The AI+ABM Inflection Point Report

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The AI+ABM Inflection Point

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Use Cases, Challenges, and Strategic Implications

Research Report

April 29, 2025The AI+ABM Inflection Point Report

The AI+ABM Inflection Point

Matt Steffen, Senior Research Advisor, ForgeX

Chandler Martin, Research Advisor, ForgeX Davis Potter, CEO & Co-founder, ForgeX

By:

Contributors:

This report provides a clear view of where AI is being used today, where adoption is likely to grow next, and the friction points that continue to stand in the way of broader implementation. For B2B marketing teams that are experimenting with or expanding their use of AI in ABM, the findings offer grounded insights into how the landscape is evolving and what that could mean for the future of Account-Based GTM.

01 02

Why This Report Matters

The application of AI for Account-Based GTM programs is still taking shape. While enthusiasm is high, many organizations are navigating uncertainty as they test early use cases, confront implementation challenges, and work to identify where AI can drive the greatest impact.

This report provides a focused analysis of how AI is currently being used to support Account- Based Go-to-Market (GTM) programs and where adoption is headed in the next 6 to 12 months. Grounded in proprietary survey research and enriched with insights from desk research and interviews with subject matter experts, the report highlights key use cases, adoption patterns, and implementation barriers that are shaping how AI is being integrated into Account-Based Marketing (ABM) today.

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Research Methodology

AI Use Cases: Now vs. Next

Table of Contents

Related Content

II.

10Current Viewpoints Regarding AI for Account-Based GTMIII.

16Barriers to AI Adoption in ABMIV.

2025 State of ABM Report: The Rise of Account-Based Go-To-Market

Account-Based GTM Certification Course(s)

19Deployment Model-Specific NuancesV.

22GuidanceVI.

27Appendix: Demographics of Survey RespondentsVII.

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Enterprise ABM & Growth ABM: Modernized Deployment Models

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ForgeX undertook a rigorous, multidimensional research process to ensure the findings presented in this report are both credible and relevant. The core of this research includes:

An online questionnaire, fielded from March 11 to April 11, 2025, garnered 142 total responses. After excluding incomplete, unqualified or duplicate responses, 115 qualified responses remained in the data set. (Due to rounding, percentages mentioned in this report might not add up to exactly 100%). Respondents represent a diverse array of B2B marketing professionals spanning industries, organization sizes, and job functions. Participants included practitioners with ABM job titles as well as other job titles such as demand generation, field marketing, and so on.

A review of secondary sources was conducted to complement the survey findings. This included identifying referenceable data points and supporting insights from credible, published research.

In-depth conversations with experienced B2B marketing professionals and AI-savvy strategists were also conducted to provide qualitative depth, helping to uncover nuances and patterns influencing how AI is being adopted and applied within Account-Based GTM programs in 2025.

Survey Research

Desk Research

Subject Matter Expert Interviews

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Research MethodologyI.

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In our survey of B2B marketing professionals, 91% indicated that their organization has adopted AI to support Account-Based GTM efforts. Furthermore, 86% of respondents reported spending at least two hours per week working with AI-enabled tools in their role as a marketer, with over half (55%) saying they spend 5 or more hours with AI weekly. A recent McKinsey study also found that marketing and sales functions are leading all others in AI adoption. 1

However, adoption varies widely by use case. According to our survey, the three most commonly adopted AI use cases in Account-Based GTM programs today are:

Copywriting (68%) Research (47%) Predictive analytics for account selection and/or prioritization (46%)

The use cases with the highest adoption rates are those that can be easily implemented and integrated into existing marketing workflows with minimal disruption. The top two use cases in particular are well-supported by the current wave of more familiar generative AI tools, specifically application interfaces that leverage large language models (LLMs) such as ChatGPT, Claude, and Google Gemini, which are already used by 9 out of 10 marketing teams. These two use cases are a natural starting point for AI adoption, since so many marketing teams are already comfortable using LLMs to develop or proofread copy or conduct deep research. Furthermore, the alternative to using AI for these top use cases is extensive manual work—much of it mundane and not the best use of the marketer’s time. It’s no surprise that use cases that save time and facilitate scale, with minimal barriers to get started, are the most likely to be baked into daily execution today.

On the other end of the spectrum, lower adoption was reported for more complex or strategic AI applications, particularly those that touch sales workflows, require reliable data ecosystems, or demand more than plug-and-play deployment. More specifically, use cases such as next-best- action recommendations (21%), measurement & reporting (22%), and personalization of websites (23%) showed the lowest levels of adoption in our survey. These use cases tend to demand access to specialized AI-enabled point solutions, tighter cross-functional collaboration, and sophisticated data operations, which are factors that can slow adoption even when interest is high.

Moreover, many of these advanced use cases rely on more than just tool functionality; they require a shift in process, training, and mindset. For example, reliable next-based-action recommendations from AI require both a deep foundational trust in AI as well as advanced

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AI Use Cases: Now vs. Next II. Current Use Cases

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai1

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https://contentmarketinginstitute.com/b2b-research/b2b-content-marketing-trends-research2

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https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai https://contentmarketinginstitute.com/b2b-research/b2b-content-marketing-trends-research

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My organization has already adopted AI for the following use cases to support its Account-Based GTM program(s) (Select all that apply)

technological infrastructure. Seeing these low-adoption use cases gain traction will therefore require niche AI tools and/or workstreams to be more familiar and widely proven in practice.

April 29, 2025

Figure 1

In addition to asking about the top current use cases for AI, our survey asked about use cases organizations plan to adopt to support Account-Based GTM program(s) within 6-12 months.

Six to twelve months from now, the top two use cases are projected to remain copywriting and research, with asset creation and/or personalization rounding out the top 3. Our survey data reveals that the majority of B2B marketing professionals are not yet preparing to implement many of the more ambitious use cases. Leveraging AI for optimization, measurement & reporting, and next-best-action recommendations is not in the plan for the majority. While the time horizon to adopt some of the more advanced AI use cases may extend beyond the 12-month window, early adopters could benefit from embracing them sooner than later. These use cases are tightly connected to core Account-Based Marketing (ABM) principles and desired outputs, including highly personalized engagement, timely responses to buying signals, and greater alignment between the sales and marketing functions.

It’s helpful to consider the types of AI technologies that power most of the current and near-term use cases, which are primarily grounded in lower-barrier applications such as copywriting, research, and certain categories of website personalization efforts. These use cases are largely driven by foundation models, including LLMs, which generate and summarize content in response to natural language prompts. Today, these models are mostly being used in isolated tasks across GTM workflows rather than powering fully autonomous processes. This reflects the "Current" stage depicted in Figure 2, where AI can assist with specific tasks under human oversight but is not yet orchestrating the workflow from objective to outcome.

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Future Use Cases

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Evolving GTM Workflows: From Human-Led to Agentic Execution

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April 29, 2025

Figure 2

The AI+ABM Inflection Point Report

At the same time, the concept of agentic AI is gaining attention, largely for its future potential. These are systems that can take action independently, make decisions, and pursue goals. While few organizations are fully deploying agents today, we’re beginning to see their emergence within existing tools as vendors introduce early forms of agentic functionality. The long-term opportunity lies in transitioning from AI that supports task-level work to AI that can orchestrate entire workflows. This means embedding agents that combine foundation models with decision engines, API integrations, memory systems, and goal-directed orchestration to move from single- step assistance to true multi-step collaboration across GTM teams.

Looking further ahead, the future of AI is moving toward multi-agent collaboration, where systems of agents autonomously coordinate complex workflows across all GTM teams. In this “Future” state, shown in Figure 2, agents are embedded at every task layer, working across platforms, tools, and teams to execute programs at scale. AI no longer simply augments GTM teams but becomes part of the team itself.

It’s no surprise that among AI-related innovations, the three developments capturing the most executive attention all relate to agentic AI, according to Deloitte’s Now Decides Next report based on quarterly interviews with leaders at large global enterprises.³ When it comes to Account-Based GTM, these systems are beginning to be used to synthesize large volumes of first-party data, intent signals, firmographic information, and external research. Early implementations focus on automating the generation of tailored content, orchestrating dynamic touchpoints across the buyer journey, and optimizing campaign elements based on real-time feedback. As capabilities mature, agentic AI is poised to play a transformational role in analyzing deal progression, recommending strategic actions, and coordinating execution across marketing, sales, and operations teams, doing so with far greater efficiency than current methods. Through this expanded role, agentic AI stands to not only streamline campaign execution, but also empower ABM programs to deliver a step change in pipeline growth, deal velocity, and overall revenue impact.

The growing interest in agentic AI’s applicability to Account-Based GTM is well-founded. These systems have the potential to improve coordination, increase autonomy, and help teams execute more efficiently at scale. As organizations gain confidence in AI and key barriers to adoption begin to diminish, we expect a growing number of B2B marketing teams to start experimenting with multi-agent ecosystems, delivering significant value to their ABM programs.

https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-q4.pdf3

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It’s key to understand the full buying committee—including those who might be less visible, like procurement, IT, or the CFO. They also need to be influenced, and each of them have entirely different reasons to support or oppose your solution. Through deep account research, we discovered that our blind spot was the IT department. To address this, we launched a targeted campaign specifically for IT, using a white paper that spoke directly to their unique questions and pain points.

— Mark Norbruis, VP, Digital Marketing, o9

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The next generation of ABM practitioners won't just be digital natives—they'll be AI natives. The winners will develop an instinctual sense of when to deploy AI across content creation, personalization, campaign orchestration, and other ABM efforts. This isn't merely about embracing AI—it's about rewiring your marketing brain to think in partnership with it.

— Bryant Aponte, VP, Global B2B Marketing, Visa

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Our survey data indicates that today’s B2B marketers overwhelmingly believe AI will have a positive impact on their Account-Based GTM efforts. 86% of respondents expect AI to benefit their programs over the next 12–24 months (32% anticipate a “significant positive” impact, while 54% expect a “somewhat positive” impact). A mere 1% foresee a “somewhat negative” impact.

The AI+ABM Inflection Point Report

Current Viewpoints Regarding AI for Account- Based GTM

III.

Source: ForgeX The AI+ABM Inflection Point Report

April 29, 2025

Figure 3

And belief in AI’s potential is showing up in meaningful day-to-day usage. To contextualize how deeply AI is being integrated into B2B marketing workflows, we categorized respondents by the number of hours per week they spend using AI-enabled tools. The result offers a directional view of usage intensity across the respondent base:

Light Users (13%): These users spend fewer than 2 hours per week using AI-enabled tools. Average Users (67%): The majority of respondents fall into this category, spending between 2 to 10 hours per week using AI-enabled tools. Power Users (19%): This cohort spends 11 or more hours per week with AI-enabled tools.

To what extent do you anticipate AI will impact your account-based GTM program(s), either positively or negatively, in the next 12-24 months?

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Distribution of Weekly Time Spent Using AI-Enabled Tools by B2B Marketers

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April 29, 2025

Note: 1% of respondents selected “Unsure” when asked about their weekly time spent using AI-enabled tools.

On average, how many hours per week do you personally spend working with AI-enabled tools in your role as a marketer? (Make your best estimate).

Figure 4

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Despite widespread optimism and confidence in AI’s potential among respondents, a notable share expressed dissatisfaction with their organization’s progress in adopting AI for Account- Based GTM specifically. While a slim majority (52%) of marketers said they are satisfied with their organization’s AI adoption efforts to support Account-Based GTM, a full quarter (25%) expressed dissatisfaction, and another 23% felt neutral. These mixed sentiments suggest that while some organizations are making meaningful progress with AI, overall adoption still has a long way to go to meet B2B marketers' expectations, reinforcing the idea that, while AI adoption has momentum, it hasn’t fully delivered on its promise within many ABM programs. A recent Deloitte report aptly sums up this dynamic with striking clarity: “GenAI technology continues to advance at incredible speed. However, most organizations are moving at the speed of organizations, not at the speed of technology.”⁴

In the section of the survey focused on use cases, respondents had the option to indicate whether their organization had not yet adopted AI for Account-Based GTM at all, and only 10 reported that their organization had not. Of those, 8 of 10 expressed they would be “somewhat supportive” or “very supportive” if their organization were to adopt it. While the sample size is limited, this datapoint suggests that enthusiasm for AI exists even within not-yet-adopting organizations, reinforcing the broader trend: B2B marketers increasingly see AI as a worthwhile opportunity.

In addition to gauging sentiment around current adoption, we also explored how marketers view AI on a use case–by–use case basis. Respondents were asked to assess the expected impact of 12 specific use cases on their Account-Based GTM efforts. For each use case, they rated how impactful they believe it will be in helping achieve desired program outcomes using a five-point scale ranging from “very impactful” to “not impactful at all.” The results were telling: in the aggregate, a clear majority of respondents viewed every use case listed as at least somewhat impactful. The highest-rated use cases included capturing and/or analyzing buying signals (94%), research (88%), and predictive analytics for account selection and/or prioritization (87%). Even more executional or technically complex use cases, such as campaign orchestration and next-best-action recommendations, were seen as impactful by more than three-quarters of respondents. These findings suggest that B2B marketers see AI as broadly applicable, given its ability to support a wide range of activities across Account-Based GTM.

Taken together, the findings from the survey questions touching on current viewpoints on AI are clear: today’s B2B marketers believe in its value but also acknowledge that making AI work in practice requires deliberate and coordinated action.

April 29, 2025

https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-q4.pdf https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-q4.pdf

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How impactful do you believe each of the following AI use cases will be in achieving the desired outcomes of your Account-Based GTM program(s)?

April 29, 2025

Figure 5

Integrating AI into ABM could drive significant competitive advantage, especially for early movers. As leaders in Edge AI technologies, we believe AI-driven workflows could be an unlock for companies that were previously struggling to operationalize personalization practices, especially at scale.

— Brent Summers, Senior Manager, Marketing, Qualcomm Technologies

I’m an AI optimist. I truly believe it can supercharge ABM. We hear a lot about AI being used to personalize content at scale, but that’s not all it can do. Its value extends much further. It can help us identify the right accounts and research them at the click of a button. It can also enable predictive engagement, support lead scoring and routing, and greatly enhance reporting. I see ABMers realizing the most value when they leverage it thoughtfully across different stages of ABM execution.

— Akriti Gupta, Director - Marketing, LinkedIn

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The subset of AI tools with applications for Account-Based GTM is evolving and expanding at breakneck speed. However, because organizations often lag in adopting emerging technologies and are slow to embrace the changes in how teams work best, the speed of innovation often outpaces the readiness of teams, infrastructure, or internal processes to support meaningful implementation. The reasons behind this gap are often complex and interrelated, with one barrier compounding another.

To better understand where friction exists, we asked survey respondents to identify and rank the three most significant barriers to adopting AI within their ABM efforts.

Barriers to AI Adoption in ABMIV.

What are the three most significant barriers to AI adoption in your organization’s Account-Based GTM program(s)? (Drag the top three barriers to the right and rank them).

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Figure 6

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The most frequently cited barrier was a “lack of internal AI expertise”, followed closely by “inadequate training and enablement”. Together, these challenges reinforce the reality that many teams are still in the early stages of building practical knowledge, even as interest in AI remains high.

Rounding out the top three was “integration challenges with existing tools”. This finding reflects a common, and often easy to underestimate, pain point. Many AI-enabled applications work best when connected to systems such as ABM platforms, CRM platforms, data processing infrastructure, and campaign orchestration tools. However, these systems may not be fully compatible or mature enough to support the functional and/or strategic outcomes that AI integrations are intended to enable. Even when integrations are technically feasible, operational hurdles such as limited team bandwidth, lengthy approval processes, and crowded implementation queues can delay progress. Further complicating execution, AI-related integrations often require heightened data governance reviews or involve systems owned by multiple teams. In many cases, issues related to data quality, accessibility, and overall organizational readiness remain insufficient to support accurate, scalable, and sustained AI deployment. As a result, the value of AI is often confined to isolated tasks, rather than scaled, widely integrated, and coordinated program impact, which is where AI is likely to deliver the greatest value.

While the top three barriers reflect the most frequently cited challenges overall, the fact that we asked respondents to rank their selections allows us to examine which obstacles were most commonly identified as the single most significant barrier. “Lack of internal AI expertise” was the most common response when aggregating first, second, and third rankings, underscoring its consistent presence as a top concern. It was also most frequently selected as the #1 barrier. However, “data privacy and security concerns” followed as the second most likely to be ranked first. Interestingly, this barrier appeared far less often in second- or third-place rankings, which suggests that, for many organizations, it's either a dominant concern or one that falls further down the priority list. This polarized response pattern may reflect differences in company size, industry, or regulatory environment, where data governance is a gating factor for some organizations but less relevant for others. Alternatively, it may reflect that some organizations have already implemented proactive and consistent data governance measures, effectively preventing this from being viewed as a barrier.

Beyond the top-ranked barriers, a number of additional challenges were cited with moderately high frequency: most notably, “poor data quality”, “unclear ROI or business case”, and “lack of budget”. Collectively, these barriers point to a mix of foundational and strategic gaps: even when the technology is available, adoption can stall without trustworthy data, sufficient funding, or a clear justification for investment.

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In contrast, “ethical concerns” and “legal concerns” were rarely selected, either in aggregate or as top-ranked barriers. This suggests that while these issues are relevant to broader conversations about AI, they are not currently viewed as primary blockers to progress in Account-Based GTM programs. Interestingly, “resistance to change within the team” was also among the least frequently selected barriers. This further reinforces the idea that the primary obstacles are rooted in knowledge gaps, technology constraints, and a lack of operational readiness, not necessarily in skepticism or a reluctance at the level of individual practitioners to shift away from the established ways of working.

April 29, 2025

Bespoke, multi-channel campaigns targeting individual high-value accounts.

Campaigns designed for small clusters of accounts with shared characteristics.

Scalable, multi-channel campaigns aimed at broader account lists.

Custom strategies focused on winning specific, high-value deals.

1:1 ABM

1:Few ABM

Growth ABM

Deal-Acceleration

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The impact of AI on Account-Based GTM execution isn’t uniform. It varies significantly depending on how ABM is operationalized through the distinct playbooks tied to each deployment model.

Deployment Model-Specific NuancesV.

For teams practicing Enterprise ABM (1:1 and 1:Few), AI represents a long-awaited relief valve. Enterprise ABM programs have traditionally relied on intensive manual work: in-depth account or account cluster research, highly tailored copywriting, detailed mapping of buying committees, and development of dynamic, account- or account cluster-specific engagement plans. Compounding this, Enterprise ABM teams frequently find themselves stepping in to fill gaps left by Sales account teams (e.g., compensating for underdeveloped account plans). With AI, a wide range of manual, time-consuming tasks, previously only marginally supported by traditional technology tools, can now be automated or accelerated. While it does not eliminate all manual work, this shift certainly repositions where efforts are focused and frees marketers from many repetitive and mundane tasks so they can spend more time on high-impact efforts like campaign planning and creative execution, where strategy and collaboration truly move the needle.

April 29, 2025

Figure 7

Enterprise ABM

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For Growth ABM teams, AI’s influence is equally transformative—if not more so—though in a different way. Because Growth ABM targets a broader set of accounts, these teams have long relied on technology to achieve scale. While a notable degree of semi-personalized engagement has been possible, the tradeoff between personalization and scale has persisted. As a result, the final experience for customers and prospects has often felt shallow or overly templated. Because humans simply don’t have the time or capacity to step in at scale, Growth ABM has historically fallen short of the depth and specificity seen in Enterprise ABM. AI is changing that. It’s now enabling Growth ABM teams to move closer to the level of relevance that once required extensive manual effort. That said, this shift also introduces new complexity, demanding thoughtful orchestration, strong data hygiene, and governance to ensure both quality and resonance in scaled personalization efforts.

April 29, 2025

Growth ABM

Turning to Deal Acceleration, AI’s application can vary significantly. This is because the deployment model is used differently by Enterprise ABM and Growth ABM teams. That said, one thing that is universally characteristic of Deal Acceleration is its sharpened focus on in-flight opportunities. This model has always relied on timely insights to identify buying signals, strong alignment with Sales, and agile content development to support and move deals forward. Achieving those goals, along with knowing exactly when and how to engage the buying group tied to a large, high-stakes opportunity, has often depended on manual processes or institutional knowledge that isn’t always well-documented or shared. AI helps close those gaps. It can surface intent signals earlier, recommend next-best-actions, and assist in generating highly relevant messaging and content that’s hyper-tailored to the specific buying group involved in the deal. That said, effectiveness depends on clean and accessible data, close sales collaboration, and a feedback loop to ensure AI is applied in context.

Deal Acceleration

With AI, the value of first-party data is only increasing. If ABM teams prioritize capturing and organizing high-quality first-party data, AI can unlock powerful opportunities—like building predictive algorithms for intent, cross-sell, upsell, and more. These models can be more robust and actionable than anything that came before. But that’s only possible if the data is structured in a way AI can understand. Don’t train your models on disorganized or incomplete data.

— Ville Murtojärvi, Head of Digital Marketing and Creative Content and Head of the AI tiger team, Luxid Group

Proficiency in AI is quickly becoming a must-have skill for B2B marketers. Those who embrace AI early and learn to apply it effectively will be better prepared for the future of work. Start by understanding your organization’s AI roadmap and look for opportunities to join pilot programs. These are practical steps most marketers can take today to gain hands-on experience, as fluency with AI tools will increasingly set you apart as demand for AI-savvy marketers continues to rise.

— Brooke Melia, Senior Director Marketing Strategic Accounts, ADP

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In our survey, only 19% of respondents said their organization currently has an AI roadmap in place. Another 38% reported plans to create one, while 34% said there are no plans at all, and 9% were unsure. That means more than 4 in 10 marketers are operating without a roadmap or any intention to build one. Without a roadmap to support AI implementation, organizations risk stalled progress, scattered or siloed adoption, and missed opportunities to fully realize AI’s potential.

Research from McKinsey reinforces the importance of an AI roadmap, noting that in larger organizations, having one strongly correlates with improved financial performance.⁵ While there’s no one-size-fits-all approach, effective roadmaps typically address readiness stages, prioritize business-aligned use cases, and define success criteria to guide implementation.

The World Economic Forum’s 2025 Future of Jobs Report forecasts that there will be a significant net increase in marketing roles in the next 5 years, while also stating that competencies related to big data and AI will top the list of most in-demand skills.⁶ To prepare for this shift, organizations must invest in structured enablement programs.

In the context of ABM, this means equipping marketers with the ability to identify when and where AI can drive the most impact, whether by accelerating account research, making personalization more meaningful, or intelligently automating additional elements of campaigns.

The same World Economic Forum report shared findings that 88% of organizations are currently running or plan to run AI enablement programs. However, it’s not yet clear if such efforts are being extended specifically to support Account-Based GTM teams. In our survey, a “lack of internal AI expertise” and “inadequate training and enablement” emerged as the most frequently cited barriers to adoption. When organizations overlook AI enablement for ABM teams, they introduce a significant risk of falling behind. After all, competitors can gain an edge simply by stepping up and making this a priority.

Don’t stop at enabling the core marketing team. Truly unlocking the value of AI in Account-Based GTM requires a broader change management effort, one that aligns people, processes, and priorities across functions. To achieve this, upskilling must ultimately extend to all cross-functional teams that contribute to ABM, including sales, operations, data, analytics, and beyond.

Establish an AI Roadmap

Address the Skills Gap

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GuidanceVI.

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai5

https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf6

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https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf

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The AI use cases most commonly adopted today, according to our survey findings, include copywriting (68%), research (47%), and predictive analytics for account selection and prioritization (46%). These use cases are also among the easiest to activate, offering an accessible starting point for teams looking to build early momentum. They require minimal technical overhead and fit naturally into existing ABM workflows, delivering quick wins without major disruption. Furthermore, they allow internal early adopters to exercise agency over their AI usage, discover how AI tools can meet the needs of their departments, and drive internal transformation.

These early gains matter. By focusing first on areas where AI can enhance efficiency, scale, or precision without overhauling core workflows, teams can build early internal support, demonstrate value, and lay the groundwork for broader adoption.The lowest-friction entry point is arguably Generative AI tools, which offer ABM teams an accessible option to begin experimenting at their own pace.

According to Bain's recent survey of over 180 large U.S. companies,⁷ marketers who identified as early adopters of generative AI (a tool widely seen as easy to adopt) have achieved significant benefits, including:

Campaign time to market reduced by up to 50% Content creation time dropped by 30% to 50% An ability to hyper-personalize campaigns more easily, boosting click-through rates by up to 40%

The AI+ABM Inflection Point Report

Strong AI adoption doesn’t just require tools, it demands trust, clear processes, and operational order coming from strong governance. As organizations increase the use of AI for Account-Based GTM, they need well-defined policies to guide ethical use, protect data, and establish guardrails to prevent unintended or risk-laden application. These policies provide teams with clarity on what's permitted, what's encouraged, and where caution is necessary. Interestingly, according to PwC’s 2024 Responsible AI Survey, only 11% of executives report that their organizations have fully implemented fundamental responsible AI capabilities, such as data governance, model management, and risk controls.⁸ In 2025, PwC built on this research, emphasizing that, “in 2025, company leaders will no longer have the luxury of addressing AI governance inconsistently or in pockets of the business.”⁹

But policy alone isn’t sufficient. The output from AI tools is only as reliable as the data that feeds them. In our survey, poor data quality ranked among the most frequently cited barriers to AI

Start with The Most Impactful, Low-Lift Use Cases

Establish AI Policies and Prioritize Data Governance

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adoption in Account-Based GTM. Issues such as duplicate records, inconsistent formats, and disconnected systems can all erode AI performance and trust in outputs.

The AI+ABM Inflection Point Report

While much of the conversation around AI focuses on its technical advantages and business value, it's also important for individual ABM practitioners to consider how it can shape their personal experience at work. A 2025 study from Harvard Business School, entitled The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise,¹⁰ found that professionals using AI realized significant gains in both individual and team performance. Strikingly, individual participants using AI improved their performance by 37%, while AI-enabled teams improved by 39%. Also, teams using AI were nearly three times more likely to produce solutions ranked in the top 10% for quality .

The study, which involved 776 professionals at Procter & Gamble, also found that individuals using AI produced work on par with two-person teams, thanks to the way AI enables idea generation, cross-functional thinking, and emotional engagement.

But the study went further by exploring the emotional and collaborative dynamics surrounding AI use. Professionals leveraging AI reported significantly higher levels of excitement, energy, and enthusiasm, along with reduced anxiety and frustration compared to peers not using AI.

These findings point to an opportunity for all of us: to use AI not only to get work done, but also to improve how we feel while doing it. The AI available today is already helping B2B marketers deliver higher-quality output with less stress and a greater sense of fulfillment and satisfaction in their work. And as the technology continues to evolve and improve, these benefits are likely to become even more impactful, especially for those who choose to lean in and level up with intention and follow-through.

Embrace AI for the Right Reasons: To Improve Your Performance—and Your Well- Being

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Establishing documented AI and data privacy policies and complementing them with training is foundational. These policies aren’t meant to stunt adoption. In fact, studies show that in the absence of these policies, people are afraid. Ensuring that the guardrails are clearly articulated tends to become an accelerator for learning, adoption. and innovation. It inspires more than it limits.

— Lisa Cole, CMO and Head of the AI Center of Excellence, 2X

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Many teams underestimate the hidden effort behind data clarity. ABM doesn’t fail because of strategy—it fails because research is fragmented, stakeholder maps are shallow, or CRM data is outdated. The same applies to AI: Without clean, context-rich data, even the most advanced models can’t deliver. Fix the data foundation, and both AI and GTM flow faster and more effectively.

— Thomas Allgeyer, Managing Director and Founder, Frenus

April 29, 2025

Take the time to test, experiment, and pilot a range of AI tools to identify which ones deliver the strongest outputs. As you explore these technologies, be mindful of data sensitivity and legal compliance to ensure responsible use.

— George Sayah, Global ABM Digital Strategist & Email Marketing Manager, Salesforce

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You can't learn drums by starting with a Neil Peart solo. I eased myself into AI usage with crude prompts, learning to engineer them over time. If you're upskilling a team on AI, do not skip steps or assume knowledge. Start from scratch, and show your people that AI is a tool designed to help them.

— Casey Patterson, Director NoAM ABM, Snowflake

April 29, 2025

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The AI+ABM Inflection Point Report

Appendix

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The AI+ABM Inflection Point Report

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The AI+ABM Inflection Point Report

Partnership Disclaimer

This report was independently researched and authored by ForgeX, adhering to the rigorous standards and best practices expected of an analyst firm. Demandbase, the partner of this report, assisted with promoting the survey, but did not influence the research process, data collection, analysis, or findings presented.

April 29, 2025

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Where Does Your AI-GTM Maturity Stand?◆◆

As AI adoption accelerates, its integration into account-based go-to-market strategies remains unbalanced. The ForgeX GTM-AI Maturity Model™ defines five distinct levels of organizational maturity, each representing a progression in AI integration and impact.

Introducing ForgeX’s AI-GTM Maturity Model™

According to our research, 86% of organizations anticipate AI will positively impact their Account- Based GTM efforts within the next 12–24 months. However, only 52% report being satisfied with their organization’s current progress toward AI adoption,and just 19% report having an established AI roadmap in place.

When viewed alongside recent findings from the ForgeX State of ABM survey, which uncovered that only 22% of organizations maintain an ABM Center of Excellence and just 33% have a documented ABM Charter, our research findings highlight a substantial maturity and readiness gap.¹¹

April 29, 2025The AI+ABM Inflection Point Report

Source: ForgeX The AI+ABM Inflection Point Report

Figure 8

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April 29, 2025The 2025 State of ABM: The Rise of Account-Based Go-To-Market

AI is not intentionally leveraged. There is a complete absence of AI-related experimentation, tooling, or planning.

Not Started

Organizations are beginning to engage with passive AI tools (e.g., AI-powered search) and prompt- based large language models (LLMs) such as ChatGPT, Claude, or Gemini. Use is informal, fragmented, and largely driven by individual curiosity rather than formal strategy or guidance.

Exploring

AI capabilities are being selectively tested across isolated marketing or sales workflows, often through GTM tools that include built-in AI features. Co-pilots and basic agent-driven functionality are introduced, but adoption remains decentralized and exploratory.

Piloting

AI workflows are formally implemented across specific GTM functions. These include human-in- the-loop and expert-in-the-loop designs, with moderate levels of automation and agent coordination introduced. API-based integrations between AI systems and GTM platforms are established, enabling more reliable and consistent performance across functions.

Operationalizing

AI is fully integrated across the GTM ecosystem. Multi-agent collaboration enables autonomous orchestration of GTM workflows across marketing, sales, and customer success. Custom-built agents, either developed in-house or with strategic partners, work across platforms, tools, and teams. AI does not merely augment GTM teams; it is part of the team, operating as a core driver of GTM execution and innovation.

While this stage is technically achievable today, reaching Embedded maturity requires advanced infrastructure, sophisticated cross-functional orchestration, and deep AI fluency across the organization. For most organizations today, it represents a longer-term ambition. Efforts to reach the Embedded stage must not be rushed.

Embedded

The Five Levels of AI Maturity in Account-Based GTM

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April 29, 2025The 2025 State of ABM: The Rise of Account-Based Go-To-Market

A common misstep is attempting to leap from “Not Started” to “Operationalizing” or even directly to “Embedded” without the necessary foundation. True AI maturity must be built thoughtfully over time, layering talent, infrastructure, data readiness, change management, and cultural buy-in.

To mature with discipline, organizations must fund and establish:

A clearly defined AI roadmap for GTM A designated cross-functional AI committee Internal champions and executive sponsors to lead use case exploration and drive scale sustainably

By deliberately stacking these foundational elements, organizations can manage their progression across maturity stages in a way that maximizes early impact while also setting the stage for deeper, more transformational value over time. When approached with discipline, AI can become the greatest force multiplier and growth engine in your GTM portfolio.

Our Recommendation: Build AI Maturity Thoughtfully and Incrementally

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The AI+ABM Inflection Point Report

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