What does sales leadership actually look like once the AI experimentation phase is over and real results are the only thing that matters?

In this episode of Tech Talks Daily, I sit down with Jason Ambrose, CEO of the Iconiq backed AI data platform People.ai, to unpack why the era of pilots, proofs of concept, and AI theater is fading fast. Jason brings a grounded view from the front lines of enterprise sales, where leaders are no longer impressed by clever demos. They want measurable outcomes, better forecasts, and fewer hours lost to CRM busywork. This conversation goes straight to the tension many organizations are feeling right now, the gap between AI potential and AI performance.

We talk openly about why sales teams are drowning in activity data yet still starved of answers. Emails, meetings, call transcripts, dashboards, and dashboards about dashboards have created fatigue rather than clarity.

Jason explains how turning raw activity into crisp, trusted answers changes how sellers operate day to day, pulling them back into customer conversations instead of internal reporting loops. The discussion challenges the long held assumption that better selling comes from more fields, more workflows, and more dashboards, arguing instead that AI should absorb the complexity so humans can focus on judgment, timing, and relationships.

The conversation also explores how tools like ChatGPT and Claude are quietly dismantling the walls enterprise software spent years building. Sales leaders increasingly want answers delivered in natural language rather than another system to log into, and Jason shares why this shift is creating tension for legacy platforms built around walled gardens and locked down APIs.

We look at what this means for architecture decisions, why openness is becoming a strategic advantage, and how customers are rethinking who they trust to sit at the center of their agentic strategies.

Drawing on work with companies such as AMD, Verizon, NVIDIA, and Okta, Jason shares what top performing revenue organizations have in common.

Rather than chasing sameness, scripts, and averages, they lean into curiosity, variation, and context. They look for where growth behaves differently by market, segment, or product, and they use AI to surface those differences instead of flattening them away. It is a subtle shift, but one with big implications for how sales teams compete.

We also look ahead to 2026 and beyond, including how pricing models may evolve as token consumption becomes a unit of value rather than seats or licenses.

Jason explains why this shift could catch enterprises off guard, what governance will matter, and why AI costs may soon feel as visible as cloud spend did a decade ago. The episode closes with a thoughtful challenge to one of the biggest myths in the industry, the belief that selling itself can be fully automated, and why the last mile of persuasion, trust, and judgment remains deeply human.

If you are responsible for revenue, sales operations, or AI strategy, this episode offers a clear-eyed look at what changes when AI stops being an experiment and starts being held accountable, so what assumptions about sales and AI are you still holding onto, and are they helping or quietly holding you back?

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