Predicting the Next Wave of Innovation in ABM Technology

Account-based marketing tools can do more today than most teams will ever switch on. The catch is that capability and results have drifted apart. In McKinsey's research on AI adoption, marketing and sales is among the functions where companies most often report using AI and seeing revenue gains. Yet most organizations say they have not yet turned generative AI into a measurable bottom-line impact.

That gap is the real story of ABM technology right now. The teams pulling ahead are not the ones who bought the most software. They are the ones who picked a few capabilities, wired them into how they sell and ran them well. So the useful question is no longer "what can ABM technology do?" It is "which of these innovations is worth operationalizing first?"

Here is where the next wave is heading, and how to decide what to adopt.

Key takeaways

• AI adoption across marketing and sales has surged, but most teams have not yet converted it into results. Closing that execution gap is where the advantage lives.
• Forrester finds that most ABM programs report 21% to 50% higher ROI than non-ABM marketing, and ABM accounts tend to carry larger deal sizes, with uplifts most often in the 11% to 50% range.
• The next wave is less about new product categories and more about four shifts: targeting that predicts, personalization that adapts, orchestration that runs continuously and ABM tied directly to revenue.
• Adopt the one or two innovations that fit how you sell today. A capability you run well beats four you bought and never used.

A capability you don't use is not an advantage

Most ABM programs have the basics handled. They can build an account list, drop a company name into an email, send across a couple of channels and report at the account level. None of that separates anyone anymore.

The capabilities that move pipeline tend to sit unused inside the same platforms: signal-based targeting, predictive account scoring, content that adapts to the buyer and real coordination across channels. It is the same pattern McKinsey sees across business functions. Adoption runs ahead of impact, because buying a feature and putting it to work are two different projects. If your ABM program has stalled, the fix is usually tighter execution, not another tool.

Keep that in mind as you read the four waves below. Each one is real. None of them helps until it changes what your team does on a Tuesday.

Targeting that predicts the buying window

For years, account selection meant describing a good-fit account: this industry, this headcount, this tech stack. It is a reasonable start and a poor finish, because fit tells you who could buy, not who is about to.

The shift underway is toward targeting that reads public buying signals and flags which accounts are entering a window before the obvious cues appear. AvairAI calls this method Pain-Signal Targeting: instead of starting with filters, you start with the problems your product solves, then watch for the public business events that reveal them. We call those events Trigger Signals, a funding round, a hiring spike, a leadership change or an acquisition.

A short example. Say you sell inventory-planning software, and the pain you remove is manual, spreadsheet-bound forecasting. Firmographic targeting hands you every mid-market manufacturer in a region. Signal-based targeting watches for the moments that show the pain is live right now: a job posting for a demand planner who "must be an Excel expert," a newly hired VP of Operations running a process review, an acquisition that just doubled the SKUs somebody has to plan. Same universe of accounts, a different short list, built from public evidence rather than guesswork.

Personalization that adapts to the account

Dropping a company name into a template stopped impressing anyone years ago. The next wave of personalization is keyed to the account's actual situation and to the role of the person reading it, and it adjusts as engagement changes. AI is what makes this affordable. Writing genuinely relevant messaging for each account and each stakeholder used to require a person assigned to every account. Now a small team can do it across a full list.

One caution, because the technology makes the mistake easy to scale: more personalization is not automatically better. Buyers can smell research-for-research's-sake, and a message stuffed with "I saw you went to State and love hiking" reads as creepy, not relevant. The version that works ties one specific, relevant observation to a problem you can solve. For a framework on getting that right, see our guide to personalized content for ABM campaigns.

From planned campaigns to continuous orchestration

The classic model is a loop: plan the campaign, run it, measure it, adjust for next time. The emerging model is closer to a thermostat. The program reads engagement continuously and responds: it escalates when an account's interest rises, shifts channels when one stops working and coordinates touches across a buying committee instead of hitting one contact over and over.

In practice that coordination spans display and LinkedIn ads, email, the website experience a known account sees, alerts to the rep and the phone. A note on the last one: calling still belongs to people. Automated AI calling is real but legally narrow under the TCPA, which restricts it to warm or opted-in contacts, so it is a secondary channel, not a cold-outbound engine. The point of orchestration is that the touches stop feeling like four disconnected tools and start building momentum. That coordination is a big part of why the future of ABM is genuinely AI-powered: it is the work humans cannot do by hand at scale.

ABM tied directly to revenue

The oldest complaint about ABM is that it is hard to measure. Activity metrics, impressions, clicks and engagement, are easy to count and easy to dismiss. The technology worth caring about connects those top-of-funnel touches to what the business tracks: influenced pipeline, sales-cycle velocity and win rate.

This is also where the numbers justify the work. Forrester reports that most ABM decision-makers see 21% to 50% higher ROI from ABM than from non-ABM marketing, and roughly a quarter see returns 51% to 200% higher. The same research finds ABM accounts tend to carry larger average deal sizes, with uplifts most often in the 11% to 50% range across regions. You only see those outcomes, though, if marketing and sales agree on what counts and measure it together. Getting both teams reading the same scoreboard is usually the hard part, and the one worth solving first.

Where to start depends on where you are

You do not adopt all four waves at once. Match the move to your maturity.

If you are early, build the foundation before the frontier: a precise definition of the accounts you want, clean data behind it, content that speaks to your buyers and measurement that ties activity to outcomes. Our complete ABM strategy guide walks through that groundwork.

If you already have a working program, the highest-return additions are signal-based targeting and personalization that adapts, in that order. Both improve who you reach and what you say, which is where most programs leak.

If you are advanced, push into prediction and revenue integration: identify accounts earlier in the buying window, and move spend toward what produces pipeline rather than what produces clicks.

Pair Selling: where the innovations meet execution

AvairAI packages these shifts into a working model rather than a wish list. Pain-Signal Targeting finds the accounts showing public evidence of the pain you solve. From there, AI builds and runs the prospecting: it verifies the contacts, writes the personalized messages, sends the emails and queues ready-to-run call and LinkedIn tasks for your reps. The input is just your website.

What it does not do is pretend to be the closer. AvairAI surfaces interested leads; your reps make the calls, build the relationships and close the deals. That division of labor is what we call Pair Selling, and it is the honest version of "AI-powered ABM." The machine handles the prospecting grind it is good at; people handle the conversations that win business. You never sell alone.

The next wave is already here

ABM technology innovation is not something to wait for. Predictive targeting, adaptive personalization, continuous orchestration and revenue measurement are all available now. The advantage is not in owning them. It is in operationalizing the one or two that fit how your team sells, and running them well while competitors stay stuck on company-name personalization.

Pick your starting point, wire it into a real campaign and measure it against pipeline. If you want the broader playbook for turning these capabilities into a predictable B2B pipeline, start there. When you are ready to put signal-based targeting and Pair Selling to work, launch your first campaign from just your website.