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Software companies are shipping AI sales agents four times faster than any other industry

·Dennis Sevilla for ZoomInfo

A concept graphic of digital transformation in business using AI. — THICHA SATAPITANON // Shutterstock
A concept graphic of digital transformation in business using AI. — THICHA SATAPITANON // Shutterstock
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The biggest advancement in AI sales agents comes from the software sector sales and revenue teams in the past 13 months, according to data from ZoomInfo. That’s more than three-and-a-half times the next-closest industry. Business services came in second, finance in third and after that, the field thins out fast.

The speed at which vendors are shipping agent capabilities into a given industry's revenue motion varies drastically, creating an enormous gap between industry leaders and laggards. This data comes from tracked product launches of AI agents over Q3 2025 to Q3 2026, segmented out by industry.

The rankings by industry

  • Software (roughly 66% of total launches). Software companies are both the biggest buyers of AI sales tooling and the biggest builders of it, so adoption compounds in a way it doesn't elsewhere. Often, a software company shipping an internal AI SDR tool is also the vendor selling a version of it to everyone else.
  • Business services (roughly 16% of total launches). Consulting and professional services firms are adopting agent tooling for client delivery as much as for their own pipeline. The line between "selling with AI" and "delivering with AI" blurs fast in this category.
  • Finance (roughly 10% of total launches). Although compliance overhead is a tangible barrier, use cases involving lead qualification and fraud prevention are clearly compelling enough to drive adoption forward.
  • Manufacturing (roughly 8% of total launches). Long, technical sales cycles turn out to be a decent fit for agent-assisted configuration and quoting.

Why the gap is this wide

ZoomInfo's own tracking points to data readiness as one clear factor: comparing legacy-system pain points to AI agent launches by industry, Software shows the lowest ratio in the group, while Insurance and Telecommunications, the two lowest-ranked industries, show ratios nine to nearly 30 times higher.

That pattern lines up with independent industry research: Deloitte puts insurance's legacy-system technical debt at over $30 billion a year, with insurers spending roughly 70% of their IT budget just maintaining old systems, and EY found 98% of telecom operators say they need to modernize their core platforms.

Deal complexity plays a role too, regulated industries typically require seven to 12 stakeholders to sign off on a purchase, compared to a 13-stakeholder average industry-wide, and in insurance and financial services specifically, compliance requirements mean no single person can approve a deal alone. And coverage capacity matters: McKinsey's 2026 B2B go-to-market research found reps managing under 30 accounts hit 118% of quota, while those managing 50-plus struggled to clear 70% — a real constraint that AI tooling is built to expand.

A decision framework for AI adoption

The ranking provides a decision framework for revenue leaders based on their industry’s current maturity. For those in the top three tiers (software, business services, and finance), the window for initial experimentation has closed. Organizations in these sectors should pivot their focus toward optimizing the performance of existing agents and refining ROI through advanced attribution modeling.

In contrast, for companies sitting lower on the list, the current gap is an opportunity to leapfrog laggards by prioritizing foundational data hygiene, rigorous workflow mapping, and proactive change management. By building this infrastructure now, these firms can ensure that when they do deploy agentic capabilities, they are built on a scalable, high-integrity foundation rather than a fragmented one.

Methodology

The industry ranking comes from ZoomInfo's tracking of AI agent product launches by industry, June 2025-July 2026, a measure of launch volume, not adoption depth or performance. The data-readiness comparison is a constructed ratio, legacy-system pain point signals divided by AI launch volume, per industry. The deal-complexity and rep-to-account context is supported by independently published research.

This story was produced by ZoomInfo and reviewed and distributed by Stacker.

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