How Preply Is Building an AI-Native GTM Organization with Attention

The Challenge

Preply is building an AI-native go-to-market organization, where every customer interaction becomes structured data, and every rep is augmented by AI.

As the B2B division, roughly 100 go-to-market people, 70 of them in presales and sales roles, scaled across Europe, the team partnered with Attention to turn sales conversations into a core source of intelligence, powering coaching, qualification, and execution at scale.

As Preply scaled its GTM organization, traditional systems (CRM, manual coaching, rep intuition) were no longer sufficient to operate at scale across languages and markets.

‍1. No structured visibility into multilingual customer conversations

Customer conversations were the richest source of insight, but remained largely unstructured and inaccessible at scale. Preply's sales managers had no reliable way to evaluate call quality across a multilingual team. With reps selling in all European languages, managers were largely dependent on what reps told them happened on calls. "The main challenge at the time was actually to have a better read of what was happening during the call," said Lucas Daniel, Sr. Director of B2B at Preply. "That was the main reason we purchased Attention: the coaching and enablement capability that we felt quite weak on before."

2. Pipeline qualification was too subjective to scale

As Preply's inbound volume grew, thanks to a large marketplace and rising brand awareness across Europe, the team faced a new problem: plenty of leads, but no systematic way to determine which ones were actually worth pursuing. Qualifying opportunities at the pre-sales stage relied on human interpretation without a system-level intelligence layer, making it nearly impossible to hold the team to a consistent pipeline standard. "I was pushing back on moving the pre-sales team on pipeline metrics,until we had Attention," Lucas said. "Otherwise, it would have been too inconsistent across teams and reps in the interpretation."

3. CRM hygiene and follow-up were eating rep time

Post-call admin, from updating Salesforce fields to drafting follow-up emails, was slow, inconsistent, and forcing high-value sellers to spend time on low-value, non-differentiated tasks

The team needed a way to automate the routine without sacrificing quality.

The Solution

Rather than treating Attention as a point solution, Preply approached it as a foundational AI layer embedded into daily workflows across the revenue organization.

1. Custom scorecards by call type for coaching at scale

Preply transformed coaching from a manual, manager-driven process into an AI-powered system that evaluates every interaction consistently across languages by building scorecards mapped to each stage of the funnel: qualification, discovery, demo, proposal, and close. This gave managers a consistent, objective view of rep performance regardless of language. Notably, leadership adopted the same scorecards. "When you're part of the leadership and the scorecard applies to you, and you see you have 5.5 out of 10, the comments are fair," Lucas shared. "In those cases, I even share it with everyone just to relax them."

Preply built scorecards mapped to each stage of the funnel: qualification, discovery, demo, proposal, etc., creating a consistent and objective view of performance across languages. Crucially, these scorecards applied to everyone, including leadership. “I also get my calls scored,” Lucas shared. “And yes, sometimes it’s uncomfortable to see you didn’t do a great job and scored a 5.5 out of 10. But the feedback is fair, and that’s what makes it powerful, you can actually play against the score and try to improve the next time.” That dynamic helped turn coaching from a top-down exercise into something closer to a shared, gamified standard across the team.

‍2. Automated ICP scoring directly in Salesforce

Preply built an automated field in Attention that evaluates every discovery and demo call against their pipeline definition and outputs a qualification decision: yes or no, with a confidence score and reasoning. "It streamlines the discussion in a way that team leads would not be able to do without a tool like that," said Lucas. "Using Attention allowed us to effectively change the way we look at the performance of the pre-sales team." This effectively introduced an AI-assisted decision-making layer into pipeline creation.

Before Attention, qualification was largely subjective. Reps and team leads had to interpret both ICP fit and pipeline quality on their own, which led to inconsistent decisions and made it difficult to enforce a clear standard across the team.

Preply built an automated field in Attention that evaluates every discovery and demo call against both their ICP definition and pipeline criteria. For each opportunity, the system outputs a qualification decision, yes or no, along with an ICP fit score, a confidence level, and clear reasoning. This gives the team a shared, structured way to assess not just whether a deal is moving forward, but whether it is the right deal to pursue in the first place.

3. AI-powered follow-up and CRM automation for reps

Rather than leading with coaching features during onboarding, Preply prioritized rep-facing value: automated CRM field updates, one-click follow-up emails, and AI-powered call summaries. The approach converted early skeptics quickly. "That resistance was overtaken by the time saved in the follow-up," Lucas said. "Some more advanced AEs have even created templates that are quite sophisticated, recapitulating the business need, the pain, the impact under the new methodology. That creativity is what has boosted adoption."

AI Adoption Strategy: Driving bottom-up and top-down adoption

Preply’s approach to AI adoption combined leadership involvement with immediate rep value:

• Lead by example: leadership used and shared their own AI-scored calls
• Start with value: prioritizing time-saving features to drive adoption
• Enable experimentation: teams built their own templates and workflows

This created a culture where AI is not mandated, but actively leveraged by the team.

The Results

1. Expanded from AE team to full GTM organization

After launch, Preply's call recording adoption climbed from 54% to 79% in a single month. Within six weeks, more than 90% of calls were being recorded. From there, Preply grew Attention's footprint from 35 AEs at launch to include the post-sales team, and is now expanding to SDRs with Outreach integration. "It was a land and expand where we started with the sales team and expanded every time with the same intention: increase visibility on team performance and make their life easier by automating the CRM notes," Lucas said.

2. Pipeline qualification became a measurable, scalable process

Before Attention, holding the pre-sales team accountable to pipeline metrics was not feasible. After deploying automated ICP scoring, Preply moved from human judgment to AI-supported qualification to a structured system with confidence indicators and documented reasoning, enabling a level of forecast accuracy that simply did not exist before.

3. Reps and leadership rely on it daily

When asked what he would miss most if Attention were turned off on a Monday, Lucas was direct: "I would miss the information on the pipeline status and ICP fit of everything coming into the pipeline, which allows us to have a clear forecast. And the reps would miss the automated CRM fields and follow-ups, because most of them have customized templates that feed their style, but powered by AI."

Why Preply chose Attention over the competition

When a competing platform approached Preply about replacing Attention, the evaluation was short. "Although their sales process was very strong, the platform itself looked too demo to be trustable," Lucas said. "With Attention, we are playing with it quite a lot. Many people are playing with it, from ops to the teams. We have built that confidence that it's the right tool for us."

The distinction Lucas draws is between a polished pitch and a platform teams actually trust in daily operations. Attention won, not on the demo, but on the depth of use.

“Attention allowed us to move from intuition-driven sales to an AI-supported revenue system.

Before, moving sales on pipeline metrics was something I was pushing back on entirely. Now we have a confidence indicator, a yes or no, and the reasons behind it."

About Preply

Preply is the human-led, AI-enabled language learning platform building the future of learning. Its B2B division is building an AI-native go-to-market organization, where data, automation, and human expertise combine to drive performance at scale across Europe.

With a team of ~100 GTM professionals, Preply leverages AI to enhance coaching, improve decision-making, and automate execution—setting a new standard for how modern revenue teams operate.

Preply’s ambition is not just to use AI tools, but to redesign how go-to-market teams operate, where every conversation becomes data, every decision is augmented, and every rep is empowered to perform at their best.

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