI improved outcomes in each of the following ABM channels or program components? (Business impact includes stronger engagement, higher conversion rates, increased pipeline influence, or higher-quality outputs.)” Q: Focusing specifically on cost efficiency (including reductions in spend, labor hours, or production time at a similar volume of work), to what extent has your use of AI improved efficiency in each of the following ABM channels or program components? ForgeX Research 2026 State of AI in ABM Benchmark Report

There is a clear pattern when you compare reported cost efficiency and business impact improvements by channel. This result compares the answers to two separate questions, one measures the effect by channel of AI on business impact, the other on cost efficiency. Overall, AI is moving the needle on cost efficiency more than business impact in 6 out of the 8 channels covered in the survey. In relationship-driven channels, respondents are much more likely to report stronger impacts on business outcomes. For executive engagement, respondents are nearly three times more likely to report that AI is affecting business impact more than cost savings (29% vs. 10%). Conversely, in high-volume channels like email marketing and digital advertising, respondents are two to three times more likely to indicates stronger improvements to cost efficiency than business impact. ForgeX Research 2026 State of AI in ABM Benchmark Report

AI Operations and Governance Top performers are facing new challenges Top 3 selected barriers to AI adoption in ABM programs (Figure 8) Q: “What are the 3 most significant barriers to AI adoption in your organization’s ABM program(s)? Select up to three.” n = 189 ForgeX Research 2026 State of AI in ABM Benchmark Report

Top performers have already deployed AI more broadly, now they are constrained by limited integrations, budget for additional rollout, and expertise across teams. Other respondents are held back by the lack of clear ROI and poor data quality. Both are likely limiting adoption to early use cases or sporadic applications. Their limited adoption means they are less likely to be hitting budget constraints. Company size also impacts barriers. Small companies face a disproportionate data quality problem. Mid-market and mid-enterprise organizations share an expertise gap as their primary distinctive challenge. Enterprises are much more likely to be constrained by governance and integration than smaller companies.

Governance is not limiting AI’s value in most ABM programs Agreement that AI governance restricts ABM potential (Figure 9) Q: “To what extent do you agree with the following statement: ‘Our organization’s AI governance or policies restricts us from fully realizing the potential of AI in ABM.’” n = 189 ForgeX Research 2026 State of AI in ABM Benchmark Report

Restrictions or limitations from corporate governance policies are not a widespread complaint or limitation for ABM teams. Across all segments, only 29% of respondents say organizational AI policies restrict their potential, while 39% actively disagree. Top performers are more likely to feel constrained by governance (32%) than others, as they push closer to the boundaries of what is allowed. Non-technology companies feel governance restricts them at a notably higher rate than tech companies (38% vs. 25% agree), likely reflecting heavier regulatory environments in financial services, healthcare, and professional services. This is one of the widest industry gaps in the dataset. The AI Tech Stack Incumbent tech providers have an advantage AI tool selection approach by company size (Figure 10) Q: “When selecting AI tools to support ABM, which approach best describes the approach your ABM team takes today?” n = 189 23% Proprietary 12%

ForgeX Research 2026 State of AI in ABM Benchmark Report

Nearly half of all respondents (48%) take a hybrid approach to AI capabilities, combining AI features from existing platforms with new AI-native solutions. Among those with a strong preference, existing vendors win decisively: 20% prioritize AI capabilities from their current tech stack, compared to just 3% who prioritize AI-native tools. Incumbent technology providers rolling out AI capabilities have a significant advantage over AI-native solutions. AI-native solutions are not the preferred approach across any size or high performer segment. Enterprises are most likely to be building proprietary AI solutions, but even enterprises are 3x more likely to pursue a hybrid approach than a proprietary build. While AI is making the Build option more accessible and vibe-coded tools drive LinkedIn sharing, Buy is still the primary tech stack approach across segments. ForgeX Research 2026 State of AI in ABM Benchmark Report

How organizations build their AI stack determines where ABM budgets are headed Expected ABM tech spending changes by AI tool selection approach (Figure 11) Q: “Do you expect AI to drive a decrease or increase in your organization's spending on ABM technology over the next 12-24 months?” n = 146 The way organizations structure their AI tech stack is the strongest predictor of ABM tech spending expectations. Hybrid adopters, those combining existing platform AI features with new AI-native tools, are most likely to expect increased tech spending. They are adding tools on top of what they already pay for, expanding both capability and cost. Organizations prioritizing existing vendors expect moderate growth, likely reflecting an assumption that AI capabilities will be bundled into current contracts at incremental cost. Proprietary builders are a significant outlier. 42% expect decreases and only 26% expect increases, the only net-negative spending outlook in the dataset. Companies building internal AI capabilities are the ones most likely to be churning from ABM tech solutions. ForgeX Research 2026 State of AI in ABM Benchmark Report

Conclusion AI adoption in ABM has passed the early-adoption phase. The question is no longer whether teams are using AI, but whether they are creating an environment where AI is adopted across the team and producing meaningful results. The difference in performance between top performers and others is stark. But notably, there is little difference in the way they look to add AI to their tech stack capabilities. Instead the differences are in their behavior, including experimentation, adoption, and planning. ABM and AI leaders need to focus on systematic applications of AI that allow them to drive and measure the benefit of AI in their organizations. The behaviors of the top performing respondents highlighted throughout this report provide additional insight into what that will require. ForgeX Research 2026 State of AI in ABM Benchmark Report

About About the author Eric Wittlake is a GTM advisor and analyst for ForgeX. He has spent more than two decades working with B2B SaaS and enterprise technology companies as a practitioner, analyst, and agency leader. A former senior analyst at TOPO and Gartner, he led TOPO’s ABM practice and authored foundational ABM research including the TOPO Account Based Funnel and Account Based Benchmark reports. He most recently led initiatives across product marketing, analyst relations, and services delivery at 6sense through a period of more than 5x revenue growth. — Additional contributor. — Additional contributor. — Additional contributor.

About ForgeX ForgeX is a research and advisory firm focused on modernizing ABM, accelerated with AI. We run primary research, build benchmark data on performance, and advise B2B teams on operating models, measurement, and AI enablement. Join Our Private Community Learn More in Our Research Library

2026 AI in ABM Benchmark Report | ForgeX

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