How to Set Up an AI R&D Lab

There’s a common misconception that rebranding as an AI company is as simple as having data, infrastructure, and off-the-shelf data and analytics. The reality is that AI is complex, high-risk, expensive, and often requires significant business transformation. Most importantly, though, it demands an ultra-specialized talent pool that, according to latest reports, currently stands at only 22,000 PhD-level experts worldwide — a remarkably small pool. To go beyond data science and do real AI, you need to hire the right people, embrace research, and adapt your culture. The “right” people are, for now, almost entirely academics. To attract them, you need to be able to offer compelling datasets and interesting problems to work on. Computational power and a strong team to provide research mentorship are also required. But the ability to pursue curiosity-driven research is the real draw.

The moment a hyped-up new technology garners mainstream attention, many businesses will scramble to incorporate it into their enterprise. The majority of these trends will splutter and die out by Q4. Artificial intelligence (AI) is unlikely to be one of them.

AI is a transformative series of tools that can accelerate productivity, drive insight, and open up unexplored revenue streams. It’s poised to revolutionize the way we do business and everyone in a leadership role should be thinking about it.

But few organizations are set up to do AI properly.

There’s a common misconception that rebranding as an AI company is as simple as having data, infrastructure, and off-the-shelf data and analytics. The reality is that AI is complex, high-risk, expensive, and often requires significant business transformation. Most importantly, though, it demands an ultra-specialized talent pool that, according to latest reports,currently stands at only 22,000 PhD-level experts worldwide — a remarkably small pool.

When you consider that theannual market value predictions for AI techniques range between $3.5 trillion and $5.8 trillion, it’s clear why the battle to recruit from this scarce group has become the industry’s defining challenge. Right now, the biggest companies in the world are scooping up these experts to populate their teams because they understand that the key to building a robust, successful AI practice is to find and retain the right talent.

So, when the Royal Bank of Canada (RBC) approached me three years ago to help them grow their AI capabilities, this is what I advised: To go beyond data science and do real AI, you need to hire the right people, embrace research, and adapt your culture.

At the moment, AI is more of an open frontier than an industry-friendly space. Its applications are new enough that actual practitioners of AI don’t yet exist at scale. This makes finding, retaining, and nurturing talent the field’s most pressing challenge. If you want this capability in your organization, you have to hire the people with the perfect balance of data intuition and state-of-the-art knowledge. These people are almost all academics.

It’s impossible to overstate the importance of this expertise level. Machine learning models run on mathematics whose subtlety requires a deep understanding of data domains. Simple tasks can be daunting to those without the right experience. Mistakes in the machine learning space are extremely common, but when applied to business they can have real-life consequences too – up to and including life-and-death.

Here’s a textbook example of how easily these mistakes can occur. In 1973, the University of California at Berkeley compiled their graduate school admissions figures and discoveredwhat appeared to be a significant bias against women applicants. The numbers were right there on the page: 44% of male applicants were admitted to graduate programs, versus 35% of female applicants.

Fearing a lawsuit, school officials shipped the data to their statistics department for a closer look.