How Managers Can Enable AI Talent to Help in Organizations

Leading a successful AI-enabled workforce requires key hiring, training, and risk management considerations.

Recent progress on the technical side of machine learning, particularly within deep learning, has followed an accelerating trend of businesses adopting AI technologies into their processes and workflows in the past decade.1 Some of these advances, such as Google DeepMind’s AlphaGo and OpenAI’s GPT-2 and GPT-3 models, have demonstrated expert-level performance in domains previously held up as examples of areas where bots would be incapable of challenging human abilities.2 With respect to business outcomes, most of the exciting developments involve using deep learning for supervised learning problems. Supervised learning is a form of machine learning where you have input and output variables and use an algorithm to learn the function that relates an input to output. The algorithm is “supervised” because it learns from training data where input and output are known in advance. These deep learning algorithms enable a different kind of software development — where instead of explicitly writing a recipe in code to complete a task, a model is trained with data to learn how to complete the task on its own. These types of algorithms are also especially useful for different types of prediction.3 Finding and enabling talented individuals to succeed in engineering these kinds of AI systems can be a daunting challenge for companies. Building organizational AI/machine learning capabilities requires fundamental reengineering of existing business processes. These efforts naturally include hiring or training technical talent.4 Effective AI management, however, is perhaps even more critical. Ultimately, managers are responsible for shaping the design and direction of the organization’s strategy to maximize the returns of any new technology. With this comes the responsibility of managing the associated risks of building AI systems. Done properly, effective AI management can drive faster productivity growth and provide companies with a competitive advantage.

Hiring and Training Considerations for Managers

The first requirement for leaders in building a successful AI system is hiring and training the right talent. The AI team is effectively a type of data science team, but it builds a different suite of products. For example, instead of running experiments to determine the effect of a new ad campaign, an AI team might build a product image classifier to determine how store shelves are organized. These teams use many of the same tools, including common programming languages like Python and R, cloudbased computing environments, and database technologies. Provisioning a team to build machine learning models involves familiarity and knowledge of the organization’s technological hierarchy. Questions for leaders and teams to keep in mind include the following:

1. Is there a way to access a lot of computational power quickly?

Running production-quality AI systems is often best handled with cloud services, but building out a data center can be a better option for some companies. Either way, AI engineers are going to need access to the right machines.

2. Is there technical talent supporting the stability of the computational systems?

Stability of data infrastructure and computational resources is key to building out systems that scale. That means hiring IT talent that can make it easy for data science and AI engineers to produce reliable models.

3. Is data collected, cleaned, and accessed in a reliable and compliant way?

Professional data engineers can make sure that the raw data inputs are available in the format and quality needed to maximize AI value while minimizing risks.

Even with a strong technical team in place, every AI-powered organization needs to successfully invest in organizational complements to maximize the return of AI.

AI, like other forms of IT, requires a lot of preexisting investment in various other assets, such as technical expertise, business processes, data, and culture, to be productive and provide value in a new context.5 Early on, all of this additional complementary investment and change management can make it seem like AI (and data science as well) is a drag on productivity. After all, more resources are committed to generating some of the same outcomes. But over time, what may have looked like initial dips in measured productivity will pay off with real returns. My research colleagues and I refer to this phenomenon as the Productivity J-Curve, and our research supports the idea that these up-front investments help organizations move toward the objectives stakeholders want to reach.6 In my own work partnering with LinkedIn’s Economic Graph Research and Insights team, I found that a major portion of the business value of AI talent is reflected in these complementary assets. This makes sense given that many of these intangible assets, such as new processes, provide more value when AI skills become easier to acquire. New tooling and platforms such as Google’s TensorFlow and PyTorch open-source machine learning libraries have made it easier to train deep learning models and build skills more quickly on AI teams. In my research, I used LinkedIn data to track the prevalence of AI skills across companies and found that the market value of publicly traded companies that were already using AI increased by as much as 3% to 7% after TensorFlow came into the market at the end of 2015.



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