The 2025 Science of B2B Report on Marketer Attitudes and Investment in AI

Introduction

In May 2018, the lead author of this report co-presented a SiriusDecisions Summit keynote titled “Building the (Artificially) Intelligent Revenue Engine.” In it, we outlined a range of AI use cases for B2B revenue teams, grouping them into two broad categories: “know everything about your buyers,” and “know everything about your own performance”.

Two use cases we strongly recommended at the time—predictive analytics for identifying ideal customer profiles (ICP) and detecting in-market buyers—were already in play and remain central to how marketers apply AI today.

Since then, while some of the AI use cases we envisioned have become common B2B applications, many have not. Meanwhile, the rise of generative AI has dramatically expanded the landscape, introducing entirely new categories. Chief among them: content creation, now one of the most widely adopted AI applications in B2B, as we will explore below. 

The turning point came just two and a half years ago, when OpenAI launched ChatGPT—followed by a wave of competitors in early 2023. Since then, AI has dominated industry conversations. But has that momentum translated into budgets and strategic plans for B2B marketers?

This research set out to answer that question. Specifically, we wanted to know if AI had moved beyond the realm of discussion and into line items in budgets? If so, where is AI being applied—and how confident are marketers in AI’s ability to deliver real value?

In the sections that follow, we examine whether and how marketing organizations are budgeting for AI in 2025, drawing on survey responses from 392 B2B marketers. There were three main questions to answer:

1. How often are companies budgeting for AI and is that new or reallocated budget? 
2. Which capabilities of AI are receiving investment?
3. How confident do marketers feel that AI will help them achieve their 2025 goals?

We also invite readers to review the companion report to this paper, 2025 Marketing Spend Report – Neither Boom Nor Gloom, which provides a broader look at how companies are allocating marketing spend and how those changes align with 2025 pipeline goals.

Current State of AI Investment in B2B Marketing

Our survey asked marketers whether they had budgeted specifically for AI in the current calendar year. As Figure 1 shows, just over half have done so. There are undoubtedly many marketing organizations taking advantage of AI that is built into applications they already use. Later in this report, we break down whether marketers expect to invest in AI that is embedded in other applications or in standalone AI tools.

Factors Influencing Whether and How Companies Budget for AI

Our survey participants were evenly split on whether they budgeted for AI, so next we looked at whether certain factors meaningfully influence that decision, and whether AI budgets were additive or reallocated from other areas of marketing.

Largely, what we found is that a handful of factors—such as revenue growth, increasing the overall marketing budget, and the type of company ownership—weakly influence whether and how marketing organizations budget for AI. Many of the underlying reasons marketers invest in AI likely fall outside what we captured in this study. It may be that much of what drives AI investment is particular to the attitudes of company leadership.

Below, we break down each factor we considered:

• Firmographic characteristics such as ownership type (public, private), industry, and size, 
• Performance attributes concerning the company’s financial performance and revenue growth,
• Marketing department parameters such as marketing’s investment priorities and budget growth.

The list that follows describes how each factor (weakly) influenced budgeting for AI.

• Company Size: Larger companies ($251M+ in revenue) show a marginally reliable increased likelihood of investing in AI (p = 0.05), but they are reliably less likely to allocate new budget to AI, and are instead more likely to reallocate existing funds (p = 0.04).
• Revenue Growth: Companies with higher revenue growth are reliably more likely to invest in AI (p = 0.01), but they tend to reallocate existing budgets instead of adding new AI budget (p = 0.01).
• Financial Performance: Financial performance does not reliably impact AI investment or budget allocation. We measured financial performance by asking participants to self-assess how their organization is performing financially compared to expectations (e.g., exceeding, meeting, or falling short of goals).
• Marketing Headcount: Companies with larger marketing teams are more likely to invest in AI (p = 0.04), but this does not affect whether they add or reallocate budget for AI.
• Marketing Budget Change: A positive marketing budget change marginally increases AI investment likelihood (p = 0.05) and is the strongest predictor of adding rather than reallocating budget for AI (p < 0.001).
• Ownership Type: Privately financed and VC-backed firms are less likely to have budgeted for AI (p < 0.05), but ownership type does not reliably predict whether the budget is additive or reallocated.
• Industry Type: Industry does not reliably predict AI investment or budget allocation.
• Investment Priorities: Companies that prioritize investment in technology are marginally more likely to investment in AI (p = 0.07), but investment priorities do not reliably affect budget allocation.

When considering all factors together, the following are the strongest predictors of whether companies had a dedicated AI budget:

• Revenue growth
• Year-over-year marketing budget increase 
• And ownership type (e.g., public, private, venture capital-backed) 

That said, each of the factors listed above accounts for less than 5% of the variability in that decision.

As for what drives budget being additive or reallocated from other areas of marketing, the primary factor was an increase in marketing’s overall budget, such that bigger increases make it more likely that AI receives its own dedicated line item. (p = 0.002).

AI: Embedded or Standalone

So far, we know that:

• Over half (52%) of companies have allocated budget for AI in 2025,
• More than half (52%) of those are adding net new budget to do so,
• A variety of factors weakly influences whether and how marketing organizations budget for AI.

There is yet another way that organizations must think about their use of AI. That is, will they be acquiring AI tools as stand-alone capabilities that could be used to build their own applications, or do they expect their marketing applications to have AI embedded in them, or both? 

On this question, marketers are again relatively evenly split, but the split is between those that expect AI to be embedded, and those that expect to use both embedded and stand-alone AI tools. Very few marketing organizations are just buying standalone AI tools.

None of the factors we tested in the prior section meaningfully influenced whether marketers want applications embedded in AI or standalone or both. 

Budgets and Confidence Are Not Tightly Aligned

The next questions we examined were: What are the AI use cases B2B marketers are investing in? And, how confident are they that their investment will help them achieve their 2025 goals? As Figure 4 below illustrates, marketers’ budgets and confidence are not perfectly aligned.

Content creation is the only use case where more marketers have invested in the capability than believe it will help them in 2025. In other words, there are marketers who have acquired AI content creation capabilities who are not confident it will help them achieve their goals.

The other three capabilities for which there is a statistically reliable difference between investment and confidence are cases in which investment trails confidence. The largest gap is for process automation, where 45% of marketers believe it could help them achieve their goals in 2025, but only 33% of marketers have budgeted for the capability. 

Industries Differ in their Adoption of AI Applications

While overall adoption of AI—measured by whether companies have budgeted for it—does not differ in a statistically reliable way across industries (see Figure 5 below), differences do emerge in the specific use cases companies are pursuing and where they expect AI to have the greatest impact. These distinctions suggest that while the decision to adopt AI may be widespread, how industries plan to use it—and what they expect to gain—varies. 

Adoption trends across specific use cases include:

• AI-powered content creation is most widely adopted in the tech sector (adoption = 63%).
• Buyer engagement agents are used by half of tech firms (adoption = 51%).
• Predictive modeling for identifying in-market buyers is more evenly distributed but still more prevalent in tech (adoption = 47%).

While the differences listed above and shown in Figure 6 are not statistically reliable, these early trends hint that tech firms may be further along in experimenting with AI-driven capabilities, whereas manufacturing appears more cautious in its adoption.

When examining the perceived potential of these capabilities in helping organizations hit their goals for the year, we see a similar pattern – tech firms express greater confidence in AI’s utility. 

• 49% of tech firms believe buyer engagement agents will help, compared to 39% in services and 36% in manufacturing.
• Process automation and workflow agents stand out in the tech sector, with the highest potential rating (Confident = 57%), far surpassing services (Confident = 42%) and manufacturing (Confident = 31%).
• Even website personalization, a relatively less adopted capability, is seen as more impactful in tech (Confident = 42%) than in services (Confident = 41%) or manufacturing (Confident = 31%).

These results suggest there is a growing recognition of AI’s strategic value across industries, particularly in tech and services, whereas manufacturing is trailing both in implementation and confidence in AI’s business impact.

Strategic Importance of Embedded AI Capabilities

As we saw earlier, marketers are looking for AI to contribute specific capabilities, such as content creation or predictive modeling, rather than being a tool to do general experimentation with. It follows, then, that many marketers are looking for solution providers to embed AI in the solutions they already use for these functions. 

Just over half (52.8%) of marketers view embedded AI capabilities as either “very important” or “important” when assessing new tools. However, a significant 43% remain neutral or consider AI only slightly important—or not important at all.

Marketers whose organizations have an AI budget are more likely to view embedded AI capabilities as essential—68% rate it as either “very important” or “important,” compared to just 39% of those without an AI budget. This gap suggests that organizations already investing in AI are more sanguine about its strategic potential. 

AI’s Role in Achieving Organizational Goals

Marketers take a mixed view of AI’s ability to help them achieve their goals. Only 1.1% of respondents expressed no confidence at all. And yet, marketers rated their confidence in the ability of AI to help in the near-term as just halfway between Moderately Confident (3) and Confident (4) on a five-point scale. 

This view spans the entire organization, with everyone from individual contributors through the C-suite rating their confidence in AI similarly — reflecting a shared, but cautious, belief in the promise of AI. Not surprisingly, confidence in AI is one of the factors that determined whether organizations devoted additive (Confidence = Confident) or reallocated (Confidence = Moderately Confident) budget for AI. 

It may be that a marketer’s needs influence their confidence. An independent samples t-test showed that marketers at organizations with increasing pipeline goals were reliably more likely to believe AI-driven content creation could help achieve those goals (p = .030). 

Specifically, marketers facing increasing pipeline targets expressed greater confidence (m = 0.597) compared to those with decreasing goals (m = 0.360). However, despite higher confidence levels, the difference in actual investment in AI for content creation between these two groups was not statistically reliable (t(14.44) = -1.321, p = .207), suggesting confidence in AI’s potential has not yet translated into direct financial commitment.

It is possible that marketers with increasing pipeline goals may be engaging in motivated reasoning – they are facing tougher goals and want to believe AI can help them achieve those goals.

Implications

While just over half of B2B marketing organizations are budgeting for AI in 2025, nearly half remain on the sidelines. This hesitation poses a substantial risk. AI is not a futuristic experiment; it is already reshaping how companies identify in-market buyers, automate processes, and create content. Marketers who fail to adapt may soon find themselves at a competitive disadvantage, playing catch-up in an AI-driven landscape.

As noted in the introduction, AI for marketing is not a passing fad or a fringe experiment in marketing, but an operational requirement. Across a wide variety of use cases, AI operates as a force-multiplier, improving operational efficiency, capacity, while surfacing insights and creating operational transparency.

Moreover, the most mature AI use cases in B2B marketing are not generative AI, but rather predictive analytics and automation, capabilities that have been productized for years. Identifying in-market buyers through predictive modeling should be table stakes for modern marketing organizations. Companies that delay adoption of these proven AI capabilities are missing out on efficiency gains and data-driven precision their competitors are already leveraging.

There’s also a faint but concerning trend that the rich are getting richer. It is often the case that feisty start-ups and scale-ups are early adopters of new technologies that help them outcompete their more sluggish rivals. That does not appear to be how adoption of AI is playing out in marketing today. Instead, larger companies with growing budgets are leading the way in AI adoption. This should be particularly alarming for organizations that remain on the sidelines — and for the organizations that invest in them. As AI-powered marketing engines become more sophisticated, the competitive gap will widen, making it harder for late adopters to keep pace.

Marketers who proactively embed AI into their workflows—whether via standalone solutions or embedded applications—will gain greater operational agility and improved decision-making, ensuring they stay ahead as AI reshapes the industry. The time for experimentation has passed; AI must be a core part of marketing strategy.

Methodology

The study surveyed 392 B2B marketers in the late months of 2024, spanning tech, services, and manufacturing. Participants were sourced through panel providers and organic channels. 

A companion to this report, 2025 Marketing Spend Report – Neither Boom Nor Gloom, was produced from the same survey data.