Account-based marketing (ABM) has moved firmly into the mainstream.

When asked how important it is for their marketing strategy to nurture individual accounts and customer relationships, 46.7% of B2B marketers say ABM is ‘very important’, according to Marketing Week’s 2025 State of B2B Marketing research.

A further 34.8% describe it as ‘important’, meaning a combined 81.5% now see ABM as a strategic priority.

That level of consensus is striking. At a time when budgets are under pressure and scrutiny from finance teams is intensifying, marketers are increasingly expected to prove not just activity, but impact.

ABM’s promise of tighter focus, stronger sales alignment and more efficient growth explains why it has become so central to B2B marketing strategies.

Most people are at the six- to 12-month stage of using AI, experimenting. Despite the fact it hasn’t shown huge ROI yet – beyond productivity improvements – everyone’s planning to invest more.

Bev Burgess, Inflexion Group

But this renewed focus on accounts is colliding with another force reshaping marketing: AI. As ABM adoption accelerates, AI is increasingly being positioned as the technology that can finally make account-based strategies scalable.

Yet while tools and platforms are advancing quickly, the deeper transformation is happening elsewhere. AI seems is changing how teams work, how decisions are made and how much trust organisations are willing to place in machines.

Bev Burgess, who is widely credited with coining the term account-based marketing, as well as co-founding consultancy firm Inflexion Group, explains that belief in AI’s potential for ABM is running ahead of proven commercial results. However, that has not slowed momentum.

“People are investing. Most people are at the six- to 12-month stage of using AI, experimenting. Despite the fact it hasn’t shown huge ROI yet – beyond productivity improvements – everyone’s planning to invest more.”

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The benefits of using AI in ABM lie in the very things that have historically made it hard to scale. Done well, ABM demands deep understanding of accounts, buying groups and individual stakeholders, combined with highly relevant engagement across channels.

As Burgess puts it: “There are two parts of the ABM process. One, you really need to understand your accounts and the individuals in your accounts and what they care about. To get that level of insight, AI is a great way of getting that more quickly and more comprehensively.”

The second “half” of ABM is where AI’s impact becomes even more apparent, in Burgess’s opinion.

“The other half of ABM is the personalisation. The fact that you can optimise anything for the industry they’re in, the company they’re in, the role they’re in and what they’re doing – and again, you can do that with AI much faster and better.”

This allows even smaller firms to scale their ABM strategy to more accounts. For an SME which is aware that ABM is its best strategy but lack lacks the resource to carry it out to its full potential, AI can help “help scale its strategy more effectively,” according to Burgess.

For Asana CMO, Prachi Gore, this move toward personalisation can go even further than the confines of ABM. “I think the construct of ABM is going to massively change the future of marketing,” she says.

“We used to have two disciplines: one-to-many campaigns and ABM. And I think all campaigns, all marketing, can be so hyper-personalised, the more you use AI technology. The construct of ABM is going to be disrupted, and all marketing is going to be ‘ABM-ed’.”

From specialist strategy to table-stakes motion

As AI reduces the operational friction involved in ABM, the discipline itself is becoming harder to ignore. For large B2B organisations in particular, operating without account-level insight is increasingly seen as outdated.

For Asana, which is currently in the process of “piloting” ABM within its sales team, AI’s access to insights and data, is allowing it “to live the dream of ABM”.

“[AI-assisted ABM gives the team] the highest propensity accounts to reach out to. So your account list is no longer static. In the old world of ABM, you came up with an account list and it stayed that way because you didn’t have your data quality,” says Gore.

Being able to produce these “hyper-personalised emails” has already shown a “massive improvement in engagement”, says Gore.

According to Rachel Truair, CMO of martech firm Demandbase, ABM has crossed the threshold from optional to essential. “I see ABM as a table-stakes motion in any really solid enterprise B2B company. Being able to understand at the account level what is actually happening, and the prospects and personas within those accounts, is no longer a question of whether or not we should be doing that.”

For her, what has changed is the nature of the challenge. “Companies aren’t really asking, should they be doing ABM. It’s more about delivering on your ABM strategy and being able to build and innovate fast enough, especially in a time of AI.”

The construct of ABM is going to be disrupted, and all marketing is going to be ‘ABM-ed’.

Prachi Gore, Asana

In that sense, AI is not driving the rise of ABM so much as reinforcing it. As growth slows and efficiency becomes non-negotiable, ABM aligns neatly with leadership demands for focus, accountability and measurable outcomes.

Despite the growing confidence in AI, most organisations are clear-eyed about where value is being delivered today. For now, AI’s impact in ABM is largely operational.

As Burgess explains: “Most of the workflows people are using AI in today are the research and insight piece, personalised messaging and a bit of multichannel orchestration. That’s where people are using it, and they’re getting benefits – but mostly productivity.”

That productivity can be transformative. Account research that once took days can be completed in minutes. Messaging can be adapted quickly for different roles or industries. Briefing papers, planning documents and campaign setup are significantly faster.

What AI has not yet consistently delivered is short-term revenue uplift, particularly in complex B2B buying journeys where sales cycles stretch over months or even years.

That gap between efficiency gains and commercial impact explains why some organisations remain cautious, even as investment continues.

The rise of agentic AI

Looking ahead, Burgess and Truair point to agentic AI as the next major inflection point for ABM.

Burgess believes this will fundamentally change how teams think about resourcing.
“I think we’re going to see teams that are made up of people and agents. At the moment, most people are still thinking about tools… but agents aren’t just finding things out or generating things – they’re actually taking actions as well.”

Truair sees this as a defining moment for go-to-market teams more broadly. “Agentic AI is life-changing for so many go-to-market teams. What teams do over the next 18 months will really define the rest of their careers. Understanding how to leverage agentic AI and start to deploy it is critical.”

As AI begins to recommend next-best actions, adjust journeys in real time and orchestrate engagement autonomously, the role of the marketer shifts. Strategy, judgement and oversight become more important, while execution increasingly sits with machines.

Trust, governance and risk

With greater autonomy comes greater risk – particularly in ABM, where mistakes are highly visible and often commercially sensitive.

According to Burgess, who conducted a study of ABMers in conjunction with Inflexion Group and Propolis, this is the dominant concern among practitioners. “When we asked people about looking ahead, especially around agents, three quarters said their biggest concern was the potential for strategic errors – making an error with an important customer. Brand reputation risks came next, followed by data security and privacy.”

As a result, she believes governance is becoming a critical success factor.
“You have to have a governance framework for adopting AI across the company. That should cover ethical use, copyright issues and the morals of how you’re using it – not just reputational risk.”

Executive sponsorship, phased rollouts and clear guardrails are emerging as the most effective ways to build trust internally, particularly as AI moves closer to decision-making.

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Burgess also discusses the regional distinction the earlier days of tech-enabled ABM at scale when it was mostly reserved for larger SaaS firms. “I think in North America, because they have such a homogenous market, and the tech was available, they were able to move to an ABM model quite quickly.”

However, she is keen to point out, “it’s not just geographical. It’s cultural. There are tech companies that have been really late to adoption because they’re risk-averse, cautious cultures.”

On the subject of tech, she believes AI’s accessibility is lowering traditional skills barriers.

“You don’t need to be a coder. You just need to be able to type in the language you want the technology to understand. That’s what makes it so available.”

Training, experimentation and shared learning are increasingly more important than specialist expertise.

Taken together, these trends suggest ABM is in the next stage of its evolution. The organisations that succeed will be those that balance speed with control, automation with human judgement, and innovation with trust.

As Truair puts it: “This is a career-defining moment for revenue teams. The decisions that are made now – whether to lean into AI, learn it and leverage it – will differentiate both individuals and the companies they work for.”