GTM Startup Enso Puts AI Agents to Work as Enterprise Marketing Models Shift

As traditional digital marketing tactics face rising costs and platform saturation, a new generation of enterprise go-to-market startups is shifting focus from tool provision to autonomous execution. Among the prominent firms entering this arena is enso, an experimental growth laboratory that automates distribution across digital platforms using artificial intelligence agents, as first reported by The Next Web. Rather than operating as standard subscription software, the company conducts controlled experiments across customer channels to identify visibility opportunities and publishes its empirical findings.

enso originally launched as an online marketplace for AI agents, securing $6 million in seed funding in a round led by venture capital firm NFX. The organization subsequently executed a strategic pivot into automated growth research, going on to raise an additional $25 million in venture capital. Instead of selling software seat subscriptions with a dashboard, enso operates customized engagements designed to test platform algorithms, measuring distribution results against control groups on behalf of enterprise clients.

The lab structures its operations around five primary research initiatives: search and AI answer-engine optimization, multi-channel automated outbound sequences across email, SMS, voice, and messaging, community forum engagement, newsletter integration, and social media distribution. This model aligns with an investment thesis outlined by Sequoia Capital, which posited that enterprise software is transitioning from selling workflow tools to delivering direct labor outcomes—a market paradigm referred to as service as a software.

The shift comes as early-generation sales automation products struggle to maintain efficiency. Automated cold-email platforms have seen declining response rates, even as enterprise software contracts start around $45,000 annually. An evaluation of 249 Y Combinator-backed go-to-market startups established since 2023 indicated that only 2 percent continue to market complete sales development representative replacement, with the vast majority repositioning their tools as copilots.

Concurrently, legacy advertising agencies are facing structural revenue contractions. Global advertising expenditure expanded by 8.6 percent in 2025, yet agency holding company revenues dropped 1.2 percent over the same period. Major holding groups have implemented significant headcount reductions, including one organization that trimmed its workforce from approximately 128,000 to 105,000 within twelve months. Research firm Forrester recorded an average 8 percent staff cut across agencies in 2025, projecting a further 15 percent reduction in 2026 as 60 percent of U.S. marketing executives report spending less on agencies due to internal AI usage, with 82 percent of major brands now operating in-house teams.

Market consolidation and pricing adjustments are also reshaping the sector. The largest data layer provider in the go-to-market space recently attained a $3.1 billion valuation, while major platform operators completed several acquisitions over a four-month span, including purchases of signal, site-visitor, and community-intelligence startups. Simultaneously, major CRM providers are altering contract terms, moving from seat licenses to performance-based fees such as $1 per qualified lead, 50 cents per resolved service chat, or $2 per enterprise conversation.

These structural shifts occur alongside mounting pressure on classic B2B lead generation tactics. Cross-industry pay-per-click rates climbed 12 percent over the past year, marking the sharpest increase since 2021. Non-branded B2B software keywords surged 29 percent to range between $8 and $14 per click, rising to $18 in security and up to $25 in CRM categories. At the same time, AI-driven search features reduced click-through traffic on informational searches by 68 percent, diverting prospective buyers from corporate websites toward summary answers generated directly by AI models.

By operating across external platform surfaces rather than relying purely on direct-mail automation or traditional search indexing, enso aims to capitalize on the migration toward model-cited answers and forum-based distribution. Although enso lacks self-serve trials or public pricing sheets, its emphasis on published experimental data and outcome-focused client engagements reflects an evolving standard for AI-driven go-to-market strategies.