AI Meets Method: Turning Chaos Into A GTM System
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AI Meets Method: Turning Chaos Into A GTM System

The ground is shifting under every revenue team, and not because of another tool—because AI now demands a real operating model. We sit down with Julia Nimchinski, founder of Hard Skill Exchange, to unpack a 2026 predictions report that brings clarity to the chaos: AI becomes a managed role centered in RevOps, enablement turns into an agentic operating layer, and systems of action finally replace systems of record. If you’ve felt the tension between flashy pilots and durable performance, this ...

The ground is shifting under every revenue team, and not because of another tool—because AI now demands a real operating model. We sit down with Julia Nimchinski, founder of Hard Skill Exchange, to unpack a 2026 predictions report that brings clarity to the chaos: AI becomes a managed role centered in RevOps, enablement turns into an agentic operating layer, and systems of action finally replace systems of record. If you’ve felt the tension between flashy pilots and durable performance, this conversation gives you the blueprint.

We dig into the phases of AI in GTM—from human sellers with AI assist, to human sellers and AI buyers, toward more agentic organizations—and what it means for job design, governance, and measurement. Julia shares why the most important shift is methodological: unify theory and practice so AI augments the right parts of the workflow, under clear guardrails, with instrumentation that proves what actually works. We explore the uncomfortable truth behind adoption numbers: while surveys boast 70-plus percent adoption, real usage often sits near 7.6 percent. That gap isn’t a failure of tech; it’s a failure of method and measurement.

From there, we get practical. How does enablement move beyond training to orchestrate agent-assisted workflows that show ROI within weeks? What telemetry proves that methodology use correlates with higher win rates and faster cycles? Why should sellers stop living in CRMs and shift into systems of action that do work on their behalf? We also tackle the convergence of B2B and B2C as buyer-side agents screen messages and shape journeys—and what sellers must change to reach real humans through that layer.

If you’re building an AI-native revenue engine, this is your edge: treat AI as a managed role, elevate enablement to execution, and measure everything so your method evolves with the market. Enjoy the conversation, share it with a colleague who runs RevOps or Enablement, and subscribe for more deep dives on the future of go-to-market.