Visium adds rare expertise at a defining moment for pharmaceutical AI

Gabriel Viela’s career has been shaped at the intersection of two of the world’s most demanding environments.

He spent six years at GSK learning how decisions are made inside a global, highly regulated pharmaceutical organisation. He then spent nine years at Apple, working with data, engineering, machine learning, and AI at extraordinary scale.

Now, he has chosen Visium for the next chapter of that career.

Gabriel joins as a Principal Machine Learning Engineer, bringing a combination of pharmaceutical knowledge, operational leadership, and advanced technology experience that is exceptionally difficult to find. His appointment is a significant addition to Visium’s pharmaceutical AI practice and a clear signal of the calibre of talent the company is attracting.

Gabriel joins us at a pivotal moment.

Pharmaceutical companies are moving rapidly from AI experimentation towards systems that may influence scientific, commercial, operational, and quality decisions. The opportunity is enormous, but so is the responsibility. A model cannot simply sound convincing. It must perform reliably, operate within clear limits, and produce evidence strong enough for people to trust its output.

This is the kind of challenge Gabriel Viela will address as part of the Visium team.

From pharmaceutical operations to AI at scale

Gabriel began his career at GSK in 2010, working across marketing, sales, planning, product introduction, and logistics.

The experience taught him how quickly decisions move through a pharmaceutical organisation. A forecast can affect supply. A commercial assumption can reshape planning. A delayed signal can travel through finance, sales, and operations before teams have time to respond.

It also taught him that accuracy alone is not enough.

“A good decision made late is not a good decision,” he says. “A decision made on time, even under uncertainty, is what creates real impact.”

That lesson stayed with him when he joined Apple in 2017.

There, he worked on forecasting and anomaly detection before leading business planning for two iPhone launches across Europe, the Middle East, India, and Africa. He later helped develop the work that became Apple’s Business Analytics function in Europe, contributing operational expertise and helping shape the data pipelines behind its models.

Over time, he moved more deeply into data engineering, machine learning, and large language models. His work included ticket triage, email management, performance summaries, question answering, and agentic systems for key account managers.

Across each role, the challenge remained the same: not simply building a model, but making it useful inside a real working environment.

A technically impressive system can still fail if it solves the wrong problem, relies on the wrong information, or does not fit the way people actually work.

That combination of operational judgement and technical depth is what Gabriel now brings to Visium.

Why evaluation matters

At Visium, Gabriel will focus on the evaluation of generative and agentic AI systems for pharmaceutical companies.

In practical terms, that means designing the tests that show what a system can do, where it becomes unreliable, and what evidence and safeguards are needed before people act on its output.

A fluent response is not evidence. An impressive pilot is not yet a dependable process.

Before a scientist, commercial team, or quality function can rely on an AI system, they need to understand the conditions under which it performs well, how its output should be reviewed, and whether its performance can be demonstrated consistently.

For Gabriel, these questions should not come after development. They should shape development from the beginning.

That view closely reflects Visium’s own approach. The company is not focused on AI that performs well only in a controlled demonstration. Its goal is to build systems that can withstand scrutiny, fit complex organisations, and earn a place in important decisions.

Why Visium, why now?

Gabriel was drawn to Visium because it brought together the two worlds he knew best.

The work demanded an understanding of pharmaceutical operations, scientific standards, and regulation. It also required the engineering discipline, speed, and willingness to experiment that he had experienced in technology.

During the interview process, he found a team with deep technical capability, strong life sciences expertise, and an appetite for difficult problems.

After nine years at Apple, he was also thinking differently about the work he wanted to do.

The question was no longer only what he could build, but what that work could contribute. Applying AI to help pharmaceutical companies improve decisions, accelerate research, or support the development of treatments offered a different kind of purpose.

“What I bring is the rigour of a long career in pharma and the agility and development practices of a company like Apple,” he says. “At Visium, that experience becomes part of a hugely talented team.”

His decision to join reflects what Visium is building: a company capable of attracting exceptional people from some of the world’s most respected organisations and allowing them to work on problems with real scientific and human significance.

Building AI that earns trust

Gabriel’s career has repeatedly begun in the same place: an incomplete picture, a consequential decision, and limited time to connect the two. At Visium, he is applying that experience to the question that will determine whether pharmaceutical AI moves from pilot to practice:

How do we build systems that do more than produce convincing answers, and instead earn a place in the decisions that matter?

At a defining moment for the industry, Gabriel has chosen Visium to help answer that question.