Fast-growing CRM Pipedrive had spent the previous year investing in AI training, helping teams understand AI, set strategy and build with new tools. While those efforts validated several promising ideas, leadership wanted to go a step further toward becoming a truly AI-native CRM. They saw two connected challenges: their workforce needed to embed AI into the way they built and their product needed to use AI to solve real customer problems, not simply add AI features onto existing workflows. To make both shifts stick, Pipedrive needed a focused way to prove what AI-native could look like in practice, then scale those learnings across the organization.
Rather than trying to become AI-native across the entire product and organization at once, Pipedrive chose to start with one discrete, high-impact problem and solve it from first principles. At ExperiencePoint, we call this approach a “beacon project” (i.e., a way to establish good practice before scaling those learnings more broadly). So, the team decided to focus on a sales workflow challenge within the CRM. Pipedrive’s VP of Product Azadeh Pak partnered with ExperiencePoint to lead a hands-on design sprint, where a small cross-functional team of 15 people, including three engineers, one product manager and one designer, explored customer pain points, prototyped solutions and began modeling new AI-enabled ways of working. The team’s limited resources became a productive constraint, forcing them to lean on AI tools to move faster, scale their capacity and challenge traditional ways of building. What emerged was a more valuable customer solution: an omnichannel communication space designed around how sales teams actually work.
The beacon project helped Pipedrive move ahead of competitors by going beyond fragmented AI features toward an AI-native product experience built around a real customer need. Having created AI toolkits during the process, the beacon team came back and made them publicly available across the organization. They also hosted open demos, sharing what worked and what didn’t, helping other teams see how AI could unlock value in practical, day-to-day work. As good practice spread, Pipedrive reinforced the shift through retrospectives, data reflection and regular reporting to prevent teams from falling back into old habits during delivery pressure. The initiative drove uptake of the new solution, helping the team arrive at Product-Market Fit (PMF). It also sparked organizational cultural change within Pipedrive, establishing a repeatable playbook for both product experiences and the way teams build in the future.
