5 AI Lessons from the Front Lines of Portfolio Value Creation

Eighty percent of value creation leaders say generative AI will have the single greatest impact on valuation over the next few years, according to our State of Value Creation Benchmark report.

Yet, only 35% feel ready to act on it. That gap should make every GTM leader uncomfortable.

In the third edition of York IE’s State of the Industry webinar series on value creation, we sat down with Blake Tiemeyer, Director of Growth Acceleration at General Atlantic, and Amy Kramer, Operating Partner for Go-to-Market at Level Equity, to unpack how portfolio operators are actually deploying AI across the go-to-market function.

The conversation was practical and full of examples from operators managing hundreds of portfolio companies between them.

Here are the five takeaways every GTM leader needs to internalize right now.

1. AI Strategy Follows Company Strategy

There is no universal AI playbook. The right approach depends entirely on where your company sits in its growth lifecycle.

General Atlantic recently studied roughly 250 companies and found a clear pattern: companies growing 30% or more year over year are investing AI resources into demand generation and top-of-funnel engines. Slower growers with higher churn are orienting their AI efforts toward protecting the base, retaining customers, and defending the long tail.

This matters because too many operators are chasing the same AI use cases regardless of their company’s strategic reality. A transformation-stage business with no SDR team should be building AI-native outbound from scratch, not hiring a team and then layering tools on top. A healthy growth-stage company with rising customer expectations cannot afford to cut product and engineering headcount just because AI makes things faster.

The takeaway: match your AI investments to your strategic position. What works for a hyper-grower will not work for a company in turnaround mode.

2. Clean Data and Aligned Definitions Come Before Any AI Tool

This is the unsexy truth that keeps coming up in every portfolio review: most companies are not ready for AI because their foundational data is a mess.

Deals sitting in the wrong pipeline stages. No gate processes. Marketing running one ICP definition while sales runs another and product builds for a third. If your teams are not aligned on basic definitions, no AI tool will fix that. You are just automating bad data faster.

Amy put it plainly during the webinar: companies get excited about AI and want to jump straight to the tooling. But if you do not have basic processes, clean CRM data, and shared definitions across the business, you are not there yet. That infrastructure work often requires real investment, whether that is people, process redesign, or both, before AI can deliver real returns.

Blake reinforced the point: you need full-funnel visibility before you can even identify where leakage exists, let alone build a business case for an AI solution to fix it. Get everyone rowing in the same direction first. Then go find the tools.

For teams looking to get this right, York IE’s Revenue Operations services help portfolio companies build the foundational data layer that makes AI adoption actually stick.

3. Inbound Is Nearly Solved. Post-Sales Is Wide Open.

The inbound SDR function is, for many companies, already automated. AI-powered tools handle 24/7 chat, ICP scoring, meeting qualification, and booking without a human in the loop. Blake noted that several GA portfolio companies have broadly deployed these capabilities across their inbound motion.

Outbound is scaling too, but with a catch. Level Equity’s annual GTM Report revealed that while meeting volumes are going up, conversion rates are actually declining. AI tools enable higher outreach volume, but that volume is creating noise. If you are not using these tools, your performance drops. If you are, do not expect a dramatic lift. It is becoming table stakes.

The bigger opportunity is after the sale. Customer success has the most low-hanging fruit but the weakest infrastructure. CS teams are typically under-invested in tooling compared to new-logo acquisition. Signal recognition, health scoring, upsell and cross-sell automation, QBR workflows: all of these are ripe for AI, but most companies have not laid the groundwork to take advantage.

Beyond CS, internal processes like deal desk, RFP generation, and pricing proposals are bottlenecks that nobody talks about because they are not “top-line sexy.” But the efficiency gains are real and immediate.

4. Shorter Contracts, Clear Hypotheses, and Real Testing Frameworks

The pace of AI innovation is outrunning the pace of AI procurement.

Blake’s tactical advice: negotiate shorter vendor contracts. Six months, monthly, or at minimum build in extensive trial periods. A massive consolidation wave is coming in the AI tooling landscape, and you do not want to be locked into a 24-month contract with a product that gets leapfrogged in six.

But contract length is only half the equation. Amy flagged a pattern she keeps seeing across the Level Equity portfolio: companies testing new tools without any structured evaluation framework. They are moving fast, swiping the credit card, and relying on gut feel. That is a recipe for wasted spend.

The fix is not complicated. Define a hypothesis before you buy. Set clear KPIs, and focus on leading indicators since many enterprise sales cycles mean you cannot wait 18 months for lagging data. Run a real A/B test against your existing workflow. Learn fast, fail fast, move on.

One advantage PE and growth equity operators have here is pattern recognition at scale. Blake described his personal rule: if he hears a tool name three times across different portfolio companies, he sets up a demo. That kind of cross-portfolio signal is invaluable for separating real traction from hype.

5. Human Relationships Are More Valuable Than Ever

Here is the counterintuitive truth: as AI automates more of the GTM function, human connection is becoming the differentiator, not less important.

Every commercial leader Blake and Amy work with wants the same thing. Get the administration, data entry, qualification, and scheduling off their sellers’ plates so those sellers can do what they were hired to do: build relationships, run complex negotiations, and close deals. No one is asking AI to handle the high-EQ work.

Events. Physical mailers. Field time. These are what’s moving the needle now. The old-school relationship selling tactics that fell out of favor during the digital-first era are resurging precisely because digital channels are so noisy.

Amy also shared an interesting data point from Spara, a conversational AI platform: 90% of their customers choose to disclose that the initial outreach is AI-powered.

Prospects are more candid and direct with an AI agent than they would be with a human SDR. That honesty produces better qualification data for the human seller who picks up the conversation.

AI’s greatest contribution to go-to-market may not be replacing people. It may be giving people more time to be people.

Go Deeper: Watch the Full Webinar

These five takeaways only scratch the surface. Blake, Amy, and Mike covered everything from how to force AI adoption through hackathons and gamification, to the build-vs-buy debate, to where KPIs are evolving for PE-backed companies.

Watch the full webinar to hear the complete conversation and get the tactical detail behind each of these insights.