How to Implement AI in Go-to-Market: Lessons from Sales Leaders

Sales leaders are applying AI across the unstructured data that already lives inside their pipeline.

As ScorePlay's Charlotte explains, sales is "almost been the easiest because it's very unstructured data, but it's quite centralized" — calls get recorded, email chains accumulate, and AI helps reason over the combined context.

Deal thinking and MEDIC qualification: AI is used to standardize how reps internalize next steps and stress-test their view of the champion.

Stakeholder mapping: Reps push transcripts and notes into models to challenge assumptions about who is really invested in the deal.

Signal-based qualification: At CEQUENS, Adis describes using AI to identify sentiment and run searches that help prioritize whether a deal can close in the next quarter, month, or year.

The toolset emerging from operators on Business AI Explained spans prospecting, customer engagement, and product research: Lemlist — described by AI PM Alexis as a prospecting tool that automates "email sequences, LinkedIn DM, so it's omnichannel" across multiple channels.

CEQUENS — a customer engagement platform with its own Gen AI layer that helps companies engage customers across SMS, Viber, WhatsApp, and Telegram.

Claude Code — Alexis uses it to fetch competitor product pages on a regular basis to monitor new feature releases and feed product ideas.

Granola transcripts — ScorePlay feeds call recordings into models to evaluate deal health scores.

David Arnoux, who advises GTM leaders on AI implementation, frames his work as sitting "inside companies" and actually building the stuff, not just advising — a sign that the winning stacks are assembled with hands-on iteration rather than off-the-shelf purchases.

CEQUENS positions itself as a customer engagement platform that unifies AI across communication channels.

As Adis puts it, the company helps customers "engage with their consumers or customers easily using different channels, like the CPAS, which is a communication platform as a service, like SMS, Viber, WhatsApp, Telegram" .

A worked example from the episode: a retailer selling tires or car parts can use an agentic layer on top of CPaaS so that a browsing user on the website can be re-engaged across messaging channels with contextual offers — combining live data with batch-run ML decisions on what to surface and when.

The architectural decision Adis flags is whether the underlying data is fetched from a "data lake" or transactional store, and whether scoring runs live or in batch — a choice that shapes the latency and personalization of every WhatsApp or SMS interaction.

Asked for his dream B2B GTM AI team, David Arnoux keeps it deliberately small: An ML / AI engineer A full-stack developer GTM strategists layered on top David also flags a structural shift in the buyer landscape: a UiPath salesperson told the podcast that "salespeople won't disappear because of AI SDRs, but because of procurement agents" — agents standardized enough to evaluate vendors objectively, insensitive to traditional sales skill.

That changes which capabilities GTM teams need to invest in.