Best use of AI Finalists: Three Teams Integrating AI

The Best Use of AI Award is for teams that have meaningfully integrated AI into their workflow or product. As a change in how they work, make decisions, or build better products. This year’s final three show three different ways AI can make a real difference: 

• Intercom R&D, for making agent-first development part of how 500 people build and ship
• Vodafone Ireland, for using AI to accelerate design thinking and move teams from assumptions to validated decisions
• PageOn, for moving from Figma handoff to AI-native product delivery

Each case study stands out because AI was used to change the workflow. One helped an R&D organisation ship significantly more. One helped product teams turn research and alignment into 90-minute workshops. One changed how designers move from idea to production.

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Intercom R&D

In June 2025, Intercom’s CTO set a clear goal: double R&D productivity within 12 months. The metric they used was merged Pull Requests per R&D employee across 500 people. Instead of treating AI as an optional tool, Intercom made agent-first development an operating principle.

Engineers shifted from writing code in IDEs to directing agents. The team first set a target of 80% agent-driven PRs within six weeks, then raised that target to 95%. The team used AI in several parts of the workflow. Designers started shipping code directly to production, beginning with small copy and CSS changes before taking on more complex work. Researchers and analysts could self-serve data through internal tooling built on Claude Code, instead of waiting in analysis queues.

One of the biggest shifts was in code review. PR reviews had become a blocker, so Intercom built an AI approval system, starting with lower-risk changes before expanding. Now, 93.6% of PRs are agent-driven, and 19.2% are approved entirely by AI with no human reviewer. Auto-approved PRs merge in around 15 minutes, compared with more than an hour across the wider organisation.

The impact of these changes was significant. After nine months, the organization hit 2x the productivity goal, and across 16 months, they reached 3x and were still climbing. The defect backlog dropped by 54%, the median time from idea to shipped fell by 39%, downtime from breaking changes dropped by 35%, and the cost per PR halved.

Vodafone Ireland

Vodafone Ireland had a different problem. Design thinking was seen as too slow, research synthesis took weeks, and teams were often making decisions based on assumptions instead of evidence. As a result, 25 to 26 key digital KPIs were blocked.

Vodafone Ireland introduced an AI-accelerated design thinking framework to reduce the time between insight, alignment, and action. Instead of relying on multi-week discovery cycles, teams used custom GPT agents to bring together fragmented inputs such as analytics, stakeholder knowledge, and previous research. The AI helped generate early personas, problem framing, opportunity areas, solution directions, UX copy, interaction patterns, and testable concepts. Every output was treated as a hypothesis and then validated through real user testing, usually with 12 to 20 users per project.

As a result, Vodafone Ireland achieved a 77% login conversion rate, 91.7% task completion on key journeys, increased clarity scores, and 30+ teams adopted the methodology. The framework helped unlock 25 to 26 previously blocked KPIs. This changed both the speed and quality of decision-making. Research synthesis and alignment moved from weeks into 90-minute workshops, which helped teams ideate faster, align earlier, and validate more often.

PageOn

PageOn shows how AI can change the relationship between design, prototyping, and production. Before that shift, even small feature work usually took around five days to move from design to release. Most of the work happened in Figma and then passed over to engineering through handoff, which created delays, rework, and limited validation of how the product would actually behave.

AI changed that workflow by moving key parts out of static mockups and into AI-assisted, runnable environments. The team used tools like Claude to explore interaction ideas faster than traditional wireframing, then gradually moved closer to the real product, where flows, states, and constraints could be tested more realistically. As the codebase became more modular and AI-friendly, designers were able to go beyond prototyping and make production-ready UI changes, with engineering review still in place.

That shift made a big difference. For smaller features, design could often be completed in one day and shipped the next. Therefore, Figma dropped from around 80% of the workflow to about 5% for this type of work as more design activity moved into runnable environments. This way, the team could explore more alternatives, test ideas faster, and reduce the risk of experimentation. If a variation did not work, it could be adjusted or replaced quickly. As people experienced realistic versions of features earlier, feedback became more concrete and decision-making became faster.

Why these three matter

These three finalists show that AI is most useful when it changes real behaviour. Intercom used it to scale delivery. Vodafone used it to speed up evidence-based decision-making. PageOn used it to remove friction between design and shipping.

The public vote counts for 25% of the final score, and now it’s over to you. The winners will be announced live on stage at UXDX EMEA 2026 in Berlin, where we’ll celebrate the teams turning evidence, research, and design into measurable outcomes. Voting closes in 7 days, so make sure your vote is counted.

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*Get your tickets here to see the winners live: https://uxdx.com/berlin/2026/tickets/