Mathematical Optimization Is Strong: So What Stands In Its Way?

It’s easy to see why many machine learning capabilities—from generative AI to facial recognition to self-driving cars—have captivated data scientists and the public alike, especially in the past year.

But while this particular branch of AI may have been a hot topic in 2023, its close cousin—mathematical optimization—has been steadily and quietly growing as well, reaching new peaks in both usage and interest.

At Gurobi, we survey the field each year through our State of Mathematical Optimization and State of Mathematical Optimization in Data Science reports. This year, our research shows that mathematical optimization is slowly joining the data science mainstream. It may not yet be a household name, but it plays an integral role across industries, from aircraft scheduling and energy distribution to supply chain network design.

And in fact, more than 80% of the companies we surveyed are already combining mathematical optimization with machine learning—a much-anticipated cross-pollination of ideas that will likely lead to even more new uses.

Let’s take a closer look at some of the other key trends our research uncovered.

New Players Are Entering The Field

According to our report, the number of practitioners reporting that they have one to three years of optimization experience grew by 25% in 2023, compared to 2022 data.

This significant increase matches our own observations:

• Companies are launching new mathematical optimization projects, which are driving the need for new talent to staff these projects.

• New people are coming into the field—either recent graduates or professionals from other fields who have acquired new mathematical optimization skills.

This growth in fresh talent is a sign of a healthy, growing field—a projection that is also supported by the Bureau of Labor Statistics (BLS), which listed operations research (a role that relies heavily on mathematical optimization) as one of the 30 fastest-growing occupations this decade. The BLS predicts a need for 24,200 new practitioners by 2032, as demand for optimization skills continues to grow.

While we were frankly a bit surprised to find operations research on their list, the data seems to be part of a larger trend, as even industry analysts like Gartner recognize the field’s promise. Indeed, Gartner recently added decision intelligence (another related field that relies heavily on mathematical optimization) to their Hype Cycle, citing a high and growing level of interest.

The Field Itself Is Expanding

Optimization has long been used to improve decision-making processes, especially in areas such as logistics, supply chain management and resource allocation.

However, over the last few years, we’ve seen that use cases are no longer limited to these traditional domains.

For example, since we issued our 2021 report, the number of respondents who cite pricing as a use case has more than doubled, now standing at 23%.

Other increasingly popular domains include finance, telecommunications, healthcare, energy and environmental management.

The fact that typical use cases have changed noticeably in recent years suggests that mathematical optimization is being used in new, increasingly innovative ways.

There’s A Thirst For More Knowledge

When we talk to data scientists, a field much larger than mathematical optimization, we find that the majority have at least some understanding of optimization, while a vast majority (81%) express an interest in learning more. Thirty percent say they look for optimization skills in job candidates.

We’ve seen this demand for advanced knowledge through our own webinar registrations. Our recent Optimization for Data Scientists series, which offers intermediate, hands-on training, has attracted thousands of views.

Meanwhile, companies that already use optimization are using it even more. Specifically, 41% of our respondents report that usage in their company is growing, while 52% said it remains steady. This is a clear sign that optimization skills are continuing to gain value.

Bridging The Knowledge Gap

While the surge in interest and talent entering the mathematical optimization field is undeniable, we must also address the existing knowledge gap, which may pose a barrier for those looking to enter—and ultimately limit the field’s growth.

Eighty-one percent of our survey’s respondents reported holding an advanced degree in a related field, with 49% of those degrees in operations research specifically.

As the expertise required to enter the field remains stubbornly high, what can business leaders do to ensure their companies remain at the forefront when it comes to using innovative methods like mathematical optimization?

With more accessible tools and resources, we can begin to bridge the knowledge gap, enabling a new generation to sustain the growth in interest that we’ve seen in the recent past. And there’s no better place to start than within your own organization.

By training your data scientists in optimization—beginning with those who have a strong mathematics background—you can empower your current employees to grow their skill sets and use their new knowledge to help your company make better business decisions.

With the use cases for mathematical optimization continuing to expand, investing in your top talent today will not only help you remain competitive but also ensure that you’re ready to meet the business challenges of tomorrow.

This article was originally published on Forbes.com.