How to Set Up an AI R&D Lab

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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 the annual 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 discovered what 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. A team led by Peter Bickel, now the school’s professor emeritus of statistics, was able to decode the figures by parsing individual departments. When analyzed this way, the data didn’t provide evidence of bias; the issue was that women were applying to harder, more competitive programs with lower admission rates than their male counterparts. In fact, women had slightly higher admission rates than men per department. (This doesn’t mean there was no gender bias at play; it simply means the evidence that was being used to allege that bias – lower admissions rates for women – was not itself an indication of bias.)

Researchers will be familiar with this phenomenon, known as Simpson’s paradox. Machine learning challenges have significantly increased in complexity since then and it takes years of training and experience to develop a well-honed intuition that can sniff these problems out. In the case of Berkeley, the administration had access to an in-house team of top-flight statisticians. But it’s easy to see how inexperienced analysis could have tied the university up in a costly, lengthy legal battle. Instead, the university emerged with a more nuanced understanding of its admissions process.

In a knowledge-based economy, research becomes the means of production. This recognition should put an end to any misconception that as an AI-enabled business you can “get away” with not conducting in-house research. What leaders tend to miss here is that the scientific progress we’ve made in AI does not automatically render the technology ready for any environment. Each business carries its own unique challenges and requirements, from proprietary data types to operational constraints and compliance requirements, which may require additional customization and scientific progress.

This makes AI an extremely high-risk, high-return pursuit. To pursue research may, in this moment, seem like a novel and bold act. But it should be the norm. There is a vast competitive advantage that comes from owning these solutions for your industry. The companies that invest in research that adapts machine learning to their industry will generate extremely valuable intellectual property (IP).

One way to de-risk this pursuit is to pair fundamental research (pure science) and applied research practices together. The scientific pursuit is by definition open-ended: you can go on forever, pushing boundaries and conquering knowledge. (This is why universities will never go out of business.) Applied research, on the other hand, is designed to solve specific real-world problems. What applied researchers bring to the table is knowing when to stop researching and to focus on delivering a solution. Each practice serves to influence the other. It’s a simultaneous push forward that is less likely to end up with no payoff.

For instance, one of the areas we’re focused on at Borealis AI (the R&D arm of RBC) is natural language processing (NLP), the field of AI that can understand language. So far, NLP has proved most powerful for parsing and analyzing bodies of text in order to extract meaningful patterns. Our particular interest in this type of machine learning involves training NLP on news datasets to predict how global events can affect the trajectories of companies. The ultimate goal is to build software that can direct financial analysts in real-time toward relevant information within their respective industries.

It’s important to note that the state-of-the-art in machine learning has not yet reached the level where it can solve every aspect of this problem. NLP algorithms perform best in constrained environments, such as question and answer systems, where a user can ask a computer questions from a finite list of possible queries. Since the computer knows what to anticipate in language form, the program can respond accordingly.

But when the goal is to understand something as dynamic as news and apply that information to the relationship between companies and world events, it requires the ability to contextualize, then track the evolution of very complex and time-dependent entities. This is where the marriage between fundamental and applied research comes into play. In our case, fundamental research aims to advance NLP to a place where it can independently perform high-level language-based reasoning and grasp complex relationships at the same level as humans do. Our applied researchers then ensure these solutions can become immediately applicable to financial services. This is how we build products while pushing the boundaries of science.

The last step to building an AI practice is to create the right environment. The world is an AI researcher’s proverbial oyster right now. In an uncertain economy, they’re among the rare few whose opportunities continue to become more fruitful and multiply. Offering potential talent a dynamic, comfortable, and unique workspace is a good start. You also 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.

When you hire from academia, you’re inviting a group of people into your organization who come from a very specific culture. They share values that are built on both the ethos of solving big, meaningful problems and having the ability to publish the results of their efforts. Researchers take pride in contributions they make, so these factors must be in place. This translates into reproducing some of the working conditions they’ve brought from academia such as while allowing the transparency of collaboration and open publication that serves to advance their community as a whole. Businesses working in closed environments need to reconsider that approach. If you are operating in this arena, it is up to you to prove your credentials and not the other way around.

Operating a business at the moment of this technological shift is a rare opportunity to seize upon an economic turning point. While we can’t yet predict how it will re-shape the market, the prevalence with which AI is already embedded into our core technologies favors early adoption.

Unlike the last industrial revolution, however, investing in big machinery won’t cut it. To truly have impact and remain relevant in the market, it’s up to executive leaders to build the bridge between research and commercialization. Only in this collaborative vein will AI’s true impact flourish.

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Many people believe that being a good manager only requires common sense, and that it is therefore easy to be one. If this were true, good managers would be commonplace at all levels of more organizations, and as a result, employee engagement and retention would be high. However, only 13% of workers worldwide are engaged at work, and employee turnover rates in the United States are at a 17-year high. As these statistics suggest, either most managers lack common sense, or good management is, in fact, quite challenging in practice.

As social scientists who study organizational behavior, we know that even in theory, being a good manager and retaining employees is difficult. Indeed, researchers have sought to understand how to cultivate a happy and productive workforce for more than 100 years. When we consider this body of work, it is evident that there are a multitude of factors that influence employee retention. In both theory and in practice, engaging and retaining employees is a complex endeavor, and it takes hard work to do it well.

When managers subscribe to the “common sense” view of management, they see little value in exerting effort when it comes to leading their teams. In turn, they become lazy managers. As explained below, we have observed at least two symptoms associated with lazy management: 1) a tendency for managers to blame low performance and turnover on employees, rather than on oneself or on the organization, and 2) a tendency for managers to look for quick fixes to complex retention problems.

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Psychologists have long recognized that people often overestimate the role of personality and underestimate the power of the situation in shaping human behavior. When managers become lazy, they tend to make this fundamental attribution error more frequently and on a larger scale, believing that employees act the way they do because of who they are. By blaming employees for performance problems or retention issues, lazy managers free themselves from doing the hard work of considering how their own management style affects employee satisfaction, performance, and turnover.

Also, because lazy managers believe that good management is simple, when things go wrong, they are drawn to simple solutions that are easy to find. For example, when employee retention becomes a problem, lazy managers may be quick to suggest pay raises or bonuses as the antidote — a costly solution that may fail to address the underlying issue(s). The latest management fads may also be more appealing to lazy managers. Indeed, the sheer volume and availability of solutions to employee engagement and retention problems through blogs, books, podcasts, and other sources is greater than ever, and the reality is that much of it targets lazy managers seeking quick fixes.

Even though most managers realize that their employees want to be treated fairly, have meaningful work, feel a sense of accomplishment, and so forth, the extent to which employees feel these needs are being satisfied can vary on a daily basis. Thus, effortful management requires that leaders be more thoughtful and persistent in trying to understand why their employees may be thinking of leaving and what time, energy, and other resources are needed to increase their engagement. Given that even good managers can sometimes fall into this trap, what can you do if you and your management team are showing signs of lazy management?

First, when employees are disengaged, rather than asking what is wrong with them, managers should instead start by considering the possibility that management is doing something wrong. After opening their minds to this possibility, managers can determine whether this is the case by collecting data. For instance, quick, frequent “pulse surveys” may be useful for keeping tabs on how employees feel about their own jobs and the job that management is doing; likewise, self-development tools, such as the Reflected Best Self exercise, a tool that helps people understand and leverage their individual talents, may provide leaders with feedback that can help them use their strengths more effectively. In short, managers need to take the uncomfortable and intentional step of gathering evidence from others to inform what they can be doing to re-engage their employees. The good news is that by simply signaling to employees that a manager is willing to work hard and make meaningful changes, some employees will feel more supported and inclined to stay.

Second, managers who are willing to make the effort will find that there are ongoing advances in the practice and study of management which offer an ever-expanding set of tools for diagnosing and addressing employee retention challenges. Not every tool fit a given manager’s style and the organization’s circumstances. Therefore, good managers must not only continually learn, but also must have the discipline to verify whether the advice they do receive, even when based on strong evidence and best practices, will apply to their team. It can be useful, then, for managers to see themselves as behavioral scientists, and become comfortable pilot testing retention-targeted changes before fully implementing them. For example, before providing employees with customer feedback in order to stoke their prosocial motivation — that is, their interest in helping customers for altruistic, unselfish reasons — a trial run with a subset of employees can provide evidence regarding whether it will improve employee attitudes and performance, and if so, by how much. Fortunately, there are resources available to managers who want to learn more about “people analytics” and how to use it to improve their organizations.

Finally, when retention issues crop up, organizational leaders should consider whether lazy management is contributing to the problem. If managers are just going through the motions when it comes to employee engagement and retention, it could indicate that they lack the necessary time, resources, and motivation to do more. Since effortful management requires energy in the short-term, but does not pay off until down the road, some managers forgo their responsibilities to their people because they are too focused on meeting short-term objectives. To discourage lazy management, then, managers must be given the support, incentives, and direction needed to motivate them to dedicate time and energy toward more actively managing their teams. In addition, rather than blaming lazy managers for retention issues, leaders should take a critical look at their selection and promotion processes to determine why these individuals were placed into managerial positions in the first place. If they were promoted to manager because they have excellent technical skills, they may have turned into a lazy manager because they were taken away from what they do best. If managerial promotion processes do not focus on identifying those who are most likely to embrace the challenge of managing well, lazy management may spread throughout the organization.

Management is not easy, and it takes a lot more than common sense to develop and retain a highly motivated workforce these days. By abandoning the “just common sense” mentality associated with lazy management, managers can learn how their actions influence employees, stop looking for easy fixes, and exert the thought and effort that is uncommon in too many workplaces.

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