With Pensero, SEAT CODE is replacing fragmented, manual measurement with an objective view of AI adoption, engineering productivity, and market performance while giving its leaders the evidence they need to guide a multi-year AI plan.

The company

Based in Barcelona, SEAT CODE brings a startup mindset to a large automotive group and builds digital products ranging from corporate websites to the mobile applications used by SEAT and CUPRA drivers.

Today, SEAT CODE has close to 300 employees, including 150 developers. Its engineering organization is distributed across five business areas and multiple cross-functional product teams working on data, mobile, web, and backend products.

The challenge: Making an AI investment without relying on assumptions

SEAT CODE takes a deliberately pragmatic approach to technology. When the company began introducing AI into engineering, it started with one pilot team and then expanded to five. During this stage, usage and cost were tracked manually.

That approach provided an initial view, but it would not scale across a large, heterogeneous engineering organization. Each business area worked on different products, for different stakeholders, and at a different pace of AI adoption. Managers also had their own ways of understanding performance, combining custom solutions with quantitative and qualitative information.

As a result, visibility was fragmented. Producing a company-wide view required significant manual effort, and SEAT CODE still lacked an objective measurable way to compare its progress with the wider market.

As AI moved from an experiment to a strategic investment, that gap increased. SEAT CODE needed to understand whether the money being invested was producing a meaningful return.

“With all the topic of AI, this is an investment. And as an investment, every investment requires a return on investment.”

Jordi Abad · CTO, SEAT CODE

For Jordi Abad, CTO of SEAT CODE, the need was clear: the company required a scalable system that could show how AI adoption was affecting engineering work, whether the strategy was moving in the right direction, and how SEAT CODE compared with other organizations.

Why Pensero: One objective framework across every team

SEAT CODE chose Pensero to create a consistent and objective way to evaluate engineering performance and AI impact across the company.

Pensero brings together the dimensions SEAT CODE already considers essential to productivity, including delivery, quality, and AI adoption. This alignment allows leaders to assess very different teams through a shared framework while still exploring the specific signals that matter for each one.

“Pensero allows us to do it in a homogeneous way. There are different projects, but we are getting the same way of measuring for each project.”

Jordi Abad · CTO, SEAT CODE

This was particularly important as SEAT CODE moved from five pilot teams to a company-wide rollout. Pensero gave the organization a common baseline from which to learn, compare progress, and make decisions without forcing managers to assemble information manually or build an internal measurement system outside the company’s core business.

From more data to the right information at the right moment

For SEAT CODE, adopting Pensero has been an evolving process rather than a one-time rollout. The platform first provided a high-level view across the organization. As teams and leaders became more familiar with the data, they began going deeper into the dimensions most relevant to each team.

One team may need to improve quality, while another may need to increase AI adoption. Pensero gives technical directors and team leads the quantitative evidence to identify those differences and bring them into performance conversations and development plans.

“It’s important that we select the right information at the right moment, based on the maturity and the knowledge that we have on the platform.”

Jordi Abad · CTO, SEAT CODE

Jordi uses Pensero regularly to monitor the rollout and understand how productivity is evolving across the company. That visibility then flows through the organization: from the CTO to technical directors, and from technical directors to team leads and individual teams.

The results: Turning a multi-year AI plan into a series of measurable decisions

SEAT CODE’s AI strategy is a multi-year plan built around progressive investment and continuous learning. Its first phase is focused on rollout: giving teams access to AI, establishing an initial spending baseline, and learning how adoption differs across the organization.

Pensero provides the measurement layer behind that plan. It connects AI usage and spending with changes in engineering performance, giving SEAT CODE a way to evaluate whether each additional euro invested is creating value.

“We have a plan, a multi-year plan on AI and investing in AI. We are in the first step, which is basically the rollout; the investment.”

Jordi Abad · CTO, SEAT CODE

This matters because both the technology and its economics are changing quickly. SEAT CODE does not assume today’s AI budget or toolset will remain right a year from now. Instead, it plans to review progress quarterly, learn from the evidence, and decide whether to increase investment, redirect it, or slow down.

The company’s current objective is to learn through the end of the year, establish reliable historical data, and use those findings to make the next investment decision with confidence.

“It’s a plan of going step by step, moving on a quarterly basis.”

Jordi Abad · CTO, SEAT CODE

An objective foundation for improving productivity

Early pilots showed productivity improvements, but also significant differences between teams. Some adopted AI quickly and saw strong gains; others moved more slowly. Rather than extrapolating a headline number from a limited sample, SEAT CODE is using Pensero to understand those differences as the rollout expands.

This disciplined approach is central to the business case: Pensero gives SEAT CODE an objective input for determining whether productivity is improving and whether its AI investment is worthwhile. It also avoids the time, cost, and effort of collecting data from every team and building an internal measurement system.

“We need a way that is objective to see that we’re increasing productivity. We need a way that is not just ideas, opinions, or subjective thoughts.”

Jordi Abad · CTO, SEAT CODE

By combining internal performance signals with external benchmarks, Pensero helps SEAT CODE understand not only whether it is improving, but whether it is keeping pace with the market. The result is a more grounded approach to AI: ambitious enough to capture the opportunity, but measured enough to remain sustainable.

For SEAT CODE, Pensero is becoming the evidence layer that connects AI adoption, productivity, and spending. This approach turns a strategic bet into a plan that can be evaluated and improved over time.

“Pensero is giving us the capacity to compare how we are working with how the market is working, while adding more quantitative data to the conversations managers have with their teams, and giving us visibility into how we are using AI and how we are spending money on it.”

Jordi Abad · CTO, SEAT CODE