Most B2B ABM programs stall at execution, and AI-native tools are being built to fix that

Multiply launched 10 Minute ABM on July 9, 2026, claiming it compresses roughly 100 hours of account-based advertising work into under 10 minutes. The timing is not accidental. Across the B2B marketing industry this summer, from product launches to conference sessions, a consistent diagnosis has surfaced: the problem with ABM was never the strategy. It has always been the execution.

The execution gap that has always held ABM back

Account-based marketing has a well-documented paradox. Marketing teams understand what good ABM looks like: personalized creative for each target account, paid media coordinated with outbound sales, messaging that evolves as accounts move through the funnel. But getting there manually is expensive and slow. According to Multiply's announcement via PR Newswire, teams typically identify target accounts, send a handful of emails, run a few ads, and then stop, because managing account-specific campaigns at scale is simply too time-consuming.

The result, as Demand Gen Report noted in its coverage of the launch, is that many ABM programs never reach their full potential. The tools existed. The intent was there. The throughput was not.

Multiply describes itself as the first AI-native paid media agency built specifically for B2B companies. Its model differs from traditional campaign automation in one key respect: rather than requiring marketers to manually rebuild and optimize each campaign, the platform is designed to learn continuously. Every campaign generates insights that feed the next one, so account-level performance compounds over time rather than resetting with each new flight. The company currently supports Google Ads, LinkedIn Ads, and ChatGPT Ads, with Meta, Reddit, and Bing listed as coming soon, according to the PR Newswire release.

Self-learning campaigns and what they change for marketing ops

The core mechanics of 10 Minute ABM center on four capabilities Multiply detailed in its PR Newswire announcement. Marketers define their target accounts, messaging, and objectives, and the platform generates account-specific creative, launches hundreds of campaigns, and then runs structured experiments to identify which messages, offers, and formats perform best for each account. Critically, the optimization is automatic, the system does not wait for a marketer to analyze results and rebuild. It surfaces top-performing campaigns and launches additional experiments continuously.

Multiply's early customer results, cited in the same PR Newswire release, are striking: companies using the platform have reported up to 700% improvements in sales meetings booked and pipeline generated from ads. The company connects this directly to its CRM and sales call integration, which it uses to understand why customers actually buy before translating those signals into ad creative.

Matt Jason, Multiply's CEO, framed the product's purpose in the company's announcement: marketers should spend their time understanding customers and refining strategy, while AI handles execution, learns what works, and continuously improves performance for each account. The pitch is explicit that the AI is not replacing judgment, it is replacing the manual rebuild cycle that currently burns weeks of time.

What B2BMX 2026 sessions reveal about the broader shift

The execution problem Multiply is targeting extends well beyond paid media. At B2BMX 2026, Hayley Ho of WordPress VIP and Gillian Hinkle of Salesforce addressed a parallel challenge in content marketing: how to use AI to increase output without losing quality, and how to tie every content touchpoint to pipeline instead of traffic. Their session, covered by Demand Gen Report's James Hickey, surfaced two practical frameworks marketing operations leaders can act on now.

Hinkle's approach at Salesforce centered on building what she called advisory agents: AI agents loaded with Salesforce's tone, voice, format rules, technical guardrails, and a grading system that flags clarity and accuracy issues. Writers attach a draft, get feedback, and fix problems before work reaches her team for review. The result, as Demand Gen Report described, is speed without quality sacrifice, and a reusable framework the entire team can operate within. Hinkle also shifted her SEO approach away from keyword chasing toward buyer cohort mapping, using Semrush, Google Analytics, and Parse.ly to understand the real decisions buyers face, such as fully managed versus self-hosted infrastructure, and then building content around those decision points.

Ho's contribution was on measurement. She mapped every content touchpoint from landing page to MQL to opportunity, then built reporting on landing pages by influenced revenue and opportunities by industry rather than traffic volumes. According to Demand Gen Report's coverage of the session, her reporting approach was built specifically to give the C-suite a revenue number, not a traffic number, with the logic that high-intent traffic from newer channels including ChatGPT would earn credibility only when tied to pipeline outcomes.

What this means for marketing ops and demand gen leaders

The convergence here is worth noting. Multiply's product launch and the B2BMX 2026 sessions independently arrive at the same operational challenge: B2B marketing teams have the strategy and increasingly have the AI tools, but the bottleneck is building the infrastructure that makes AI output reliable, measurable, and continuously improving. Whether the context is paid ABM campaigns or content production pipelines, the teams making progress are the ones investing in structure: account-level learning systems, advisory agents with brand guardrails, and attribution models that report on revenue rather than reach.

• Evaluate whether your ABM execution infrastructure can sustain continuous per-account optimization, or whether campaigns are effectively one-time projects that reset manually.
• Audit your content AI workflow for guardrails: do your AI tools operate with your brand voice, tone, format rules, and a quality-grading mechanism, or are writers working without a standard framework?
• Shift at least one marketing report from traffic and MQL volume to influenced revenue and pipeline by content touchpoint or account segment before your next C-suite review.
• Assess current ABM platform coverage across ad channels, including whether ChatGPT Ads and emerging placements are part of your account-level targeting mix.

Multiply's next platform expansions to Meta, Reddit, and Bing will be a concrete marker to watch: broader channel coverage is where AI-native ABM tools either prove they can scale account-level personalization consistently or reveal the limits of the model.