Builders of AI: Meet Christina

Valore and her team built an internal tool designed to help creative teams move faster at the very beginning of the process. A user enters a prompt. Within seconds, the tool generates up to four image drafts. That’s it. No over-engineering. No expectation of perfection. The outputs are not meant to be final assets. They are starting points, something tangible that designers can react to, refine and evolve into campaign-ready work. It shifts the job from creating something from nothing to improving something that already exists. And that shift matters. By removing the friction of ideation, creative teams spend less time searching for direction and more time shaping it. The result is a shorter path from idea to launch and more energy spent on storytelling instead of setup.

What stands out is not just the tool itself. It is how quickly it came together. The core functionality was built in six weeks. In a world where traditional creative production timelines can stretch for months, that speed comes from a deliberate choice to ship something useful early and improve it over time. That meant close collaboration across data science, engineering and creative teams. It meant aligning on what good enough to use actually looks like. It also meant resisting the urge to overcomplicate. The outcome is a working product that delivers value now, not a concept waiting to be perfected.

Working in generative AI means the ground is constantly shifting. What feels cutting-edge today can quickly become outdated. That is exactly what keeps Valore engaged. “Working on the bleeding edge of AI is exhilarating,” she says. “New capabilities appear frequently, and the focus is on staying ahead of how these tools can actually make work easier for clients and for teams.” The shift from image generation to video is a good example. Early models like Veo 3.1 can already produce short video clips. These are rough eight-second sequences that still need significant refinement. The direction, however, is clear. The next frontier is not just static content. It is dynamic, generative media. The challenge is not access to these tools. It is making them reliable enough for real production environments. That is where the work becomes meaningful.

Generative models are powerful, but they are also unpredictable. Outputs can vary. Quality is not guaranteed. Without structure, experimentation can quickly lose focus. Valore approaches this with a scientist’s mindset. Her team combines continuous learning, reading research and testing new tools, with structured experimentation. They run controlled tests, track outputs and build feedback loops that improve performance over time. Inside Publicis Sapient, that learning is shared openly. Monthly data science forums highlight what teams are building and where breakthroughs are happening. Weekly stand-ups create space to solve challenges in real time and stay aligned on long-term direction. It is not just about keeping up. It is about turning constant change into something teams can actually use.

With so much evolving so quickly, one of the biggest risks is losing focus. “As scientists, you can easily go down rabbit holes,” Valore explains. “Discipline is what keeps experiments tied to product goals.” That discipline shows up in how teams prioritize. Not every new capability gets pursued. Not every idea becomes a feature. The goal is to build tools that solve real problems, not chase novelty. Just as important is how teams work together. Valore is clear about what accelerates growth in this environment. Ask questions early. Stay curious. Do not let uncertainty slow you down. “If you’re confused, speak up. Curiosity and humility accelerate learning,” she says.

There is no clean roadmap for building in a space that is still being defined. Growth often comes from stepping into challenges before you feel fully prepared. For Valore, that has been a constant. Taking on leadership roles early, navigating ambiguity and learning through mistakes were not side effects of the job. They were the job. “Being thrown into difficult assignments is one of the fastest ways to learn,” she says. “You won’t get everything right, and that’s part of it.” It is a mindset that mirrors the way her team builds. Start, test, learn and improve.

From the outside, generative AI can feel abstract, full of possibilities but disconnected from day-to-day impact. Valore’s work shows a different reality. It is practical. It is iterative. And it is grounded in making things that people actually use. A tool that generates rough image drafts might not sound revolutionary. But when it cuts weeks out of a process and helps teams move faster with more confidence, the impact is real. That is the difference between exploring AI and operationalizing it. And it is where the work gets meaningful.