But the number of qualified candidates is falling short, according to the report, with the supply of such talent only reaching less than 645,000 in the next two years in the US.

Sarah Elk, Americas head of AI, Insights, and Solutions at Bain & Company, previously noted that executives see the AI talent gap as a major roadblock to innovation.

“Companies navigating this increasingly competitive hiring landscape need to take action now, upskilling existing teams, expanding hiring strategies, and rethinking ways to attract and retain AI talent,” Elk said in a previous statement.

Quantum science is one of today’s most talked-about fields, full of buzz and seemingly limitless potential to reshape how we understand the world — and what technology can achieve. Including subsets like quantum information science and quantum mechanics, the field is a subject more people have heard of than can explain, often surrounded by bold claims, from floating, earthquake-proof cities to making time travel possible.

But for Anastasia Pipi, the focus remains grounded in real science rather than in science fiction. Growing up in Cyprus, Pipi was always fascinated by physics. But explaining her desire to make it a career was sometimes a challenge.

“Physics didn’t seem like a common career path among the people I knew; many saw it as limiting,” she said. “But I was naturally drawn to it — it just made sense to me. I knew that pursuing it could open many more doors.”

Excelling in science throughout high school, Pipi was captivated by her first physics class, where her teacher kindled her curiosity by opening each chapter with deceptively simple questions — such as how an object would move in the vacuum of space — inviting students to reason from first principles before they had learned the formal laws.

Intrigued by the challenge of theorizing about the unknown and driven by a love for math, she went on to study mathematical physics at the University of Edinburgh, where she was first introduced to quantum science.

Eager to innovate in a cutting-edge field, she traveled to the U.S. to join UCLA’s master’s program in quantum science and technology, or MQST.

“I was excited that UCLA offered opportunities to explore not only theory, but also the computational and experimental sides,” Pipi said. “It was a great way to learn how to apply my skills in practice — and it was incredibly motivating to see everyone here pushing boundaries at such an inspiring, accelerated pace.”

Roger Lee/UCLA

What is quantum science?

The power of quantum, Pipi says, lies in its ability to revolutionize secure communication, offering unprecedented protection for sensitive data in an increasingly digital world; to tackle complex pharmaceutical challenges such as personalized medicine and targeted drug design; and to explore fundamental questions in physics, from the nature of gravity to the mystery of dark matter and beyond.

Still, she emphasizes that the foremost goal — both for her and her colleagues — is to solve the practical challenges that stand in the way of making quantum technologies truly viable.

“When we think about the future of quantum, it’s easy to get swept up in the hype,” she said. “But the real excitement lies in the tangible, transformative progress we’re making — even if it comes with big challenges.”

But what, exactly, is quantum?

“In a nutshell, quantum physics is our framework for understanding nature at the smallest scales,” Pipi said. “While Newtonian physics helps us make sense of things like planetary motion or how a ball rolls across the floor, those laws break down when we look at microscopic particles. The behavior of something like an electron is probabilistic — instead of tracing a neat, predictable path, we can only calculate the likelihood of where it might be at any given time.”

Pipi’s scientific curiosity and drive to explore the potential of quantum technologies made her a natural fit for UCLA’s MQST program.

“Anastasia was a standout member of our inaugural cohort and represents exactly the type of student our program was designed for,” said Richard Ross, MQST program director. “She showed an impressive aptitude and curiosity for this interdisciplinary field and is well prepared to make her mark in it.”

Bringing research to life with Nvidia, Caltech and more

Pipi’s time at UCLA was so rewarding that she stayed on after earning her MQST degree to pursue a doctorate in physics under the mentorship of Professor Prineha Narang, a leader in physical sciences and electrical and computer engineering. With Narang’s guidance, Pipi is advancing research at the intersection of fundamental physics and emerging technology, developing quantum control methods powered by artificial intelligence in atomic, molecular and optical systems, in collaboration with scientists at Caltech and the technology company Nvidia.

As she looks beyond her graduation, Pipi is eager to deepen her work on developing computational tools that can help make quantum technologies more practical and scalable. In the meantime, she’s fully embraced life on and off campus, steadily building her international profile as a researcher. In addition to presenting her work on quantum logic spectroscopy as a lead author at the American Physical Society, she traveled to Denmark earlier this year to attend the prestigious AI4Quantum: Accelerating Quantum Computing with AI conference, organized by the global health care company Novo Nordisk.

But Pipi’s interests extend far outside the lab. A certified open-water diver, she is also passionate about ballet, piano and snow skiing. She sees creativity not as separate from science, but as an essential part of it — a perspective that continues to shape her approach to research and life as she continues to explore new and exciting horizons.

“Physics offers a unique outlet for creativity,” she said. “Science is an art form where imagination can be just as important as logic.”

Explore more of the UCLA College’s State of Mind

Q: “Collecting data for efficiency isn’t enough, you must translate it into the system’s language.” How do you enable this transformation for brands? How do you overcome resistance in transitioning from manual to automated systems?

A: Actually, for brands, the real challenge is not gathering data – it’s transforming data into a decision ready language. Typically, data lives outside systems – in spreadsheets, emails, field notes… when data is recorded, it’s easy to systematise, but many insights are internally processed by individuals and not formally documented.

So we begin by focusing on both recorded and informal data, then plan how to formalise that data. In this process we map data sources, note frequency, and establish a data ownership framework. Then we convert this data into a mathematical language the system can understand: normalising, labelling, building relational structures. Finally, we process it through our models and connect it with decision-makers – augmenting workflows as decision support and expert systems.

When moving from manual to automated systems, resistance often arises because users fear losing control. That’s why we design automation to assist, not replace humans. Our recommendation systems also explain the reasons behind decisions. Users can see not only what should be done but why. As trust grows, resistance fades and turns into collaborative engagement.

Q: Near-future demand forecasting is increasingly important. How do your AI enabled systems predict the immediate future? How often do they update? How do they adapt?

A: Merely looking at historical data or knowing “what’s happening today” is now insufficient. We need to anticipate tomorrow.

In our systems, near-future forecasts run not just on past data but on real-time behavioral signals, market pulse, local shifts, pricing and promotional inputs. For example, when a product’s turnover rate changes in a store, it’s interpreted not just as “low stock,” but as a “change in demand pattern” signal.

We monitor such changes daily, not weekly, because missing a week in retail means missing a season. Updates involve not just retraining but context specific shifts: models reprioritise variables, adjust feature importance.

We don’t use AI only to forecast based on historical data – we complement forecasting algorithms with optimisation tools that adapt to uncertain environments, offer scenario-based modeling, and propose solution sets satisfying all possible outcomes.

Q: Many chains still rely on regional managers’ intuition for ordering. How should efficiency and intuitive decisions be balanced? How can technology optimise this?

It’s a very real situation. Many large chains still make order decisions based on “I know that region.” But the real question is: knowing versus feeling. Experience is certainly valuable, but if it isn’t systematic, it’s not sustainable.

We don’t replace intuition – we strengthen it with data. For instance, when the system generates an order recommendation, it tells the user: “This recommendation worked previously on this specific behavior.” So decision-making isn’t just about numbers – it has context and narrative.

Technology here strikes a balance: it doesn’t exclude intuition but makes it measurable and testable. Users sometimes override the system; we record and feed those interventions back. Thus the system learns over time, enabling both efficiency and expert insight to coexist.

Q: Which KPIs do you recommend retailers track to measure the benefits from your systems? For example: stock-out time, shrinkage rate, product availability score?

A: At RNV.ai, we go beyond delivering forecasting accuracy. We also observe how forecast accuracy impacts corporate culture, operations, and profitability – crucial both for clarifying ROI and making AI’s real effect visible.

We track metrics across operational, financial, and decision-quality dimensions: stock holding time, inventory turnover, stock-out rate, product availability, etc. Plus, our self-service BI tools allow end users to create their own data sets and reports.

Q: As summer 2025 begins, which product groups see the most forecasting errors? How do demand forecasting systems adapt to such seasonal fluctuations?

A: The year 2025 has been a period when retail has been more sensitive than ever to macroeconomic factors. Consumer purchasing behavior changed significantly – decisions once made easily became delayed and scrutinised.

Special holiday promotions underperformed, and campaigns no longer drew the same reaction. It wasn’t just economic slowdown – nature driven factors also challenged retailers: for instance, a delayed summer season or regionally extended heat waves led to large deviations in seasonal launch timing.

These changes present serious problems for traditional forecasting systems, which still rely on old behaviour patterns – leading to underperformance. We address these issues with dynamic forecast adaptation. When the gap between forecasts and actual sales for certain product groups becomes meaningful, models are retrained with different feature sets.

Declines are interpreted via causality-based algorithms, and feature weightings are adjusted accordingly. As a result, I can confidently say: in this period, the most successful brands aren’t those with the highest accuracy – they are those that adapt fastest. RNV.ai systems are designed for exactly this flexibility. We read changes, recognise signals, and recalculate recommendations.