Marketing Week’s 2025 State of B2B Marketing research found that 46.7% of B2B marketers rate nurturing individual accounts as very important. Another 34.8% call it important. Together, that is 81.5% of marketers treating account-level focus as a strategic priority.
The timing makes sense. Budgets are tight, finance teams want evidence, and marketers are expected to show business impact rather than just activity. ABM promises sharper targeting, closer alignment with sales and more efficient growth, so it fits the moment.
Why AI and ABM fit together
ABM has always been hard to scale because it asks a lot of marketers. You need to understand each account, the buying group inside it and what each stakeholder cares about. Then you have to tailor your outreach to match.
Bev Burgess, a consultant often credited with coining the term ABM and co-founder of Inflexion Group, says AI helps with both halves of that job. It speeds up the research needed to understand accounts and the people in them. It also makes personalisation faster, so messaging can be adapted to a prospect’s industry, company and role.
That matters most for smaller businesses. An SME may know ABM is its best route to growth but lack the people to run it properly. AI can close part of that gap, letting it cover more accounts than it could manually.
From specialist tactic to default approach
Prachi Gore, CMO at Asana, thinks the impact will reach well beyond ABM. Marketing used to split into two camps: one-to-many campaigns and account-based programmes. As AI makes personalisation cheaper, she expects nearly all marketing to take on an account-based character.
Asana is piloting ABM with its sales team, and Gore describes a real shift in how account lists work. In the past, lists were static because data quality was too poor to update them. With AI, teams can surface the accounts most likely to buy and reach out to them first. Gore reports that hyper-personalised emails have already lifted engagement significantly.
Rachel Truair, CMO at Demandbase, goes further and calls ABM table stakes for solid enterprise B2B companies. In her view, the question is no longer whether to do it. It’s whether you can deliver on your strategy and innovate quickly enough while AI keeps moving.
In that sense, AI is reinforcing ABM rather than creating it. When growth slows and efficiency matters, a focused, accountable approach is an easy sell to leadership.
What AI delivers today
Enthusiasm is running ahead of proven results. Burgess notes that most organisations are six to twelve months into experimenting with AI. Beyond productivity, the return on investment hasn’t been demonstrated yet, though almost everyone plans to spend more.
The current use cases are fairly consistent: account research and insight, personalised messaging and some multichannel orchestration. The productivity gains are real. Research that once took days now takes minutes. Messaging adapts quickly to different roles and sectors, and briefs, plans and campaign set-up move faster.
What AI hasn’t reliably delivered is short-term revenue growth. B2B buying journeys can run for months or years, so it’s hard to link an efficiency gain to a closed deal. That gap explains why some companies stay cautious even as they invest.
Agentic AI is the next shift
Burgess and Truair both see agentic AI as the next big change. Most teams still treat AI as a tool for finding information or generating content. Agents go further by taking actions themselves.
Burgess expects teams made up of both people and agents. Truair calls the next 18 months a defining period for go-to-market teams, arguing that learning to deploy agentic AI will shape careers.
As AI starts recommending next-best actions, adjusting customer journeys in real time and running engagement on its own, the marketer’s role changes. Strategy, judgement and oversight matter more, while execution shifts to machines.
Trust, governance and risk
More autonomy brings more risk, and ABM raises the stakes because its mistakes are visible and often commercially sensitive. In a study Burgess ran with Inflexion Group and Propolis, three quarters of practitioners named strategic errors as their top worry about agents, such as getting something wrong with an important customer. Brand reputation came next, followed by data security and privacy.
Her advice is to put a company-wide governance framework in place before adopting AI widely. It should cover ethical use and copyright, not just reputation. Executive sponsorship, phased rollouts and clear guardrails help build trust inside the organisation.
Skills are less of a barrier than they used to be. Burgess points out that you don’t need to code to use AI; you only need to describe clearly what you want. That puts the emphasis on training, experimentation and sharing what works.
Conclusion
ABM is entering its next stage. The companies that do well will balance speed with control, automation with human judgement and innovation with trust. As Truair frames it, the choices teams make now about learning and using AI will set apart both individuals and the businesses they work for.
