Scaling ABM with AI

"How do you scale?" It's a question I've been asked many times, and luckily it just got a little bit easier. Here is my answer

If your SaaS business is targeting enterprise or multi-stakeholder organisations then Account-Based Marketing (ABM) should be a part your growth strategy.

In my experience, the concept of hyper-personalised 1:1 campaigns sounds great, yet in practice it creates bottlenecks, which is why most businesses end up with a less effective ‘ABM Lite’ style programme. That’s where AI can really help.

7 ways to scale ABM with AI

Whether you're just setting up an ABM programme or trying to scale from a few accounts to many, AI can make scaling campaigns and content personalisation a lot more achievable.

Here are seven suggestions on how you could use AI to scale your ABM programme:

1. Using AI to Set Up Your ABM Programme

Setting up an effective ABM programme starts with identifying the right target accounts. AI-powered intent data platforms (like 6sense, Bombora, or Demandbase) can identify which enterprise prospects are actively in-market based on their online behaviour. Instead of relying on static firmographics or gut feel, you can use real-time data to prioritise the accounts that fit your ICP and are actively researching solutions like yours.

AI tools also assist in account segmentation and tiering, helping marketers group accounts into Tier 1 (1:1), Tier 2 (1:Few), and Tier 3 (1:Many) based on opportunity size, intent, and fit. This reduces guesswork and ensures you invest resources where they’ll deliver the most impact.

2. AI-Powered Research and Deep Account Insight

Before outreach begins, ABM success hinges on knowing your accounts inside and out. AI can compile intelligence from multiple sources to build detailed account profiles – including in-house sources. This includes company news, org charts, job openings, social sentiment, technology stack, and even competitor relationships.

Tools like ZoomInfo, Clay, and Apollo all use AI to enrich contact data and reveal buying decision making units (DMUs) helping you build account specific engagement strategies. This level of insight used to take days per account. AI can deliver it in minutes, enabling faster go-to-market action.

Combining this with those all-important person-level insights and unique nuggets of information your sales team hold can all be collated and summarised using AI as your team’s personal agent.

3. Scaling Personalisation at the 1:1 Level

True 1:1 ABM for enterprise clients involves bespoke messaging, content, and experiences. AI can scale personalisation in several ways:

• Content customisation: Tools like Claude or Jasper can personalise messaging on microsites, landing page copy, or outreach emails tailored to each account’s specific pain points or industry.

• Reports & Research: AI can be used to summary industry research papers, extracting single page summaries and branding specific to each target account.

• Smart sequencing: AI-driven email platforms (e.g., Salesloft, Outreach) can adjust messaging based on how prospects engage, dynamically modifying tone or timing for optimal conversion.

• Chatbots and Conversational AI: These can provide tailored conversations on account-specific landing pages—giving buyers exactly what they need in real-time and putting them in touch with their relevant account representative at the most relevant time.

With AI doing the heavy lifting, your marketing and sales teams can spend less time writing and more time building strategic relationships.

4. Scaling From 1:1 to 1:Few by Clustering and Content Matching

Scaling from 1:1 to 1:Few requires balancing efficiency with relevance. AI helps group accounts by shared attributes such as industry, business model, or current lifecycle stage and dynamically match them to tailored campaigns.

Natural Language Processing (NLP) can analyse company websites or content engagement data to spot shared themes across accounts. From there, AI tools can auto-recommend or even generate microsites, nurture flows, or ad variants that speak directly to those shared needs.

This not only makes your team more productive, but it ensures your messaging stays relevant without requiring full-scale manual content development for every segment.

5. Optimising Channels: AI and Omnichannel ABM Execution

Effective ABM isn’t about choosing one channel, it’s about maintaining a consistent message across all multiple. Traditionally this has meant limiting channels for quality control reasons, however AI can help you extend your channel choices:

• Email: AI tools optimise send times, subject lines, and copy to maximise engagement.

• Content syndication: Platforms like PathFactory or Uberflip personalise the content journey based on intent signals and interaction history.

• Sales enablement: AI suggests the best timing, content, and approach for sales reps to use when engaging specific contacts.

• Webinars & events: AI can identify accounts most likely to attend and engage, while also helping personalise pre- and post-event messaging. It can also help you refine invite and reminder automations to maximise attendance rates.

• Social and PPC: AI-powered platforms (e.g., Demandbase, Metadata.io, Influ2) allow for account-level ad targeting and optimisation, dynamically adjusting campaigns based on performance.

• Website dynamic personalisation: AI tools can modify website content in real-time to show different messages or CTAs based on visitor company and persona.

The key is consistency: AI helps coordinate these touchpoints so your buyers see one joined-up experience, not a patchwork of disconnected messages.

6. Measurement and Continuous Learning

One of the most powerful benefits of AI in ABM is its ability to constantly learn and improve. AI-driven analytics tools can track engagement across the entire buying committee, attributing pipeline impact across channels and touchpoints.

Machine learning models then use this data to refine audience targeting, prioritise higher-intent accounts, and suggest new tactics. Instead of relying on campaign-level metrics like CTR or impressions, you can track true revenue outcomes—shortening learning cycles and improving ROI.

7. Practical Steps for B2B SaaS Teams

You don’t need to go all-in on AI to start seeing benefits. Begin by identifying one or two areas where your team is resource-constrained e.g. account research, content personalisation, or campaign analytics and test AI tools there.

Here’s a suggested roadmap:

1. Start with AI-assisted intent data and contact enrichment tools.

2. Layer on AI-generated content and outreach personalisation.

3. Use AI to scale campaigns across clustered account segments.

4. Introduce AI analytics to improve performance measurement and attribution.

As your programme matures, AI will become less of a “nice-to-have” and more of a central engine powering your ABM strategy.

Final thoughts and avoid the AI pitfall

Whilst ABM is a requirement, AI is an option: The reality is that most of us are all still trying to get our heads around AI, AI Agents and figure out how to apply it. So keep it simple, start small and take it from there.

Don’t lose the human element and build trust: The core of ABM is about the deep understanding marketing and sales build around each target account. The one-to-one business relationships that are built through human interactions, understanding and trust.

If you become overly reliant on AI, then you run the risk of undermining how you build the intimate understanding of an account that can only be gained when you interact with its business and its people.

Get the balance right: Apply AI where it makes sense and to enhance your capability, but AI is fallible. Always have the people in your business who understand the account oversee everything from content to messages. It is often the small details that make the greatest difference and these can be missed. Your people should remain central to your ABM programme for success.