Getting-to-Market in the Age of AI
July 2026
Alline Akintore & Matt Compton
Workshop Recap: Go-to-Market in the Age of AI
Earlier this month we brought together a small group of portfolio CEOs, board members, and venture partners for a working session on how the go-to-market playbook is evolving with the AI platform shift.
To keep it concrete, we anchored the conversation around three portfolio companies building at different stages: Justin Vandehey, Founder/CEO of Thread (pre-seed, automating customer onboarding and delivery); Chas Ballew, Founder/CEO of Conveyor (Series B, AI-powered sales workflow automation for vendors); and John Schoenstein, CRO at Customer.io ($100M+-ARR customer engagement platform). Here are a few things that stuck with us:
With GTM teams possessing more leverage, roles are getting blurred
GTM teams are operating with more leverage; sales teams are absorbing tasks that used to belong to ops and customer success. Not surprising given that the lines between engineering, product, and design are blurring, with designers committing front-end code and PMs prototyping directly with AI. GTM leaders are having to step back and reassess their org structures.
One tangible example came from one of the companies, which cut its BDR team by 40%. Their new BDR profile is tech-savvy, curious, and AI-fluent rather than a high-volume dialer. Those hires cost 20-30% more each, but total cost is down because there are far fewer of them. And the performance of the team speaks for itself: conversion-to-close roughly tripled, from about 1-in-10 to 3-in-10, and sales cycles shortened by around half, by focusing reps on pre-scored, high-intent prospects. The honest wrinkle is that this new BDR profile doesn't ladder cleanly into the AE role the way the old one did and some are moving toward RevOps or solutions engineering instead.
The GTM stack is augmenting teams as real operating infrastructure
We spent quite a bit of time unpacking the evolution of tooling. One company has built a custom agent that pulls from their CRM tool, sales enablement tools, its product data warehouse, and website signals; two use cases made the value concrete for them:
First, RevOps forecasting: an agent pre-builds the forecast analysis before the Monday morning meeting, turning what used to be 4-5 hours of manual report-building into showing up with a point of view, so that the conversation jumps straight to gap plans and actions.
Second, a home-built competitive intelligence dashboard that surfaced open, competitive deals worth quantified millions at risk, letting the GTM leader drill into any deal, and pull win rates by competitor –ultimately making forecasts sharper and rep-reported confidence easier to probe.
A recurring theme: every tool in the stack now has to be AI-first, and vendors that aren't innovating are being replaced.
"Headless" is the framing to watch on build-vs-buy for customers
A useful mental model emerged for where software value is migrating: SMBs are increasingly building their own tooling, while enterprise and mid-market still buy. With the "headless" shift the UI layer is commoditizing, but the underlying data, systems of record and business logic engines that remain are genuinely hard to build – something we continue to underline here at OVF.
The next layer to watch is agent-to-agent orchestration, which will likely create demand for new "AI ops" roles to maintain and evolve workflows as customers’ businesses grow and adapt.
In outbound, precision is beating volume
We spoke about the all-too-familiar barrage of AI-generated sales emails that we are all wrestling with in our inboxes. Our speakers shared that the winning formula is the inverse: fewer, highly targeted messages, with deep pre-research done by an agent and reps personalizing the human layer on top.
The results back it up: one of the companies lifted strategic account response rates from 2% to 6% with this approach. Another company runs targeted campaigns against specific competitor cohorts. But there was a sharp lesson attached – simply handing everyone an agent wasn’t enough to maximize results, what drove adoption was enablement and best-practice sharing, including internal non-engineering demo days.
Pricing is drifting toward outcomes, albeit carefully
The speakers are navigating the same question of how to price AI-delivered value, and each of the three is at a different point:
One company charges a flat platform fee plus an outcome-based "promotion" fee when a customer goes live, aligning its success with the customer's success. The next iteration would price on agent effectiveness, holding agents accountable for outcomes.
A second is usage-based today, but that model misses chatbot-style interactions and positions the product as a productivity tool rather than a sales play. They are weighing whether to add a seat component and where outcome/connection-based pricing fits
The last hasn't moved to outcome-based pricing but has moved to outcome-based selling – its product ties retention and conversion outcomes to dollars.
It was underlined that the product, target customer profile, company readiness, and what it might mean for annual contract sizes, are all key for leaders considering the shift to outcome-based pricing.
The real bottleneck may be culture, not necessarily technology
The strongest consensus of the day was that change management, above all, determines who captures the value.
CEO buy-in is the multiplier; this includes setting AI-forward expectations at every all-hands meeting and mitigating employee anxiety in two ways: creating a genuine safe zone to experiment and by being specific about new role expectations, so that people can adapt rather than act out of fear.
Two practices stood out:
One of the companies runs a "looping" cadence – observe, orient, decide and act, on a 24-hour default cycle – which forces focus on the 1-to-3 things that truly matter, as most issues resolve or deprioritize themselves.
And second, across all three companies, curiosity has become the new hiring filter, sitting above the old "hungry, humble, smart" framework. With something like 90% of AI experiments failing, the willingness to experiment, fail, and keep going is what matters in these fast evolving times.
Our thanks to John, Chas and Justin for sharing so candidly. If you’re a founder in Oregon and Southern Washington, looking for the right partner to invest, reach out to our partner, Deepthi Madhava.